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• What Engineers Do

Engineers apply scientific principles to analyze, design, invent, code, build, and create to solve all sorts of problems and make the world a better place. One of their most important tools is their own creativity.

What Is an Engineer?

Engineers are experts in their fields , creating and innovating constantly. As practitioners of engineering , engineering professionals deal with complex systems, structures, devices, and materials to fulfill functional requirements while also considering the limitations imposed by regulation, safety, cost, and more. Because of existing limitations, engineering has sometimes been called design under constraint.

What Do Engineers Do?

Engineers solve problems using math, science, and technology. As a problem-solver , every potential answer an engineer devises must be weighed against the realities of the physical world and other concerns such as public safety, a client's requirements, regulations, available materials, and a finite budget. It takes creativity to get successfully from problem to solution, all while navigating a tangle of constraints.

• Engineers Are Creative Problem-solvers

Engineers Make a World of Difference

Engineering is essential to our health, happiness + safety, engineers help shape the future, engineers are creative problem-solvers.

There is never just one way to solve an engineering design challenge ; there is no single, "right" answer to a problem. Engineers must accept a degree of uncertainty regarding a solution's endpoint, and creativity helps here, too. Engineering requires a sense of vision that goes beyond constraints to "see" a solution that others might miss or dismiss as farfetched.

• A damaged heart stretches its walls dangerously thin when the muscle expands, in an effort to increase pumping capacity. Engineers envision a gel-like "sleeve" that can surround it to prevent this expansion. After the heart is healed, the sleeve safely dissolves into the body.
• Cholera is a major health problem in African villages. Engineers devise a water filtration system that not only stops the spread of the disease but is cheap and easy to use, and can be implemented in a culturally sensitive manner.
• A large-capacity "green" office building is needed in a coastal town. Engineers design a 30-story building with one highly unusual feature: there are no internal support columns affording the structure maximal office space. The structure is built with eco-friendly materials but can withstand hurricane-force winds. Even better, it stands out in the town's skyline and becomes a local landmark.

Engineers today work in diverse and diffuse teams, often across time zones and national borders. At the same time, the problems engineers are being called upon to solve have become larger and more complex: reconstructing habitats in the Florida Everglades; protecting the integrity and security of the nation's electrical grid; moving the United States toward greater energy independence. The modern engineer must be able to synthesize a broad range of disciplinary knowledge while keeping the systemic nature of the problem within her view. As we take on the challenges facing us, it will be engineers and their creativity that design the world we want and turn ideas into reality.

Source: National Academy of Engineering

Most of the things that make our lives safer, more enjoyable, and more productive are products of engineering.

• In the late 1970s, computer data were stored on 8-inch "floppy disks" that held 1.2 megabytes of data. Today's flash drives are as small as 0.5-inch across and can hold 32 gigabytes or more—25,000 times as much memory as the old floppies. The engineers responsible for this dramatic improvement have made possible such modern essentials as smartphones, digital media players, and digital cameras.
• For most of human history, doctors had little way of directly seeing what was happening inside a body. Over the past century, however, medical engineers have created a host of imaging devices. X-rays, ultrasound, magnetic resonance imaging (MRI), computerized axial tomography (CAT), positron emission tomography (PET), and other techniques allow doctors to diagnose and treat injuries and disease far more effectively than ever before.
• The final battle of the War of 1812, the Battle of New Orleans, took place on January 8, 1815. Due to the slow-moving communications of that era, the battle famously occurred a full fortnight after the signing of the Treaty of Ghent, which had already formally ended the war. Modern communication allows today's military personnel to communicate with each other in real time across continents. Commanders implement strategy, and soldiers in the field converse with loved ones back home using Internet technologies such as Skype.

A major difference between science and engineering is that scientists deal with the world that is, while engineers envision the world that could be. It is the job of the engineer to determine what people need or want and figure out the best way to provide it. This can involve something as simple as an improved garbage bag that is inexpensive yet tear-resistant, or as complex as the MS Oasis of the Seas, a cruise ship that is as long as four football fields, as high as a 24-story building, and carries 6,000 passengers on 16 passenger decks.

Modern humans interact with two worlds at once. The first is the natural world—the earth, water, air, plants, and animals that exist independently of any human intervention. The second is the human-made world—all the things people have created for themselves in order to improve their lives: their cities and farms and factories, their clothes, their televisions, their medicines, their musical instruments, their eyeglasses, their credit cards. Without engineers, this human-made world could not exist.

Engineering's ubiquity makes it key to society's health, happiness and safety.

• A growing urban area has a major river running through it that is little more than an open sewer. Disease, spread by this water supply, ravages the populace, and during one summer the stench is so bad that all government offices close. After engineers design and implement an extensive sewer system, health improves dramatically and London becomes a leader on the world stage, one of the most important cities of the 20th century.
• A soldier loses his arm to an IED in Iraq and returns home depressed, wondering how he'll assimilate to life with his new disability. But equipped with engineer Dean Kamen's "Luke Arm," a full-arm prosthetic, he can feed his kids, return to work, and enjoy many of the things he did before his injury.
• A few friends set sail in the mid-Atlantic on a fishing expedition, only to find their boat taking on water 20 miles from land. An on-board GPS-enabled satellite messaging and emergency communications device – designed by engineers – sends a location-based message to an emergency rescue coordination center that dispatches the Coast Guard to rescue the group.

Engineering surrounds us, part of every aspect of our lives. From the moment we wake up, we interact with engineered devices and systems, from the obvious—the alarm clock that stirs us; lights that illuminate our way; and the car that takes us to our destination—to the more subtle—breakfast cereal brought to our grocer in a truck routed with logistics engineering; timed traffic lights; and "self-healing" concrete used in the buildings where we live and work.

Engineering enables some of the very things that make us feel human. We can bond and connect with others over the Internet; we can help each other, perhaps by using an automated external defibrillator in an emergency that is engineered so that even non-medical professionals can use it; and we experience joie de vivre playing Wii with our families, flying through the air on massive roller coasters, and being whisked away to 3-D worlds while at the cinema.

Engineering is so pervasive in advancements in our health, happiness, and safety that it becomes hard to separate its influence from that of other disciplines in technology like artificial hearts and computer security. The very success of engineering is in part the fact that it is sometimes subsumed.  Good design seems effortless, and therefore can be invisible; this leads to the continual problem that engineers do not get the credit they deserve for their many contributions.

The challenges engineers must overcome change and evolve over time, based on the needs and demands of society. From something as seemingly mundane as a sewer, to something as advanced as satellite technology, engineers are consistently imagining solutions to our problems, creating the world we want, impacting us in every aspect of our lives. It is difficult to point to areas of our well-being not brought about in at least some measure by what can be called "engineering."

No one can say what the future will bring, but one thing is certain: Engineers will play a major role in shaping the world of tomorrow.

• The carbon dioxide released by burning fossil fuels is changing Earth's climate. If this looming disaster is to be averted, it will be thanks to engineers developing alternative energy sources and ways of minimizing the effects of the carbon dioxide.
• Engineers specializing in robotics are collaborating with specialists in cognitive science to develop the type of intelligent robots that have been a mainstay of science fiction for decades. These visionaries speak of robots that can navigate a battlefield to disarm explosive devices or enter a burning building to find people trapped inside and carry them to safety.

Anyone can dream about the future, but the people who actually turn those dreams into reality will be engineers. Traversing the path from concept to practical creation requires an understanding of the relevant science and familiarity with current technologies but also the vision to see beyond the world as it is and create something new. This is the job of the engineer: to combine the knowledge and tools of today with dreams of tomorrow to create the world of the future.

The power of engineers to shape the future is clear. Consider such powerful handheld devices as the iPod or the Blackberry, or the coming generation of practical electric cars. Not long ago, each of these was no more than a dream, but today each is a reality thanks to the vision of engineers. We cannot say with certainty what our world will look like 10 or 20 or 30 years from now, but we do know that whatever new wonders appear, engineers will have played a major role in bringing them to life.

Types of Engineering Fields

While there are many different ways to organize the various engineering disciplines, according to the National Academy of Engineering (NAE) , there are twelve types of board engineering categories 1 :

• How to Become an Aerospace Engineer
• What is Biomedical Engineering?
• Chemical Engineering
• What is Civil Engineering?
• What is Computer Engineering?
• What is Computer Science?
• What is Software Engineering?
• What is Electrical Engineering?
• Electronics, Communication, and Information Systems Engineering
• What is Mechatronics?
• Manufacturing Engineering vs. Industrial Engineering
• What is Robotics Engineering?
• What is Materials Science and Engineering?
• What is Mechanical Engineering?
• Earth Resources Engineering
• Engineering vs. Engineering Technology

1: Categories from the National Academy of Engineering , accessed October 2023

Master of Engineering vs Master of Science

Engineering Careers

There is no doubt that engineering continues to be a growing field with excellent job diversity, job placement prospects, and salary outcomes. To become an engineer you need a degree in engineering that will provide you with a broad background in math, science, and technology, as engineers use these skills to solve problems on a daily basis. Learn more about the various fields of engineering, engineering salaries , job outlooks, degree options, and what Michigan Technological University offers.

• Michigan Tech's Engineering Departments
• Michigan Tech Engineering Majors
• Michigan Tech's Engineering Department Chairs

Engineering Salaries

What is an engineering degree worth? Year after year, engineering jobs in the United States and abroad are paid the highest average starting salary. Compensation can vary widely based on factors such as the type of engineering field or type of degree. View our engineering salaries data for additional information.

Engineering Problem-Solving

• First Online: 21 September 2022

Cite this chapter

• Michelle Blum 2  

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You are becoming an engineer to become a problem solver. That is why employers will hire you. Since problem-solving is an essential portion of the engineering profession, it is necessary to learn approaches that will lead to an acceptable resolution. In real-life, the problems engineers solve can vary from simple single solution problems to complex opened ended ones. Whether simple or complex, problem-solving involves knowledge, experience, and creativity. In college, you will learn prescribed processes you can follow to improve your problem-solving abilities. Also, you will be required to solve an immense amount of practice and homework problems to give you experience in problem-solving. This chapter introduces problem analysis, organization, and presentation in the context of the problems you will solve throughout your undergraduate education.

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https://www.merriam-webster.com/dictionary , viewed June 3, 2021.

Mark Thomas Holtzapple, W. Dan Reece (2000), Foundations of Engineering, McGraw-Hill, New York, New York, ISBN:978-0-07-029706-7.

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Aide, A.R., Jenison R.D., Mickelson, S.K., Northup, L.L., Engineering Fundamentals and Problem Solving, McGraw-Hill, New York, NY, ISBN: 978-0-07-338591-4.

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End of Chapter Problems

1.1 ibl questions.

IBL1: Using standard problem-solving technique, answer the following questions

If you run in a straight line at a velocity of 10 mph in a direction of 35 degree North of East, draw the vector representation of your path (hint: use a compass legend to help create your coordinate system)

If you run in a straight line at a velocity of 10 mph in a direction of 35 degree North of East, explain how to calculate the velocity you ran in the north direction.

If you run in a straight line at a velocity of 10 mph in a direction of 35 degree North of East, explain how to calculate the velocity you ran in the east direction.

If you run in a straight line at a velocity of 10 mph in a direction of 35 degree North of East, explain how to calculate how far you ran in the north direction.

If you run in a straight line at a velocity of 10 mph in a direction of 35 degree North of East, explain how to calculate how far you ran in the east direction.

If you run in a straight line at a velocity of 10 mph in a direction of 35 degree North of East, how far north have you traveled in 5 min?

If you run in a straight line at a velocity of 10 mph in a direction of 35 degree North of East, how far east have you traveled in 5 min?

What type of problem did you solve?

IBL2: For the following scenarios, explain what type of problem it is that needs to be solved.

Scientists hypothesize that PFAS chemicals in lawn care products are leading to an increase in toxic algae blooms in lakes during summer weather.

An engineer notices that a manufacturing machine motor hums every time the fluorescent floor lights are turned on.

The U.N. warns that food production must be increased by 60% by 2050 to keep up with population growth demand.

Engineers are working to identify and create viable alternative energy sources to combat climate change.

1.2 Practice Problems

Make sure all problems are written up using appropriate problem-solving technique and presentation.

The principle of conservation of energy states that the sum of the kinetic energy and potential energy of the initial and final states of an object is the same. If an engineering student was riding in a 200 kg roller coaster car that started from rest at 10 m above the ground, what is the velocity of the car when it drops to 2.5 m above the ground?

Archimedes’ principle states that the total mass of a floating object equals the mass of the fluid displaced by the object. A 45 cm cylindrical buoy is floating vertically in the water. If the water density is 1.00 g/cm 3 and the buoy plastic has a density of 0.92 g/cm 3 determine the length of the buoy that is not submerged underwater.

A student throws their textbook off a bridge that is 30 ft high. How long would it take before the book hits the ground?

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Blum, M. (2022). Engineering Problem-Solving. In: An Inquiry-Based Introduction to Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-91471-4_6

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• Jun 29, 2020

The Problem Solving Steps all Engineers Should Know

Imagine walking into a room, everyone is clamoring for answers and after a few moments you know exactly what everyone should do to fix the problem.

You deal with problems on a daily basis as an Engineer but sometimes you run into the situation where you solve the wrong problem, or senior engineers get frustrated with how long it's taking to complete a task - perhaps they gave you some vague problem statement and when you asked for some direction it was still high level because it should be "obvious".

I think where people get caught is the Senior engineers giving out tasks aren't necessarily looking to walk you through a solution, they want a problem to go away, they want to spend as close to zero brain cells on the problem (at this point in time). So your job is to make it go away and not to use their brain cycles.

But this is counter intuitive, if I don't know where to start or I take too long then that will also be frustrating since the problem will still be there.

Correct. So you are caught in between a rock and a hard place. But it's not the worst and we can certainly equip ourselves with the skills we need to handle these situations.

What's the situation?

Problem Solving and reducing our "mean-time to solve". There's a spectrum of problems one can consider and if you realize this you can see that more complex problems do require more time to solve - there's an "expected" time to solve. So you want to perform in such a way that you are below this line as much as is practicable.

I've worked in Engineering for over a decade now and I can tell you that for sure there are specific tricks to solving particular problems specific to the industry, company, field, technology, etc. You gain these by purely time. Working on problems and solutions in that area. This is why experience is king - but it is also overrated sometimes.

Someone with 5 years more experience may not be very good and if you only looked at the number of years you would be none the wiser.

So how can we overcome this hurdle and forget the number of years we've worked and just perform better?

Use the book 10+1 Steps to Problem Solving: An Engineers Guide.

Here I created simple steps to follow that looks at a more birds-eye view but is so practical you can apply it to any situation.

But this isn't some "one-size fits all" methodology, nor is it "how do I calculate the potential energy in this craft", "how do you enable this features in this software". Don't get it twisted.

But it does help formalize your approach, use the right mindset and ask the right questions at the various stages of problem solving.

What's wrong with Steps to Problem Solving lists out there? They are mostly correct, but the primary issue is they are so generic and have little practicality. They lay out steps around identification of the problem, analysis, breaking it down to small bits, evaluating. But more often than not they spend half the time talking about implementation, working out the kinks, timing, etc.

This presents 2 problems:

It is super slow

It is solution focused

I'm not saying you shouldn't plan out your solutions and have implementation plans, timings, schedules, documentation - you need these (at the solution stage). But when you plan out how you are going to try to fix something and spend all this time pondering - you could have simply tried and moved on.

You either fixed it or you got more data.

You iterate faster through your questions, quick testing of the obvious things, getting eyes on the situation in the correct way, checking your fundamentals and proceeding from there. (These are still in the first three steps by the way).

The rest of the steps are still focused on going deeper into the rabbit hole to solve your problems. This is when you are stuck, for hours, days, weeks!

So what are the steps?

Here's direct extract of the index:

The Question

The Obvious

Check Yourself

The RTFM Protocol

What about the Environment?

Phone-A-Friend

The Secret Step

The book goes on to explain each of these steps and provide a checklist style summary at the end of each. You can practically use this as a framework to approach problems, particularly tricky ones so that you can reduce the average amount of time you spend fixing things. There's real examples from easy to difficult ones covered so you gain context on how to fix.

I really wanted to help as many people as I can with this so I actually made the book completely free. You can get online access and read the whole thing from my website here .

It will require you create an account but other than that you are good.

At the time of this writing only the first 2 chapters are available, but you are getting early access as the book isn't set to release until the 4th Quarter of this year! (In time for Christmas).

You can register to get notified when the release is coming out so you can be first in line to get your own copy.

What's the advantage of problem solving this way?

So if you remember to the opening of this article we did cover some of the pain points and frustrations that can happen in an engineering career. So think of it this way, if you can consistently solve problems and make things go away, or better yet, things seem to get fixed faster when you are around - then you'll be wanted around.

This tends to have a compounding effect where you help others solve their problems simply by understanding this method and asking the right questions to get them to their own answer, and now people want you on bigger projects.

You do this and gain more responsibility and then now you have the foundation for increasing your pay, your role and your impact. (There's challenges here of course but I will have courses and free content to address these). You can become one of the "go to" engineers in your company.

Every Engineer should be aware of these problem solving steps.

Get your free book access today.

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Science News Explores

Engineering: the route to problem-solving.

Young researchers learn how math and science are used in the real world, from protecting eggs to delivering tap water

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By Helen Fields

February 6, 2013 at 5:24 pm

The delicate egg hatched some heavy discussion among the five young scientists inspecting a pile of squishy packing materials. “So, bubble wrap?” asked Samuel Coulson, 14, of West Platte High School in Weston, Mo.

The team, working with only the materials at hand, had to devise a way of protecting a raw egg from a series of increasingly higher drops. The challenge pit these students against five other teams at the second annual Broadcom MASTERS competition. It was  held in Washington, D.C., from Sept. 28 through Oct. 3.

As the team pondered its options, some plastic wrap burst with a satisfying sound. “Stop popping it,” said Maria Elena Grimmett, 13, from Oxbridge Academy of the Palm Beaches in West Palm Beach, Fla.

The 7th- to 9th-graders qualified as finalists in the competition based on a science fair project from the previous academic year. But that project would count for only one-quarter of each contestant’s score here at the finals. The majority of each score would reflect how well a student performed in a series of group challenges — including the egg drop.

“Guys, I seriously think packing peanuts, due to the fact that they actually slide against each other,” suggested Chase Lewis, 13, who is home-schooled in Chapel Hill, N.C.

The team events would award creativity and cooperation as the students spent a long weekend together brainstorming solutions to various challenges. At stake: national bragging rights and a top educational award of $25,000. Additional awards would go to participants who demonstrated great innovation and outstanding performance in applying science, technology, engineering and mathematics (or STEM).

“We’re just going to have bubble wrap as the outer layer,” declared Daniel Lu, 13, from Concord-Carlisle Regional High School in Concord, Mass. “Then we can fill the rest up with packing peanuts.”

And in the end, that’s what the White Team did. On Monday, Oct. 1, the four, along with Anna Lou, 12, from Oxford Academy in Cypress, Calif., layered the inside of a brown paper lunch bag with packing peanuts and bubble wrap. The layers cocooned a zippered plastic bag filled with more packing peanuts — and a single egg. The team then dropped the egg from waist-height. Would it survive the fall?

E is for engineering

This year’s Broadcom competition focused on engineering. Engineering means using math and science to design new things or to solve practical problems — such as cushioning a dropped egg.

As Samuel held out the White Team’s egg and let it drop, his teammates timed its fall. The students all remained confident the egg would land intact. Later, the team would use the time and the distance the egg fell to calculate how fast it was traveling when it hit the floor.

The competition gave the students a sense of the many different types of engineers. Software engineers, for instance, design computer programs. Structural engineers may design a bridge to handle heavy loads, to span especially long distances or to withstand harsh conditions. Chemical engineers create new foods or fuels. And civil engineers design sewers, tunnels, buildings and other structures that we reply upon in everyday life.

“ Thunk! ” The White Team’s egg hit the bottom of a plastic bin on the carpeted floor of the hotel where the competition was taking place. The five expectantly peered inside: The egg had survived!

Among the many problems that engineers tackle, protecting eggs is one that most consider pretty much solved. Egg cartons work well, and most people take care not to drop eggs anyway. Still, the egg drop provides a good challenge for young engineers — especially as the height increases.

For the second drop, the White Team had to release its egg from a height of about 6 feet (1.8 meters). Chase climbed onto a chair, held out the egg and let go. Would the egg survive this time?

The Broadcom finals challenged the competitors to think of the egg drop as analogous, or similar, to a car crash. Engineers design automobiles to protect the people inside, just like a paper bag filled with packing peanuts and bubble wrap can protect an egg.

Unfortunately, the White Team’s second drop didn’t end so well for the egg: “It broke into so many pieces!” Chase moaned. On the next try, the students added more bubble wrap, which solved the problem.

To reinforce the crash analogy, the six teams started the egg drop challenge by watching two videos from the Insurance Institute for Highway Safety. This organization studies how to keep people safe during car crashes. The videos showed crashes created under laboratory conditions. The vehicles contained no real drivers or passengers.

In the first video, a car ran into the back of a truck, but the crash wasn’t too bad. In the second video, the same type of car ran into the same type of truck. This time, the car was nearly destroyed as a portion of the truck pierced the car’s windshield — right where the driver’s head would have been.

Just like that second crash, not every egg drop turned out well. Eventually, the White Team figured out how to protect an egg enough to allow it to survive a drop from nearly 8 feet (2.4 meters) above the ground. All the other teams protected their eggs too — although not always on the first try.

To help the Broadcom MASTERS finalists understand how apparently similar crashes could turn out so differently, the Insurance Institute provided detailed measurements from the collisions it had conducted. The finalists then used these data to understand some critical differences. For instance, in the second crash, the car was traveling 10 miles (16 kilometers) an hour faster. Using this information, the students calculated how much harder the car struck the truck in the second crash. Then, each team applied the same principles to calculate how dropping their eggs from different heights would affect their speed.

Engineering safer conditions

Probing the dynamics of crashes (or drops) and how to limit damage is a good example of engineering. On the Sunday of the Broadcom finals weekend, engineer Julio Vargas offered others.

Vargas is president of the Washington, D.C., chapter of Engineers Without Borders. His group helps people in developing countries. In the last few years, his chapter has worked with people in Santa Clara, a rural town in the Central American country of El Salvador. Together, they are helping to bring clean drinking water to homes there.

The simple goal required some complicated engineering. The engineers first had to decide how to clean the water they retrieved from a well. Then they had to pump it uphill into a water tank. The engineers also had to design a system to distribute the stored water to about 400 separate households. Vargas and his coworkers spoke to the Broadcom finalists during a field trip to the Maryland Science Center in Baltimore. The engineers reviewed some of the math they used in figuring out how to distribute the water from the elevated holding tank to all of those homes.

Water leaving the holding tower runs downhill. But if the tower isn’t high enough, the water won’t leave it with enough energy to reach each home. So the engineers used a gauge to measure the water pressure at different heights. During the Broadcom MASTERS competition, student researchers simulated the problem using two buckets, some tubing and a stepladder.

“I had no idea that giving water to a city involved so much math,” said Varun Iyer, 14, a Purple Team member from Springfield High School in Illinois. Through this exercise, he noted, “I learned how you can use math in the real world.”

STEM’s least understood component

Varun is already well on his way to becoming an engineer — you could say it is a family tradition. His parents are software engineers, an uncle is a mechanical engineer and his grandfather was a civil engineer. Young Varun is interested in biomedical engineering. One day, he mused, he might design robots that help surgeons make precise cuts on delicate organs. He suspects even his little sister might join the profession. “She’s pretty good at math,” he said, and “likes science a lot.”

The Broadcom MASTERS focused on engineering in part because many young people don’t know what it is. Vargas explained to the finalists how he became interested in engineering when he happened to take a drafting class in high school.

Drafting involves the precise drawing of very detailed plans that others can use to build things, such as machines or buildings. Vargas began drafting using pencils and paper. Today, most engineers use computers. His early start in drafting inspired him to study engineering in college.

“Engineering is the ability to decipher a problem or need and come up with a solution,” Vargas explained. For example, Vargas works at IBM, where he makes sure that the computers people use to check in at airports are safe. Those computers may not seem dangerous, but that’s because of people like Vargas. A computer “could fall on you, it could shock you, it could have a sharp edge. It could fail and start a fire,” he said. So Vargas anticipates every possible way a computer might hurt you. He then designs ways to reduce the chance that any of those might happen.

Elizabeth Hubler, a student at George Washington University in Washington, D.C., also attended the Sunday event to share the joys of engineering with the finalists. Hubler is in her third year of college and majoring in mechanical engineering. Mechanical engineers design, build and run all types of machines. Hubler got interested in engineering in high school while competing with other young people to build robots that met certain challenges. “I was like, oh, this exists? That would be really cool to study in college!” Hubler said. Now she and a professor study how air flows when you talk — and how that changes when there’s a problem with your vocal cords.

Engineer Shane Lansing lives in California, but flew across the country to tell the young researchers about careers in engineering. Lansing works at Broadcom, the company that sponsors the MASTERS competition. There, he directs a team that designs the computer chips used in all types of communications devices, including the Wii video game console and iPhone.

“I loved being the kid that was always solving the math problems that none of the other kids could solve,” Lansing said. Work still offers that kind of competitive excitement, he said. In fact, most of his coworkers have held onto their childhood enthusiasm for problem solving, Lansing explained. Even as grownups, the Broadcom employees still tinker with things and play video games.

If you’re interested in engineering, play can be a great place to begin, Vargas said. “Buy a Lego set and just start experimenting,” he recommended. “That’s what I did.” Vargas would start by following the instructions. “Then I’d take it all apart and build it the way I wanted to build it.” Eventually, he would build something better — just as an engineer would.

Power Words

biomedical engineer An expert who uses science and math to find solutions to problems in biology and medicine; for example, they might create medical devices such as artificial knees.

civil engineer An engineer who creates buildings, tunnels, water systems and other large projects that improve everyday life.

computer chip (also integrated circuit ) The computer component that processes and stores information.

developing country A poorer country with relatively little industry and a lower standard of living than industrial countries, such as the United States and Canada.

drafting The creation of detailed plans or drawings that others can use to build something.

engineering The use of math and science to solve practical problems.

mechanical engineer An engineer who designs, builds or operates machines.

modem An electronic device that connects your computer to the Internet.

pressure gauge A device that measures pressure.

software engineer An engineer who designs and works with computer programs.

vocal cords Two folds in the human throat that vibrate to produce your voice.

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Tips for Solving Engineering Problems Effectively

Problem solving is the process of determining the best feasible action to take in a given situation. Problem solving is an essential skill for engineers to have. Engineers are problem solvers, as the popular quote says:

“Engineers like to solve problems. If there are no problems handily available, they will create their own problems.” – Scott Adams

Engineers are faced with a range of problems in their everyday life. The nature of problems that engineers must solve differs between and among the various disciplines of engineering. Because of the diversity of problems there is no universal list of procedures that will fit every engineering problem. Engineers use various approaches while solving problems.

Engineering problems must be approached systematically, applying an algorithm, or step-by-step practice by which one arrives at a feasible solution. In this post, we’ve prepared a list of tips for solving engineering problems effectively.

#1 Identify the Problem

Evaluating the needs or identifying the problem is a key step in finding a solution for engineering problems. Recognize and describe the problem accurately by exploring it thoroughly. Define what question is to be answered and what outputs or results are to be produced. Also determine the available data and information about the problem in hand.

An improper definition of the problem will cause the engineer to waste time, lengthen the problem solving process and finally arrive at an incorrect solution. It is essential that the stated needs be real needs.

As an engineer, you should also be careful not to make the problem pointlessly bound. Placing too many limitations on the problem may make the solution extremely complex and tough or impossible to solve. To put it simply, eliminate the unnecessary details and only keep relevant details and the root problem.

#2 Collect Relevant Information and Data

After defining the problem, an engineer begins to collect all the relevant information and data needed to solve the problem. The collected data could be physical measurements, maps, outcomes of laboratory experiments, patents, results of conducted surveys, or any number of other types of information. Verify the accuracy of the collected data and information.

As an engineer, you should always try to build on what has already been done before. Don’t reinvent the wheel. Information on related problems that have been solved or unsolved earlier, may help engineers find the optimal solution for a given problem.

#3 Search for Creative Solutions

There are a number of methods to help a group or individual to produce original creative ideas. The development of these new ideas may come from creativity, a subconscious effort, or innovation, a conscious effort.

You can try to visualize the problem or make a conceptual model for the given problem. So think of visualizing the given problem and see if that can help you gain more knowledge about the problem.

#4 Develop a Mathematical Model

Mathematical modeling is the art of translating problems from an application area into tractable mathematical formulations whose theoretical and numerical analysis provides insight, answers, and guidance useful for the originating application.

To develop a mathematical model for the problem, determine what basic principles are applicable and then draw sketches or block diagrams to better understand the problem. Then define and introduce the necessary variables so that the problem is stated purely in mathematical terms.

Afterwards, simplify the problem so that you can obtain the required result. Also identify the and justify the assumptions and constraints in the mathematical model.

#5 Use Computational Method

You can use a computational method based on the mathematical method you’ve developed for the problem. Derive a set of equations that enable the calculation of the desired parameters and variables as described in your mathematical model. You can also develop an algorithm, or step-by-step procedure of evaluating the equations involved in the solution.

To do so, describe the algorithm in mathematical terms and then execute it as a computer program.

#6 Repeat the Problem Solving Process

Not every problem solving is immediately successful. Problems aren’t always solved appropriately the first time. You’ve to rethink and repeat the problem solving process or choose an alternative solution or approach to solving the problem.

Bottom-line:

Engineers often use the reverse-engineering method to solve problems. For example, by taking things apart to identify a problem, finding a solution and then putting the object back together again. Engineers are creative , they know how things work, and so they constantly analyze things and discover how they work.

Problem-solving skills help you to resolve obstacles in a situation. As stated earlier, problem solving is a skill that an engineer must have and fortunately it’s a skill that can be learned. This skill gives engineers a mechanism for identifying things, figuring out why they are broken and determining a course of action to fix them.

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3 What is Problem Solving?

Chapter table of contents, what is problem solving.

• What Does Problem Solving Look Like?

Developing Problem Solving Processes

Summary of strategies, problem solving:  an important job skill.

The ability to solve problems is a basic life skill and is essential to our day-to-day lives, at home, at school, and at work. We solve problems every day without really thinking about how we solve them. For example: it’s raining and you need to go to the store. What do you do? There are lots of possible solutions. Take your umbrella and walk. If you don’t want to get wet, you can drive, or take the bus. You might decide to call a friend for a ride, or you might decide to go to the store another day. There is no right way to solve this problem and different people will solve it differently.

Problem solving is the process of identifying a problem, developing possible solution paths, and taking the appropriate course of action.

Why is problem solving important? Good problem solving skills empower you not only in your personal life but are critical in your professional life. In the current fast-changing global economy, employers often identify everyday problem solving as crucial to the success of their organizations. For employees, problem solving can be used to develop practical and creative solutions, and to show independence and initiative to employers.

what does problem solving look like?

The ability to solve problems is a skill at which you can improve.  So how exactly do you practice problem solving? Learning about different problem solving strategies and when to use them will give you a good start. Problem solving is a process. Most strategies provide steps that help you identify the problem and choose the best solution. There are two basic types of strategies: algorithmic and heuristic.

Algorithmic strategies are traditional step-by-step guides to solving problems. They are great for solving math problems (in algebra: multiply and divide, then add or subtract) or for helping us remember the correct order of things (a mnemonic such as “Spring Forward, Fall Back” to remember which way the clock changes for daylight saving time, or “Righty Tighty, Lefty Loosey” to remember what direction to turn bolts and screws). Algorithms are best when there is a single path to the correct solution.

But what do you do when there is no single solution for your problem? Heuristic methods are general guides used to identify possible solutions. A popular one that is easy to remember is IDEAL [Bransford & Stein [1] ] :

IDEAL is just one problem solving strategy. Building a toolbox of problem solving strategies will improve your problem solving skills. With practice, you will be able to recognize and use multiple strategies to solve complex problems.

What is the best way to get a peanut out of a tube that cannot be moved? Watch a chimpanzee solve this problem in the video below [Geert Stienissen [2] ].

Problem solving is a process that uses steps to solve problems. But what does that really mean? Let's break it down and start building our toolbox of problem solving strategies.

What is the first step of solving any problem? The first step is to recognize that there is a problem and identify the right cause of the problem. This may sound obvious, but similar problems can arise from different events, and the real issue may not always be apparent. To really solve the problem, it's important to find out what started it all. This is called identifying the root cause .

Example: You and your classmates have been working long hours on a project in the school's workshop. The next afternoon, you try to use your student ID card to access the workshop, but discover that your magnetic strip has been demagnetized. Since the card was a couple of years old, you chalk it up to wear and tear and get a new ID card. Later that same week you learn that several of your classmates had the same problem! After a little investigation, you discover that a strong magnet was stored underneath a workbench in the workshop. The magnet was the root cause of the demagnetized student ID cards.

The best way to identify the root cause of the problem is to ask questions and gather information. If you have a vague problem, investigating facts is more productive than guessing a solution. Ask yourself questions about the problem. What do you know about the problem? What do you not know? When was the last time it worked correctly? What has changed since then? Can you diagram the process into separate steps? Where in the process is the problem occurring? Be curious, ask questions, gather facts, and make logical deductions rather than assumptions.

When issues and problems arise, it is important that they are addressed in an efficient and timely manner. Communication is an important tool because it can prevent problems from recurring, avoid injury to personnel, reduce rework and scrap, and ultimately, reduce cost, and save money. Although, each path in this exercise ended with a description of a problem solving tool for your toolbox, the first step is always to identify the problem and define the context in which it happened.

There are several strategies that can be used to identify the root cause of a problem. Root cause analysis (RCA) is a method of problem solving that helps people answer the question of why the problem occurred. RCA uses a specific set of steps, with associated tools like the “5 Why Analysis" or the “Cause and Effect Diagram,” to identify the origin of the problem, so that you can:

Once the underlying cause is identified and the scope of the issue defined, the next step is to explore possible strategies to fix the problem.

If you are not sure how to fix the problem, it is okay to ask for help. Problem solving is a process and a skill that is learned with practice. It is important to remember that everyone makes mistakes and that no one knows everything. Life is about learning. It is okay to ask for help when you don’t have the answer. When you collaborate to solve problems you improve workplace communication and accelerates finding solutions as similar problems arise.

One tool that can be useful for generating possible solutions is brainstorming . Brainstorming is a technique designed to generate a large number of ideas for the solution to a problem. The goal is to come up with as many ideas as you can, in a fixed amount of time. Although brainstorming is best done in a group, it can be done individually.

Depending on your path through the exercise, you may have discovered that a couple of your coworkers had experienced similar problems. This should have been an indicator that there was a larger problem that needed to be addressed.

In any workplace, communication of problems and issues (especially those that involve safety) is always important. This is especially crucial in manufacturing where people are constantly working with heavy, costly, and sometimes dangerous equipment. When issues and problems arise, it is important that they be addressed in an efficient and timely manner.  Because it can prevent problems from recurring, avoid injury to personnel, reduce rework and scrap, and ultimately, reduce cost and save money; effective communication is an important tool..

One strategy for improving communication is the huddle . Just like football players on the field, a huddle is a short meeting with everyone standing in a circle.   It's always important that team members are aware of how their work impacts one another.  A daily team huddle is a great way to ensure that as well as making team members aware of changes to the schedule or any problems or safety issues that have been identified. When done right, huddles create collaboration, communication, and accountability to results. Impromptu huddles can be used to gather information on a specific issue and get each team member's input.

"Never try to solve all the problems at once — make them line up for you one-by-one.” — Richard Sloma

Problem solving improves efficiency and communication on the shop floor. It increases a company's efficiency and profitability, so it's one of the top skills employers look for when hiring new employees.  Employers consider professional skills, such as problem solving, as critical to their business’s success.

The 2011 survey, "Boiling Point? The skills gap in U.S. manufacturing [3] ," polled over a thousand manufacturing executives who reported that the number one skill deficiency among their current employees is problem solving, which makes it difficult for their companies to adapt to the changing needs of the industry.

• Bransford, J. & Stein, B.S. (). The Ideal Problem Solver: A Guide For Improving Thinking, Learning, And Creativity . New York, NY: W.H. Freeman. ↵
• National Geographic. [Geert Stienissen]. (2010, August 19). Insight learning: Chimpanzee Problem Solving [Video file]. Retrieved from http://www.youtube.com/watch?v=fPz6uvIbWZE ↵
• Report: Boiling Point: The Skills Gap in U.S. Manufacturing Deloitte / The Manufacturing Institute, October 2011. Retrieved from http://www.themanufacturinginstitute.org/Hidden/2011-Skills-Gap-Report/2011-Skills-Gap-Report.aspx ↵

Introduction to Industrial Engineering Copyright © 2020 by Bonnie Boardman is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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• Problem Solving

Lesson Problem Solving

Grade Level: 8 (6-8)

(two 40-minute class periods)

Lesson Dependency: The Energy Problem

Subject Areas: Physical Science, Science and Technology

• Print lesson and its associated curriculum

Curriculum in this Unit Units serve as guides to a particular content or subject area. Nested under units are lessons (in purple) and hands-on activities (in blue). Note that not all lessons and activities will exist under a unit, and instead may exist as "standalone" curriculum.

• Energy Forms and States Demonstrations
• Energy Conversions
• Watt Meters to Measure Energy Consumption
• Household Energy Audit
• Light vs. Heat Bulbs
• Efficiency of an Electromechanical System
• Efficiency of a Water Heating System
• Solving Energy Problems
• Energy Projects

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Scientists, engineers and ordinary people use problem solving each day to work out solutions to various problems. Using a systematic and iterative procedure to solve a problem is efficient and provides a logical flow of knowledge and progress.

• Students demonstrate an understanding of the Technological Method of Problem Solving.
• Students are able to apply the Technological Method of Problem Solving to a real-life problem.

Educational Standards Each TeachEngineering lesson or activity is correlated to one or more K-12 science, technology, engineering or math (STEM) educational standards. All 100,000+ K-12 STEM standards covered in TeachEngineering are collected, maintained and packaged by the Achievement Standards Network (ASN) , a project of D2L (www.achievementstandards.org). In the ASN, standards are hierarchically structured: first by source; e.g. , by state; within source by type; e.g. , science or mathematics; within type by subtype, then by grade, etc .

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Scientists, engineers, and ordinary people use problem solving each day to work out solutions to various problems. Using a systematic and iterative procedure to solve a problem is efficient and provides a logical flow of knowledge and progress.

In this unit, we use what is called "The Technological Method of Problem Solving." This is a seven-step procedure that is highly iterative—you may go back and forth among the listed steps, and may not always follow them in order. Remember that in most engineering projects, more than one good answer exists. The goal is to get to the best solution for a given problem. Following the lesson conduct the associated activities Egg Drop and Solving Energy Problems for students to employ problem solving methods and techniques. 

Lesson Background and Concepts for Teachers

The overall concept that is important in this lesson is: Using a standard method or procedure to solve problems makes the process easier and more effective.

The specific process of problem solving used in this unit was adapted from an eighth-grade technology textbook written for New York State standard technology curriculum. The process is shown in Figure 1, with details included below. The spiral shape shows that this is an iterative, not linear, process. The process can skip ahead (for example, build a model early in the process to test a proof of concept) and go backwards (learn more about the problem or potential solutions if early ideas do not work well).

This process provides a reference that can be reiterated throughout the unit as students learn new material or ideas that are relevant to the completion of their unit projects.

Brainstorming about what we know about a problem or project and what we need to find out to move forward in a project is often a good starting point when faced with a new problem. This type of questioning provides a basis and relevance that is useful in other energy science and technology units. In this unit, the general problem that is addressed is the fact that Americans use a lot of energy, with the consequences that we have a dwindling supply of fossil fuels, and we are emitting a lot of carbon dioxide and other air pollutants. The specific project that students are assigned to address is an aspect of this problem that requires them to identify an action they can take in their own live to reduce their overall energy (or fossil fuel) consumption.

The Seven Steps of Problem Solving

1.  Identify the problem

Clearly state the problem. (Short, sweet and to the point. This is the "big picture" problem, not the specific project you have been assigned.)

2.  Establish what you want to achieve

• Completion of a specific project that will help to solve the overall problem.
• In one sentence answer the following question: How will I know I've completed this project?
• List criteria and constraints: Criteria are things you want the solution to have. Constraints are limitations, sometimes called specifications, or restrictions that should be part of the solution. They could be the type of materials, the size or weight the solution must meet, the specific tools or machines you have available, time you have to complete the task and cost of construction or materials.

3.  Gather information and research

• Research is sometimes needed both to better understand the problem itself as well as possible solutions.
• Don't reinvent the wheel – looking at other solutions can lead to better solutions.
• Use past experiences.

4.  Brainstorm possible solutions

List and/or sketch (as appropriate) as many solutions as you can think of.

5.  Choose the best solution

Evaluate solution by: 1) Comparing possible solution against constraints and criteria 2) Making trade-offs to identify "best."

6.  Implement the solution

• Develop plans that include (as required): drawings with measurements, details of construction, construction procedure.
• Define tasks and resources necessary for implementation.
• Implement actual plan as appropriate for your particular project.

7.  Test and evaluate the solution

• Compare the solution against the criteria and constraints.
• Define how you might modify the solution for different or better results.
• Egg Drop - Use this demonstration or activity to introduce and use the problem solving method. Encourages creative design.
• Solving Energy Problems - Unit project is assigned and students begin with problem solving techniques to begin to address project. Mostly they learn that they do not know enough yet to solve the problem.
• Energy Projects - Students use what they learned about energy systems to create a project related to identifying and carrying out a personal change to reduce energy consumption.

The results of the problem solving activity provide a basis for the entire semester project. Collect and review the worksheets to make sure that students are started on the right track.

Learn the basics of the analysis of forces engineers perform at the truss joints to calculate the strength of a truss bridge known as the “method of joints.” Find the tensions and compressions to solve systems of linear equations where the size depends on the number of elements and nodes in the trus...

Through role playing and problem solving, this lesson sets the stage for a friendly competition between groups to design and build a shielding device to protect humans traveling in space. The instructor asks students—how might we design radiation shielding for space travel?

A process for technical problem solving is introduced and applied to a fun demonstration. Given the success with the demo, the iterative nature of the process can be illustrated.

The culminating energy project is introduced and the technical problem solving process is applied to get students started on the project. By the end of the class, students should have a good perspective on what they have already learned and what they still need to learn to complete the project.

Hacker, M, Barden B., Living with Technology , 2nd edition. Albany NY: Delmar Publishers, 1993.

Other Related Information

This lesson was originally published by the Clarkson University K-12 Project Based Learning Partnership Program and may be accessed at http://internal.clarkson.edu/highschool/k12/project/energysystems.html.

Contributors

Supporting program, acknowledgements.

This lesson was developed under National Science Foundation grants no. DUE 0428127 and DGE 0338216. However, these contents do not necessarily represent the policies of the National Science Foundation, and you should not assume endorsement by the federal government.

Last modified: August 16, 2023

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Problem-solving for Engineers: Root Cause Analysis Fundamentals (Virtual Classroom)

Credits: CEUs: 2.3 | PDHs: 23.0

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Learn root cause analysis (RCA) fundamentals, explore RCA tools' purpose and application, and perform RCA on real-world problems to find solutions.

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This course commences at 9:30 AM and ends at 6PM each day, with breaks scheduled throughout.

Even with the best quality systems and training, problems can happen. Root cause analysis (RCA) describes a wide range of approaches, tools, and techniques used to uncover causes of problems. For engineers, this could be applied to failure analysis in engineering and maintenance, quality control problems, safety performance, and computer systems or software analysis. The goal of RCA is to identify the origin of a problem using a systematic approach and determine:

• What happened
• Why it happened
• How to reduce the likelihood that it happens again
• How to launch a solution implementation plan

This three-day course provides a collaborative and dynamic learning environment that affords the participant the ability to perform RCA on real-world problems and overlay solutions to the problems. Each RCA tool is presented in an easy-to-follow structure: a general description of the tool, its purpose and typical applications, the procedure when using it, an example of its use, a checklist to help you make sure it is applied properly, and different forms and templates.

The examples used can be tailored to many different industries and markets, including manufacturing, robotics, bioengineering, energy, and pressure technology. The layout of this course has been designed to help speed participants’ learning through short videos depicting well-known scenarios for analysis in class. Course Materials (included in purchase of course):  Digital course notes via ASME’s Learning Platform 

By participating in this course, you will learn how to successfully:

• Explain the concept of root cause analysis
• Describe how to use tools for problem cause brainstorming
• Ask the right questions; establish triggers that drive you to the RCA process
• Develop strategies for problem cause data collection and analysis
• Deploy tools for root cause identification and elimination
• Perform a cost-benefit analysis
• Practice ways of implementation solutions

Who should attend? This course is intended for engineers and technical professionals involved in flow of complex processes, materials and equipment, or those who serve in a project or product management function. This  ASME Virtual Classroom  course is held live with an instructor on our online learning platform. A Certificate of Completion will be issued to registrants who successfully attend and complete the course. Can't make one of the scheduled sessions? This course is also available On Demand.

• Introduction to Root Cause Analysis (RCA)
• The need and the practice
• Defining a Problem
• Strategies to Solve Problems
• Understanding Causes and Its Levels
• Finding Root Causes
• Eliminating Root Causes
• Proactive Problem Solving
• Case Studies & Hands-on Activity
• Defining Root Cause Analysis
• Conducting Root Cause Analysis
• Case Study & Group Activity
• Problem Understanding
• The Purpose and Applications of Flowcharts
• Using Flowcharts
• Using Critical Incidents
• Using Performance Matrices
• Problem Cause Brainstorming
• The Purpose and Application of Brainstorming
• Brainstorming Recording Templates
• Problem Cause Data Collection
• Taking Advantage of Samplings
• Steps in Using Samplings
• Taking Advantage of SurveysUsing Check Sheets
• Problem Cause Data Collection Checklist
• Understanding Problem Cause Data Analysis
• The Purpose and Application of Histograms
• Using and Interpreting Histograms
• Using Relations Diagram
• Case Study & Hands-on Activity
• Fundamentals of Root Cause Identification
• Using Cause-and-Effect Diagrams
• Using the Five Whys Method
• Using the Fault Tree Analysis Technique
• An Overview of Root Cause Elimination
• Using DeBono’s Six Hats
• Overview of Solution Implementation
• Organizing the Implementation
• Developing an Implementation Plan
• Using Tree Diagrams
• Creating Change Acceptance
• The Purpose and Application of Force-Field Analysis
• What to Watch for When Using Tools and Techniques
• Selecting the Right Tool
• Example Cases and Practice

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7 Surprising Ways Engineering Has Solved Everyday Problems

In a culture where hacking and repurposing are commonplace, engineering-minded designers transform everyday items into innovative solutions. By following design-cycle steps, they turn science fiction into reality, addressing pervasive everyday problems. This post explores real-world challenges, showcasing the transformative power of engineering solutions—from the coffee cup sleeve to the selfie stick and beyond. Consider inventive creations that improve accessibility and tackle health issues. Discover the limitless potential of engineering expertise to address contemporary challenges and improve lives.

Expertise to Create the Unexpected

We live in a hacking culture where we break down and repurpose everything from IKEA furniture to power tools, redesigning them to fill a need or solve a problem for which they were not originally intended. By applying some of the basic design-cycle steps of Ask, Research, Imagine, Plan, Create, Test and Improve, engineering-minded product designers are turning what might have once been considered science fictional solutions into reality.

By sharpening your engineering skill set , you can put yourself in a unique position to address some pervasive everyday problems. Which would you like to take on? For a little inspiration, take a look at some real-world everyday challenges, big and small, that have been alleviated by some rather innovative engineering solutions.

Squeezing Out the Last Drop of Liquid

We’ve all experienced the frustration of attempting to squeeze the last drop of ketchup or toothpaste from their containers. That could end very soon, all thanks to a unique slippery coating that keeps thick, gooey substances from sticking to solid surfaces.

Called LiquiGlide, this material was initially was created to line oil and gas pipelines to protect against buildup. 1 It worked so well that the team developing this technology at MIT decided to explore other commercial applications for it. They researched and tested different combinations of materials to create new variations of LiquiGlide, including food-grade and medical-grade versions. These can help reduce product waste and enable viscous liquid medications to efficiently empty from tubes to improve proper dosing.

Holding Hot Coffee Without Spilling It

The coffee cup sleeve: With such deceptively simple design and such obvious value, it’s hard to believe it wasn’t invented sooner than it was, back in 1991. The idea was born two years prior, when piping hot coffee in a paper to-go cup burned the hands (and subsequently spilled on the lap) of future Java Jacket founder Jay Sorensen.

Sorensen did considerable research on the potential market demand for such a product, the kinds of materials that could be used to cost-effectively create it and the most successful physical design. He produced and tested several iterations of the sleeve before landing on the prototype that is still used today. 2 Now, the nearly ubiquitous coffee cup sleeves are helping save the fingers (and laps) of countless hot-java-drinking commuters—not to mention engineers.

A Far-Reaching Solution for Getting the Group Shot

By freeing us from having to rely on a willing passerby to take a group photo in front of a tourist attraction or a silhouette shot against a stunning sunset, the selfie stick has certainly made an impact in today’s social-media-savvy world.

Wayne Fromm didn’t invent camera-on-a-stick technology, but in 2005 he did patent a version that could hold almost any camera and, eventually, nearly any smartphone. 3 That’s the version that began to resonate with consumers worldwide.

Since then, the original selfie stick concept has evolved into several iterations by Fromm and other manufacturers to answer the demand for more uses—including ones that extend telescopically at the push of a button so you can fit more people or more background into your shot, that allow you to snap a shot via Bluetooth without needing to set the camera timer, or that take blur-free photographs and video while skydiving or partaking in other action sports.

Walking Your Way to Health at Work

Dr. James Levine, a medical doctor who researches obesity, found that sitting for several hours at a time negatively impacts our health much more than initially thought, even for those who regularly go to the gym. He argued that our increasingly sedentary lifestyle, fueled by demands at work requiring us to be at our desks, has contributed to a culture of people with poor posture, lack of energy, and increased risk of heart disease and diabetes.

Levine came up with a rather unusual solution: He rigged a used treadmill under a raised bedside tray. 4 Perhaps this prototype he created in 1999 wasn’t the most attractive setup, but its goal was clear: to give people a way to be active while working and help reduce sitting-related health risks.

Levine worked with a manufacturer to produce the first official treadmill desk, released in 2007. Today, many companies promoting a healthier workplace offer employees the option to have such a desk instead of a traditional one.

Overcoming Fear of Public Speaking

Sophia Velastegui, an influential engineer in the technology sector, applied several engineering design steps early in her career to conquer a common phobia: speaking in front of a crowd. 5

Velastegui did this by:

• Identifying specific problems to address: her shyness and fear of public speaking
• Looking into ways to work on them (such as volunteering to speak at company meetings)
• Setting up a plan of action to overcome her shyness with strangers: research people to meet at conferences, contact them, choose discussion topics and maintain regular contact
• Continuing to improve her speaking and networking skills through constant practice

Velastegui’s process improved her public speaking—and her confidence and management skills—so thoroughly that it has been invaluable to her rise through desirable positions at top companies. Not only that, she was named to Business Insider's list of most powerful female engineers in 2017.

Eating With Confidence, Without Spilling

Many of us take the simple act of feeding ourselves for granted. But for anyone with trembling hands, it can be a frustrating struggle to keep food on a fork or spoon long enough to reach their mouth without it winding up on the table or their clothing. Liftware Level™ utensils were created by inventors with loved ones experiencing such limitations.

Liftware uses sensor technology that makes real-time adjustments to accommodate any mild-to-severe shaking and trembling movements. 6 This improves accessibility and independence for those suffering from conditions such as Parkinson’s disease.

Liftware developers are taking their testing to a new level: They created an app that records motion data using an accelerometer sensor found in smartphones. They use this data when creating prototypes for versions of other common products that can be used by people with disabilities.

Diagnosing Deep Gastrointestinal Diseases

In 1981, inspired by a friend experiencing small intestine pain with no apparent source, rocket engineer Gavriel Iddan wondered if there was a way to create a “missile”—complete with a camera—that could be launched into the intestine to snap photographs in order to help physicians make accurate diagnoses.

Applying his knowledge of rocket engineering to a completely unrelated problem led to his invention of the ingestible camera. “PillCam” actually took 17 years to become reality, thanks to Iddan’s diligence and the development of micro cameras, transmitters and LED lights that could fit into a large pill-sized capsule. 7

Now the diagnostic standard, doctors can properly identify conditions that are deep in the digestive tract, areas previously unreachable by other nonsurgical methods.

Put Your Engineering Skills to Use

The world is full of countless challenges waiting for that one solution to be created or tweaked that can make life just a little easier, healthier or better. What problems are you planning on tackling with an engineering approach? What inefficiencies are you improving? And better yet, how many more opportunities might present themselves as you continue to hone your engineering expertise?

Using your engineering knowledge, there’s no limit to what you can do. Explore our online graduate engineering degree programs at Case Western Reserve University to get started improving the world around you today.

• Retrieved on September 8, 2018, from liquiglide.com/
• Retrieved on September 8, 2018, from smithsonianmag.com/arts-culture/how-the-coffee-cup-sleeve-was-invented-119479/
• Retrieved on September 8, 2018, from businessinsider.com/wayne-fromm-is-the-inventor-of-the-modern-selfie-stick-2015-8
• Retrieved on September 8, 2018, from newyorker.com/magazine/2013/05/20/the-walking-alive
• Retrieved on September 8, 2018, from businessinsider.com/how-this-engineer-hacked-her-career-and-became-a-gm-at-microsoft-2018-2
• Retrieved on September 8, 2018, from launchforth.io/blog/post/invention-spotlight-liftware-level/2335/
• Retrieved on September 8, 2018, from epo.org/learning-events/european-inventor/finalists/2011/iddan/impact.html

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10 major engineering challenges of the next decade

Challenges related to climate change continue to threaten the world's population. But solutions exist, and engineers will play a major role in solving them. Here are 10 major challenges and what engineers can do about them in 2023.

1. Upgrading the aging U.S. infrastructure

The American Society of Civil Engineers gives our  aging infrastructure opens in new tab/window  a C- grade and  estimates  that the U.S. is spending just over half of what is needed. Significant action is needed to bring our roads opens in new tab/window , bridges, opens in new tab/window   water opens in new tab/window ,  electrical opens in new tab/window  and  sewage systems opens in new tab/window  to proper safe working order.

2. Educating first world engineers to understand how to solve third world problems

The Renewable Resources Journal  reports  that the world’s population will grow by two billion over the next two decades, 95% of this in developing or underdeveloped countries. Engineers must learn new ways to  solve problems opens in new tab/window in these countries.

3. Promoting  green engineering opens in new tab/window  to improve sustainability and reduce the carbon footprint in manufacturing opens in new tab/window

According to the U.S. Office of Energy Efficiency & Renewable Energy, manufacturing in the U.S. produced 19,237 trillion BTUs and 1,071 million metric tons of  carbon dioxide opens in new tab/window .

4. Identifying viable  and renewable energy opens in new tab/window  sources

The contributions to our energy production from renewables opens in new tab/window and other  new fuel opens in new tab/window  sources are growing at 6% per year according to BP  and will contribute 45% of the increment in  energy production opens in new tab/window  by 2035.

5. Rethinking how the  city opens in new tab/window  looks and works

54% of the world’s population lives in cities. Europe leads the way in sustainability opens in new tab/window , with seven out of the world’s top 10 most sustainable cities, according to the  ARCADIS Sustainable Cities Index.

6. Making  STEM more appealing opens in new tab/window  to young students

By 2018, the United States  will have  more than 1.2 million unfilled STEM jobs. Meanwhile, according to  a UCLA study, 40% of students enrolled as STEM majors switched subjects or failed to get a degree.

7. Safeguarding our personal data and wealth from cyberattacks opens in new tab/window

34% of data breaches happen at financial institutions; 11% target retail companies; while 13% target government institutions opens in new tab/window , according to the  2014 Data Breach Investigation Report.

8. Addressing  climate change opens in new tab/window through engineering innovation

Six of the 10 cities with the largest annual flood opens in new tab/window costs by 2050  are in India and China.  Three are in the U.S.: New York, Miami and New Orleans.

9. Feeding our growing population through cutting-edge bio-engineering and  agricultural innovations opens in new tab/window

The  U.N. warns  that we must produce 60% more food by 2050 to keep up with demand, but how do we do this sustainably? Food opens in new tab/window and  water opens in new tab/window  access will be major issues in the future, and research must begin now.

10. Improving our health and well-being through life sciences, nanotechnology opens in new tab/window  &  bio-engineering opens in new tab/window

Administration on Aging,  by 2060 the population of Americans aged 65 and older will have more than doubled in size from 2011. This puts a lot of pressure on new drug creation and also on innovative engineering techniques to deliver drugs opens in new tab/window .

The Engineering Method: A Step-by-Step Process for Solving Challenging Problems

Getting stuck before you even begin to work on an engineering problem is more common than you think. Use this method to help you break a problem down, find a path toward a solution, and avoid mistakes.

Have you ever experienced the feeling of panic when presented with a problem you’ve never seen before, you’re on a deadline, and all eyes are on you? Maybe it’s your manager asking you to work on something urgent and mission-critical, or in an interview for your dream job and you’re across the table from the one person standing in your way of getting the offer. Most of us have felt this moment of panic at some point or another. In my experience as a tech lead and engineering manager at companies like Microsoft and Meta (then Facebook), the people who are most successful in these situations aren’t always the ones who are the smartest or have the most experience. Rather, it’s the people who used effective strategies for solving problems they hadn’t seen before. The junior engineers who do this well build experience and grow quickly. The higher-level engineers who have honed these skills are the key players on their teams and are often the best equipped to mentor the junior engineers. But how do people consistently make progress when working on something new and challenging? What do you do when you really have no idea where to start? We created The Engineering Method , a simple set of steps that can help you break a problem down, find a path toward a solution, and avoid mistakes. Here at Formation, we apply the method to software engineering problems, but the same steps can be applied to many different fields.

Step 1: Thoroughly understand the problem

A. ask clarifying questions.

Good questions often fall into one of two categories. They’re either questions that:

• Gather requirements to precisely understand what the goal is or isn’t
• Search for subtle edge cases or exceptions to the rules of the problems

B. Come up with your own “happy case” examples

A “happy case” refers to a normal input that isn’t trying to test the edge cases of the question. Happy path and “golden path” are also terms used to describe this. What does the feature, algorithm, or system need to do in the normal cases? What do these normal cases look like? At this stage, you are using the information gathered to and testing your understanding. You want to be able to generate 2-3 more examples that illustrate the requirements. These examples are going to help you find the patterns that will lead you towards possible approaches.

C. Come up with edge cases

Once you’ve come up with some “happy cases” and understand the problem, test the limits. Try not to go straight to the easy ones like, “what if it’s null”. This is useful for the implementation but is unlikely to inform the algorithm. Instead, try to think about logical edge cases that are more unique to that specific problem. Some edge cases are easier to deal with in some designs versus others. Listing out these edge cases now will help you choose between multiple implementation options later. For an algorithmic problem, here are some of the most common categories of edge cases:

• Negative numbers
• Empty cases (Empty array, string)
• Out of bounds
• Cycles in lists
• In problems with arrays or matrices, often the literal edges of the data have interesting quirks

Step 2: Identify and explore possible solutions

Always try to identify multiple solutions. Weigh the pros and cons of the different solutions and then select one to try. Sometimes, an attempt doesn’t end up working out, but the data gathered from these unsuccessful ideas often informs better ideas. For example, let’s say you’re attempting to solve something with depth-first search. While testing the idea on an example, you might discover that it doesn’t work. The way it doesn’t work might lead you to discover that breadth-first search does work. Keeping your work on these unsuccessful ideas might help inform your eventual correct solution.

A. Identify a simple solution

Try the simple thing first. If no obvious solution comes to you right away, work through a list of major solution archetypes for the type of problem.

• Algorithmic problem: stack, queue, BFS, DFS, dynamic programming, etc.
• Systems: map/reduce, key/value store, work queue

B. Work through some example cases manually

Take the sample input or ideas from the exploration phase and try to solve it manually. As you solve it, try to see if you can generalize your decisions. If you can't, try making small variations to your input or using input ideas from Step 1 to see if you can identify the patterns.

C. Work out the expected time and space complexity of each idea

Runtime and space complexity is one important part of comparing solutions. Often, but not always, we want to implement the solution with optimal complexity.

Step 3: Choose a solution

A big part of engineering is making decisions and choosing between different implementation options. Proactively point out the advantages/disadvantages of your ideas. If there is more than one solution on the table, you should consider which makes the most sense build. Often, there is no one best answer, so think about trade-offs and discuss the reasoning behind your decision with your team. Consider time/space complexity, but remember this is only one consideration. You should also consider difficulty of implementation. Remember, simple solutions are often best, especially in a time crunch.

Step 4: Make a plan

You’ve decided on a basic approach, an algorithm, or a high-level system design. It’s time to build it now, right? Not quite. Even for simple problems, a little bit of planning can go a long way. For something small, it might be as simple as splitting out some helper functions. Oftentimes, just starting at the top can lead to confusion and bugs. Starting with small, testable building blocks is often better.

Step 5: Build it

At this point, with a clear understanding of the solution and an implementation plan, the actual coding will go much more smoothly. As problems become more difficult, it becomes more and more important to explicitly split out these steps. Doing so will avoid costly mistakes and, while it feels slow, it will often be much faster in the long run. For problems that seem simple enough that you can effectively jump into coding quickly, remember that you’ve not actually skipped the preceding steps. You just did them ahead of time in your past work or practice and are utilizing that past experience.

Step 6: Test it

You've written a solution. You're done. Right? NO. As an engineer, you are always responsible for the results and quality of your work. As you uncover potential issues, you will be moving back and forth between 4 and 5. The good news in this step is that you should have already done most of the work! Some test cases should have come from Steps 1 and 2.

A. Test the happy cases

Start with a standard happy case . For example, for sorting, you might test a standard unsorted array: [3, 5, 4, 1, 2]. Pick a few important test cases you have identified earlier in Steps 2 and 3.

B. Test the edge cases

You should be picking from edge cases that you already identified earlier. Once you’ve written the code, you may also discover some implementation-level edge cases. A good way to make sure that you’ve covered all edge cases is to ensure that every line of code is executed at least once ! This is also the time to handle truly malformed input.

Wrapping Up

As engineers, our job is to bring new ideas to life. This often means doing something new, something different than we’ve done before, or maybe even building something that has never existed before. This means we can expect to encounter problems we haven’t seen previously. Experience can build some intuition that can seep things up, but the people who solve new problems the best are the ones who can make progress even when experience and intuition can’t help. Feeling panic set in? Take a deep breath, then follow the Engineering Method. Looking for structure in your job hunt? Apply below to close your most urgent interviewing skill gaps and interview with confidence.

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What Is Problem Solving? How Software Engineers Approach Complex Challenges

From debugging an existing system to designing an entirely new software application, a day in the life of a software engineer is filled with various challenges and complexities. The one skill that glues these disparate tasks together and makes them manageable? Problem solving . 

Throughout this blog post, we’ll explore why problem-solving skills are so critical for software engineers, delve into the techniques they use to address complex challenges, and discuss how hiring managers can identify these skills during the hiring process. 

What Is Problem Solving?

But what exactly is problem solving in the context of software engineering? How does it work, and why is it so important?

Problem solving, in the simplest terms, is the process of identifying a problem, analyzing it, and finding the most effective solution to overcome it. For software engineers, this process is deeply embedded in their daily workflow. It could be something as simple as figuring out why a piece of code isn’t working as expected, or something as complex as designing the architecture for a new software system. 

In a world where technology is evolving at a blistering pace, the complexity and volume of problems that software engineers face are also growing. As such, the ability to tackle these issues head-on and find innovative solutions is not only a handy skill — it’s a necessity. 

The Importance of Problem-Solving Skills for Software Engineers

Problem-solving isn’t just another ability that software engineers pull out of their toolkits when they encounter a bug or a system failure. It’s a constant, ongoing process that’s intrinsic to every aspect of their work. Let’s break down why this skill is so critical.

Driving Development Forward

Without problem solving, software development would hit a standstill. Every new feature, every optimization, and every bug fix is a problem that needs solving. Whether it’s a performance issue that needs diagnosing or a user interface that needs improving, the capacity to tackle and solve these problems is what keeps the wheels of development turning.

It’s estimated that 60% of software development lifecycle costs are related to maintenance tasks, including debugging and problem solving. This highlights how pivotal this skill is to the everyday functioning and advancement of software systems.

Innovation and Optimization

The importance of problem solving isn’t confined to reactive scenarios; it also plays a major role in proactive, innovative initiatives . Software engineers often need to think outside the box to come up with creative solutions, whether it’s optimizing an algorithm to run faster or designing a new feature to meet customer needs. These are all forms of problem solving.

Consider the development of the modern smartphone. It wasn’t born out of a pre-existing issue but was a solution to a problem people didn’t realize they had — a device that combined communication, entertainment, and productivity into one handheld tool.

Increasing Efficiency and Productivity

Good problem-solving skills can save a lot of time and resources. Effective problem-solvers are adept at dissecting an issue to understand its root cause, thus reducing the time spent on trial and error. This efficiency means projects move faster, releases happen sooner, and businesses stay ahead of their competition.

Improving Software Quality

Problem solving also plays a significant role in enhancing the quality of the end product. By tackling the root causes of bugs and system failures, software engineers can deliver reliable, high-performing software. This is critical because, according to the Consortium for Information and Software Quality, poor quality software in the U.S. in 2022 cost at least $2.41 trillion in operational issues, wasted developer time, and other related problems.

Problem-Solving Techniques in Software Engineering

So how do software engineers go about tackling these complex challenges? Let’s explore some of the key problem-solving techniques, theories, and processes they commonly use.

Decomposition

Breaking down a problem into smaller, manageable parts is one of the first steps in the problem-solving process. It’s like dealing with a complicated puzzle. You don’t try to solve it all at once. Instead, you separate the pieces, group them based on similarities, and then start working on the smaller sets. This method allows software engineers to handle complex issues without being overwhelmed and makes it easier to identify where things might be going wrong.

Abstraction

In the realm of software engineering, abstraction means focusing on the necessary information only and ignoring irrelevant details. It is a way of simplifying complex systems to make them easier to understand and manage. For instance, a software engineer might ignore the details of how a database works to focus on the information it holds and how to retrieve or modify that information.

Algorithmic Thinking

At its core, software engineering is about creating algorithms — step-by-step procedures to solve a problem or accomplish a goal. Algorithmic thinking involves conceiving and expressing these procedures clearly and accurately and viewing every problem through an algorithmic lens. A well-designed algorithm not only solves the problem at hand but also does so efficiently, saving computational resources.

Parallel Thinking

Parallel thinking is a structured process where team members think in the same direction at the same time, allowing for more organized discussion and collaboration. It’s an approach popularized by Edward de Bono with the “ Six Thinking Hats ” technique, where each “hat” represents a different style of thinking.

In the context of software engineering, parallel thinking can be highly effective for problem solving. For instance, when dealing with a complex issue, the team can use the “White Hat” to focus solely on the data and facts about the problem, then the “Black Hat” to consider potential problems with a proposed solution, and so on. This structured approach can lead to more comprehensive analysis and more effective solutions, and it ensures that everyone’s perspectives are considered.

This is the process of identifying and fixing errors in code . Debugging involves carefully reviewing the code, reproducing and analyzing the error, and then making necessary modifications to rectify the problem. It’s a key part of maintaining and improving software quality.

Testing and Validation

Testing is an essential part of problem solving in software engineering. Engineers use a variety of tests to verify that their code works as expected and to uncover any potential issues. These range from unit tests that check individual components of the code to integration tests that ensure the pieces work well together. Validation, on the other hand, ensures that the solution not only works but also fulfills the intended requirements and objectives.

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Evaluating Problem-Solving Skills

We’ve examined the importance of problem-solving in the work of a software engineer and explored various techniques software engineers employ to approach complex challenges. Now, let’s delve into how hiring teams can identify and evaluate problem-solving skills during the hiring process.

Recognizing Problem-Solving Skills in Candidates

How can you tell if a candidate is a good problem solver? Look for these indicators:

• Previous Experience: A history of dealing with complex, challenging projects is often a good sign. Ask the candidate to discuss a difficult problem they faced in a previous role and how they solved it.
• Problem-Solving Questions: During interviews, pose hypothetical scenarios or present real problems your company has faced. Ask candidates to explain how they would tackle these issues. You’re not just looking for a correct solution but the thought process that led them there.
• Technical Tests: Coding challenges and other technical tests can provide insight into a candidate’s problem-solving abilities. Consider leveraging a platform for assessing these skills in a realistic, job-related context.

Assessing Problem-Solving Skills

Once you’ve identified potential problem solvers, here are a few ways you can assess their skills:

• Solution Effectiveness: Did the candidate solve the problem? How efficient and effective is their solution?
• Approach and Process: Go beyond whether or not they solved the problem and examine how they arrived at their solution. Did they break the problem down into manageable parts? Did they consider different perspectives and possibilities?
• Communication: A good problem solver can explain their thought process clearly. Can the candidate effectively communicate how they arrived at their solution and why they chose it?
• Adaptability: Problem-solving often involves a degree of trial and error. How does the candidate handle roadblocks? Do they adapt their approach based on new information or feedback?

Hiring managers play a crucial role in identifying and fostering problem-solving skills within their teams. By focusing on these abilities during the hiring process, companies can build teams that are more capable, innovative, and resilient.

Key Takeaways

As you can see, problem solving plays a pivotal role in software engineering. Far from being an occasional requirement, it is the lifeblood that drives development forward, catalyzes innovation, and delivers of quality software. 

By leveraging problem-solving techniques, software engineers employ a powerful suite of strategies to overcome complex challenges. But mastering these techniques isn’t simple feat. It requires a learning mindset, regular practice, collaboration, reflective thinking, resilience, and a commitment to staying updated with industry trends. 

For hiring managers and team leads, recognizing these skills and fostering a culture that values and nurtures problem solving is key. It’s this emphasis on problem solving that can differentiate an average team from a high-performing one and an ordinary product from an industry-leading one.

At the end of the day, software engineering is fundamentally about solving problems — problems that matter to businesses, to users, and to the wider society. And it’s the proficient problem solvers who stand at the forefront of this dynamic field, turning challenges into opportunities, and ideas into reality.

This article was written with the help of AI. Can you tell which parts?

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Problems Worth Solving Competition

Check Out the 2024 Winners

Steve Jobs famously said, “You've got to start with the customer experience and work back toward the technology, not the other way around.”

Understanding your customers’ experiences requires that you become experts in the progress they are trying to make in their businesses and personal lives and the problems they encounter. The Problems Worth Solving Competition asks participants to explore this critical part of the entrepreneur’s journey.

This competition does not require a solution to the problem, only an important problem that could be the seed of a startup or investment made by an established enterprise.

Contest entries will use an online form that asks for short answers and to upload a short 60-second video. One application per person will be limited, and only the first 75 entries will be accepted.

Five finalists will be invited to present their problems live in front of judges at the main event. The presentations will consist of five minutes of a student pitching their problem and five minutes of Q&A. No slides will be used during the presentation, but you may bring one visual aid if appropriate.

There will also be three honorable mention prizes awarded. Honorable Mention winners will receive a cash award and an invitation to a private reception with judges and finalists at the Main Event.

Each finalist will receive a cash prize. Judges will determine the prize winners as follows:

• $2,500 - First Place
• $2,000 - Second Place
• $1,500 - Third Place
• $1,000 - Fourth Place
• $500 - Fifth Place
• $250 - Honorable Mention (3 awarded)

In addition to a $2,500 award, the first-place finisher will also be awarded the honor of being the guest sponsor of a 2024-2025  Aggies Invent . One Aggies Invent for next school year will feature the winner’s problem as the theme for the event, and the winner will present their problem, provide coaching during the event and serve as one of the event judges.

Judging Criteria

We will have judges representing Texas A&M faculty from across campus as well as entrepreneurs, innovators and experts from industry. 

Judges will evaluate the entries based on the following:

• Is the problem clear and concise?
• Is it clear what people and/or organizations have the problem?
• Is a compelling case made that the people/organizations with the problem perceive it to be worth spending money to solve?
• Is the target market large enough to support a high-growth business?
• Is it clear that the existing solutions to solve the problem are inadequate or absent?
• Do not pitch your business or product idea. Doing so will disqualify your application. Pitch problems only.

In addition to these criteria, finalists will be judged on their ability to communicate through their presentations and during Q&A.

Eligibility

All current Texas A&M College Station students are eligible.

You must be able to be physically present for the finalist workshop and final event. Do not apply if you cannot be on campus for these events.

Finalists from previous Problems Worth Solving competitions are not eligible.

Application Tips

The best problems will combine the popularity (how many people or organizations have the problem), severity (how much pain, cost or opportunity is created) and importance (with what significance do people perceive the problem).

The written portion of the application should be used to illustrate the details and specifics surrounding your selected problem. A general problem statement won’t differentiate your application. "Everyone" is not specific enough to describe who has the problem you are presenting.

Use credible external sources to support any examples and estimates. Be sure to cite those sources.

The videos may be recorded using your laptop, smartphone or a more sophisticated camera. Pay attention to the lighting, background noises, recording volume and other distractions. Please make sure you are visible for the majority of the video as we would like to see you present the problem. You may use multimedia, advanced editing software and other embellishments, but they are not required. The judges will use the video to determine how well you know the problem and your ability to communicate it.

Additional Resources

How to Evaluate Startup Ideas , a video featuring Kevin Hale from Y Combinator incubator that explains attributes of good problems for a startup to solve

The Problem Statement Canvas for Startups and Innovation Teams , an article by Marius Ursache about what makes a good problem statement and how to make yours better

99 Startup Problems , a summary of problem statements from some of the most successful tech companies

Intellectual Property

All intellectual property is owned by the competitors unless otherwise assigned. In developing their application, pitch and presentation materials, competitors should again keep in mind that it is their full responsibility to protect all proprietary and confidential information.

Competitors concerned about the protection of intellectual property may research intellectual property protection at the Texas A&M University Libraries Patent & Trademark Resource Center or the United States Patent and Trademark Office.

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How Chain-of-Thought Reasoning Helps Neural Networks Compute

March 21, 2024

Writing out intermediate steps can make it easier to solve problems.

Nick Slater for Quanta Magazine

Introduction

Your grade school teacher probably didn’t show you how to add 20-digit numbers. But if you know how to add smaller numbers, all you need is paper and pencil and a bit of patience. Start with the ones place and work leftward step by step, and soon you’ll be stacking up quintillions with ease.

Problems like this are easy for humans, but only if we approach them in the right way. “How we humans solve these problems is not ‘stare at it and then write down the answer,’” said Eran Malach , a machine learning researcher at Harvard University. “We actually walk through the steps.”

That insight has inspired researchers studying the large language models that power chatbots like ChatGPT. While these systems might ace questions involving a few steps of arithmetic, they’ll often flub problems involving many steps, like calculating the sum of two large numbers. But in 2022, a team of Google researchers showed that asking language models to generate step-by-step solutions enabled the models to solve problems that had previously seemed beyond their reach. Their technique, called chain-of-thought prompting, soon became widespread, even as researchers struggled to understand what makes it work.

Now, several teams have explored the power of chain-of-thought reasoning by using techniques from an arcane branch of theoretical computer science called computational complexity theory. It’s the latest chapter in a line of research that uses complexity theory to study the intrinsic capabilities and limitations of language models. These efforts clarify where we should expect models to fail, and they might point toward new approaches to building them.

“They remove some of the magic,” said Dimitris Papailiopoulos , a machine learning researcher at the University of Wisconsin, Madison. “That’s a good thing.”

Training Transformers

Large language models are built around mathematical structures called artificial neural networks. The many “neurons” inside these networks perform simple mathematical operations on long strings of numbers representing individual words, transmuting each word that passes through the network into another. The details of this mathematical alchemy depend on another set of numbers called the network’s parameters, which quantify the strength of the connections between neurons.

To train a language model to produce coherent outputs, researchers typically start with a neural network whose parameters all have random values, and then feed it reams of data from around the internet. Each time the model sees a new block of text, it tries to predict each word in turn: It guesses the second word based on the first, the third based on the first two, and so on. It compares each prediction to the actual text, then tweaks its parameters to reduce the difference. Each tweak only changes the model’s predictions a tiny bit, but somehow their collective effect enables a model to respond coherently to inputs it has never seen.

Researchers have been training neural networks to process language for 20 years. But the work really took off in 2017, when researchers at Google introduced a new kind of network called a transformer.

“This was proposed seven years ago, which seems like prehistory,” said Pablo Barceló , a machine learning researcher at the Pontifical Catholic University of Chile.

What made transformers so transformative is that it’s easy to scale them up — to increase the number of parameters and the amount of training data — without making training prohibitively expensive. Before transformers, neural networks had at most a few hundred million parameters; today, the largest transformer-based models have more than a trillion. Much of the improvement in language-model performance over the past five years comes from simply scaling up.

Transformers made this possible by using special mathematical structures called attention heads, which give them a sort of bird’s-eye view of the text they’re reading. When a transformer reads a new block of text, its attention heads quickly scan the whole thing and identify relevant connections between words — perhaps noting that the fourth and eighth words are likely to be most useful for predicting the 10th. Then the attention heads pass words along to an enormous web of neurons called a feedforward network, which does the heavy number crunching needed to generate the predictions that help it learn.

Real transformers have multiple layers of attention heads separated by feedforward networks, and only spit out predictions after the last layer. But at each layer, the attention heads have already identified the most relevant context for each word, so the computationally intensive feedforward step can happen simultaneously for every word in the text. That speeds up the training process, making it possible to train transformers on increasingly large sets of data. Even more important, it allows researchers to spread the enormous computational load of training a massive neural network across many processors working in tandem.

To get the most out of massive data sets, “you have to make the models really large,” said David Chiang , a machine learning researcher at the University of Notre Dame. “It’s just not going to be practical to train them unless it’s parallelized.”

However, the parallel structure that makes it so easy to train transformers doesn’t help after training — at that point, there’s no need to predict words that already exist. During ordinary operation, transformers output one word at a time, tacking each output back onto the input before generating the next word, but they’re still stuck with an architecture optimized for parallel processing.

As transformer-based models grew and certain tasks continued to give them trouble, some researchers began to wonder whether the push toward more parallelizable models had come at a cost. Was there a way to understand the behavior of transformers theoretically?

The Complexity of Transformers

Theoretical studies of neural networks face many difficulties, especially when they try to account for training. Neural networks use a well-known procedure to tweak their parameters at each step of the training process. But it can be difficult to understand why this simple procedure converges on a good set of parameters.

Rather than consider what happens during training, some researchers study the intrinsic capabilities of transformers by imagining that it’s possible to adjust their parameters to any arbitrary values. This amounts to treating a transformer as a special type of programmable computer.

“You’ve got some computing device, and you want to know, ‘Well, what can it do? What kinds of functions can it compute?’” Chiang said.

These are the central questions in the formal study of computation. The field dates back to 1936, when Alan Turing first imagined a fanciful device , now called a Turing machine, that could perform any computation by reading and writing symbols on an infinite tape. Computational complexity theorists would later build on Turing’s work by proving that computational problems naturally fall into different complexity classes defined by the resources required to solve them.

In 2019, Barceló and two other researchers proved that an idealized version of a transformer with a fixed number of parameters could be just as powerful as a Turing machine. If you set up a transformer to repeatedly feed its output back in as an input and set the parameters to the appropriate values for the specific problem you want to solve, it will eventually spit out the correct answer.

That result was a starting point, but it relied on some unrealistic assumptions that would likely overestimate the power of transformers. In the years since, researchers have worked to develop more realistic theoretical frameworks.

One such effort began in 2021, when William Merrill , now a graduate student at New York University, was leaving a two-year fellowship at the Allen Institute for Artificial Intelligence in Seattle. While there, he’d analyzed other kinds of neural networks using techniques that seemed like a poor fit for transformers’ parallel architecture. Shortly before leaving, he struck up a conversation with the Allen Institute for AI researcher Ashish Sabharwal , who’d studied complexity theory before moving into AI research. They began to suspect that complexity theory might help them understand the limits of transformers.

“It just seemed like it’s a simple model; there must be some limitations that one can just nail down,” Sabharwal said.

The pair analyzed transformers using a branch of computational complexity theory, called circuit complexity, that is often used to study parallel computation and had recently been applied to simplified versions of transformers. Over the following year, they refined several of the unrealistic assumptions in previous work. To study how the parallel structure of transformers might limit their capabilities, the pair considered the case where transformers didn’t feed their output back into their input — instead, their first output would have to be the final answer. They proved that the transformers in this theoretical framework couldn’t solve any computational problems that lie outside a specific complexity class. And many math problems, including relatively simple ones like solving linear equations, are thought to lie outside this class.

Basically, they showed that parallelism did come at a cost — at least when transformers had to spit out an answer right away. “Transformers are quite weak if the way you use them is you give an input, and you just expect an immediate answer,” Merrill said.

Thought Experiments

Merrill and Sabharwal’s results raised a natural question — how much more powerful do transformers become when they’re allowed to recycle their outputs? Barceló and his co-authors had studied this case in their 2019 analysis of idealized transformers, but with more realistic assumptions the question remained open. And in the intervening years, researchers had discovered chain-of-thought prompting, giving the question a newfound relevance.

Merrill and Sabharwal knew that their purely mathematical approach couldn’t capture all aspects of chain-of-thought reasoning in real language models, where the wording in the prompt can be very important . But no matter how a prompt is phrased, as long as it causes a language model to output step-by-step solutions, the model can in principle reuse the results of intermediate steps on subsequent passes through the transformer. That could provide a way to evade the limits of parallel computation.

Meanwhile, a team from Peking University had been thinking along similar lines, and their preliminary results were positive. In a May 2023 paper, they identified some math problems that should be impossible for ordinary transformers in Merrill and Sabharwal’s framework, and showed that intermediate steps enabled the transformers to solve these problems.

In October, Merrill and Sabharwal followed up their earlier work with a detailed theoretical study of the computational power of chain of thought. They quantified how that extra computational power depends on the number of intermediate steps a transformer is allowed to use before it must spit out a final answer. In general, researchers expect the appropriate number of intermediate steps for solving any problem to depend on the size of the input to the problem. For example, the simplest strategy for adding two 20-digit numbers requires twice as many intermediate addition steps as the same approach to adding two 10-digit numbers.

Examples like this suggest that transformers wouldn’t gain much from using just a few intermediate steps. Indeed, Merrill and Sabharwal proved that chain of thought only really begins to help when the number of intermediate steps grows in proportion to the size of the input, and many problems require the number of intermediate steps to grow much larger still.

The thoroughness of the result impressed researchers. “They really pinned this down,” said Daniel Hsu , a machine learning researcher at Columbia University.

Merrill and Sabharwal’s recent work indicates that chain of thought isn’t a panacea — in principle, it can help transformers solve harder problems, but only at the cost of a lot of computational effort.

“We’re interested in different ways of getting around the limitations of transformers with one step,” Merrill said. “Chain of thought is one way, but this paper shows that it might not be the most economical way.”

Back to Reality

Still, researchers caution that this sort of theoretical analysis can only reveal so much about real language models. Positive results — proofs that transformers can in principle solve certain problems — don’t imply that a language model will actually learn those solutions during training.

And even results that address the limitations of transformers come with caveats: They indicate that no transformer can solve certain problems perfectly in all cases. Of course, that’s a pretty high bar. “There might be special cases of the problem that it could handle just fine,” Hsu said.

Despite these caveats, the new work offers a template for analyzing different kinds of neural network architectures that might eventually replace transformers. If a complexity theory analysis suggests that certain types of networks are more powerful than others, that would be evidence that those networks might fare better in the real world as well.

Chiang also stressed that research on the limitations of transformers is all the more valuable as language models are increasingly used in a wide range of real-world applications, making it easy to overestimate their abilities.

“There’s actually a lot of things that they don’t do that well, and we need to be very, very cognizant of what the limitations are,” Chiang said. “That’s why this kind of work is really important.”

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25 essential System Design Interview questions in 2024

Over my 10+ years as a systems engineer and hiring manager at Microsoft and Facebook, I led hundreds of software engineer candidates through System Design interviews .

Surprisingly, I found that even the best developers often struggled with System Design problems. Why? I think it's because System Design questions can be open-ended, and therefore require creativity and problem solving skills not practiced in other coding interview challenges.

While SDI questions tend to evolve over time, many have remained popular across the industry. These questions are well-suited to evaluate candidates on two important levels:

Test the candidate's understanding of System Design fundamentals

Evaluate the candidate's ability to apply those fundamentals in real-world applications

Today, we’ll break down the top 25 System Design Interview questions for 2024. These are essential questions asked at top companies like Google, Amazon, Meta, and more. Mastering these problems, and their solutions, will give you a huge leg up in your System Design interview prep.

Finally, I will leave you with a few battle-tested strategies that you can use to confidently take on any System Design question you encounter.

Top System Design Interview questions

I have divided these 25 essential System Design problems into three difficulty levels:

Easy System Design Interview Questions

Design an API rate limiter for sites like Firebase or GitHub

Design a pub/sub system like Kafka

Design a URL-shortening service like TinyURL or bit.ly

Design a scalable content delivery network (CDN)

Design a web crawler

Design a distributed cache

Medium System Design Interview Questions

Design a chat service like Facebook Messenger or WhatsApp

Design a mass social media service like Facebook or Instagram

Design a proximity service like Yelp or nearby places/friends

Design a search engine-related service like Typeahead

Design a video streaming service like YouTube or Netflix

Design a ride-sharing service like Uber or Lyft

Design a recommendation service

Design a file-sharing service like Google Drive

Design a social network and message board like Reddit or Quora

Hard System Design Interview Questions

Design a social media newsfeed service

Design a collaborative editing service like Google Docs

Design Google Maps

Design a payment gateway like Stripe

Design a food-delivery service like Uber Eats or DoorDash

Design a distributed locking service like Google Chubby locking

Design a coordination system like ZooKeeper

Design a scalable distributed storage system like Bigtable

Design an online multiplayer game system

Design video conference service

Before we start breaking down specific questions, I want to give you some high-level System Design tips that will enable you to confidently approach any problem.

Tips for any SDI question

Start each problem by stating what you know: List all required features of the system, common problems you expect to encounter with this sort of system, and the traffic you expect the system to handle. The listing process lets the interviewer see your planning skills and correct misunderstandings before you begin the solution.

Narrate any trade-offs: Every System Design choice matters. At each decision point, list at least one positive and negative effect of that choice.

Ask your interviewer to clarify: Most System Design questions are purposefully vague. Ask clarifying questions to show the interviewer how you view the question and your knowledge of the system’s needs. Also be sure to state your assumptions before diving into the components.

Know your architectures: Most modern services are built upon a flexible microservice architecture . Unlike the monolithic architectures of tech companies in the past, microservices allow smaller, agile teams to build independently from the larger system. Some older companies will have legacy systems, but microservices can function parallel to legacy code and help refresh the company architecture.

Discuss emerging technologies: Conclude each question with an overview of how and where the system could benefit from machine learning. This will demonstrate that you’re prepared for not only current solutions but also future solutions.

16. Design a social media newsfeed service

Problem statement: Design a scalable and efficient social media newsfeed system that delivers personalized, real-time content updates to users, ensuring low latency, high availability, and scalability.

Follow these requirements for the design:

• Newsfeed generation
• Newsfeed contents
• Newsfeed display

In the following high-level design of a newsfeed system, clients post or request their newsfeed through the app, which the load balancer redirects to a web server for authentication and routing. Whenever a post is created via the post service and available from the friends (or followers) of a user, the notification service informs the newsfeed generation service, which generates newsfeeds from the posts of the user’s friends (followers) and keeps them in the newsfeed cache. Similarly, the generated feeds are published by the newsfeed publishing service to the user’s timeline from the news feed cache. If required, it also appends multimedia content from the blob storage with a news feed.

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Boeing Starliner astronauts may be 'stuck' aboard space station until February

Persistent problems aboard a Boeing Starliner spacecraft may extend the stay of two American astronauts aboard the International Space Station from eight days to eight months , NASA officials say.

Astronauts Sunita "Suni" Williams and Barry “Butch” Wilmore have been on the space station for 70 days, far beyond their original 10-day mission to test the Starliner capsule. They could be there until February.

The crew may return on Starliner. Alternatively, they could join NASA's SpaceX Crew-9 mission, scheduled for liftoff on Sept. 24. In that scenario, the Crew-9's SpaceX Dragon would launch with two astronauts instead of four.

Williams and Wilmore would become part of Crew-9, stay aboard the space station into 2025, and return to Earth on a SpaceX Dragon . NASA says it will make a decision by the end of August .

In the meantime, the two – who have been aboard the space station on previous missions – are assisting with research and station maintenance.

What went wrong with Starliner?

After technical delays , the two pilots launched aboard the Starliner crew capsule on June 5. It was the Starliner’s first crewed mission, known as Crew Flight Test , to test its flight and docking capabilities at the space station.

However, a helium leak developed that affected control of the capsule’s thrusters, used for docking and maneuvering.

Starliner docked at the space station on June 6. It remains there while engineers try to figure out what went wrong and decide the best option to get Williams and Wilmore back to Earth.

NASA and Boeing say the Starliner astronauts aren't "stranded" on the space station. While their extended mission is unexpected, the space station has room and resources to accommodate the Starliner crew.

An uncrewed supply ship delivery included clothes and personal items for Williams and Wilmore. It arrived at the space station on Aug. 6.

Where do astronauts sleep – or camp out on – the space station?

The International Space Station has seven permanent Crew Quarters , sleeping spaces for astronauts. U.S. Crew Quarters, with two laptops, personal items and sleeping bags , are about 74 cubic feet, or a little larger than an old-time phone booth , astronauts say.

But there are other places at the space station where astronauts can sleep: extra space that's used during handovers , the short periods in which new crew members replace existing ones.

"When there are more astronauts aboard the station than crew quarters, crew members work with flight controllers to identify temporary “campout” locations for the crew to sleep during the short handover period," NASA says.

Those locations include the U.S. Quest Airlock , attached to Node 1, or the Japanese Experiment Module Kibo . There's also CASA, or Crew Alternate Sleep Accommodation , in the European Columbus Module, the European Space Agency says.

Astronauts can even sleep in docked spacecraft, NASA says.

Sleeping locations and their current occupants:

• ◾ U.S. Harmony Module /Node 2 : U.S. astronauts Tracy Dyson, Mike Barratt, Matthew Dominick and Jeanette Epps.
• ◾ European Columbus Module : Williams.
• ◾ Japanese Experiment Module Kibo : Wilmore.
• ◾ Russian Zvezda Module /Service Module : Two Russian cosmonauts.
• ◾ Russian Multipurpose Laboratory Module Nauka : One Russian cosmonaut.

While nine astronauts might seem like a lot, the space station hosted 13 in July 2009 . That included the six space station crew members and seven astronauts on the Endeavor STS-127 mission, which delivered modules to the station.

How do you sleep on the space station?

The space station doesn't have conventional beds. Astronauts in microgravity rest in specially designed sleeping bags, which can be tied to walls.

The arrangement might look uncomfortable, says astronaut Chris Hadfield in this NASA/CSA video . But without gravity, "you can completely relax," he says.

Space station astronauts usually get eight hours of sleep per day. In addition to the scientific and maintenance work they do, crew members must physically exercise about two hours every day. This is to counteract the absence of Earth's gravity, which weakens muscles and bones .

What's docked at the space station besides Starliner?

Starliner problems started before launch.

Starliner’s problems started before its June 5 launch.

The original launch date was May 6. That was delayed when a problem with an oxygen relief valve was found on the Atlas V rocket's Centaur Stage.

Engineers found a small helium leak in the spacecraft’s service module on May 21. That delayed the launch until June 1. However, a computer problem canceled that launch.

Starliner successfully launched on June 5. NASA later said two more helium leaks were discovered after Starliner separated from its Atlas V rocket and entered Earth orbit.

The craft loses five thrusters and delays docking at the space station. Engineers reset the thrusters and Starliner docked at the space station on June 6.

However, Starliner’s return, originally scheduled for June 13, was delayed three times as engineers continue to analyze its propulsion systems.

Contributing: Brooke Edwards, Florida Today; Eric Lagatta, Jonathan Limehouse, USA TODAY

Source: USA TODAY Network reporting and research; NASA; European Space Agency; Canadian Space Agency

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Research: How IT Can Solve Common Problems in DEI Initiatives

• Monideepa Tarafdar
• Marta Stelmaszak

Lessons from three organizations that successfully leveraged IT to drive structural change.

The authors’ research found that three persistent problems plague DEI initiatives: They do not connect to operational or strategic goals and objectives; they do not include the rank-and-file; and they are often implemented through periodic efforts like annual diversity training that aren’t integrated into day-to-day work processes. Organizations can overcome these problems by using IT in three ways.

Diversity, equity, and inclusion (DEI) programs are under attack. Confronted by high costs, mixed outcomes , unclear organizational benefits , and a political and regulatory backlash , organizations are rolling back their initiatives. Google and Meta, for example, recently reduced investment in their DEI programs and let go of DEI staff.

• Monideepa Tarafdar is Charles J. Dockendorff Endowed Professor at the Isenberg School of Management at the University of Massachusetts Amherst.
• Marta Stelmaszak is an assistant professor of information systems at the Isenberg School of Management at the University of Massachusetts Amherst.

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Title: diversity empowers intelligence: integrating expertise of software engineering agents.

Abstract: Large language model (LLM) agents have shown great potential in solving real-world software engineering (SWE) problems. The most advanced open-source SWE agent can resolve over 27% of real GitHub issues in SWE-Bench Lite. However, these sophisticated agent frameworks exhibit varying strengths, excelling in certain tasks while underperforming in others. To fully harness the diversity of these agents, we propose DEI (Diversity Empowered Intelligence), a framework that leverages their unique expertise. DEI functions as a meta-module atop existing SWE agent frameworks, managing agent collectives for enhanced problem-solving. Experimental results show that a DEI-guided committee of agents is able to surpass the best individual agent's performance by a large margin. For instance, a group of open-source SWE agents, with a maximum individual resolve rate of 27.3% on SWE-Bench Lite, can achieve a 34.3% resolve rate with DEI, making a 25% improvement and beating most closed-source solutions. Our best-performing group excels with a 55% resolve rate, securing the highest ranking on SWE-Bench Lite. Our findings contribute to the growing body of research on collaborative AI systems and their potential to solve complex software engineering challenges.

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