We would like to talk to you a little bit about deep learning. Some experts are predicting the dawn of a new era, which will also lead to the development of a wholly new set of software. What do you think of such predictions? What would this software 2.0 be like? Uwe Friedrichsen: I think that right now the magic crystal ball is still very cloudy; a prognosis is still difficult to make from my point of view.

Some call it "strong" AI, others "real" AI, "true" AI or artificial "general" intelligence (AGI)… whatever the term (and important nuances), there are few questions of greater importance than whether we are collectively in the process of developing generalized AI that can truly think like a human -- possibly even at a superhuman intelligence level, with unpredictable, uncontrollable consequences. This has been a recurring theme of science fiction for many decades, but given the dramatic progress of AI over the last few years, the debate has been flaring anew with particular intensity, with an increasingly vocal stream of media and conversations warning us that AGI (of the nefarious kind) is coming, and much sooner than we'd think. Latest example: the new documentary Do you trust this computer?, which streamed last weekend for free courtesy of Elon Musk, and features a number of respected AI experts from both academia and industry. The documentary paints an alarming picture of artificial intelligence, a "new life form" on planet earth that is about to "wrap its tentacles" around us. There is also an accelerating flow of stories pointing to an ever scarier aspects of AI, with reports of alternate reality creation (fake celebrity face generator and deepfakes, with full video generation and speech synthesis being likely in the near future), the ever-so-spooky Boston Dynamics videos (latest one: robots cooperating to open a door) and reports about Google's AI getting "highly aggressive" However, as an investor who spends a lot of time in the "trenches" of AI, I have been experiencing a fair amount of cognitive dissonance on this topic.

At Sentient, we have an entire team dedicated to research and experimentation in AI. Over the past few years, the team has focused on developing new methods in Evolutionary Computation (EC), i.e. designing artificial neural network architectures, building commercial applications, and solving challenging computational problems using methods inspired by natural evolution. This research builds upon more than 25 years of research at UT Austin and other academic institutions, and coincides with related efforts recently at OpenAI, DeepMind, Google Brain, and Uber. There is significant momentum building in this area; indeed, we believe evolutionary computation may well be the next big thing in AI technology. Like Deep Learning (DL), EC was introduced decades ago, and it is currently experiencing a similar boost from the available big compute and big data.

Artificial intelligence is hitting its stride, already giving us machines that can drive themselves, talk to us, fight in our wars, perform our surgeries and beat humanity's best in a game of Go or Jeopardy. Recently five companies at the forefront of AI research met to discuss these advancements – all of which have been rapid, few of these solutions existed even five years ago – and figure out how to regulate even more powerful systems in the future – the near future. Researchers from Facebook, Alphabet, Amazon, Microsoft and IBM are looking at the practical consequences of AI, such as how it will impact transportation, jobs and welfare and while the group doesn't have a name or an official credo its general goal is to ensure AI research focuses on benefiting people, not harming them. In 2015, Elon Musk, Stephen Hawking, the founders of Google DeepMind and dozens of other researchers signed an open letter calling for robust investigations into the impact of AI and ways to ensure it remains a benign tool at humanity's disposal. But the industry partnership is notable because it represents a renewed, active effort among disparate tech companies – although interestingly, not the regulators – to address some of the ethical and moral issues posed by AI. The companies are expected to announce the group officially later this month even though the consortium could grow in the meantime since Google DeepMind has asked to participate separately from Alphabet, its parent company.

A couple of month's ago Google's Artificial Intelligence (AI) group, DeepMind, unveiled the latest incarnation of its Go playing program, AlphaGo Zero, an AI so powerful that it managed to cram thousands of years of human knowledge of playing the game, before inventing better moves of its own, into just three days. Hailed as a major breakthrough in AI learning because, unlike previous versions of AlphaGo, which went on to beat the world Go champion as well as take the Go online player community to the cleaners, AlphaGo Zero mastered the ancient Chinese board game from nothing more than a clean slate, with no more help from humans than being told the rules of the game. However, and as if that wasn't already impressive enough, it took its predecessor, AlphaGo, the AI that famously beat Lee Sedol, the South Korean grandmaster, to the cleaners as well, hammering it 100 games to nil. AlphaGo Zero's ability to learn for itself, and without human input, is a milestone on the road to one day realising Artificial General Intelligence (AGI), something that the same company, DeepMind, published an architecture for last year, and it will undoubtedly help us create the next generation of more "general" AI's that can do a lot more than just thrash humans at board games. AlphaGo Zero amassed its impressive skills using a technique called Reinforcement Learning, and at the heart of the program are a group of software "neurons" that are connected together to form a digital neural network.

You can be excused for not noticing that a scientist named Daniel Buehrer, a retired professor from the National Chung Cheng University in Taiwan, recently published a white paper proposing a new class of mathematics that many feel could one day lead to the birth of machine "consciousness," and perhaps even Artificial Super Intelligence (ASI) itself which is slated to arrive circa 2045. After all, keeping up with all the breakthroughs in the field of Artificial Intelligence (AI), from the development of new Artificial General Intelligence (AGI) architectures to the AI's, for example, from DeepMind, that are self-evolving and fighting each other, can be exhausting. Robot consciousness, or sentient machines, have long been a touchy subject if for no other reason than the fact that as of yet we still aren't able to describe what consciousness really is, let alone how it came to be, and this therefore makes it a touchy subject for anyone in AI circles. In order to have a discussion around the idea of a computer that can'feel' and'think,' and that has its own aspirations and motivations, you first have to find two people who actually agree on the semantics of sentience. And if you manage that, you'll then have to wade through a myriad of hypothetical objections to any theoretical living AI you can come up with.

Much as IBM's Watson once demonstrated the power of AI by becoming a Jeopardy champion, DeepMind's AlphaGo has been beating the world's best Go players, a long-time aspiration of AI researchers which once seemed unobtainable. Here at the F8 conference, Facebook CTO Mike Schroepfer lavished praise on DeepMind (a division of Google) for its accomplishment--and then began talking about ELF OpenGo, Facebook's own reimplementation of DeepMind's technology. Though he readily admitted that Facebook's version isn't the world's best Go-playing technology, it recently took on four top-30 human Go players--running on a computer with a single GPU powering its computations--and won 14-0.

Check out the "Software Development in the Age of Deep Learning" session at the AI Conference in San Francisco, September 4-7, 2018. Hurry--best price ends June 8. Machine learning today resembles the dawn of aviation. In 1903, dramatic flights by the Wright brothers ushered in the Pioneer Age of aviation, and within a decade, there was widespread belief that powered flight would revolutionize transportation and society more generally. Machine learning (ML) today is also rapidly advancing.

The power of deep learning systems is that they determine their own parameters or features. Just give them a task or purpose, point them at the data, and they handle the rest. For example, the autotune capability in SAS Visual Data Mining and Machine Learning can figure out the best result for itself. But people are still the most critical part of the process. "Humans solve problems, not machines," explains Mary Beth Ainsworth, an AI specialist at SAS. "Machines can surface the information needed to solve problems and then be programmed to address that problem in an automated way – based on the human solution provided for the problem."

In a recent conversation, Facebook AI research scientist Moustapha Cissé told me, "You are what you eat, and right now we feed our models junk food." Well, just like you can't eat better if you don't know what's in your food, you can't train less biased models if you don't know what's in your training data. That's why the recent paper "Datasheets for Datasets" is so interesting. In it, Timnit Gebru and her coauthors from Microsoft Research and elsewhere propose the equivalent of food nutrition labeling for datasets. Given that many machine learning and deep learning model development efforts use public datasets such as ImageNet or COCO -- or private datasets produced by others -- it's important to be able to convey the context, biases, and other material aspects of a training dataset to those interested in using it.