Patrick Winston, a beloved professor and computer scientist at MIT, died on July 19 at Massachusetts General Hospital in Boston. A professor at MIT for almost 50 years, Winston was director of MIT's Artificial Intelligence Laboratory from 1972 to 1997 before it merged with the Laboratory for Computer Science to become MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). A devoted teacher and cherished colleague, Winston led CSAIL's Genesis Group, which focused on developing AI systems that have human-like intelligence, including the ability to tell, perceive, and comprehend stories. He believed that such work could help illuminate aspects of human intelligence that scientists don't yet understand. "My principal interest is in figuring out what's going on inside our heads, and I'm convinced that one of the defining features of human intelligence is that we can understand stories,'" said Winston, the Ford Professor of Artificial Intelligence and Computer Science, in a 2011 interview for CSAIL.

Fernando "Corby" Corbató, an MIT professor emeritus whose work in the 1960s on time-sharing systems broke important ground in democratizing the use of computers, died on Friday, July 12, at his home in Newburyport, Massachusetts. Decades before the existence of concepts like cybersecurity and the cloud, Corbató led the development of one of the world's first operating systems. His "Compatible Time-Sharing System" (CTSS) allowed multiple people to use a computer at the same time, greatly increasing the speed at which programmers could work. It's also widely credited as the first computer system to use passwords. After CTSS Corbató led a time-sharing effort called Multics, which directly inspired operating systems like Linux and laid the foundation for many aspects of modern computing.

If your CV was good enough to get you an interview, that's great, but looking good on paper is just the starting point. At interview, you have to demonstrate that you have the skills to do the job and will be a good fit with the team. An interview is an audition – your opportunity to shine and prove you are the perfect person for the role. The actor Harlan Hogan is famous for delivering the catchphrase, 'you never get a second chance to make a first impression…' and it certainly pays to be well prepared. The interview is not, however, just an exercise in self-promotion. The hiring manager has a specific brief and, in effect, you are there to convince the interviewer that you can solve their hiring problem.

The presumed data owners' right to explanations brought about by the General Data Protection Regulation in Europe has shed light on the social challenges of explainable artificial intelligence (XAI). In this paper, we present a case study with Deep Learning (DL) experts from a research and development laboratory focused on the delivery of industrial-strength AI technologies. Our aim was to investigate the social meaning (i.e. meaning to others) that DL experts assign to what they do, given a richly contextualized and familiar domain of application. Using qualitative research techniques to collect and analyze empirical data, our study has shown that participating DL experts did not spontaneously engage into considerations about the social meaning of machine learning models that they build. Moreover, when explicitly stimulated to do so, these experts expressed expectations that, with real-world DL application, there will be available mediators to bridge the gap between technical meanings that drive DL work, and social meanings that AI technology users assign to it. We concluded that current research incentives and values guiding the participants' scientific interests and conduct are at odds with those required to face some of the scientific challenges involved in advancing XAI, and thus responding to the alleged data owners' right to explanations or similar societal demands emerging from current debates. As a concrete contribution to mitigate what seems to be a more general problem, we propose three preliminary XAI Mediation Challenges with the potential to bring together technical and social meanings of DL applications, as well as to foster much needed interdisciplinary collaboration among AI and the Social Sciences researchers.

The Bank of England's new 50-pound note will feature mathematician Alan Turing, honoring the code-breaker who helped lay the foundation for computer science. The Bank of England's new 50-pound note will feature mathematician Alan Turing, honoring the code-breaker who helped lay the foundation for computer science. Alan Turing, the father of computer science and artificial intelligence who broke Adolf Hitler's Enigma code system in World War II -- but who died an outcast because of his homosexuality -- will be featured on the Bank of England's new 50-pound note. The new note will be printed on polymer and will bear a 1951 photo of Turing, the bank announced Monday. It's expected to enter circulation by the end of 2021. It will include a quote from Turing: "This is only a foretaste of what is to come and only the shadow of what is going to be." Turing was just 41 when he died from poisoning in 1954, a death that was deemed a suicide.

Dr Mark Woods is one of our panellists in the afternoon. Mark leads the Autonomy and Robotics group at SCISYS UK. We invited him to tell us more about himself ahead of the conference. Mark, thank you so much for accepting our invitation to be a panellist. And you've developed autonomous/AI/ML based systems for Mars Exploration including the European Space Agency's first robotic mission to Mars!

More people around the world are being diagnosed with cancer every year. Thankfully, researchers and clinicians continue to devise ever more effective and sophisticated techniques to combat the disease, resulting in better patient outcomes and improved survival rates for most forms of cancer. But both of these important trends have created a data challenge in the clinic: how to collect, process and analyse increasing amounts of data to extract the useful information that will deliver the most effective treatment plans and the best outcomes for patients. Artificial intelligence is emerging as a vital tool to tackle this data deluge. Using computer algorithms to search for important signals in the noise can reduce treatment times, improve the quality of care, and make the best use of valuable resources.

These days, you can't attend a conference or have a conversation with leaders of the aerospace and defense community without touching on the modernization of the sector. This includes the integration of new technologies and processes to address aging equipment and fleets. The scale of the challenges in this space is too large, the costs are too high, and the impact of failure is too great to disregard the upside that's possible from a complete rethinking of how technology enhances these applications. Although the industry has experienced the effects of modernization in small ways for some time, we're starting to see how the adoption of digital technology is driving innovation on a much broader scale. New technologies like artificial intelligence, ubiquitous connectivity, advanced data analytics, blockchain, and robotics/autonomous systems are taking center stage and empowering cutting-edge applications.

Well, what natural language processing that we use does is it actually goes in and identifies what's the thing that this it actually about. So, it's keeping track as it reads through and says, "Okay, I recognize that there a few things "that we're talking about here, "here are some ideas that are very similar." And it pulls all that together and says… "Okay, at the end, I think that this was really "about these three or four topics. "Like, that's what I'm going to put in front of you "and say this is what this piece "of content was actually about." And, so that's what we use the natural language processing for.

Considering various factors such as the research areas, research focus, courses offered, duration of the program, location of the university, honors, awards and job prospects, we came up with the best universities to help you in your choosing process. This article is most suited for individuals who'd like to pursue a PhD with a focus in machine learning and need some guidance on their decision making. Feel free to jump to the end if you are looking for only the names of the Universities. Note: The universities mentioned below are in no particular order.