Geoffrey Hinton Distinguished Professor Emeritus, Computer Science Department, University of Toronto Geoffrey Hinton received his PhD in Artificial Intelligence from Edinburgh in 1978. After five years as a faculty member at Carnegie Mellon he became a fellow of the Canadian Institute for Advanced Research and moved to the Department of Computer Science at the University of Toronto where he is now an emeritus professor. Geoffrey Hinton was one of the researchers who introduced the backpropagation algorithm and the first to use backpropagation for learning word embeddings. His other contributions to neural network research include Boltzmann machines, distributed representations, time-delay neural nets, mixtures of experts, variational learning and deep learning. His research group in Toronto made major breakthroughs in deep learning that revolutionized speech recognition and object classification.

The increasing attention being paid to artificial intelligence raises important questions about its integration with social sciences and humanity, according to David De Cremer, founder and director of the Centre on AI Technology for Humankind at the National University of Singapore Business School. He is the author of the recent book, Leadership by Algorithm: Who Leads and Who Follows in the AI Era? While AI today is good at repetitive tasks and can replace many managerial functions, it could over time acquire the "general intelligence" that humans have, he said in a recent interview with AI for Business (AIB), a new initiative at Analytics at Wharton. Headed by Wharton operations, information and decisions professor Kartik Hosanagar, AIB is a research initiative that focuses on helping students expand their knowledge and application of machine learning and understand the business and societal implications of AI. According to De Cremer, AI will never have "a soul" and it cannot replace human leadership qualities that let people be creative and have different perspectives. Leadership is required to guide the development and applications of AI in ways that best serve the needs of humans. "The job of the future may well be [that of] a philosopher who understands technology, what it means to our human identity, and what it means for the kind of society we would like to see," he noted. An edited transcript of the interview appears below. AI for Business: A lot is being written about artificial intelligence. What inspired you to write Leadership by Algorithm?

Can broader datasets help developers avoid accidentally perpetuating deep-rooted biases in vital institutions like healthcare and education? AI in healthcare has a bias problem. Last year, it came to light that six algorithms used on an estimated 60-100 million patients nationwide were prioritizing care coordination for white patients over black patients for the same level of illness. The algorithm was trained on costs in insurance claims data, predicting which patients would be expensive in the future based on who was expensive in the past. Historically, less is spent on black patients than white patients, so the algorithm ended up perpetuating existing bias in healthcare.

How to Do It is Slate's sex advice column. Send it to Stoya and Rich here. Every week, the crew responds to a bonus question in chat form. I recently met a guy on Tinder, where I usually don't have much luck because I'm not conventionally attractive and want to date, not just hook up. But after talking to this guy for a few days we seem practically perfect for each other!

Today is the era of intelligence in machines. With the advances in Artificial Intelligence, machines have started to impersonate different human traits, a chatbot is the next big thing in the domain of conversational services. A chatbot is a virtual person who is capable to carry out a natural conversation with people. They can include skills that enable them to converse with the humans in audio, visual, or textual formats. Artificial intelligence conversational entities, also called chatbots, conversational agents, or dialogue system, are an excellent example of such machines. Obtaining the right information at the right time and place is the key to effective disaster management. The term "disaster management" encompasses both natural and human-caused disasters. To assist citizens, our project is to create a COVID Assistant to provide the need of up to date information to be available 24 hours. With the growth in the World Wide Web, it is quite intelligible that users are interested in the swift and relatedly correct information for their hunt. A chatbot can be seen as a question-and-answer system in which experts provide knowledge to solicit users. This master thesis is dedicated to discuss COVID Assistant chatbot and explain each component in detail. The design of the proposed chatbot is introduced by its seven components: Ontology, Web Scraping module, DB, State Machine, keyword Extractor, Trained chatbot, and User Interface.

As vice president of innovation at logistics company DHL, Gina Chung oversees a 28,000-square-foot innovation facility in Chicago. Fascinated with supply chains since college ("I think it's something to do with the fact that I'm from New Zealand and grew up in a pretty isolated part of the world," she explains), she spearheads AI and robotics projects focused on front-line operations -- like automated pallet inspection and stacking, delivery route optimization, and aircraft utilization. Your reviews are essential to the success of Me, Myself, and AI. For a limited time, we're offering a free download of MIT SMR's best articles on artificial intelligence to listeners who review the show. Send your review screenshot to smrfeedback@mit.edu to receive the download. Gina Chung is vice president, Innovation Americas, at DHL, where she is responsible for DHL's Americas Innovation Center, a purpose-built platform to engage customers, startups, and industries on the future of logistics. She manages a portfolio of projects focused on the rapid testing and adoption of technologies such as collaborative robotics and artificial intelligence across logistics operations. Gina notes that "the first day for AI is the worst day": The technology improves with human input over time, achieving accuracy to a level where people trust and embrace it. She describes how success requires closely collaborating with key stakeholders, integrating change management, bringing teams along when introducing new technology, and designing solutions with the end user in mind.

Wherever that will lead is, at the time of the writing of this article, still not certain, but regardless of the direction, it's clear that advancing progress with artificial intelligence is a key strategic element for both major parties. Over the course of the past few years, governments around the world have taken strong positions on advancing their strategies around AI adoption. Certainly heading into the new year it seems that the pace of adoption won't be slowing any time soon. At the recent Data for AI conference, we had an opportunity to get insights into how the government plans to continue and accelerate its adoption of AI in an interview with Ellery Taylor, Acting Director of the Office of Acquisition Management and Innovation Division, at the US General Services Administration (GSA). In this article he shares his outlook for the future of AI and how it is being adopted in the government.

So it turns out that Kim Kardashian whisking her friends and family off to a private island in the middle of a pandemic was only the second craziest thing about her 40th birthday celebration. On Thursday, Kardashian revealed what her husband, Kanye West, got her the birthday gift at the top of every woman's wish list: her very own hologram. And not just any hologram: It was a so-called holographic resurrection of her late father, Robert Kardashian, who died in 2003. Kaleida, a "multimedia hologram company," published a page to its website taking credit for the creation (Kardashian and West have not yet confirmed the hologram's origins). Reached by Slate, Kaleida director and producer Daniel Reynolds declined to discuss any specifics of the Kardashian hologram, but agreed to speak about the company and its technology more generally. How exactly do you order a hologram of a late relative?

It is 2020 and it's all about technology these days. You knowingly or unknowingly use machine learning in your day-to-day life. Let me give you some examples, Google spam filter, Netflix recommendation system, Facebook face recognition, weather forecast, and much more. All of these are Machine Learning. It has become one of the hottest industries and has become like a trend for beginners.

In early 2018, officials at University College London were shocked to learn that meetings organized by "race scientists" and neo-Nazis, called the London Conference on Intelligence, had been held at the college the previous four years. The existence of the conference was surprising, but the choice of location was not. UCL was an epicenter of the early 20th-century eugenics movement--a precursor to Nazi "racial hygiene" programs--due to its ties to Francis Galton, the father of eugenics, and his intellectual descendants and fellow eugenicists Karl Pearson and Ronald Fisher. In response to protests over the conference, UCL announced this June that it had stripped Galton's and Pearson's names from its buildings and classrooms. After similar outcries about eugenics, the Committee of Presidents of Statistical Societies renamed its annual Fisher Lecture, and the Society for the Study of Evolution did the same for its Fisher Prize. In science, these are the equivalents of toppling a Confederate statue and hurling it into the sea. Unlike tearing down monuments to white supremacy in the American South, purging statistics of the ghosts of its eugenicist past is not a straightforward proposition. In this version, it's as if Stonewall Jackson developed quantum physics. What we now understand as statistics comes largely from the work of Galton, Pearson, and Fisher, whose names appear in bread-and-butter terms like "Pearson correlation coefficient" and "Fisher information." In particular, the beleaguered concept of "statistical significance," for decades the measure of whether empirical research is publication-worthy, can be traced directly to the trio. Ideally, statisticians would like to divorce these tools from the lives and times of the people who created them. It would be convenient if statistics existed outside of history, but that's not the case.