All architectures are compatible with both TensorFlow and Theano, and upon instantiation the models will be built according to the image dimension ordering set in your Keras configuration file at /.keras/keras.json. For instance, if you have set image_dim_ordering tf, then any model loaded from this repository will get built according to the TensorFlow dimension ordering convention, "Width-Height-Depth". Weights can be automatically loaded upon instantiation (weights'imagenet' argument in model constructor). Weights are automatically downloaded if necessary, and cached locally in /.keras/models/. Note that using these models requires the latest version of Keras (from the Github repo, not PyPI). Additionally, don't forget to cite Keras if you use these models.

Last week, Google parent Alphabet Inc (NASDAQ:GOOGL) released a report about their new milestone in artificial inteligence [AI] speech recognition that according to the company outperforms existing technology by 50%. Microsoft Corporation (NASDAQ:MSFT) did not want to be left behind as it too announced an amazing breakthrough for this market. According to Xuedong Huang, chief speech scientist for Microsoft, their researchers achieved a word error rate (WER) of 6.3%. It is considered the lowest in the industry. Microsoft recorded the benchmark rate by combining neural network based acoustic and language modeling on the US National Institute of Standards and Technology (NIST) 2000 Switchboard speech recognition task. This is a conversational telephone speech recognition test used as an industry standard.

This Wednesday, the tech billionaire investor announced an investment in Luminance, a newly launched startup that uses artificial technology to read contracts in order help law firms with the arduous process of due diligence for mergers and acquisitions (M&A). It's not a "sexy" piece of technology, Lynch argues -- but one that has huge implications for the way we live our lives, and is indicative of a quiet revolution in artificial intelligence. What this is is probably an example of what's going to be changing a lot of things. If you can get machine technology to be reading contracts, it's going to be changing a lot of the world around us ... you're seeing the beginning of a new age." He has since founded venture capital firm Invoke Capital -- the vehicle through which the investment in Luminance was made. This week, Business Insider sat down with the investor to discuss Luminance, Brexit, his augmented reality plans, and why he likes having an "unfair advantage." Mike Lynch is an investor in Luminance -- but was also instrumental in helping create it. "The bit that makes it possible is the machine learning, and that was being done by some research people at Cambridge, and I actually have a connection because my PhD a long, long time ago was in machine learning," Lynch said. "I was introduced to them, and what they were doing looked great, but I said to them'look, you gotta go and meet some real world people.' "So they started getting real data and they met up with [law firm] Slaughter and May, and basically the machine learnt from Slaughter and May how to do these thing and at that point they made a little company. They got a CEO who is a lady who'd actually been involved in a lot of M&A deals over their career and we funded it, and it's been developing the product, and today it comes out into the bright lights of day."

Matthew Ramirez was teaching writing classes to students at the University of California at Berkeley when he started to get frustrated. Mixing his experience as a teacher with some advanced learning technology, he and his business partner started WriteLab – a Berkeley, California-based software company that helps students strengthen their writing skills by providing quick, customized feedback. WriteLab can even adapt its feedback over time to students' individual writing styles. "Focus on problems that eliminate waste – wasted time, wasted energy, or wasted space," Ramirez advises.

Matthew Ramirez was teaching writing classes to students at the University of California at Berkeley when he started to get frustrated. He was spending the majority of his time giving repetitive feedback to students, and there wasn't enough time to provide truly constructive, in-depth feedback to each individual before the next essay was due. "Given the time constraints of a semester and the number of students in a class, it wasn't humanly possible to respond to everything I wanted to," Ramirez said in an interview. "Nor was it possible to work through multiple drafts with individual students." Ramirez took it upon himself to build a solution.

With new neural network architectures popping up every now and then, it's hard to keep track of them all. Knowing all the abbreviations being thrown around (DCIGN, BiLSTM, DCGAN, anyone?) can be a bit overwhelming at first. So I decided to compose a cheat sheet containing many of those architectures. Most of these are neural networks, some are completely different beasts. Though all of these architectures are presented as novel and unique, when I drew the node structures… their underlying relations started to make more sense. One problem with drawing them as node maps: it doesn't really show how they're used. For example, variational autoencoders (VAE) may look just like autoencoders (AE), but the training process is actually quite different. The use-cases for trained networks differ even more, because VAEs are generators, where you insert noise to get a new sample. AEs, simply map whatever they get as input to the closest training sample they "remember". I should add that this overview is in no way clarifying how each of the different node types work internally (but that's a topic for another day).

IBM has launched three Power8 Linux servers designed to accelerate artificial intelligence, deep learning, and advanced analytics applications. The new systems tap the Nvidia NVLink technology to move data five times faster than any competing platform, said Stefanie Chiras, an IBM vice president, in an interview with VentureBeat. These systems and their operating systems are part of a larger business group that generates about 2 billion a quarter for IBM. And the A.I. markets they're going after have exploded in the past couple of years. IBM claims that the combination of Power8 processors and Linux software results in systems that deliver 80 percent more performance per dollar than the latest x86-based (Intel or AMD) servers.

One of the biggest concerns surrounding #artificialintelligence is that the technology will eliminate #jobs as it becomes increasingly more sophisticated. The first field to be impacted by artificial intelligence - chatbots, digital assistants, robotic process automation, and cognitive computing - will be customer service, followed by truck and taxi drivers and then the automotive industry. The general theme is that jobs that can be easily automated will be threatened by #robots, as companies will likely prefer cost-efficient robots over humans.

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In her opening keynote at the Intel Analytics Summit, which was mostly about machine learning, Intel's executive vice president Diane Bryant said that we are now "reaching a tipping point where data is the game changer." With the rapid growth of machine-to-machine data exchange we should expect more, according to Bryant: Autonomous vehicles will produce 4 terabytes of data each day, a connected plane will transmit 40 terabytes of data, and the automated, connected factory will generate one petabyte (one million gigabytes) daily. Another presenter, CB Bohn, Senior Database Engineer at Etsy, the online marketplace, speculated that the tipping point has already happened--when the value of the data exceeded the cost of its storage. Historical data has lots of value left in it, so "why throw it away?" Cheap storage, added Debora Donato, Director of R&D at Mix Tech, a content discovery platform, has changed the attitudes of businesses towards data and what they can do with it.