By The New York Times
May 24, 2017
Google DeepMind’s AlphaGo program on Tuesday beat a Chinese world champion in the first of three Go games held this week, in what is being hailed as a victory for artificial intelligence (AI).
The human player, Ke Jie, notes the program has improved rapidly after its 2016 defeat of a South Korean Go player. “AlphaGo is like a different player this year compared to last year,” Ke says.
DeepMind co-founder Demis Hassabis says AlphaGo uses methods in which it learns experientially from playing a large number of games. For the new contest, Hassabis notes the program adopted a strategy that enables it to learn more by playing games against itself.
“Last year it was still quite humanlike when it played,” Ke says. “But this year, it became like a god of Go.”
Researchers say similar AI methods could be used to perform many tasks, such as improving basic scientific research.
From The New York Times
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Abstracts Copyright © 2017 Information Inc., Bethesda, Maryland, USA
Half-assed examples I can think of:
- Schizophrenia. You have "AI" in your head, completely artificial personalities (fabricated by your brain) that are going haywire.
- Alan Turing writing a chess algorithm for a computer and exclusively following it while playing against someone to test it out.
- Beyblades (you "let it rip" then watch a "fight" play out, without any further interaction).
- Playing a game like Candyland or War, one based entirely on luck, in which the outcome is the same whether a second player is taking the actions or you are for them.
- Raising a baby.
from Artificial Intelligence http://ift.tt/2qWErDD
Inspired by how mammals see, a new “memristor” computer circuit prototype at the University of Michigan has the potential to process complex data, such as images and video orders of magnitude, faster and with much less power than today’s most advanced systems.
Faster image processing could have big implications for autonomous systems such as self-driving cars, says Wei Lu, U-M professor of electrical engineering and computer science. Lu is lead author of a paper on the work published in the current issue of Nature Nanotechnology.
Lu’s next-generation computer components use pattern recognition to shortcut the energy-intensive process conventional systems use to dissect images. In this new work, he and his colleagues demonstrate an algorithm that relies on a technique called “sparse coding” to coax their 32-by-32 array of memristors to efficiently analyze and recreate several photos.
Memristors are electrical resistors with memory — advanced electronic devices that regulate current based on the history of the voltages applied to them. They can store and process data simultaneously, which makes them a lot more efficient than traditional systems. In a conventional computer, logic and memory functions are located at different parts of the circuit.
“The tasks we ask of today’s computers have grown in complexity,” Lu said. “In this ‘big data’ era, computers require costly, constant and slow communications between their processor and memory to retrieve large amounts data. This makes them large, expensive and power-hungry.”
But like neural networks in a biological brain, networks of memristors can perform many operations at the same time, without having to move data around. As a result, they could enable new platforms that process a vast number of signals in parallel and are capable of advanced machine learning. Memristors are good candidates for deep neural networks, a branch of machine learning, which trains computers to execute processes without being explicitly programmed to do so.
“We need our next-generation electronics to be able to quickly process complex data in a dynamic environment. You can’t just write a program to do that. Sometimes you don’t even have a pre-defined task,” Lu said. “To make our systems smarter, we need to find ways for them to process a lot of data more efficiently. Our approach to accomplish that is inspired by neuroscience.”
A mammal’s brain is able to generate sweeping, split-second impressions of what the eyes take in. One reason is because they can quickly recognize different arrangements of shapes. Humans do this using only a limited number of neurons that become active, Lu says. Both neuroscientists and computer scientists call the process “sparse coding.”
“When we take a look at a chair we will recognize it because its characteristics correspond to our stored mental picture of a chair,” Lu said. “Although not all chairs are the same and some may differ from a mental prototype that serves as a standard, each chair retains some of the key characteristics necessary for easy recognition. Basically, the object is correctly recognized the moment it is properly classified — when ‘stored’ in the appropriate category in our heads.”
Similarly, Lu’s electronic system is designed to detect the patterns very efficiently — and to use as few features as possible to describe the original input.
In our brains, different neurons recognize different patterns, Lu says.
“When we see an image, the neurons that recognize it will become more active,” he said. “The neurons will also compete with each other to naturally create an efficient representation. We’re implementing this approach in our electronic system.”
The researchers trained their system to learn a “dictionary” of images. Trained on a set of grayscale image patterns, their memristor network was able to reconstruct images of famous paintings and photos and other test patterns.
If their system can be scaled up, they expect to be able to process and analyze video in real time in a compact system that can be directly integrated with sensors or cameras.
from Artificial Intelligence News — ScienceDaily http://ift.tt/2rU5QFJ
May 24, 2017
Artificial intelligence is not just creeping into our personal lives and workplaces — it’s also beginning to appear in the doctor’s office. The prospect of being diagnosed by an AI might feel foreign and impersonal at first, but what if you were told that a robot physician was more likely to give you a correct diagnosis?
AI raises profound questions regarding medical responsibility. Usually when something goes wrong, it is a fairly straightforward matter to determine blame. A misdiagnosis, for instance, would likely be the responsibility of the presiding physician. A faulty machine or medical device that harms a patient would likely see the manufacturer or operator held to account. What would this mean for an AI?
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May 24, 2017
Imagine your teacher was a computer. A computer science professor at Georgia Tech is using artificial intelligence — or AI — to help him answer some of students’ frequently asked questions.
Tasnim Shamma from member station WABE in Atlanta has the story.
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DeepMind’s board game-playing AI, AlphaGo, may well have won its first game against the Go world number one, Ke Jie, from China – but but most Chinese viewers could not watch the match live.
The Chinese government had issued a censorship notice to broadcasters and online publishers, warning them against livestreaming Tuesday’s game, according to China Digital Times, a site that regularly posts such notices in the name of transparency.
“Regarding the go match between Ke Jie and AlphaGo, no website, without exception, may carry a livestream,” the notice read. “If one has been announced in advance, please immediately withdraw it.” The ban did not just cover video footage: outlets were banned from covering the match live in any way, including text commentary, social media, or push notifications.
It appears the government was concerned that 19-year-old Ke, who lost the first of three scheduled games by a razor-thin half-point margin, might have suffered a more damaging defeat that would hurt the national pride of a state which holds Go close to its heart.
After the game Ke said AlphaGo had become too strong for humans. “I feel like his game is more and more like the ‘Go god,’” he said. “Really, it is brilliant”.
The ban underscores the esteem in which Go is held across east Asia, where it has been played in more or less unmodified form for over 2,000 years. First invented in China in 500BC, it was considered one of the four arts a scholarly Chinese gentleman should master, along with playing the guqin, calligraphy and painting.
Go was formalised in Japan, where the game arrived in the 7th century. The country developed a system of Go houses, for training and supporting players, and for hundreds of years the houses would compete in the annual castle games for the privilege of playing in the shogun’s presence.
In Korea, where the game arrived in the 5th century, high level Go players are celebrities in their own right. DeepMind’s first public victory took place against Lee Sedol, the Roger Federer of the game, after the AI won four of five matches covered by media from across the region.
Despite the ban, several Chinese streaming sites such as bilibili.com offered versions of the game for viewers to watch live, replicating it move by move on their own boards. None had actual shots from the event, however. According to business news site Quartz, one Shanghai-based livestreaming site had sent staff to the venue before receiving the ban and withdrawing them on Friday.
DeepMind is streaming all three games of the match live, on YouTube. But the video site is blocked in China, along with the rest of Google’s services.
from Artificial intelligence (AI) | The Guardian http://ift.tt/2qjxXNj