Microsoft co-founder Bill Gates has been working to improve the state of global health through his nonprofit foundation for 20 years, and today he told the nation’s premier scientific gathering that advances in artificial intelligence and gene editing could accelerate those improvements exponentially in the years ahead.
“We have an opportunity with the advance of tools like artificial intelligence and gene-based editing technologies to build this new generation of health solutions so that they are available to everyone on the planet. And I’m very excited about this,” Gates said in Seattle during a keynote address at the annual meeting of the American Association for the Advancement of Science. Read More
Tag Archives: Deep Learning
MIT CSAIL: Introduction to Deep Learning
NTM: Neural Turing Machines
We discuss Neural Turing Machine(NTM), an architecture proposed by Graves et al. in DeepMind. NTMs are designed to solve tasks that require writing to and retrieving information from an external memory, which makes it resemble a working memory system that can be described by short-term storage(memory) of information and its rule-based manipulation. Compared with RNN structure with internal memory, NTMs utilize attentional mechanisms to efficiently read and write an external memory, which makes them a more favorable choice for capturing long-range dependencies. But, as we will see, these two are not independent of each other and can be combined to form a more powerful architecture. Read More
Going Beyond GAN? New DeepMind VAE Model Generates High Fidelity Human
Generative adversarial networks (GANs) have become AI researchers’ “go-to” technique for generating photo-realistic synthetic images. Now, DeepMind researchers say that there may be a better option.
In a new paper, the Google-owned research company introduces its VQ-VAE 2 model for large scale image generation. The model is said to yield results competitive with state-of-the-art generative model BigGAN in synthesizing high-resolution images while delivering broader diversity and overcoming some native shortcomings of GANs. Read More
Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
Constructing agents with planning capabilities has long been one of the main challenges in the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge success in challenging domains, such as chess and Go, where a perfect simulator is available. However, in real-world problems the dynamics governing the environment are often complex and unknown. In this work we present the MuZero algorithm which, by combining a tree-based search with a learned model, achieves superhuman performance in a range of challenging and visually complex domains, without any knowledge of their underlying dynamics.MuZero learns a model that, when applied iteratively, predicts the quantities most directly relevant to planning: the reward, the action-selection policy, and the value function. When evaluated on 57 different Atari games – the canonical video game environment for testing AI techniques, in which model-based planning approaches have historically struggled -our new algorithm achieved a new state of the art. When evaluated on Go, chess and shogi, without any knowledge of the game rules,MuZero matched the superhuman performance of the AlphaZero algorithm that was supplied with the game rules. Read More
An Introduction to Deep Reinforcement Learning
Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine.Thus, deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. This manuscript provides an introduction to deep reinforcement learning models, algorithms and techniques.Particular focus is on the aspects related to generalization and how deep RL can be used for practical applications. We assume the reader is familiar with basic machine learning concepts. Read More
Complete Hands-Off Automated Machine Learning
Here’ a proposal for real ‘zero touch’, ‘set-em-and-forget-em’ machine learning from the researchers at Amazon. If you have an environment as fast changing as e-retail and a huge number of models matching buyers and products you could achieve real cost savings and revenue increases by making the refresh cycle faster and more accurate with automation. This capability likely will be coming soon to your favorite AML platform. Read More
Read Amazon’s paper here
MIT Deep Learning Basics: Introduction and Overview with TensorFlow
As part of the MIT Deep Learning series of lectures and GitHub tutorials, we are covering the basics of using neural networks to solve problems in computer vision, natural language processing, games, autonomous driving, robotics, and beyond.
This blog post provides an overview of deep learning in 7 architectural paradigms with links to TensorFlow tutorials for each. It accompanies the following lecture on Deep Learning Basics as part of MIT course 6.S094. Read More
Inside DeepMind's epic mission to solve science's trickiest problem
DeepMind is best known for its breakthroughs in machine learning and deep learning that have resulted in highly publicised events in which neural networks combined with algorithms have mastered computer games, beaten chess grandmasters and caused Lee Sedol, the world champion of Go – widely agreed to be the most complex game man has created – to declare: “From the beginning of the game, there was not a moment in time when I thought that I was winning.”
For Demis Hassabis,Shane Legg, and Mustafa Suleyman, the proof points offered by gameplay will define the next ten years: namely, to use data and machine learning to solve some of the hardest problems in science. Read More
It’s Sentient — Meet the classified artificial brain being developed by US intelligence programs
At the final session of the 2019 Space Symposium in Colorado Springs, attendees straggled into a giant ballroom to listen to an Air Force official and a National Geospatial-Intelligence Agency (NGA) executive discuss, as the panel title put it, “Enterprise Disruption.” The presentation stayed as vague as the title until a direct question from the audience seemed to make the panelists squirm.
Just how good, the person wondered, had the military and intelligence communities’ algorithms gotten at interpreting data and taking action based on that analysis? They pointed out that the commercial satellite industry has software that can tally shipping containers on cargo ships and cars in parking lots soon after their pictures are snapped in space. “When will the Department of Defense have real-time, automated, global order of battle?” they asked.
“That’s a great question,” said Chirag Parikh, director of the NGA’s Office of Sciences and Methodologies. “And there’s a lot of really good classified answers.” Read More
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