Tag Archives: machine learning

Nightmare Machine


Now that the abject terror of the US presidential election is over — at least for a while — we have to turn our minds to new forms of pain and horror.

In recent years a growing number of illustrious scientists and technologists has described artificial intelligence (AI) as the greatest existential threat to humanity. They worry, rightfully, that a well-drilled, unfettered AI could eventually out-think and out-smart us at every level. Eventually, a super-intelligent AI would determine that humans were either peripheral or superfluous to its needs and goals, and then either enslave or extinguish us. This is the stuff of real nightmares.

Yet, at a more playful level, AI can also learn to deliver imagined nightmares. This Halloween researchers at MIT used AI techniques to create and optimize horrifying images of human faces and places. They called their AI the Nightmare Machine.

For the first step, researchers fed hundreds of thousands of celebrity photos into their AI algorithm, known as a deep convolutional generative adversarial network. This allowed the AI to learn about faces and how to create new ones. Second, they flavored the results with a second learning algorithm that had been trained on images of zombies. The combination allowed the AI to learn the critical factors that make for scary images and to selectively improve upon upon them. It turns out that blood on the face, empty eyeball sockets, and missing or misshaped teeth tend to illicit the greatest horror and fear.

While the results are not quite as scary as Stephen Hawkins’ warning of AI-led human extinction the images are terrorizing nonetheless.

Learn more about the MIT Media Lab’s Nightmare Machine here.

Image: Horror imagery generated by artificial intelligence. Courtesy: MIT Media Lab.

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Beware the Beauty of Move 37

AlphaGo-Lee-Sedol-Game 2

Make a note of the date, March 15, 2016. On this day, AlphaGo the Go playing artificial intelligence (AI) system from Google’s DeepMind unit, wrapped up its five game series. It beat Lee Sedol, a human and one of the world’s best Go players, by 4 games to 1.

This marks the first time a machine has beaten a human at Go, an ancient and notoriously complex board game.  AlphaGo’s victory stunned the Go-playing world, but its achievement is merely the opening shot in the coming AI revolution.

The AlphaGo system is based on deep neural networks and machine learning, which means it is driven by software that learns. In fact, AlphaGo became an expert Go player by analyzing millions of previous Go games and also by playing itself tens of millions of times, and learning and improving in the process.

While the AI technology that underlies AlphaGo has been around for decades, it is now reaching a point where AI-based systems can out-think and outperform their human masters. In fact, many considered it impossible for a computer to play Go at this level due to the immeasurable number of possible positions on the board, mastery of strategy, tactical obfuscation, and the need for a human-like sense of intuition.

Indeed, in game 2 of the series AlphaGo made a strange, seemingly inexplicable decision on move 37. This turned the game to AlphaGo’s favor and Lee Sedol never recovered. Commentators and AlphaGo’s human adversary noted move 37 as extraordinarily unexpected and “beautiful”.

And, from that story of beauty comes a tale of caution from David Gelernter, professor of computer science at Yale. Gelernter rightly wonders what an AI with an IQ of 5,000 would mean. After all, it is only a matter of time — rapidly approaching — before we have constructed machines with a human average IQ of 100, then 500.

Image: Game 2, first 99 moves, screenshot. AlphaGo (black) versus Lee Sedol (white), March 10, 2016. Courtesy of Wikipedia.

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Software That Learns to Eat Itself

Google became a monstrously successful technology company by inventing a solution to index and search content scattered across the Web, and then monetizing the search results through contextual ads. Since its inception the company has relied on increasingly sophisticated algorithms for indexing mountains of information and then serving up increasingly relevant results. These algorithms are based on a secret sauce that ranks the relevance of a webpage by evaluating its content, structure and relationships with other pages. They are defined and continuously improved by technologists and encoded into software by teams of engineers.

But as is the case in many areas of human endeavor, the underlying search engine technology and its teams of human designers and caregivers are being replaced by newer, better technology. In this case the better technology is based on artificial intelligence (AI), and it doesn’t rely on humans. It is based on machine or deep learning and neural networks — a combination of hardware and software that increasingly mimics the human brain in its ability to aggregate and filter information, decipher patterns and infer meaning.

[I’m sure it will not be long before yours truly is replaced by a bot.]

From Wired:

Yesterday, the 46-year-old Google veteran who oversees its search engine, Amit Singhal, announced his retirement. And in short order, Google revealed that Singhal’s rather enormous shoes would be filled by a man named John Giannandrea. On one level, these are just two guys doing something new with their lives. But you can also view the pair as the ideal metaphor for a momentous shift in the way things work inside Google—and across the tech world as a whole.

Giannandrea, you see, oversees Google’s work in artificial intelligence. This includes deep neural networks, networks of hardware and software that approximate the web of neurons in the human brain. By analyzing vast amounts of digital data, these neural nets can learn all sorts of useful tasks, like identifying photos, recognizing commands spoken into a smartphone, and, as it turns out, responding to Internet search queries. In some cases, they can learn a task so well that they outperform humans. They can do it better. They can do it faster. And they can do it at a much larger scale.

This approach, called deep learning, is rapidly reinventing so many of the Internet’s most popular services, from Facebook to Twitter to Skype. Over the past year, it has also reinvented Google Search, where the company generates most of its revenue. Early in 2015, as Bloomberg recently reported, Google began rolling out a deep learning system called RankBrain that helps generate responses to search queries. As of October, RankBrain played a role in “a very large fraction” of the millions of queries that go through the search engine with each passing second.

Read the entire story here.

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