Background: Statistical mechanics results (Dauphin et al. (2014); Choromanska et al. (2015)) suggest that local minima with high error are exponentially rare in high dimensions. However, to prove low error guarantees for Multilayer Neural Networks (MNNs), previous works so far required either a heavily modified MNN model or training method, strong assumptions on the labels (e.g., "near" linear separability), or an unrealistic hidden layer with $\Omega\left(N\right)$ units. Results: We examine a MNN with one hidden layer of piecewise linear units, a single output, and a quadratic loss. We prove that, with high probability in the limit of $N\rightarrow\infty$ datapoints, the volume of differentiable regions of the empiric loss containing sub-optimal differentiable local minima is exponentially vanishing in comparison with the same volume of global minima, given standard normal input of dimension $d_{0}=\tilde{\Omega}\left(\sqrt{N}\right)$, and a more realistic number of $d_{1}=\tilde{\Omega}\left(N/d_{0}\right)$ hidden units. We demonstrate our results numerically: for example, $0\%$ binary classification training error on CIFAR with only $N/d_{0}\approx 16$ hidden neurons.

High performance gaming and artificial intelligence computing giant NVIDIA launched its Deep Learning Institute (DLI) last year, and is now offering the first courses on applying this technology to the finance vertical. "There's not a lot of academic research that shows how to take these neural network techniques and adapt them to finance. It became clear to us that was sorely needed," Andy Steinbach, head of AI in financial services and senior director at NVIDIA, said. Newsweek is hosting an AI and Data Science in Capital Markets conference in NYC, Dec. 6-7. "We set out to develop labs that would show how to marry the basic building blocks like auto-encoders, recurrent neural networks, reinforcement learning, with very relevant finance problems like algorithmic trading, statistical arbitrage, optimising trade execution, and so we have done that."

New products/services paving the way for data scientists, developers and IT departments to deploy and scale deep-learning models into new and legacy applications. Hewlett Packard Enterprise wants to make it easier for enterprises to adopt artificial intelligence into their IT systems and software products. For starters, it's a good revenue stream for the company; secondly, it's because this is what more and more IT managers have been requesting during the last 12 or so months. To answer this call, Palo Alto, Calif.-based HPE on Oct. 25 introduced several new purpose-built platforms and services capabilities to help companies do this, with an initial focus on deep learning. Deep learning, as a subset of AI, is typically deployed for tasks such as image and facial recognition, image classification and voice recognition.

With artificial intelligence all the rage these days, market trackers are attempting to gauge just where the technology is headed and which industry sectors will lead development for specific big data and other enterprise use cases. The latest attempt comes from Evans Data Corp. in the form of an AI and big data survey released on Wednesday (Jan. The survey of 440 AI developers found that more than one-third of respondents are focusing on deep learning techniques, with most targeting the financial and insurance sectors. Other sectors where deep learning implementations are expected to have an impact include the Internet of Things (14.9 percent) and "non-computer" manufacturing (12.5 percent), reported the market researcher based in Santa Cruz, Calif. Nearly one-third of AI developers focused on deep learning implementations are relying on numerical inputs as the most common data type, Evans Data added.

If you wonder why we need a better way to predict hurricanes, just ask the people of Houston. Authorities knew Hurricane Harvey was heading to south Texas, but forecasters couldn't say precisely which areas would be hardest hit. So, most Houstonians stayed put. The consequences: more than 75 deaths, 30,000 people in shelters and tens of thousands who needed rescuing. And Harvey was just the start.

A company called Zebra Medical Vision (Zebra-Med) has unveiled a new service called Zebra AI1 that uses algorithms to examine your medical scans for a dollar each. The deep learning engine can examine CT, MRI and other scans and automatically detect lung, liver, heart and bone diseases. New capabilities like lung and breast cancer, brain trauma, hypertension and others are "constantly being released," the company says. The results are then passed on to radiologists, saving them time in making a diagnosis or requesting further tests. Engadget met Zebra-Med CEO and co-founder Elad Benjamin at the Hello Tomorrow startup conference in Paris, where he delivered the news about the scans.

This guide is a collection of distributed training examples (that can act as boilerplate code) and a tutorial of basic distributed TensorFlow. Many of the examples focus on implementing well-known distributed training schemes, such as those available in Distriubted Keras which were discussed in the author's blog post. Almost all the examples can be run on a single machine with a CPU, and all the examples only use data-parallelism (i.e. The motivation for this guide stems from the current state of distributed deep learning. Deep learning papers typical demonstrate successful new architectures on some benchmark, but rarely show how these models can be trained with 1000x the data which is usually the requirement in industy.

Technologies that can make a coherent summary take into account variables such as length, writing style and syntax.Automatic data summarization is part of machine learning and data mining. The main idea of summarization is to find a subset of data which contains the information of the entire set. Such techniques are widely used in industry today. Search engines are an example; others include summarization of documents, image collections and videos. Document summarization tries to create a representative summary or abstract of the entire document, by finding the most informative sentences, while in image summarization the system finds the most representative and important (i.e.

Remember (vaguely) how you learned to walk, talk, ride a bike, or drive? It was messy and full of mistakes, but the skills you learned that way stayed. Outside of living systems, it's been challenging to structure strong enough algorithms to take in "real life experience" and develop sticky, adaptable behaviors for artificial intelligence. "It starts from a blank slate and figures out only for itself, only from self-play, and without any human knowledge, or any human data, or features, or examples, or intervention from humans. It discovers how to play the game of Go from first principles," says DeepMind's professor David Silver.