Machine Learning Lecture at CMU by Ke Li, Ph.D. Candidate at the University of California, Berkeley Lecturer: Ke Li Carnegie Mellon University Abstract: In this talk, Li presents his team's work on overcoming two long-standing problems in machine learning and algorithms: 1. Mode collapse in generative adversarial nets (GANs) Generative adversarial nets (GANs) are perhaps the most popular class of generative models in use today. Unfortunately, they suffer from the well-documented problem of mode collapse, which the many successive variants of GANs have failed to overcome. I will illustrate why mode collapse happens fundamentally and show a simple way to overcome it, which is the basis of a new method known as Implicit Maximum Likelihood Estimation (IMLE). It turns out that this problem is not insurmountable - I will explain how the curse of dimensionality arises and show a simple way to overcome it, which gives rise to a new family of algorithms known as Dynamic Continuous Indexing (DCI). Bio: Ke Li is a recent Ph.D. graduate from UC Berkeley, where he was advised by Prof. Jitendra Malik, and will join Google as a Research Scientist and the Institute for Advanced Study (IAS) as a Member hosted by Prof. Sanjeev Arora.

Is bias in AI self-reinforcing? Decision-making systems that impact criminal justice, financial institutions, human resources, and many other areas often have bias. This is especially true of algorithmic systems that learn from historical data, which tends to reflect existing societal biases. In many high-stakes applications, like hiring and lending, these decision-making systems may even reshape the underlying populations. When the system is retrained on future data, it may become not less but more detrimental to historically disadvantaged groups.

"If somebody graduated from a great university, that may be an indication that they will be capable of great things, but it's not necessarily the case. If you look at, say, people like Bill Gates or Larry Ellison, Steve Jobs, these guys didn't graduate from college, but if you had a chance to hire them, of course that would be a good idea," Musk said. Instead, Musk said he looks for "evidence of exceptional ability. And if there is a track record of exceptional achievement, then it is likely that that will continue into the future," he told Auto Bild. Tesla needs artificial intelligence talent to work on its self-driving vehicle ambitions.

Welcome to the home page of SOCIAL, the UKRI Centre for Doctoral Training in Socially Intelligent Artificial Agents. The overarching goal of SOCIAL is to train the next generation of experts in Artificial Social Intelligence, the AI domain aimed at endowing artificial agents with social intelligence, the ability to deal appropriately with users' attitudes intentions, feelings, personality and expectations. SOCIAL is based at the University of Glasgow and it is a collaboration between the School of Computing Science, the School of Psychology, the Institute of Neuroscience and Psychology, the Adam Smith Business School, the School of Critical Studies, the School of Engineering, the Scottish Graduate School of the Social Sciences and 16 industrial partners (BRAIQ, Context-Scout, Dimensional Imaging, Dyson, FLASH Robotics, Furhat Robotics, Micro-phonics, Microsoft, Neurodata-Lab, PolyAI, Qumodo, Rakuten, Schumberger, SICSA, Telefonica, The Data Lab). For any inquiry, please contact us at social-cdt@glasgow.ac.uk.

Leonard Hobbs Bio: Leonard graduated from University College Cork Ireland in 1986 with a 1st class honours degree in Electrical Engineering and was awarded the title of'graduate of the year' by the college. He completed a Masters degree at the NMRC (now called Tyndall), at UCC in 1988. He has been one of Ireland's leading technologists in the ICT sector with close to 30 years of experience, mostly with Intel, spanning leading edge research to advanced manufacturing. His last role at Intel was Director of Public Affairs with responsibility for driving Intel Ireland's policy, communications, education and community agendas. Leonard is currently the Director of Research and Innovation at Trinity College Dublin where he owns the definition and implementation of the research, innovation and enterprise strategy for the University spanning research programs development, contract management, technology transfer, entrepreneurship and enterprise partnership liaison.

The lack of deep learning skills is hampering the performance of British businesses, according to new research from operational AI firm Peltarion. Its survey of firms across the UK and Nordic regions found 83 percent of AI decision-makers believe the deep learning skills shortage is affecting their business's ability to compete in the market. Almost half (49 percent) said the shortage is delaying projects, while 44 percent see the shortage as posing a major barrier to further investment in deep learning. The talent shortage is a cause for serious concern among businesses, who see deep learning (a sub-field of artificial intelligence) as an avenue to optimising processes and creating more intelligent data-driven products. As it stands, 71 percent of businesses are actively recruiting in an effort to remedy the skills gap.

Is there any similarity in learning process between humans and machines. Human learning process varies from person to person. Once a learning process is set into the minds of people, it is difficult to change it. But, in Machine Learning (ML), it is easy to change the learning method by selecting a different algorithm. In ML, we have well defined processes to understand and estimate the accuracy in learning.

Can you explain what is underfitting and overfitting in the context of machine learning? Describe it in a way even a non-technical person will grasp. Your ability to explain this in a non-technical and easy-to-understand manner might well decide your fit for the data science role! Even when we're working on a machine learning project, we often face situations where we are encountering unexpected performance or error rate differences between the training set and the test set (as shown below). How can a model perform so well over the training set and just as poorly on the test set?

Edge is all about intelligence, but those smarts must be squeezed into ever tinier form factors. Developers of artificial intelligence (AI) applications must make sure that each new machine learning (ML) model they build is optimized for fast inferencing on one or more target platforms. Increasingly, these target environments are edge devices such as smartphones, smart cameras, drones, and embedded appliances, many of which have severely constrained processing, memory, storage, and other local hardware resources. The hardware constraints of smaller devices are problematic for the deep neural networks at the heart of more sophisticated AI apps. Many neural-net models can be quite large and complex. As a result, the processing, memory, and storage requirements for executing those models locally on edge devices may prove excessive for some mass-market applications that require low-cost commoditized chipsets.

Humans are natural pattern recognizers. Whether, as in prehistoric times, we were recognizing danger in a telltale rustle of the bushes or skimming a page of letters and numbers today, we use patterns to derive meaning without having to do a more detailed inspection. Futurist and entrepreneur Ray Kurzweil considers pattern recognition so important that in his recent book, How to Create A Mind, he argued that pattern recognition and intelligence are essentially the same thing. Expertise, in essence, is the familiarity of patterns of a specific field. Today, machines are learning to recognize patterns as well.