It was heartbreaking to read over the weekend about how some Googlers in the US found out about the company's abrupt cull. Dan Russell, a research scientist who has worked on Google Search for over 17 years, wrote how he had gone to the office to finish off some work at 4 a.m., only to find out his entry badge didn't work. Economists predict the US economy may enter a recession this year amid a highly uncertain global economic outlook. Big tech companies have started to feel the squeeze. In the past, economic downturns have shut off the funding taps for AI research.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. DeepMind, the U.K.-based AI lab that seeks to develop artificial general intelligence, has finally become profitable, according to the company's latest financial report. Since being acquired by Google (now Alphabet Inc.) in 2014, DeepMind has struggled to break even with its growing expenses. And now, it is finally giving its parent company and shareholders hopeful signs that it has earned its place among Alphabet's constellation of profitable businesses. This could be wonderful news for the AI lab, which has been hemorrhaging large sums throughout its entire life.

It's wonderful to be in an area like computer science because, as we expand its reach, we encounter problems for which we don't even have the appropriate abstractions to be able to think about them. When you imagine the future of the field, what areas do you think hold the most promise? AHO: That is a great question. Particularly with fields like AI, we're starting to replace people who do routine cognitive jobs with computer programs. What will the job market of the future be with this increasing capacity and power of computing?

The modern world would be a pale shade of itself if not for the myriad foundational technologies developed at the Bell Telephone Labs. Its engineers invented the transistor and photovoltaic cell, charge-coupled devices, frickin' lasers -- even Unix and the C programming language. Those same engineers also worked with some of the Cold War era's most influential artists -- including Andy Warhol, Robert Rauschenberg, and Yvonne Rainer -- to create a wholly new style of artistic expression. In his new book, Making Art Work: How Cold War Engineers and Artists Forged a New Creative Culture, W. Patrick McCray follows the exploits of often-unsung technicians like rocket pioneer cum kinetic artist, Frank J. Malina and Bell Labs electrical engineer and Experiments in Art and Technology founder Billy Klüver, as they leveraged their technological prowess in the pursuit of creating compelling new works. The following excerpt is reprinted from Making Art Work: How Cold War Engineers and Artists Forged a New Creative Culture by W. Patrick McCray.

Each generation of cellular communication systems is marked by a defining disruptive technology of its time, such as orthogonal frequency division multiplexing (OFDM) for 4G or Massive multiple-input multiple-output (MIMO) for 5G. Since artificial intelligence (AI) is the defining technology of our time, it is natural to ask what role it could play for 6G. While it is clear that 6G must cater to the needs of large distributed learning systems, it is less certain if AI will play a defining role in the design of 6G itself. The goal of this article is to paint a vision of a new air interface which is partially designed by AI to enable optimized communication schemes for any hardware, radio environment, and application.

Gender balance in AI conference line-ups has been noticeably poor. But women in AI exist and their work is multifaceted and extraordinary. Artificial intelligence (AI) is the hot ticket in tech at the moment. Unfortunately, too many events and conferences delving into the subject, and its related disciplines of machine learning (ML) and data science, feature few women – if at all – among their speakers. To prove it doesn't have to be like this, and as a follow-on from International Women's Day, we at Silicon Republic have found 20 terrific women in AI, ML and data science who we think should be turning up in speaker line-ups across the country (and around the world).

Gender balance in AI conference line-ups has been noticeably poor. But women in AI exist and their work is multifaceted and extraordinary. Artificial intelligence (AI) is the hot ticket in tech at the moment. Unfortunately, too many events and conferences delving into the subject, and its related disciplines of machine learning (ML) and data science, feature few women – if at all – among their speakers. To prove it doesn't have to be like this, and as a follow-on from International Women's Day, we at Silicon Republic have found 20 terrific women in AI, ML and data science who we think should be turning up in speaker line-ups across the country (and around the world).

Chess is a complicated game. It's a game of strategy between two opponents, but with no hidden information and all of the potential moves known by both players at the outset. With each turn, players communicate their intent and try to anticipate the possible countermoves. The ability to envision several moves in advance is a recipe for victory, and one that mathematicians and logicians have long found intriguing. Despite some early mechanical chess-playing machines--and at least one chess-playing hoax--mechanized chess play remained hypothetical until the advent of digital computing.

Deep learning may need a new programming language that's more flexible and easier to work with than Python, Facebook AI Research director Yann LeCun said today. It's not yet clear if such a language is necessary, but the possibility runs against very entrenched desires from researchers and engineers, he said. LeCun has worked with neural networks since the 1980s. "There are several projects at Google, Facebook, and other places to kind of design such a compiled language that can be efficient for deep learning, but it's not clear at all that the community will follow, because people just want to use Python," LeCun said in a phone call with VentureBeat. "The question now is, is that a valid approach?"

Deep learning may need a new programming language that's more flexible and easier to work with than Python, Facebook AI Research director Yann LeCun said today. It's not yet clear if such a language is necessary, but the possibility runs against very entrenched desires from researchers and engineers, he said. LeCun has worked with neural networks since the 1980s. "There are several projects at Google, Facebook, and other places to kind of design such a compiled language that can be efficient for deep learning, but it's not clear at all that the community will follow, because people just want to use Python," LeCun said in a phone call with VentureBeat. "The question now is, is that a valid approach?"