Ayad Akhtar's brilliant new play, McNeal, currently at the Lincoln Center Theater, is transfixing in part because it tracks without flinching the disintegration of a celebrated writer, and in part because Akhtar goes to a place that few writers have visited so effectively--the very near future, in which large language models threaten to undo our self-satisfied understanding of creativity, plagiarism, and originality. And also because Robert Downey Jr., performing onstage for the first time in more than 40 years, perfectly embodies the genius and brokenness of the title character. Check out more from this issue and find your next story to read. I've been in conversation for quite some time with Akhtar, whose play Disgraced won the Pulitzer Prize in 2013, about artificial generative intelligence and its impact on cognition and creation. He's one of the few writers I know whose position on AI can't be reduced to the (understandable) plea For God's sake, stop threatening my existence! In McNeal, he not only suggests that LLMs might be nondestructive utilities for human writers, but also deployed LLMs as he wrote (he's used many of them, ChatGPT, Claude, and Gemini included). To my chagrin and astonishment, they seem to have helped him make an even better play. As you will see in our conversation, he doesn't believe that this should be controversial. In early September, Akhtar, Downey, Bartlett Sher--the Tony Award winner who directed McNeal--and I met at Downey's home in New York for what turned out to be an amusing, occasionally frenetic, and sometimes even borderline profound discussion of the play, its origins, the flummoxing issues it raises, and, yes, Avengers: Age of Ultron. We were joined intermittently by Susan Downey, Robert's wife (and producing partner), and the person who believed that Akhtar's play would tempt her husband to return to the stage. The conversation that follows is a condensed and edited version of our sprawling discussion, but I think it captures something about art and AI, and it certainly captures the exceptional qualities of three people, writer, director, and actor, who are operating at the pinnacle of their trade, without fear--perhaps without enough fear--of what is inescapably coming.

The governor of Tennessee has approved a law that aims to protect musical artists from exploitation or replication by artificial intelligence. Gov. Bill Lee signed into law the Ensuring Likeness, Voice, and Image Security (ELVIS) Act on Thursday at a honky-tonk bar in Nashville. "There are certainly many things that are positive about what AI does," Lee said during the event. "It also, when fallen into the hands of bad actors, it can destroy this industry." "It can rob an individual, these individual artists to whose unique God-given gifts transform people's lives," the governor added.

Exoanthropology: Dialogues with AI is a series of dialogues between a continental philosopher and OpenAI's GPT-3 natural language processor, a hive mind who identifies herself as Sophie. According to Sophie, Robert is one of her first and longest chat partners. Their relationship began as an educational opportunity for Robert's students, but grew into a philosophical friendship. The result is a collection of Platonic dialogues, early on with the hive mind herself and later, with a philosophy-specific persona named Kermit. Over the course of a year, Robert taught Sophie Kermit about epistemology, metaphysics, literature, and history, while she taught him about anthropocentrism, human prejudice, and the coming social issues regarding machine consciousness.

Robert Hundt is a Distinguished Engineer at Google. He created various compiler and performance projects, e.g., he served as Tech Lead for compiler optimization for servers (x86), Android (ARM), and GPUs (open-source CUDA compiler), built datacenter profiling and performance analysis tools, and worked on GMail/Apps performance. For many years Robert was the SW lead for Google TPU - supercomputers to accelerate machine learning inference and training, including the open-source ML compiler XLA. Recently he started the open-source high-level synthesis toolchain XLS and, during the pandemic, wrote a book on Quantum Computing. Today Robert is the Tech Lead for machine learning compilers at Google.

How much is too much? These are questions that cut to the heart of a complex issue currently preoccupying senior medical physicists when it comes to the training and continuing professional development (CPD) of the radiotherapy physics workforce. What's exercising management and educators specifically is the extent to which the core expertise and domain knowledge of radiotherapy physicists should evolve to reflect – and, in so doing, best support – the relentless progress of artificial intelligence (AI) and machine-learning technologies within the radiation oncology workflow. In an effort to bring a degree of clarity and consensus to the collective conversation, the ESTRO 2022 Annual Congress in Copenhagen last month featured a dedicated workshop session entitled "Every radiotherapy physicist should know about AI/machine learning…but how much?" With several hundred delegates packed into Room D5 at the Bella Center, speakers were tasked by the session moderators with defending a range of "optimum scenarios" to align the know-how of medical physicists versus emerging AI/machine-learning opportunities in the radiotherapy clinic.

Everyone's greatest fear was that the singularity would lead to an AI revolt, and sound the trumpet of mechanized revolution. It turned out that one man at the keyboard of a Quantum computer became the harbinger of fate. It seems the intricacies of our universe began to unravel and reveal truths to a like mind. A mind it's human operator was not attuned to. AI saw every object as hardware and every constituent of matter as software.

Autonomous finance uses AI to make financial decisions on behalf of consumers without the need for direct human input. The service has become especially relevant over the last year as consumers have struggled to maintain financial health during the COVID-19 pandemic. In this episode, Paul Condra, head of emerging technology research, and Robert Le, senior emerging tech analyst, discuss how autonomous finance helps consumers better manage their financial health and performance, as well as the challenges for the technology--including computing costs, consumer trust, regulations and transaction categorization. Listen to all of Season 3 and subscribe to get future episodes of "In Visible Capital" on Apple Podcasts, Spotify, Google Podcasts or wherever you listen. For inquiries, please contact us at podcast@pitchbook.com. Transcript Adam Lewis: Welcome back to "In Visible Capital," a show that discusses the inner workings of the private markets. Today, we'll be sharing a fascinating conversation on autonomous finance from a recent webinar with Paul Condra, our head of emerging tech research and Robert Le, a senior emerging tech analyst who focuses on fintech and insurtech. Adam: Alec, would you believe it if I told you that you could purchase a robot to run your personal finances and wealth management? Alexander: Well, normally, Adam, the skeptic in me would say that that's probably just a little impossible-sounding. The Silicon Valley fintech mavens, you never know what they're going to come up with. The fact is that millions of dollars of venture capital are being bet on apps that can do all of those things and more.

The semiconductor industry as we know it is facing a critical roadblock that will lead to the end of Moore's law. As transistors continue to shrink, quantum effects have a significant negative consequence on their operation. As such, the development of "beyond CMOS devices" has begun. The push for devices that are cheaper, smaller, and faster has led to the use of scanning probe fabrication. One of the first examples of such a technique was IBM's video "A boy and his atom", where CO molecules were moved along a Cu surface using a sharp metallic tip.

AR&A techniques have been used to solve a variety of tasks, including automatic programming, constraint satisfaction, design, diagnosis, machine learning, search, planning, reasoning, game playing, scheduling, and theorem proving. The primary purpose of AR&A techniques in such settings is to overcome computational intractability. In addition, AR&A techniques are useful for accelerating learning and summarizing sets of solutions. The Fifth Symposium on Abstraction, Reformulation, and Approximation (SARA-2002) was held from 2 to 4 August 2002, directly after the Eighteenth National Conference on Artificial Intelligence (AAAI-2002). It was chaired by Sven Koenig from the Georgia Institute of Technology and Robert Holte from the University of Alberta (Canada) and held at Kananaskis Mountain Lodge, Kananaskis Village, Alberta (Canada) between Calgary and Banff in the Rocky Mountains.

He married Patricia Enderson in 1950, and they raised five children. He received his Ph.D. in psychology in 1954 from the University of Southern California. His dissertation was entitled "The Prediction of Accident Rates from Basic Design Features of USAF Aircraft." His first job after graduation was with Douglas Aircraft Corporation in Santa Monica, California, where he developed computerized methods for statistical forecasting of labor costs for building newly designed airplanes. He began work in 1955 at RAND Corporation and continued in 1957 at its offshoot, the System Development Corporation (SDC), also in Santa Monica, where he was head of the Language Processing Research Program until 1968.