The dream of one day having an entity with artificial intelligence diagnose your condition and recommend the best treatment may still be years away, but at IBM Watson Health and elsewhere, the technology and capability is evolving at such a rapid pace that such a function could well be part of regular healthcare practices. About a month ago I interviewed Deborah DiSanzo, who is IBM's General Manager for Watson Health. She was previously the CEO of Phillips Healthcare but now spearheads the development of Watson Health into a multi-billion-dollar business unit for IBM. "I was at one of our larger partners who is actually using our application from IBM called Clinical Trial Matching, which enables oncologists to, from the hundreds of thousands of clinical trials that are going on, match the appropriate clinical trial to the patient. And the breast oncologist that I was speaking to said it is fantastic because it "enables me to speak to my patients better, I turn the screen around and I show her what the particular type of breast cancer she has, how that matches with the top three clinical trials that she could go on.""

The estimation of normalizing constants is a fundamental step in probabilistic model comparison. Sequential Monte Carlo methods may be used for this task and have the advantage of being inherently parallelizable. However, the standard choice of using a fixed number of particles at each iteration is suboptimal because some steps will contribute disproportionately to the variance of the estimate. We introduce an adaptive version of the Resample-Move algorithm, in which the particle set is adaptively expanded whenever a better approximation of an intermediate distribution is needed. The algorithm builds on the expression for the optimal number of particles and the corresponding minimum variance found under ideal conditions. Benchmark results on challenging Gaussian Process Classification and Restricted Boltzmann Machine applications show that Adaptive Resample-Move (ARM) estimates the normalizing constant with a smaller variance, using less computational resources, than either Resample-Move with a fixed number of particles or Annealed Importance Sampling. A further advantage over Annealed Importance Sampling is that ARM is easier to tune.

Intel is bigger than all but 50 other U.S. companies, and that's because of something called the CPU. If you were around in the '90s or the early aughts, you saw the TV ads. For decades, Intel has supplied a majority of the chips that sit at the heart of our personal computers, including desktops as well as laptops. These chips are called central processing units, CPUs for short. They handle most all of the digital calculations that drive our PCs.

Those gazing into the night sky have speculated about life beyond Earth since Zeus was a boy. Deep learning now holds the promise of zeroing in on an answer. A deep learning system devised by astronomers at University College London sifts through data from telescopes trained on faraway solar systems to detect planets with the potential to sustain life. "We want to know which planets are worth further study and which aren't, and we want to automate that," said Ingo Waldmann, the University College London post-doctoral researcher who leads the development team. He calls the GPU-accelerated deep learning program RobERt, short for Robotic Exoplanet Recognition.

This is the third article in a series taken from The insideHPC Guide to The Industrialization of Deep Learning. The recent introduction of new high end processors from Intel combined with accelerator technologies such as NVIDIA Tesla GPUs and Intel Xeon Phi provide the raw'industry standard' materials to cobble together a test platform suitable for small research projects and development. When combined with open source toolkits some meaningful results can be achieved, but wide scale enterprise deployment in production environments raises the infrastructure, software and support requirements to a completely different level. If we begin by considering a deep learning focused rack mount system that is designed for production use such as the HPE Apollo 6500 system, density and performance are extremely impressive. However, such capability brings other considerations to the forefront from the infrastructure perspective.

Below there's the whole Gist page containing the full Python code. It has been developed and tested with Theano/GPU support, but it can easily work with CPU-only support. Any comment or suggestion is welcome!

It has been developed and tested with Theano/GPU support, but it can easily work with CPU-only support. Any comment or suggestion is welcome!

Any aspiring science fiction writer looking for a good protagonist could do worse than ripping off the Wikipedia page for Demis Hassabis: He grew up in England as a chess prodigy and built absurdly sophisticated video games before getting a degree in computer science from Cambridge, started studying neuroscience and publishing respected papers on amnesia and other topics, and then proceeded to co-found one of the hottest artificial-intelligence startups. Now that his company, DeepMind, has been snapped up by Google for a reported 400 million to 500 million (depending on your tech blog of choice), exactly how this latest twist will change his story remains to be seen--but there's a decent chance Hassabis will ultimately become commander of an army of humanoid Googlebots. Google's acquisition of Hassabis and the rest of the DeepMind team points to the surging interest in the field of deep learning, a funky part of computer science seen as key to building truly intelligent machines. It centers on having computers learn to do tasks and find patterns on their own. Google, for example, received attention a couple of years ago, when its network of self-learning computers were able to understand the concept of a cat and find cats in YouTube videos.

Sessions include: 1) Setting the Scene: The Machine Intelligence Landscape in Renewables; 2) Predicting Energy Consumption Using Machine Learning; 3) Deep Learning for Multiple Weather Prediction Models; and 4) Discussion: What Are The Next Steps?

Chipmaker Intel today announced that it has acquired Nervana, a startup that has been developing artificial intelligence software and hardware. Terms of the deal weren't disclosed, but a source familiar with the matter told VentureBeat Intel paid more than 350 million. "Nervana's Engine and silicon expertise will advance Intel's AI portfolio and enhance the deep learning performance and TCO of our Intel Xeon and Intel Xeon Phi processors," Diane Bryant, executive vice president and general manager of Intel's Data Center Group, said in a blog post. While Intel has a running business in high-performance computing (HPC), it has taken a back seat to Nvidia, another HPC supplier, when it comes to creating chips for deep learning, a trendy type of artificial intelligence that involves training artificial neural networks on lots of data and then getting them to make inferences on new data. Google has deployed competing chips named tensor processing units (TPUs) that can handle Google's TensorFlow open source deep learning framework.