abstract for artificial intelligence paper presentation

A paper presentation abstract

This document is a paper presentation on artificial intelligence by J.G.M.Jagagdeesh Kumar from DJR College of Engineering and Technology in Vijayawada, India. The paper discusses the history and goals of artificial intelligence, including creating systems that think like humans through the Turing test and systems that think rationally. It also examines different definitions of AI and the tension between modeling human and rational thought. Read less

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• 1. A PAPER PRESENTATION ON Artificial Intelligence J.G.M.Jagagdeesh Kumar Department of C.S.E. (III year) Affiliated to JNTU K DJR College of Engineering and Technology, Gudavalli, Vijayawada Krishna (dt.), Andhra Pradesh, India. Contact details: J.G.M.Jagagdeesh Kumar Mobile number:9700234518 Email Id:[email protected]
• 2. Introduction In which we try to explain why we consider artificial intelligence to be a subject most worthy of study, and in which we try to decide what exactly it is, this being a good thing to decide before embarking. Humankind has given itself the scientific name homo sapiens--man the wise--because our mental capacities are so important to our everyday lives and our sense of self. The field of artificial intelligence, or AI, attempts to understand intelligent entities. Thus, one reason to study it is to learn more about ourselves. But unlike philosophy and psychology, which are also concerned with intelligence, AI strives to build intelligent entities as well as understand them. Another reason to study AI is that these constructed intelligent entities are interesting and useful in their own right. AI has produced many significant and impressive products even at this early stage in its development. Although no one can predict the future in detail, it is clear that computers with human-level intelligence (or better) would have a huge impact on our everyday lives and on the future course of civilization. AI addresses one of the ultimate puzzles. How is it possible for a slow, tiny brain{brain}, whether biological or electronic, to perceive, understand, predict, and manipulate a world far larger and more complicated than itself? How do we go about making something with those properties? These are hard questions, but unlike the search for faster-than-light travel or an antigravity device, the researcher in AI has solid evidence that the quest is possible. All the researcher has to do is look in the mirror to see an example of an intelligent system. AI is one of the newest disciplines. It was formally initiated in 1956, when the name was coined, although at that point work had been under way for about five years. Along with modern genetics, it is regularly cited as the ``field I would most like to be in'' by scientists in other disciplines. A student in physics might reasonably feel that all the good ideas have already been taken by Galileo, Newton, Einstein, and the rest, and that it takes many years of study before one can contribute new ideas. AI, on the other hand, still has openings for a full-time Einstein. The study of intelligence is also one of the oldest disciplines. For over 2000 years, philosophers have tried to understand how seeing, learning, remembering, and reasoning could, or should, be done. The advent of usable computers in the early 1950s turned the learned but armchair speculation concerning these mental faculties into a real experimental and theoretical discipline. Many felt that the new ``Electronic Super-Brains'' had unlimited potential for intelligence. ``Faster Than Einstein'' was a typical headline. But as well as providing a vehicle for creating artificially intelligent entities, the computer provides a tool for testing theories of intelligence, and many theories failed to withstand the test--a case of ``out of the armchair, into the fire.'' AI has turned out to be more difficult than many at first imagined, and modern ideas are much richer, more subtle, and more interesting as a result. AI currently encompasses a huge variety of subfields, from general-purpose areas such as perception and logical reasoning, to specific tasks such as playing chess, proving mathematical theorems, writing poetry{poetry}, and diagnosing diseases. Often, scientists in other fields move gradually into artificial intelligence, where they find the tools and vocabulary to systematize and automate the intellectual tasks on which they have been working all their lives. Similarly, workers in AI can choose to apply their methods to any area of human intellectual endeavor. In this sense, it is truly a universal field. What is AI? We have now explained why AI is exciting, but we have not said what it is. We could just say, ``Well, it has to do with smart programs, so let's get on and write some.'' But the history of science shows that it is helpful to aim at the right goals. Early alchemists, looking for a potion for eternal life and a method to turn lead into gold, were probably off on the wrong foot. Only when the aim changed, to that of finding explicit theories that gave accurate predictions of the terrestrial world, in the same
• 3. way that early astronomy predicted the apparent motions of the stars and planets, could the scientific method emerge and productive science take place. Definitions of artificial intelligence according to eight recent textbooks are shown in the table below. These definitions vary along two main dimensions. The ones on top are concerned with thought processes and reasoning, whereas the ones on the bottom address behavior. Also, the definitions on the left measure success in terms of human performance, whereas the ones on the right measure against an ideal concept of intelligence, which we will callrationality. A system is rational if it does the right thing. ``The exciting new effort to make computers think ... machines with minds, in the full and literal sense'' (Haugeland, 1985) ``The automation of activities that we associate with human thinking, activities such as decision- making, problem solving, learning ...'' (Bellman, 1978) ``The study of mental faculties through the use of computational models'' (Charniak and McDermott, 1985) ``The study of the computations that make it possible to perceive, reason, and act'' (Winston, 1992) ``The art of creating machines that perform functions that require intelligence when performed by people'' (Kurzweil, 1990) ``The study of how to make computers do things at which, at the moment, people are better'' (Rich and Knight, 1991) ``A field of study that seeks to explain and emulate intelligent behavior in terms of computational processes'' (Schalkoff, 1990) ``The branch of computer science that is concerned with the automation of intelligent behavior'' (Luger and Stubblefield, 1993) This gives us four possible goals to pursue in artificial intelligence: Systems that think like humans. Systems that think rationally. Systems that act like humans Systems that act rationally Historically, all four approaches have been followed. As one might expect, a tension exists between approaches centered around humans and approaches centered around rationality. (We should point out that by distinguishing between human and rational behavior, we are not suggesting that humans are necessarily ``irrational'' in the sense of ``emotionally unstable'' or ``insane.'' One merely need note that we often make mistakes; we are not all chess grandmasters even though we may know all the rules of chess; and unfortunately, not everyone gets an A on the exam. Some systematic errors in human reasoning are cataloged by Kahneman et al..) A human- centered approach must be an empirical science, involving hypothesis and experimental confirmation. A rationalist approach involves a combination of mathematics and engineering. People in each group sometimes cast aspersions on work done in the other groups, but the truth is that each direction has yielded valuable insights. Let us look at each in more detail. Acting humanly: The Turing Test approach The Turing Test, proposed by Alan Turing (Turing, 1950), was designed to provide a satisfactory operational definition of intelligence. Turing defined intelligent behavior as the ability to achieve human-level performance in all cognitive tasks, sufficient to fool an interrogator. Roughly speaking, the test he proposed is that the computer should be interrogated by a human via a teletype, and passes the test if the interrogator cannot tell if there is a computer or a human at the other end. Chapter 26 discusses the details of the test, and whether or not a computer is really intelligent if it passes. For now, programming a computer to pass the test provides plenty to work on. The computer would need to possess the following capabilities:
• 4. natural language processing to enable it to communicate successfully in English (or some other human language); knowledge representation to store information provided before or during the interrogation; automated reasoning to use the stored information to answer questions and to draw new conclusions; machine learning to adapt to new circumstances and to detect and extrapolate patterns. Turing's test deliberately avoided direct physical interaction between the interrogator and the computer, because physical simulation of a person is unnecessary for intelligence. However, the so-called total Turing Testincludes a video signal so that the interrogator can test the subject's perceptual abilities, as well as the opportunity for the interrogator to pass physical objects ``through the hatch.'' To pass the total Turing Test, the computer will need computer vision to perceive objects, and robotics to move them about. Within AI, there has not been a big effort to try to pass the Turing test. The issue of acting like a human comes up primarily when AI programs have to interact with people, as when an expert system explains how it came to its diagnosis, or a natural language processing system has a dialogue with a user. These programs must behave according to certain normal conventions of human interaction in order to make themselves understood. The underlying representation and reasoning in such a system may or may not be based on a human model. Thinking humanly: The cognitive modelling approach If we are going to say that a given program thinks like a human, we must have some way of determining how humans think. We need to get inside the actual workings of human minds. There are two ways to do this: through introspection--trying to catch our own thoughts as they go by--or through psychological experiments. Once we have a sufficiently precise theory of the mind, it becomes possible to express the theory as a computer program. If the program's input/output and timing behavior matches human behavior, that is evidence that some of the program's mechanisms may also be operating in humans. For example, Newell and Simon, who developed GPS, the ``General Problem Solver'' (Newell and Simon, 1961), were not content to have their program correctly solve problems. They were more concerned with comparing the trace of its reasoning steps to traces of human subjects solving the same problems. This is in contrast to other researchers of the same time (such as Wang (1960)), who were concerned with getting the right answers regardless of how humans might do it. The interdisciplinary field of cognitive science brings together computer models from AI and experimental techniques from psychology to try to construct precise and testable theories of the workings of the human mind. Although cognitive science is a fascinating field in itself, we are not going to be discussing it all that much in this book. We will occasionally comment on similarities or differences between AI techniques and human cognition. Real cognitive science, however, is necessarily based on experimental investigation of actual humans or animals, and we assume that the reader only has access to a computer for experimentation. We will simply note that AI and cognitive science continue to fertilize each other, especially in the areas of vision, natural language, and learning. The history of psychological theories of cognition is briefly covered on page 12. Thinking rationally: The laws of thought approach The Greek philosopher Aristotle was one of the first to attempt to codify ``right thinking,'' that is, irrefutable reasoning processes. His famous syllogisms provided patterns for argument structures that always gave correct conclusions given correct premises. For example, ``Socrates is a man; all men are mortal; therefore Socrates is mortal.'' These laws of thought were supposed to govern the operation of the mind, and initiated the field oflogic.
• 5. The development of formal logic in the late nineteenth and early twentieth centuries, which we describe in more detail in Chapter 6, provided a precise notation for statements about all kinds of things in the world and the relations between them. (Contrast this with ordinary arithmetic notation, which provides mainly for equality and inequality statements about numbers.) By 1965, programs existed that could, given enough time and memory, take a description of a problem in logical notation and find the solution to the problem, if one exists. (If there is no solution, the program might never stop looking for it.) The so-called logicist tradition within artificial intelligence hopes to build on such programs to create intelligent systems. There are two main obstacles to this approach. First, it is not easy to take informal knowledge and state it in the formal terms required by logical notation, particularly when the knowledge is less than 100% certain. Second, there is a big difference between being able to solve a problem ``in principle'' and doing so in practice. Even problems with just a few dozen facts can exhaust the computational resources of any computer unless it has some guidance as to which reasoning steps to try first. Although both of these obstacles apply to any attempt to build computational reasoning systems, they appeared first in the logicist tradition because the power of the representation and reasoning systems are well- defined and fairly well understood. Acting rationally: The rational agent approach Acting rationally means acting so as to achieve one's goals, given one's beliefs. An agent is just something that perceives and acts. (This may be an unusual use of the word, but you will get used to it.) In this approach, AI is viewed as the study and construction of rational agents. In the ``laws of thought'' approach to AI, the whole emphasis was on correct inferences. Making correct inferences is sometimes part of being a rational agent, because one way to act rationally is to reason logically to the conclusion that a given action will achieve one's goals, and then to act on that conclusion. On the other hand, correct inference is not all of rationality, because there are often situations where there is no provably correct thing to do, yet something must still be done. There are also ways of acting rationally that cannot be reasonably said to involve inference. For example, pulling one's hand off of a hot stove is a reflex action that is more successful than a slower action taken after careful deliberation. All the ``cognitive skills'' needed for the Turing Test are there to allow rational actions. Thus, we need the ability to represent knowledge and reason with it because this enables us to reach good decisions in a wide variety of situations. We need to be able to generate comprehensible sentences in natural language because saying those sentences helps us get by in a complex society. We need learning not just for erudition, but because having a better idea of how the world works enables us to generate more effective strategies for dealing with it. We need visual perception not just because seeing is fun, but in order to get a better idea of what an action might achieve--for example, being able to see a tasty morsel helps one to move toward it. The study of AI as rational agent design therefore has two advantages. First, it is more general than the ``laws of thought'' approach, because correct inference is only a useful mechanism for achieving rationality, and not a necessary one. Second, it is more amenable to scientific development than approaches based on human behavior or human thought, because the standard of rationality is clearly defined and completely general. Human behavior, on the other hand, is well-adapted for one specific environment and is the product, in part, of a complicated and largely unknown evolutionary process that still may be far from achieving perfection. This book will therefore concentrate on general principles of rational agents, and on components for constructing them. We will see that despite the apparent simplicity with which the problem can be stated, an enormous variety of issues come up when we try to solve it. Chapter 2 outlines some of these issues in more detail. One important point to keep in mind: we will see before too long that achieving perfect rationality--always doing the right thing--is not possible in complicated environments. The
• 6. computational demands are just too high. However, for most of the book, we will adopt the working hypothesis that understanding perfect decision making is a good place to start. It simplifies the problem and provides the appropriate setting for most of the foundational material in the field. Chapters 5 and 17 deal explicitly with the issue of limited rationality--acting appropriately when there is not enough time to do all the computations one might like. The ``History of AI'' sections from the book are omitted from this online version. The State of the Art International grandmaster Arnold Denker studies the pieces on the board in front of him. He realizes there is no hope; he must resign the game. His opponent, Hitech, becomes the first computer program to defeat a grandmaster in a game of chess. ``I want to go from Boston to San Francisco,'' the traveller says into the microphone. ``What date will you be travelling on?'' is the reply. The traveller explains she wants to go October 20th, nonstop, on the cheapest available fare, returning on Sunday. A speech understanding program named Pegasus handles the whole transaction, which results in a confirmed reservation that saves the traveller $894 over the regular coach fare. Even though the speech recognizer gets one out of ten words wrong, it is able to recover from these errors because of its understanding of how dialogs are put together. An analyst in the Mission Operations room of the Jet Propulsion Laboratory suddenly starts paying attention. A red message has flashed onto the screen indicating an ``anomaly'' with the Voyager spacecraft, which is somewhere in the vicinity of Neptune. Fortunately, the analyst is able to correct the problem from the ground. Operations personnel believe the problem might have been overlooked had it not been for Marvel, a real-time expert system that monitors the massive stream of data transmitted by the spacecraft, handling routine tasks and alerting the analysts to more serious problems. Cruising the highway outside of Pittsburgh at a comfortable 55 mph, the man in the driver's seat seems relaxed. He should be--for the past 90 miles, he has not had to touch the steering wheel. The real driver is a robotic system that gathers input from video cameras, sonar, and laser range finders attached to the van. It combines these inputs with experience learned from training runs and succesfully computes how to steer the vehicle. A leading expert on lymph-node pathology describes a fiendishly difficult case to the expert system, and examines the system's diagnosis. He scoffs at the system's response. Only slightly worried, the creators of the system suggest he ask the computer for an explanation of the diagnosis. The machine points out the major factors influencing its decision, and explains the subtle interaction of several of the symptoms in this case. The expert admits his error, eventually. From a camera perched on a street light above the crossroads, the traffic monitor watches the scene. If any humans were awake to read the main screen, they would see ``Citroen 2CV turning from Place de la Concorde into Champs Elysees,'' ``Large truck of unknown make stopped on Place de la Concorde,'' and so on into the night. And occasionally, ``Major incident on Place de la Concorde, speeding van collided with motorcyclist,'' and an automatic call to the emergency services. These are just a few examples of artificial intelligence systems that exist today. Not magic or science fiction--but rather science, engineering, and mathematics, to which this book provides an introduction. Summary This chapter defines AI and establishes the cultural background against which it has developed. Some of the important points are as follows: Different people think of AI differently. Two important questions to ask are: Are you concerned with
• 7. thinking or behavior? Do you want to model humans, or work from an ideal standard? In this book, we adopt the view that intelligence is concerned mainly with rational action. Ideally, an intelligent agent takes the best possible action in a situation. We will study the problem of building agents that are intelligent in this sense. Philosophers (going back to 400 B.C.) made AI conceivable by considering the ideas that the mind is in some ways like a machine, that it operates on knowledge encoded in some internal language, and that thought can be used to help arrive at the right actions to take. Mathematicians provided the tools to manipulate statements of logical certainty as well as uncertain, probabilistic statements. They also set the groundwork for reasoning about algorithms. Psychologists strengthened the idea that humans and other animals can be considered information processing machines. Linguists showed that language use fits into this model. Computer engineering provided the artifact that makes AI applications possible. AI programs tend to be large, and they could not work without the great advances in speed and memory that the computer industry has provided. The history of AI has had cycles of success, misplaced optimism, and resulting cutbacks in enthusiasm and funding. There have also been cycles of introducing new creative approaches and systematically refining the best ones. Recent progress in understanding the theoretical basis for intelligence has gone hand in hand with improvements in the capabilities of real systems. Refferences Links http://www.aaai.org/ http://www-formal.stanford.edu/ http://insight.zdnet.co.uk/hardware/emergingte ch/ http://www.genetic-programming.com/

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Artificial Intelligence (AI) has revolutionized numerous industries, transforming the way we work, live, and interact. As we delve into an AI overview, several key highlights emerge that showcase its profound impact and potential. From machine learning algorithms to natural language processing, AI technologies continue to evolve at a rapid pace, offering unprecedented opportunities for innovation and efficiency.

In this presentation, we'll explore the fundamental concepts of AI, its current applications, and future prospects. We'll examine how AI is reshaping various sectors, including healthcare, finance, and manufacturing, while also addressing the ethical considerations and challenges that come with its widespread adoption. By understanding these AI overview highlights, we can better prepare for a future where intelligent machines play an increasingly significant role in our daily lives and business operations.

Core Components of an AI Overview Highlights

When crafting an AI overview for a presentation, it's crucial to highlight key components that provide a comprehensive understanding. Begin by introducing the fundamental concept of AI as a branch of computer science focused on creating intelligent machines. Explain how AI systems simulate human intelligence processes, including learning, reasoning, and self-correction.

Next, outline the core types of AI: narrow or weak AI, general AI, and superintelligent AI. Discuss machine learning as a subset of AI, emphasizing its ability to improve performance through experience. Highlight deep learning and neural networks as advanced techniques within machine learning. Address the ethical considerations surrounding AI development and implementation, including privacy concerns and potential job displacement. Finally, showcase real-world applications of AI across various industries, such as healthcare, finance, and transportation, to illustrate its transformative potential and current impact on society.

Defining Artificial Intelligence

Artificial Intelligence (AI) has emerged as a transformative force across industries, revolutionizing how we approach complex tasks and decision-making processes. At its core, AI refers to computer systems designed to mimic human intelligence, capable of learning, reasoning, and self-correction. These systems utilize advanced algorithms and vast amounts of data to perform tasks that typically require human cognition.

The field of AI encompasses various subsets, including machine learning, natural language processing, and computer vision. Machine learning enables systems to improve their performance over time without explicit programming. Natural language processing allows computers to understand, interpret, and generate human language. Computer vision empowers machines to analyze and interpret visual information from the world around them. Together, these components form the foundation of AI's capabilities, driving innovation and efficiency across diverse sectors.

Key Applications in Various Industries

Artificial Intelligence (AI) has become a transformative force across various industries, revolutionizing processes and unlocking new possibilities. In healthcare, AI-powered diagnostic tools assist medical professionals in detecting diseases earlier and with greater accuracy. Financial institutions harness AI algorithms for fraud detection and risk assessment, enhancing security and decision-making capabilities.

Manufacturing benefits from AI through predictive maintenance and quality control, optimizing production lines and reducing downtime. In retail, AI-driven personalization engines analyze customer data to deliver tailored product recommendations and improve shopping experiences. The transportation sector employs AI for route optimization and autonomous vehicle development, paving the way for safer and more efficient travel. As AI continues to evolve, its applications expand, promising innovative solutions to complex challenges across diverse fields.

Structuring Your PPT for an AI Overview Highlights

When crafting a PowerPoint presentation on AI, structuring your content effectively is crucial for engaging your audience. Begin by outlining the core components of artificial intelligence, such as machine learning, natural language processing, and computer vision. Next, highlight the transformative impact of AI across various industries, including healthcare, finance, and manufacturing.

Consider dedicating slides to real-world AI applications, showcasing how these technologies are solving complex problems and improving efficiency. Address both the benefits and potential challenges associated with AI adoption, such as ethical considerations and job market disruptions. Conclude your presentation with a forward-looking perspective, discussing emerging AI trends and their potential to shape our future. By organizing your PPT in this manner, you'll provide a comprehensive AI overview that captivates and informs your audience.

Essential Slides to Include

When crafting an AI overview for your presentation, it's crucial to highlight key points that capture the essence of artificial intelligence. Start with a slide defining AI and its core principles, emphasizing machine learning and neural networks. Follow this with a timeline showcasing AI's evolution, from early rule-based systems to today's deep learning models.

Next, dedicate a slide to AI's current applications across various industries, such as healthcare, finance, and transportation. Highlight how AI is transforming these sectors with concrete examples. Include a slide on the ethical considerations surrounding AI, touching on topics like bias in algorithms and data privacy concerns. Finally, conclude with a forward-looking slide discussing potential future developments in AI and their implications for society and business. This structure ensures a comprehensive yet concise AI overview that engages your audience and sparks meaningful discussions.

Best Practices for Presentation Design

When crafting an AI overview for a PowerPoint presentation, it's crucial to highlight key aspects that capture the essence of artificial intelligence. Begin by defining AI in clear, accessible terms, emphasizing its ability to mimic human intelligence and learn from data. Next, outline the main types of AI, such as narrow AI and general AI, to provide context for the audience.

Delve into the core components that power AI systems, including machine learning algorithms, neural networks, and deep learning. Illustrate these concepts with real-world applications, such as virtual assistants, autonomous vehicles, or predictive analytics in business. Address both the potential benefits and ethical considerations of AI adoption, touching on topics like improved efficiency, job displacement, and data privacy. Conclude your overview by discussing future trends and the potential impact of AI on various industries, encouraging viewers to consider its role in shaping our technological landscape.

Conclusion: Summarizing AI Overview Highlights

In summarizing the key points of our AI overview, several crucial aspects stand out. The potential of artificial intelligence to revolutionize various industries is evident, from healthcare to finance and beyond. However, it's essential to approach AI implementation with caution and ethical considerations.

As we've explored, AI's ability to process vast amounts of data and identify patterns offers unprecedented opportunities for innovation. Yet, challenges such as data privacy, algorithmic bias, and the need for human oversight remain significant concerns. Moving forward, striking a balance between harnessing AI's power and maintaining human-centric values will be paramount in shaping the future of this transformative technology.

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15 Best Presentations On Artificial Intelligence And Machine Learning in 2024

• by Jeevan Biswas

For a quick overview of a subject or a breakdown of concepts, SlideShare serves as a go-to platform for many. The recapitulations found in many of the presentations are both concise and informative.

The most popular presentations topics are the ones that have received the most number of likes and have been viewed more than the other presentations in a particular category.

AIM brings you the 14 most popular ppt topics on Artificial Intelligence, Machine Learning. Deep Learning and everything else in between.

Find: Top AI PPT Maker Tools

1) Artificial Intelligence and Law Overview

People who are not aware of what artificial intelligence is will find the topic presented in a very simple manner here.

Along with the explanation of what AI is, the two major approaches towards AI are discussed– logic and rules-based approach, and machine learning approach. Special emphasis on the machine learning approach can be seen in the slides devoted to its detailed examination. The examination goes beyond the rudimentary explanation of what machine learning is and presents examples of proxies that seem like machine learning but are not.

The presentation lists examples of AI in the field of law and identifies some of the limitations of AI technology.

2)  What is Artificial Intelligence – Artificial Intelligence Tutorial For Beginners

For the uninitiated, this presentation offers an ideal rundown of AI. The question of AI being a threat is raised at the very beginning. However, as the presentation progresses, it discusses the basics necessary for understanding AI. The most basic question of what is artificial intelligence is answered.

A brief history of AI and the discussion on recent advances in the field of AI is also found. The various areas where AI currently sees practical application have been listed. Fascinating uses that AI can be put to in the future are also found in the presentation. The two approaches of achieving AI, machine learning and deep learning, is touched upon.

All in all, this presentation serves as a simple introduction to AI.

3) Why Social Media Chat Bots Are the Future of Communication

An exciting application of AI can be found in chatbots. Here, the limitless scope of chatbots is explored. The various milestones reached by leading players  in bot technology such as Facebook, Skype and KIK are enumerated.

The evolution of chatbots and its absorption of more AI in the future is also looked into. E-Commerce is touted as the biggest beneficiary of the advancement in chatbots and that bot technology will owe its rise to services and commerce.

Two tech giants, Facebook and Google, have been pitted against each other based on their ongoing developments in this area and the question of who will emerge as the best is raised.

4) AI and the Future of Work

This presentation talks about the far-fetching applicability of AI and ML,and the perils of that applicability. In order to derive a better understanding of this presentation, it is advisable to first watch the original talk.

During the course of the presentation, many examples of how machines can learn and perform any human task that is repetitive in nature are cited.

Other possibilities suggested include the creation of new unheard jobs for human beings as a result of aggressive use of AI and other allied technologies. Qualities that are characteristic only of human beings, may be the basis on which these jobs will be created is also suggested.

It concludes with a message- Ride the train, don’t jump in front of it.

5) AI and Machine Learning Demystified

In this presentation, Carol Smith establishes that AI cannot replace humans. Smith conveys that AI can serve the purpose of enabling human beings in making better decisions.

The slides talk about how the actions of AI are the result of the human inputs going into its programming. An AI’s bias is not its own, but the human bias with which it has been programmed, is emphasised on.

Other issues such as the need for regulations and other considerations within it that require deliberation are also touched upon. The presentation leaves you with a message – Don’t fear AI, Explore it.

6) Study: The Future of VR, AR and Self-Driving Car

Though no descriptive breakdown of topics related to AI is found, the presentation offers interesting numerical insights into many questions. Statistics on three main subjects – artificial intelligence, virtual reality and wearable technology, is provided here.

A variety of questions and the numerical representations of their responses are found under four main categories:

• Will you  purchase a self-driving car when they become available?
• Are you concerned with the rise of Artificial Intelligence?
• Is wearable technology part of your daily life?
• Do you own or intend to purchase a Virtual Reality headset in the next twelve months?

From consumer opinions to overall consensus of countries, the numbers show current trends and the possible trends in the future based on increasing development in the mentioned technologies.

7) Artificial Intelligence

There are many who have been introduced to AI only recently due to the buzz surrounding it and may not be aware of the early developments that led to its current status.

This presentation from 2009 offers a simple yet informative introduction to the rudiments of AI. AI’s history and a timeline of all the significant milestones in AI up to 2009 can be found. The presentation also provides an introduction to AI programming languages such as LISP and PROLOG.

For those who would like to have a crash course on the basics of AI in order to catch up with it current trends, this presentation serves the purpose.

8) Solve for X with AI: a VC view of the Machine Learning & AI landscape

While the concepts of  AI or ML are not spoken about, light is shed on other important aspects of it. The presentation discusses about how many known tech giants such as Google are bolstering their AI capabilities through mergers and acquisitions.

The role of venture capital(VC) in the landscape of AI and machine learning,and the involvement of VC in the firms that were acquired are mentioned.

Another point highlighted is how large companies are moving towards ML and re-configuring themselves around ML, and how it is not a US-centric phenomenon. Key points have been expressed in the form of self-explanatory graphical representations. Rounding off the presentation is the possible direction that ML can take and a few pointers on achieving success in ML.

9) Deep Learning – The Past, Present and Future of Artificial Intelligence

This presentation provides a comprehensive insight into deep learning. Beginning with a brief history of AI and introduction to basics of machine learning such as its classification, the focus shifts towards deep learning entirely.

Various kinds of networks such as recurrent neural nets and generative adversarial networks have been discussed at length. Emphasis has been given to important aspects of these networks and other mechanisms such as natural language processing (NLP).

Detailed examples of practical applications and the scope of deep learning are found throughout the presentation. However, this presentation may prove difficult for first time learner’s of AI to comprehend.

10) The Future Of Work & The Work Of The Future

The subject of self-learning of robots and machines is explored here. Talking about the fictional Babel fish, it is suggested that the advancements in technology leading to improved learning and translations by machines  made the Babel fish a near-real entity.

New ‘power’ values such as speed, networked governance, collaboration and transparency, among others, have been put forth and juxtaposed against older ones that are not fully technology  driven.

Going against the popular assumption that robots and machines will replace human beings, the presentation proposes that we are on the brink of the largest job creation period in humanity.

11) Asia’s Artificial Intelligence Agenda

This presentation is a briefing paper by the MIT Technological Review and talks about how the global adoption of AI is being sped up by Asian countries. It suggests that Asia will not only benefit greatly from the rise in AI technology, but will also define it.

The data collected for the review has been summarized in the form of simple info-graphics. They are a numerical reflection of the mood surrounding the adoption of AI across different industries and how it could possibly impact human capital.  The review also suggests that while there is awareness about AI in Asia, only a small percentage of companies are investing in it.

Pointers for business leaders in Asia to capitalize on AI is offered in the end along presentation with an info-graphic timeline of the history of AI.

Download review report in pdf

12) 10 Lessons Learned from Building Machine Learning Systems

While they are two separate presentations, they talk about the same subject- machine learning. The presentations are a summary of the analysis of machine learning adopted by two platforms, Netflix and Quora.

In case of Netflix, emphasis has been given to the choice of the right metric and the type of data used for testing and training. It also emphasises the need to understand the dependence between the data used and the models employed. The advice to optimize only areas that matter is offered.

The second presentation on Quora, talks about teaching machines only what is necessary. It stresses on the need the to focus on feature engineering and being thoughtful about the ML infrastructure. Another point it highlights is the combination of supervised and unsupervised being the key in ML application.

13) Design Ethics for Artificial Intelligence

With 135 slides, this presentation provides an exhaustive insight into the creation of an ethically sound AI. An introduction to the subject of User Experience(UX) design is followed by the rules that have to be considered during the designing process.

The chronological progression of UX, beginning with experience design and ending with intelligence design, and the direction in which this process is headed is also discussed.

Supported by powerful visuals, the presentation touches upon many essential considerations such as nature of intelligence, purpose of existence, awareness of self and the need for which the AI is created.

It raises a pertinent point that while creating AI, human beings are creating something that embodies qualities that they lack.

14) Artificial Intelligence

Made for a school competition in 2009, it provides many examples of cutting-edge applications of AI at the time.

Many of the examples, such as mind controlled prosthetic limbs, Ultra Hal Assistant and Dexter- the robot provide a trip down the AI memory lane where the applications of AI seemed like a page out of a sci-fi novel. It presents a list of areas where AI can assist human beings.

It concludes with  a series of questions, some of which, are still being debated. Such as machines replacing human beings’ and human unemployment due to the use of machines.

15) Artificial Intelligence (AI) – Impact on Different Sectors

The presentation provides an overview of Artificial Intelligence (AI), covering its definition, history, and applications. It explores various AI techniques such as machine learning, neural networks, and expert systems. The presentation discusses the impact of AI on different sectors, including healthcare, finance, and transportation. It also delves into the ethical considerations and potential risks associated with AI development. The future of AI is examined, highlighting emerging trends and potential advancements. The presentation concludes by emphasizing the importance of responsible AI development and its potential to revolutionize various aspects of human life, while also addressing concerns about job displacement and privacy.

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