Until now, the borderless, open-source software movement that has helped bring together AI developers and tech from the U.S. and China has risen above geopolitical tensions. Could national security crackdowns tear it apart? When Ben Wu, an engineer in China, wanted to install Facebook's open-source AI framework PyTorch in 2017, he visited its online community on GitHub and asked for some pointers. Soumith Chintala, a Facebook AI research engineer based in New York, showed him how he could download it quickly. PyTorch has become a foundational component of AI technology, thanks in large part to knowledge-sharing exchanges like the one between Wu and Chintala that happen every day.

In brief Everyone agrees that text-to-image models are here to stay, though opinions are divided over AI-generated art.… Some artists are enthralled by the ability to create completely new digital images using text prompts and see it as a new tool to be creative. Other artists, however, detest the technology and believe it will take away their jobs and devalue their work. A machine can be trained to recreate a particular artist's style and outpace human artists, as RJ Palmer, a concept artist, told the BBC. "Right now, if an artist wants to copy my style, they might spend a week trying to replicate it. That's one person spending a week to create one thing. With this machine, you can produce hundreds of them a week/" AI is "directly stealing their essence in a way", and artists are currently powerless to stop it from happening.

In brief Everyone agrees that text-to-image models are here to stay, though opinions are divided over AI-generated art. Some artists are enthralled by the ability to create completely new digital images using text prompts and see it as a new tool to be creative. Other people who make their living from art, however, detest the technology – believing it will cost them their jobs and devalue their work. A machine can be trained to recreate a particular artist's style and outpace human artists, as RJ Palmer, a conceptual artist, told the BBC. "Right now, if an artist wants to copy my style, they might spend a week trying to replicate it. That's one person spending a week to create one thing. With this machine, you can produce hundreds of them a week."

VentureBeat recently interviewed five of the most intelligent, expert minds in the AI field and asked them to make their predictions for where AI is heading over the course of the year to come. Chintala, the creator of Pytorch, which is arguably the most popular machine learning framework at the moment, predicted that 2020 will see a greater need for neural network hardware accelerators and methods of boosting model training speeds. Chintala expected that the next couple of years will see an increased focus on how to use GPUs optimally and how compiling can be done automatically for new hardware. Beyond this, Chintala expected that the AI community will begin pursuing other methods of quantifying AI performance more aggressively, placing less importance on pure accuracy. Factors for consideration include things like the amount of energy needed to train a model, how AI can be used to build the sort of society we want, and how the output of a network can be intuitively explained to human operators.

AI is no longer poised to change the world someday; it's changing the world now. As we begin a new year and decade, VentureBeat turned to some of the keenest minds in AI to revisit progress made in 2019 and look ahead to how machine learning will mature in 2020. We spoke with PyTorch creator Soumith Chintala, University of California professor Celeste Kidd, Google AI chief Jeff Dean, Nvidia director of machine learning research Anima Anandkumar, and IBM Research director Dario Gil. Everyone always has predictions for the coming year, but these are people shaping the future today -- individuals with authority in the AI community who treasure scientific pursuit and whose records have earned them credibility. While some predict advances in subfields like semi-supervised learning and the neural symbolic approach, virtually all the ML luminaries VentureBeat spoke with agree that great strides were made in Transformer-based natural language models in 2019 and expect continued controversy over tech like facial recognition. They also want to see the AI field grow to value more than accuracy.

Chintala's take is that some people would have to be assigned on something like this anyway. If PyTorch had not been created, the other option would be to tweak some existing framework, which would end up requiring the same resources too.

Fraudulent images have been around for as long as photography itself. Take the famous hoax photos of the Cottingley fairies or the Loch Ness monster. Photoshop ushered image doctoring into the digital age. Now artificial intelligence is poised to lend photographic fakery a new level of sophistication, thanks to artificial neural networks whose algorithms can analyze millions of pictures of real people and places--and use them to create convincing fictional ones. These networks consist of interconnected computers arranged in a system loosely based on the human brain's structure.

There's a high-stakes race under way in Silicon Valley to develop software that makes it easy to weave artificial intelligence technology into almost everything, and Google has sprinted into the lead. Google computer scientists including Jeff Dean and Greg Corrado built software called TensorFlow, which simplifies the programming of key systems that underpin artificial intelligence. That helps Google make its products smarter and more responsive. It's important for other companies too because the software makes it dramatically easier to create computer programs that learn and improve automatically. What's more, Google gives it away.

While the costs of human violence have attracted a great deal of attention from the research community, the effects of the network-on-network (NoN) violence popularised by Generative Adversarial Networks have yet to be addressed. In this work, we quantify the financial, social, spiritual, cultural, grammatical and dermatological impact of this aggression and address the issue by proposing a more peaceful approach which we term Generative Unadversarial Networks (GUNs). Under this framework, we simultaneously train two models: a generator G that does its best to capture whichever data distribution it feels it can manage, and a motivator M that helps G to achieve its dream. Fighting is strictly verboten and both models evolve by learning to respect their differences. The framework is both theoretically and electrically grounded in game theory, and can be viewed as a winner-shares-all two-player game in which both players work as a team to achieve the best score. Experiments show that by working in harmony, the proposed model is able to claim both the moral and log-likelihood high ground. Our work builds on a rich history of carefully argued position-papers, published as anonymous YouTube comments, which prove that the optimal solution to NoN violence is more GUNs.

Try typing "the machines" into Google and chances are that one of the top results the artificial intelligence-powered search engine will return is the phrase: "The Machines are Coming". After a 2016 filled with high-profile advances in artificial intelligence (AI), leading technologists say this could be a breakout year in the development of intelligent machines that emulate humans. Asia, until now lagging Silicon Valley in AI, will play a bigger role as the field cements itself at the pinnacle of the technology world in 2017, the experts say. AI – technically, a computing field that involves the analysis of large troves of data to predict outcomes and patterns – is as old as modern computers but its esoteric nature means it has long endured caricatures of its actual potential – think for example, the 1960s space age cartoon The Jetsons, which featured a sentient robot maid and automated flying cars (both of which we are still waiting for, even 50 years on). Now, a confluence of factors has given rise to hopes that computers with human-like cognitive ability may soon be a reality.