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Ai's Leap Forward, Huge Potential and Undoubted APRIL 2021 An academic perspective: AI’S LEAP FORWARD, HUGE POTENTIAL AND UNDOUBTED LIMITATIONS Advances in computing power and data availability have accelerated AI’s evolution and it is now entering our daily lives. Even so, it is still in the foothills of the Himalayan task of developing systems with deep understanding. Interview with Professor David Barber, Director of the UCL Centre for Artificial Intelligence and fellow of the Turing Institute. QUICK READ AI suffers from a blurring of the boundaries – it’s about machines being able to mimic the way humans work, rather than simply the analysis of large data sets. Computing power and data availability have combined to enable a period of accelerated development in machine learning, a major data-driven sub-field of AI. AI works best when carrying out limited and well-defined tasks where large amounts of data are available to train the algorithms effectively. While the prospect of “artificial general intelligence” remains distant, the real-world applications of AI are going to be hugely economically significant. AN ACADEMIC PERSPECTIVE: AI’S LEAP FORWARD, HUGE POTENTIAL AND UNDOUBTED LIMITATIONS / 2 Professor David Barber Director of the UCL Centre for Artificial Intelligence Our everyday lives are increasingly spent Innovations in robotics, a closely related Why are we talking about AI now? interacting with technology that replicates field that offers exciting possibilities in Dr David Barber, Professor of Machine abilities. Advances in AI have resulted areas such as driverless cars, warehouse Learning at UCL and Director of the UCL in voice recognition software that allows automation and personal care for the Centre for Artificial Intelligence, points us to give instructions to Siri or Alexa. elderly or infirm, also depend heavily on out that attempts to create human-like They have created sophisticated advances in AI. These machines all use abilities in man-made systems date predictive text functions in email AI to replicate humans’ ability to interpret back centuries. Indeed, Prof Barber programs, online customer service and interact with the physical environment, is a fellow of the Turing Institute, chatbots and the telephone-based as well as drawing on insights from which recognises the pioneering role systems now being deployed in call neuroscience into how humans function. of Alan Turing, who died in 1954, in centres, all of which depend on natural The emergence of AI as an increasingly the development of the discipline. language processing. Translation tools common feature of modern life suggests Turing and fellow mathematician and and digital assistants that are capable we are on the cusp of a transformation economist David Champernowne wrote of turning speech into text work in the that will produce vast changes in the their groundbreaking chess program, same way. Image recognition software, way humans live and work. However, to Turochamp, in 1948 during their research as deployed in facial or number plate interpret and navigate the effects that into AI. But the algorithm that powered recognition systems and autonomous AI is likely to have on society and the Turochamp was too complex to run on vehicles, also represent everyday commercial world, we must explore how the computers of the time and Turing was examples of AI in action. and why it appears to have undergone only ever able to execute the program such a major leap forward in recent years, manually, using paper calculations. and appreciate its current limitations as well as its undoubted potential. 3 / AN ACADEMIC PERSPECTIVE: AI’S LEAP FORWARD, HUGE POTENTIAL AND UNDOUBTED LIMITATIONS Computing power’s unstoppable rise This anecdote illustrates an important created and stored have multiplied rapidly point. The basis of many of the over recent years, so data sets large 2 ears of Moore’s La algorithms in use today are not new. enough to train algorithms to high levels What has unlocked their potential and, of accuracy and proficiency – for example, M R V T T I C NVIDIA TITAN X 1E0 GTX 480 therefore, that of AI, is the abundance images for use in teaching object 1E0 of computing power that has become recognition – have been created. IBM BLUE GENE available in recent years as processing POWER Machine learning emerges as the 1E0 MAC speeds have increased. An advanced dominant approach 1,000 IBM ASCI image recognition system takes about a WHITE IBM PC PENTIUM PC week to train using today’s single NVIDIA Taken together, these two factors – 10 CRAY 1 DATA GENERAL APPLE computing power and data availability NOVA SUN 1 MACINTOSH GPU-based computers. Performing the 01 same number of calculations using the – have combined to enable a period of WHIRLWIND accelerated development in machine ENIAC IBM 360 best workstations available in the early 0001 DEC PDP-1 1990s would have taken hundreds of learning (ML), a major, data-driven sub- COLOSSUS 1E0 thousands of years. Gains in computing field of AI. As a result, over the past Calculations per second constant dollar IBM TABULATOR HOLLERITH speed, compounded over decades, have 15 years or so, ML has become the 1E0 TABULATOR ANALYTICAL dominant paradigm within AI and is ENGINE delivered hardware capable of allowing 1E0 AI to operate in real-time. largely responsible for the advances that 100 110 120 10 10 10 10 10 10 10 2000 2010 2020 underpin the applications we are most Year The second vital factor in the emergence familiar with today. of AI has been the increasing availability Source: As of 2018. https://www.britannica.com/technology/Moores-law. of data. As the quantities of digital data Information and opinions provided by third parties have been obtained from sources believed to be reliable, but accuracy and completeness cannot be guaranteed. The information is not intended to be used as the sole basis for investment decisions, nor should it be construed as advice designed to meet the particular needs of an individual investor. AN ACADEMIC PERSPECTIVE: AI’S LEAP FORWARD, HUGE POTENTIAL AND UNDOUBTED LIMITATIONS / 4 Within ML, a key avenue of development immediately acquired by Google. Hinton’s Prof Barber observes: “A story that ML dating back decades involved neural team then went on to quickly create a researchers like to tell is that we had a networks – systems loosely based on speech recognition system far ahead of machine that could beat the best human “What’s ultimately important the structure of the human brain. any previous system. ML and its variants, chess player in 1997, but we still don’t is not the ability to play Having been largely out of favour for such as deep learning, had become the really have a robot that can smoothly and chess or Go, but the many years, neural networks became the key technique within AI. reliably pick up a chess piece and move delivery of systems that key focus of ML research once again in it.” High-profile achievements like these Deep Blue, AlphaGo and the limits of will be useful in our daily 2006, when a small group of researchers are important to generate attention, but gameplaying lives. The rest is largely demonstrated that with access to enough in pure research terms they have turned entertainment computing power, the technique offered The best-known landmarks in AI tend to out to be far less significant than many significant improvements in the results be moments such as the victory of IBM’s assume, he argues. “What’s ultimately ” obtained.1 Other major advances followed Deep Blue computer over world chess important is not the ability to play chess quickly. Soon afterwards, researchers champion Gary Kasparov in 1997, or of or Go, but the delivery of systems that were able to adapt graphics processing Google-owned Deepmind’s AlphaGo over will be useful in our daily lives. The rest units (GPUs) developed for computer Korean Go champions Lee Se-dol in is largely entertainment.” gaming to speed up the process of 2016 and Ke Jie in 2017. These highly The most important challenge for AI, he training ML algorithms 100-fold. symbolic events naturally appeal to our argues, is moving it beyond the enclosed, fascination with the idea of machines that Armed with rapidly improving technology rules-based world of games and making can outwit humans. But how significant tools, a research group led by Geoffrey it good enough to operate alongside are they, in fact? Hinton made a breakthrough in image humans in the much more complicated recognition using ML in 2012 and was environment of our daily lives. 1 One particular milestone is reported here https://science.sciencemag.org/content/313/5786/504 in which neural networks were shown to vastly outperform traditional methods for image compression. 5 / AN ACADEMIC PERSPECTIVE: AI’S LEAP FORWARD, HUGE POTENTIAL AND UNDOUBTED LIMITATIONS Where today’s AI works best characteristics with games. The scope encounters in our highly complex of the task the AI is asked to carry out everyday environment is neither limited If the research significance of AI systems is limited and well defined, and large nor well defined. Consequently, even “Speech recognition is that can beat chess or Go champions amounts of data are available to train the most advanced image recognition a good example where may be overstated, however, in one the algorithms effectively. Applications systems fall short [as we explore in the we have quite good respect perhaps these achievements do ranging from facial and number plate interview that follows with Dr Ali Shafti]. performance now. carry some wider significance. Games recognition, to the machine’s ability to But it’s still very superficial such as Go or chess are extremely A chatbot can successfully handle recognise and decode the phonemes – the machine doesn’t complex, rules-based problems for which simpler banking or insurance enquiries, that make up human speech, or even actually understand in very large quantities of game-play data because the range of tasks it needs to the visual characteristics that define are available that the ML algorithms can perform is limited by the nature of the a deep sense what everyday objects all, to a greater or lesser learn from.
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