ACM fellow Dana Stewart Scott, the recipient jointly with Michael Rabin of the 1976 A.M. Turing Award for the concept of nondeterministic finite automata, has made seminal contributions spanning computing science, mathematics, philosophy, automata theory, modal logic, model theory, set theory, and the theory of programming languages. After receiving a B.A. in mathematics from the University of California, Berkeley, in 1950, and a Ph.D. from Princeton University in 1958, he held faculty positions at the University of Chicago, UC Berkeley, and at Stanford, Princeton, Oxford, and Carnegie Mellon Universities. He retired as University Professor from CMU in 2003. The distinguished theoretical computer scientist Gordon Plotkin conducted a series of four oral history interviews of Scott between November 2020 and February 2021. The interviews, the transcripts and videos of which are online,a cover primarily the period leading up to the 1976 ACM A.M. Turing Award. Presented here is a condensed and highly edited version, which includes some additional post-interview material provided by Scott. I was born in 1932 in Berkeley, CA, where I am now in retirement. We lived on a farm near Susanville when I started first grade in a one-room school-house.

The Machine Learning Center at Georgia Tech (ML@GT) is home to many talented students from across campus, representing all six of Georgia Tech's colleges and the Georgia Tech Research Institute (GTRI). These students have diverse backgrounds and a wide variety of interests both inside and outside of the classroom. Today, we'd like you to meet Cusuh Ham, a first-year machine learning Ph.D. student who hopes to live abroad after graduation and ultimately become a professor. Tell us about your research interests. Where might people be impacted by them in everyday life?

Inevitably, you might find yourself stuck in a life-threatening situation where your car won't be able to stop in time to avoid a collision. It has a choice--either collide with one of the other vehicles endangering another passenger's life or put your life in harm's way. What do you think it would do? If we were driving a car in manual mode, whichever way we chose, it would be considered a reaction to the situation as opposed to a deliberate decision--an instinctual, potentially panicked reaction with no forethought or malice. However, if a programmer were to instruct the car to take the same call in a life-threatening situation, it could be interpreted as a premeditated homicide.

The International Conference on Machine Learning (ICML) Outstanding Paper awards are given to papers from the current conference that are "strong representatives of solid theoretical and empirical work in the field". This year, there were 15 awards. Monarch: Expressive structured matrices for efficient and accurate training Tri Dao, Beidi Chen, Nimit Sohoni, Arjun Desai, Michael Poli, Jessica Grogan, Alexander Liu, Aniruddh Rao, Atri Rudra, Christopher Re Abstract: Large neural networks excel in many domains, but they are expensive to train and fine-tune. A popular approach to reduce their compute or memory requirements is to replace dense weight matrices with structured ones (e.g., sparse, low-rank, Fourier transform). These methods have not seen widespread adoption (1) in end-to-end training due to unfavorable efficiency–quality tradeoffs, and (2) in dense-to-sparse fine-tuning due to lack of tractable algorithms to approximate a given dense weight matrix.

UNSW Engineering Professor Flora Salim has been honoured for her pioneering work in computing and machine learning by Women in AI, a global advocacy group for women in the artificial intelligence (AI) field. The 2022 Women in AI Awards Australia and New Zealand recognised women across various industries committed to excellence in AI. Finalists were judged on innovation, leadership and inspiring potential, global impact, and the ability of the AI solution to provide a social good for the community. Prof. Salim was recognised for her AI achievements in the Defence and Intelligence award category. The award acknowledged her research in the cross-cutting areas of ubiquitous computing and machine learning, with a focus on efficient, fair, and explainable machine learning for multi-dimensional sensor data, towards enabling situational and behaviour intelligence for multiple applications.

"It is a far, far better thing that I do, than I have ever done," Sydney Carton thinks on his way to the guillotine. That far better thing is dying tragically, for many reasons: to save an innocent man, to fulfill his own redemption, and--of course--to make us cry at the end of A Tale of Two Cities. The death scene is one of the sharpest tools in a writer's toolbox, as likely to wound the writer themself as the reader--for if a well-written death scene can be thrilling, terrifying, or filled with despair, so can a poorly written one be bathetic, stupid, and eye-rolling. But let's not talk about those. Let's talk about the good ones, the deathless death scenes. We've assembled the 50 greatest fictional deaths of all time--the most moving, most funny, most shocking, most influential scenes from books, movies, TV, theater, video games, and more. Spoilers abound: It's a list that spans nearly 2,500 years of human culture, from Athens to A24, and is so competitive that even poor Sydney Carton and his famous last words couldn't make it. We've also talked to many of the creators behind the scenes on our list to ask them how they wrote them, why they killed off characters we loved, what makes a great death scene, and what final moments from fiction have stuck with them all their lives. We've made this list during a pandemic, as real-life death has stalked us all, more tangible than ever. After all, one of the many things art can do is to help us navigate the pitfalls of life, and there's no deeper pitfall than the final one. Here are the scenes that have shown us all what the big goodbye might actually be like, when it comes. Imagine Imagine the horror in Athens' Theatre of Dionysus at the premiere of Medea, as the audience heard the desperate cries of Medea's two sons while she ruthlessly stabbed them to death.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 – 28. SAN FRANCISCO – VentureBeat is proud to announces the winners of the 4th Women in AI Awards and its 4th AI Innovation Awards. The Women in AI Awards honors women leaders, mentors, researchers and entrepreneurs who are transforming the AI industry. This year, over 200 women were nominated over five categories, and we're delighted to introduce our winners. VentureBeat's 4th AI Innovation Awards recognizes and awards noteworthy, compelling, innovative and successful AI initiatives in five categories: Conversational AI, Applied AI, AI on the Edge, AI for Good and AI Innovators (startups less than two years old and with no more than $30 million in funding).

Engineering a high-performance race car requires a direct consideration of the human driver using real-world tests or Human-Driver-in-the-Loop simulations. Apart from that, offline simulations with human-like race driver models could make this vehicle development process more effective and efficient but are hard to obtain due to various challenges. With this work, we intend to provide a better understanding of race driver behavior and introduce an adaptive human race driver model based on imitation learning. Using existing findings and an interview with a professional race engineer, we identify fundamental adaptation mechanisms and how drivers learn to optimize lap time on a new track. Subsequently, we use these insights to develop generalization and adaptation techniques for a recently presented probabilistic driver modeling approach and evaluate it using data from professional race drivers and a state-of-the-art race car simulator. We show that our framework can create realistic driving line distributions on unseen race tracks with almost human-like performance. Moreover, our driver model optimizes its driving lap by lap, correcting driving errors from previous laps while achieving faster lap times. This work contributes to a better understanding and modeling of the human driver, aiming to expedite simulation methods in the modern vehicle development process and potentially supporting automated driving and racing technologies.

If you ask me, the hiring managers are not looking for the correct answers. They want to evaluate your work experience, technical knowledge, and logical thinking. Furthermore, they are looking for data scientists who understand both the business and technical sides. For example, during an interview with a top telecommunication company, I was asked to come up with a new data science product. I suggested an open-source solution and let the community contribute to the project.

GT caught up with state technology leaders at the recent National Association of State Chief Information Officers Midyear conference. Here's what they had to say about artificial intelligence, chatbots, blockchain and other headline-grabbing technologies. Amanda Crawford, Texas: We use AI today. We certainly use robotic process automation in a variety of applications and projects across the state. One of the exciting areas for us is using AI for security.