A bonus: it can also help those just learning about AI to better understand … "Python Machine Learning: Machine Learning and Deep Learning with …

A bonus: it can also help those just learning about AI to better understand … "Python Machine Learning: Machine Learning and Deep Learning with …

We propose DeepHuman, a deep learning based framework for 3D human reconstruction from a single RGB image. Since this problem is highly intractable, we adopt a stage-wise, coarse-to-fine method consisting of three steps, namely inner body estimation, outer surface reconstruction and frontal surface detail refinement. Once an inner body is estimated from the given image, our method generates a dense semantic representation from the inner body to encode body shape and pose and to bridge the 2D image plane and 3D space. An image-guided volume-to-volume translation CNN is introduced to reconstruct the outer surface given the input image and the dense semantic representation. One key feature of our network is that it fuses different scales of image features into the 3D space through volumetric feature transformation, which helps to recover details of the subject's outer surface geometry.

What initially got you interested in artificial intelligence? I was reading a book on probability when I came across a famous theorem. At the time, I naively wondered if I could apply this theorem into a natural language problem I was attempting to solve at work. As it turns out, the algorithm already existed unbeknownst to me, it was called the Naïve Bayes, a very famous and simple generative model used in classical machine learning. That theorem was Bayes theorem.

The idea of reinforcement learning--or learning based on reward--has been around for so long it's easy to forget we don't really know how it works. If DeepMind's new bombshell paper in Nature is any indication, a common approach in AI, one that's led to humanity's defeat in the game of Go against machines, may have the answer. We all subconsciously learn complex behaviors in response to positive and negative feedback, but how that works in the brain remains a century-long mystery. By examining a powerful variant of reinforcement learning, dubbed distributional reinforcement learning, that outperforms original methods, the team suggests that the brain may simultaneously represent multiple predicted futures in parallel. Each future is assigned a different probability, or chance of actually occurring, based on reward.

"In #distributional #Reinforcement_Learning, the #AI algorithm predicts a full spectrum of future rewards: some are more optimistic and amplify their reward signals when the reward is larger than expected; others more pessimistic, lowering their reward signals when it's smaller than predicted." "Partnering with Harvard, the teams tested out their idea in the brains of mice. In contrast to neuroscience canon, the team said, reward neurons didn't act as one. Rather than collectively encoding for a single expected outcome, they were each "tuned" to a different prediction, with some expecting a larger amount of reward, and others less hopeful, predicting smaller volumes" "We found that reward neurons in the brain were each tuned to different levels of pessimism or optimism. If they were a choir, they wouldn't all be singing the same note, but harmonizing" "In other words, they seemed to operate on very similar principles to distributed reinforcement learning, a powerful method in #AI." https://lnkd.in/grTTXeA

They are some of the most well-known and loved poems in the world whose insightful sentiments have withstood the test of time over hundreds of years. But now a student at the University of Glasgow has used a "Robot Burns" artificial intelligence (AI) poem generator programme to create almost "instant works" by Robert Burns, in honour of a fellow student, Chris, who took his own life on Burns Day, on 25 January 2016. Perry Gibson, who is studying for a PhD in computing science, is donating all proceeds of his limited edition Robot Burns pamphlet to The Samaritans, the suicide prevention charity. The Robot Burns pamphlet includes 20 works across a variety of subjects and is accompanied by illustrations by Alasdair Currie, co-founder of independent Scottish publisher Hooltet Publishing. Mr Gibson gathered a data set of 2,000 poems by Burns, used an algorithm and created a specific coding programme which were then analysed by the GPT-2 programme operated by OpenAI whose funders include technology entrepreneur Elon Musk."When the first Burns'poem' came out, I blinked. I hadn't thought of what the consequences would be. "When I started, I just wanted to learn more about the technology, I hadn't really thought too much about what the text was going to be.

One of the most popular deep learning models used for natural language processing is BERT (Bidirectional Encoder Representations from Transformers). Due to the significant computation required, inferencing BERT at high scale can be extremely costly and may not even be possible with strict latency constraints. Recently, we shared how Bing has improved BERT inference on GPU for its real-time service needs, serving more than one million BERT inferences per second within Bing's latency limits. We are excited to announce that Microsoft has open sourced enhanced versions of these optimizations into the ONNX Runtime and extended them to work on both GPU and CPU. With ONNX Runtime, AI developers can now easily productionize large transformer models with high performance across both CPU and GPU hardware, using the same technology Microsoft uses to serve their customers.

Last month, researchers at OpenAI in San Francisco revealed an algorithm capable of learning, through trial and error, how to manipulate the pieces of a Rubik's Cube using a robotic hand. It was a remarkable research feat, but it required more than 1,000 desktop computers plus a dozen machines running specialized graphics chips crunching intensive calculations for several months. The effort may have consumed about 2.8 gigawatt-hours of electricity, estimates Evan Sparks, CEO of Determined AI, a startup that provides software to help companies manage AI projects. A spokesperson for OpenAI questioned the calculation, noting that it makes several assumptions. But OpenAI declined to disclose further details of the project or offer an estimate of the electricity it consumed.

Today we will focus on vector data and linear algebra handling when building this type of model. Note that data preprocessing may be ignored due to the things we are focusing on is not there this time. Vector data, also known as 2D tensors. If we say a single data is a vector, then 2D vector data is just when we handle more than one single data at a time. Suppose we are implementing a 3-layers multilayer perceptron for the Iris dataset for classification on TensorFlow 1.X.