KREUZADER (Posts tagged deep learning)

AlphaGo, in context“AlphaGo is made up of a number of relatively standard techniques: behavior cloning (supervised learning on human demonstration data), reinforcement learning (REINFORCE), value functions, and Monte Carlo Tree Search (MCTS).... — AlphaGo, in context
AlphaGo is made up of a number of relatively standard techniques: behavior cloning (supervised learning on human demonstration data), reinforcement learning (REINFORCE), value functions, and Monte Carlo Tree Search (MCTS). However, the way these components are combined is novel and not exactly standard. In particular, AlphaGo uses a SL (supervised learning) policy to initialize the learning of an RL (reinforcement learning) policy that gets perfected with self-play, which they then estimate a value function from, which then plugs into MCTS that (somewhat surprisingly) uses the (worse!, but more diverse) SL policy to sample rollouts. In addition, the policy/value nets are deep neural networks, so getting everything to work properly presents its own unique challenges (e.g. value function is trained in a tricky way to prevent overfitting). On all of these aspects, DeepMind has executed very well. That being said, AlphaGo does not by itself use any fundamental algorithmic breakthroughs in how we approach RL problems.

Source: medium.com

alphago google brain alphabet artificial intelligence machine learning deep learning neural networking

Meet the Most Nimble-Fingered Robot Yet“Inside a brightly decorated lab at the University of California, Berkeley, an ordinary-looking robot has developed an exceptional knack for picking up awkward and unusual objects. What’s stunning, though, is... — Meet the Most Nimble-Fingered Robot Yet
Inside a brightly decorated lab at the University of California, Berkeley, an ordinary-looking robot has developed an exceptional knack for picking up awkward and unusual objects. What’s stunning, though, is that the robot got so good at grasping by working with virtual objects.
The robot learned what kind of grip should work for different items by studying a vast data set of 3-D shapes and suitable grasps. The UC Berkeley researchers fed images to a large deep-learning neural network connected to an off-the-shelf 3-D sensor and a standard robot arm. When a new object is placed in front of it, the robot’s deep-learning system quickly figures out what grasp the arm should use.
The bot is significantly better than anything developed previously. In tests, when it was more than 50 percent confident it could grasp an object, it succeeded in lifting the item and shaking it without dropping the object 98 percent of the time. When the robot was unsure, it would poke the object in order to figure out a better grasp. After doing that it was successful at lifting it 99 percent of the time. This is a significant step up from previous methods, the researchers say.

Source: technologyreview.com

robot neural networking deep learning machine vision

Taking his last stand against AI, humanity’s best Go player says the robots have already won“Earlier today, AlphaGo, an artificial intelligence program developed by Google’s DeepMind team, defeated Ke Jie, the world’s reigning top-ranked player of... — Taking his last stand against AI, humanity’s best Go player says the robots have already won
Earlier today, AlphaGo, an artificial intelligence program developed by Google’s DeepMind team, defeated Ke Jie, the world’s reigning top-ranked player of the board game Go. The AI won by half a point, the smallest margin possible. Two more games are slated for May 25 and 27, as part of Google’s AI and Go summit being held in an eastern coastal town in China, Wuzhen.
[…]
But the odds for humans aren’t looking good, according to Ke Jie himself. On the eve of his first game against AlphaGo, Ke took to China’s Twitter-like Weibo to announce (link in Chinese) that the three games this week will be the last match he’ll play against robots.
“I believe the future belongs to AI,” he said.
But Ke added that he won’t go down without a fight as he takes a last stand against robots. What makes him better than his AI opponent? The answer, he says, is passion.
Here’s the full text of his letter, titled “The Last Battle,” translated [link]

Source: qz.com

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Mind-Reading Algorithms Reconstruct What You’re Seeing Using Brain-Scan Data“The difficulty, of course, is finding ways to efficiently process the data from functional magnetic resonance imaging (fMRI) scans. The task is to map the activity in... — Mind-Reading Algorithms Reconstruct What You’re Seeing Using Brain-Scan Data
The difficulty, of course, is finding ways to efficiently process the data from functional magnetic resonance imaging (fMRI) scans. The task is to map the activity in three-dimensional voxels inside the brain to two-dimensional pixels in an image.
That turns out to be hard. fMRI scans are famously noisy, and the activity in one voxel is well known to be influenced by activity in other voxels. This kind of correlation is computationally expensive to deal with; indeed, most approaches simply ignore it. And that significantly reduces the quality of the image reconstructions they produce.
So an important goal is to find better ways to crunch the data from fMRI scans and so produce more accurate brain-image reconstructions.
Today, Changde Du at the Research Center for Brain-Inspired Intelligence in Beijing, China, and a couple of pals, say they have developed just such a technique. Their trick is to process the data using deep-learning techniques that handle nonlinear correlations between voxels more capably.

Source: technologyreview.com

neural networking deep learning neuroscience brain

Deep learning-based artificial vision for grasp classification in myoelectric hands“Computer vision-based assistive technology solutions can revolutionise the quality of care for people with sensorimotor disorders. The goal of this work was to enable... — Deep learning-based artificial vision for grasp classification in myoelectric hands
Computer vision-based assistive technology solutions can revolutionise the quality of care for people with sensorimotor disorders. The goal of this work was to enable trans-radial amputees to use a simple, yet efficient, computer vision system to grasp and move common household objects with a two-channel myoelectric prosthetic hand.

Source: iopscience.iop.org

bionics deep learning

Lyrebird will offer an API to copy the voice of anyone. It will need as little as one minute of audio recording of a speaker to compute a unique key defining her/his voice. This key will then allow to generate anything from its corresponding voice. The API will be robust enough to learn from noisy recordings. The following sample illustrates this feature, the samples are not cherry-picked.
Please note that those are artificial voices and they do not convey the opinions of Donald Trump, Barack Obama and Hillary Clinton.

Source: lyrebird.ai

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