Elements of AI Luxembourg - session 2
- 1. Elements of AI : AI: Reflections, History and Challenges Nouzri Sana Postdoctoral Researcher at AI Robolab Department of Computer Science UNIVERSITY OF LUXEMBOURG Nouzri Sana 1
- 2. Agenda I. History of AI II. What is artificial intelligence? III. Domains covered by AI and Related fields IV. Demand for AI V. AI applications VI. AI Art applications VII.Types of AI VIII.Implications Nouzri Sana 2
- 3. History of AI 1950 Alan Turing published a landmark paper in which he speculated about the possibility of creating machines that think. Alan Turing (1912-1954) was an English mathematician and logician. He is rightfully considered to be the father of computer science Turing Test
- 4. History of AI 1950 1951 Alan Turing published a landmark paper in which he speculated about the possibility of creating machines that think. Christopher Strachey wrote a checkers program. And Dietrich Prinz wrote one for chess Christopher S. Strachey (16 November 1916 – 18 May 1975) was a British computer scientist. He was one of the founders of denotational semantics, and a pioneer in programming language design and computer time-sharing.
- 5. History of AI 1950 1951 1956 Alan Turing published a landmark paper in which he speculated about the possibility of creating machines that think. Christopher Strachey wrote a checkers program. And Dietrich Prinz wrote one for chess John McCarthy first coined the term of “Artificial intelligence” at the Dartmouth Conference The Dartmouth Summer Research Project on Artificial Intelligence was a 1956 summer workshop widely considered to be the founding event of artificial intelligence as a field. John McCarthy (September 4, 1927 – October 24, 2011) was an American computer scientist and cognitive scientist
- 6. History of AI 1950 1951 1956 1959 Alan Turing published a landmark paper in which he speculated about the possibility of creating machines that think. Christopher Strachey wrote a checkers program. And Dietrich Prinz wrote one for chess John McCarthy first coined the term of “Artificial intelligence” at the Dartmouth Conference MIT AI lab first set up. The research on AI began Massachusetts Institute of Technology
- 7. History of AI 1950 1951 1956 1959 1960 Alan Turing published a landmark paper in which he speculated about the possibility of creating machines that think. Christopher Strachey wrote a checkers program. And Dietrich Prinz wrote one for chess John McCarthy first coined the term of “Artificial intelligence” at the Dartmouth Conference MIT AI lab first set up. The research on AI began First robot was introduced to general motors assembly line
- 8. History of AI 1950 1951 1956 1959 1960 1966 Alan Turing published a landmark paper in which he speculated about the possibility of creating machines that think. Christopher Strachey wrote a checkers program. And Dietrich Prinz wrote one for chess John McCarthy first coined the term of “Artificial intelligence” at the Dartmouth Conference MIT AI lab first set up. The research on AI began First robot was introduced to general motors assembly line First AI chatbot called Eliza was introduced by by MIT professor Joseph Weizenbaum, a German American computer scientist
- 9. History of AI 1950 1951 1956 1959 1960 1966 1997 Alan Turing published a landmark paper in which he speculated about the possibility of creating machines that think. Christopher Strachey wrote a checkers program. And Dietrich Prinz wrote one for chess John McCarthy first coined the term of “Artificial intelligence” at the Dartmouth Conference MIT AI lab first set up. The research on AI began First robot was introduced to general motors assembly line First AI chatbot called Eliza was introduced IBM's Deep Blue was a chess-playing computer developed by IBM, it beats the world champion Garry Kasparov, in the game of chess 9 ………. AI Winter
- 10. History of AI 1950 1951 1956 1959 1960 1966 1997 2002 Alan Turing published a landmark paper in which he speculated about the possibility of creating machines that think. Christopher Strachey wrote a checkers program. And Dietrich Prinz wrote one for chess John McCarthy first coined the term of “Artificial intelligence” at the Dartmouth Conference MIT AI lab first set up. The research on AI began First robot was introduced to general motors assembly line First AI chatbot called Eliza was introduced IBM's Deep Blue beats the world champion Garry Kasparov, in the game of chess In 2002, iRobot Roomba can autonomously navigate and clean homes 10
- 11. History of AI 2005 Stanford's racing team’s autonomous robotic car, Stanly win the 2005 DARPA Grand Challenge The DARPA Grand Challenge is a prize competition for American autonomous vehicles
- 12. History of AI 2002 2011 IBM Watson is a question-answering computer system capable of answering questions posed in natural language, it won the $1M prize for winning in Jeopardy
- 13. History of AI 2002 2011 Personal assistants like Siri, Alexa, Google duplex, Cortana use speech recognition to answer questions and perform simple tasks 2011-2014
- 14. History of AI 2002 2011 2011-2014 2014 Ian Goodfellow comes up with Generative Adversarial Networks (GAN)
- 15. History of AI 2002 2011 2011-2014 2014 Ian Goodfellow comes up with Generative Adversarial Networks (GAN) AlphaGo beats beats world champion Ke Jie in the complex board game of Go 2016
- 16. History of AI 2002 2011 2011-2014 2014 Ian Goodfellow comes up with Generative Adversarial Networks (GAN) Most universities have courses in Artificial Intelligence, AI is for every one 2016 2018-2021 And more
- 17. What is AI? John McCarty definition John McCarthy first coined in the year 1956 John McCarthy defined AI as the science and engineering of making intelligent machines 17
- 18. What is AI ? AI is currently a "hot topic": media coverage and public discussion about AI is almost impossible to avoid. AI means different things to different people. AI is about artificial life-forms that can surpass human intelligence Almost any data processing technology can be called AI How should we define AI? Nouzri Sana 18
- 19. What is AI? The theory and development of computer systems able to perform task that normally require human intelligence, such as visual perception, speech recognition and decision making. + = Intelligence Artificial Artificial Intelligence visual perception speech recognition decision making 19
- 20. What is, and what isn't AI? Not an easy question! Reason 1: no officially agreed definition Even AI researchers have no exact definition of AI. The field is rather being constantly redefined when some topics are classified as non-AI, and new topics emerge. Automatic methods for search and planning Methods for processing uncertain information non-AI Nouzri Sana 20
- 21. What is, and what isn't AI? Not an easy question! Reason 2: the legacy of science fiction The confusion about the meaning of AI is made worse by the visions of AI present in various literary and cinematic works of science fiction. Nouzri Sana 21
- 22. What is, and what isn't AI? Not an easy question! Reason 3: what seems easy is actually hard… Another source of difficulty in understanding AI is that it is hard to know which tasks are easy and which ones are hard. While easy for you, grasping objects by a robot is extremely hard, and it is an area of active study …and what seems hard is actually easy By contrast, the tasks of playing chess and solving mathematical exercises can seem to be very difficult, requiring years of practice to master Nouzri Sana 22
- 23. So what would be a more useful definition? Autonomy The ability to perform tasks in complex environments without constant guidance by a user. Adaptivity The ability to improve performance by learning from experience. Nouzri Sana 23
- 24. Words can be misleading “Intelligent“ : system is intelligent, perhaps because it delivers accurate navigation instructions or detects signs of melanoma in photographs of skin lesions. “Understand” : a computer vision system understands images because it is able to segment an image into distinct objects such as other cars, pedestrians, buildings, the road, and so on Intelligence is not a single dimension; different AI systems cannot be compared on a single axis or dimension in terms of their intelligence Nouzri Sana 24
- 25. AI vs ML vs DL Artificial intelligence Machine learning Artificial Intelligence A technique which enables machines to mimic human behavior Machine learning Subset of AI technique which use statistical method to enable machines to improve with experience Nouzri Sana 25 Deep learning Deep learning Subset of machine learning that uses the concept of neural networks to solve complex problems.
- 26. Domains covered by AI Nouzri Sana 26
- 27. Related fields Data science is a recent umbrella term (term that covers several sub disciplines) that includes machine learning and statistics, certain aspects of computer science including algorithms, data storage, and web application development. Robotics means building and programming robots so that they can operate in complex, real- world scenarios. In a way, robotics is the ultimate challenge of AI since it requires a combination of virtually all areas of AI. Nouzri Sana 27
- 28. Demand for AI More computational power BIG DATA Better algorithms Broad Investment Nouzri Sana 28
- 29. AI applications google predictive search engine JP Morgan chase’s contract intelligence Platform IBM AI technology (healthcare) Nouzri Sana 29
- 30. AI applications Facebook (Facial features) Twitters' AI (filter out any offensive content) virtual assistants (siri, alexa, google duplex) Nouzri Sana 30
- 31. AI applications self driving cars Netflix (recommendation ) Gmail Spam filter Nouzri Sana 31
- 32. AI Art applications Can the machine be creative ? Nouzri Sana 32
- 33. AI Art applications Computational Creativity in Art Computational creativity is the study of building software that exhibits behavior that would be deemed creative in humans. Such creative software can be used for autonomous creative tasks, such as, writing poems, painting pictures, and composing music. AI Generated Music/Sound AI Generated Movement/Dance AI Generated Voice AI Generated Images / Pictures AI Generated Data Visualization AI Drawing / AI Painting Nouzri Sana 33
- 34. AI Art applications Living Archive: A tool for choreography powered by AI. Wayne McGregor is an award-winning British choreographer who uses AI to expand the possibilities of human movement. Yamaha Artificial Intelligence (AI) Transforms a Dancer into a Pianist Computational creativity in dance Living Archive by Wayne McGregor November 2019 | By Bastien Girschig, Google Arts & Culture Lab Yamaha
- 35. Magenta AI Art applications Album composed and produced with AI. Taryn Southern is an artist and futurist who created the first solo album composed and produced with AI. Magenta Studio – A collection of music plugins built on Magenta’s open source tools and models. MuseNet – Generate 4-minute musical compositions with 10 instruments Computational creativity in music 35 I AM AI Album Open AI MuseNet
- 36. AI Art applications Portraits of Imaginary People By Mike Tyka. By using GAN, it explores the latent space of human faces by training a neural network to imagine and then depict portraits of people who don’t exist Computational creativity in visual arts Nouzri Sana 36
- 37. AI Art applications Deep dream generator is a creative AI tool that create impressive paintings. With this AI- enabled tool, you can change your photos to cool paintings in different styles. Nouzri Sana 37
- 38. Deepfake
- 39. GANs are Dangerous Style GAN GAN can generate almost a human-like face if trained sufficiently
- 40. Types of AI ANI Artificial Narrow Intelligence Weak AI Narrow capability AGI Artificial General Intelligence General AI AI that equals human intelligence ASI Artificial Super Intelligence Super AI AI that exceeds human intelligence Present Future ? Possible ? Nouzri Sana 40
- 41. Thank you for your attention Nouzri Sana 41
Editor's Notes
- #24: The most useful definition of AI consists of 2 characteristics: Autonomy The ability to perform tasks in complex environments without constant guidance by a user. +One of the ultimate goals of artificial intelligence is the ability for machines to operate on their own, with little or any human interaction, The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly. Adaptivity The ability to improve performance by learning from experience. +Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- #25: “Intelligent“ : system is intelligent, perhaps because it delivers accurate navigation instructions or detects signs of melanoma in photographs of skin lesions. the word "intelligent" easily suggests that the system is capable of performing any task an intelligent person is able to perform: going to the grocery store, peel the vegetables, washing and folding laundry, and so on. “Understand” : a computer vision system understands images because it is able to segment an image into distinct objects such as other cars, pedestrians, buildings, the road, and so on. The word "understand" easily suggests that the system also understands that even if a person is wearing a t-shirt that has a photo of a road printed on it, it is not okay to drive on that road (and over the person)
- #26: Artificial Intelligence (AI) is not a single piece of hardware or software, but rather a constellation of technologies AI is sort of a process or it's a methodology in which you make machines mimic the behavior of human beings Machine learning is a method through which you can feed a lot of data to a machine and make it learn and make its own decisions. Deep learning, on the other hand, is a subset of machine learning that uses the concept of neural networks to solve complex problems. Artificial intelligence, machine learning, and deep learning, are interconnected fields. Machine learning and deep learning aids artificial intelligence by providing a set of algorithms to solve data-driven problems.
- #27: +Machine learning can be said to be a subfield of AI, which itself is a subfield of computer science. Machine learning enables AI solutions that are adaptive. +Deep learning is a subfield of machine learning. refers to the complexity of a mathematical model (neural networks), it have been applied to fields including computer vision, machine vision, speech recognition, natural language processing, +A knowledge base (KB) is a technology used to store complex structured and unstructured information used by a computer system. +Expert system is a computer system emulating the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning through bodies of knowledge. +Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos, detect objects in an image, estimate their position or handle them with care
- #29: The first reason is the computational power, AI requires a lot of computing power, one of the greatest technologies that made this possible are GPUs (A graphics processing unit) Second most important reason is lot of data, the data is generated at an immeasurable pace through social media, trough devices, every possible way, there’s a lot of data, Next reason is better algorithms, very effective algorithms which are based on neural networks, the concept behind deep learning Another reason is that universities, governments, startup are all investing in AI because they believe that AI is the future
- #30: google predictive search engine, when you begin typing a search term, google makes recommendations for you to choose, so predictive searches are based on data that google collect about you, such as your browser history, your location, your age and other personal details, so by using AI, google attempts to guess what you be trying to find, now behind this is ML, DML involved finance sector, JP Morgan chase’s contract intelligence Platform uses ML, AI and image recognition software to analyze legal documents, manually reviewing around 12 000 agreement took over 36 000 hours that o lot of time but as soon as this task was replaced by AI machine, it was able to do this in a matter of seconds so that the difference between AI and manual or human work, even though AI cannot think and reason like humans but their computational power is very strong compared to humans healthcare, IBM is one of the pioneers that has developed AI software specifically for medicine
- #31: social media platforms like Facebook. All the auto tagging feature that you see in Facebook only AI behind it in order to detect facial features and tag your friends, Twitters' AI which is being used to identify any sort of hate speech and terroristic languages in tweets. virtual assistants, like siri, alexa right now, and newly another released, Google’s virtual assistant called the Google duplex, which has astonished millions of people around the world
- #32: self-driving cars, so AI implements computer vision, image detection, Deep learning in order to build cars that can automatically detect any objects or any obstacles and drive around without human intervention, netflix, Netflix has developed a personalized movie recommendation for each of its users. 75% of what you watch are recommended by Netflix gmail uses AI, to separate sections, for example, primary section, social section, and all of that, spam section, gmail uses AI to classify emails as spam and non spam
- #33: Will a robot take my job as an artist? How is AI likely to change or improve my job in the next ten years? If we teach the machine about art and styles and push it to generate novel images, what would it generate? If we teach the machine about sound and push it to generate new sounds, can it make music?
- #39: GANs’ potential for both good and evil is huge, because they can learn to mimic any distribution of data. That is, GANs can be taught to create worlds eerily similar to our own in any domain: images, music, speech, prose. They are robot artists in a sense, and their output is impressive. But they can also be used to generate fake media content, and are the technology underpinning Deepfakes.
- #41: Artificial narrow Intelligence, (weak AI), it involves applying AI only to specific task, So, many currently existing systems that claim to use artificial intelligence are actually operating as weak AI focused on a narrowly defined specific problem. Artificial general Intelligence (strong AI), it involves machines that possess the ability to perform any intelligent task that a human being can. Machines don’t possess human like abilities, they have very strong processing unit that can perform high level computations, but they ‘re not yet capable of doing the simple and the most reasonable things that a human being can. Artificial super Intelligence (super AI) is a term referring to the time when the capabilities of computers will surpass humans, presently it’s seen as a hypothetical situation as depicted in movies and any science-fiction books.