Frontier AI: How far are we from artificial "general" intelligence, really?

#artificialintelligence

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.


12 Amazing Deep Learning Breakthroughs of 2017

@machinelearnbot

The quest to give machines a mind of their own occupied the brightest AI specialists in 2017. Machine learning (and especially the newly hip branch, deep learning) practically delivered all of the most stunning achievements in artificial intelligence so far -- from systems that beat us at our own games to art-producing neural networks that rival human creativity. At the onset and in hindsight, experts have heralded 2017 as "The Year of AI". Following its stunning win over the best human Go player in 2016, AlphaGo was upgraded a year later into a generalized and more powerful incarnation, AlphaZero. Free of any human guidance except the basic game rules, AlphaZero learned how to play master-level chess by itself in just four hours.


12 Amazing Deep Learning Breakthroughs of 2017

#artificialintelligence

The quest to give machines a mind of their own occupied the brightest AI specialists in 2017. Machine learning (and especially the newly hip branch, deep learning) practically delivered all of the most stunning achievements in artificial intelligence so far -- from systems that beat us at our own games to art-producing neural networks that rival human creativity.


12 Amazing Deep Learning Breakthroughs of 2017

#artificialintelligence

The quest to give machines a mind of their own occupied the brightest AI specialists in 2017. Machine learning (and especially the newly hip branch, deep learning) practically delivered all of the most stunning achievements in artificial intelligence so far -- from systems that beat us at our own games to art-producing neural networks that rival human creativity.


Geoffrey Hinton and Demis Hassabis: AGI is nowhere close to being a reality

#artificialintelligence

That's just a sampling of artificial intelligent (AI) systems' achievements in 2018, and evidence of how rapidly the field is advancing. At the current pace of change, analysts at the McKinsey Global Institute predict that, in the U.S. alone, AI will help to capture 20-25 percent in net economic benefits (equating to $13 trillion globally) in the next 12 years. Some of the most impressive work has arisen from the study of deep neural networks (DNNs), a category of machine learning architecture based on data representations. They're loosely modeled on the brain: DNNs comprise artificial neurons (i.e., mathematical functions) connected with synapses that transmit signals to other neurons. Said neurons are arranged in layers, and those signals -- the product of data, or inputs, fed into the DNN -- travel from layer to layer and slowly "tune" the DNN by adjusting the synaptic strength -- weights -- of each neural connection.