Microsoft has announced it will be partnering with the Elon Musk-backed OpenAI initiative in a bid to make "significant contributions to advance the field of AI". As part of its stated mission to'democratise AI', Microsoft will work closely with OpenAI in a joint effort to help ensure artificial intelligence is used to tackle some of the world's most challenging problems. The company outlined the new partnership in a blog post, where it also explained that OpenAI will use its Azure cloud platform, to help advance research and "create new tools and technologies that are only possible with the cloud". OpenAI is a nonprofit artificial intelligence research organization co-founded by Elon Musk, Sam Altman, Greg Brockman and Ilya Sutskever. The organisation will be using Microsoft's Azure N-Series Virtual Machines, which are designed to carry out intensive tasks such as deep learning, simulations, rendering and the training of neural networks.

General Electric builds jet engines and wind turbines and medical gear. But the 124-year-old industrial giant is also transforming itself for the digital age. It's fashioning software that pulls data from all this hardware, hoping to gain an insight into industrial operations that was never possible in the past. The problem is that analyzing all this data is difficult, and the talent needed to make it happen is scarce. So GE is going shopping.

But the newest artificial intelligence system from Google's DeepMind division does indeed dream, metaphorically at least, about finding apples in a maze. Researchers at DeepMind wrote in a paper published online Thursday that they had achieved a leap in the speed and performance of a machine learning system. It was accomplished by, among other things, imbuing technology with attributes that function in a way similar to how animals are thought to dream. The paper explains how DeepMind's new system -- named Unsupervised Reinforcement and Auxiliary Learning agent, or Unreal -- learned to master a three-dimensional maze game called Labyrinth 10 times faster than the existing best AI software. It can now play the game at 87 percent the performance of expert human players, the DeepMind researchers said.

At Supercomputer 16 (SC16) IBM and NVIDIA have announced what they call the fastest deep learning enterprise solution. The system is based on IBM Power System S822LC platforms that were announced in September. These systems contain the latest version of the IBM POWER8 processor that has NVIDIA NVLink embedded in it. IBM has also released a new deep learning toolkit called IBM PowerAI. The solution is capable of running AlexNet with Caffe up to 2x faster than equivalent systems. It is also capable of outperforming systems running AlexNet with BVLC Caffe using 8 M40 GU-based x86 systems.

Classifiers for the semi-supervised setting often combine strong supervised models with additional learning objectives to make use of unlabeled data. This results in powerful though very complex models that are hard to train and that demand additional labels for optimal parameter tuning, which are often not given when labeled data is very sparse. We here study a minimalistic multi-layer generative neural network for semi-supervised learning in a form and setting as similar to standard discriminative networks as possible. Based on normalized Poisson mixtures, we derive compact and local learning and neural activation rules. Learning and inference in the network can be scaled using standard deep learning tools for parallelized GPU implementation. With the single objective of likelihood optimization, both labeled and unlabeled data are naturally incorporated into learning. Empirical evaluations on standard benchmarks show, that for datasets with few labels the derived minimalistic network improves on all classical deep learning approaches and is competitive with their recent variants without the need of additional labels for parameter tuning. Furthermore, we find that the studied network is the best performing monolithic ('non-hybrid') system for few labels, and that it can be applied in the limit of very few labels, where no other system has been reported to operate so far.

Intel is taking a new direction in chip development as it looks to the future of artificial intelligence, with the company betting the technology will pervade applications and web services. The company on Thursday said it is developing new chips that will handle AI workloads, which will increasingly be a part of its chip future. For now, the AI chips will be released as specialized primary chips or co-processors in computers and separate from the major product lines. But over time, Intel could adapt and integrate the AI features into its mainstream server, IoT, and perhaps even PC chips. The AI features could be useful in servers, drones, robots, and autonomous cars.

When tagging a photo on Facebook, an AI algorithm suggests who's in it before you can type their name, and more practical speech recognition software is being integrated into a variety of devices. AI-dependent technologies like these have improved considerably in the last few years, partially due to advances in Deep Learning. Now, Neural Networks are learning to reason like a human. Deep learning uses AI neural networks to solve problems from input data. Researches "train" neural networks by inputting a particular algorithm and subject data, and then ask the AI to solve a particular problem using the given data.

In this post, we'll show how easy it is to use MLDB to build your own real time image classification service. We will use different brand of cars in this example, but you can adapt what we show to train a model on any image dataset you want. We will be using a TensorFlow deep convolutional neural network, transfer learning, and everything will run off MLDB. At a high level, transfer learning allows us to take a model that was trained on one task and use its learned knowledge on another task. We use the Inception- v3 model, a deep convolutional neural network, that was trained on the ImageNet Large Visual Recognition Challenge dataset.