MetaMind and Sentient Technologies come to mind for me, in terms of independent AI startups. MetaMind provides APIs for particular AI jobs and designs the backend for them. Sentient Technologies appears to be a more research-y company. I think some of the best teams overall in AI probably exist in the larger companies, like Facebook AI Lab or Google DeepMind or Google Brain. Also keep in mind that AI is still a very nascent field, with high knowledge barriers of entry and limited applications (with respect to similar fields like machine learning).

This group is a huge step forward, breaking down barriers for AI teams to share best practices, research ways to maximize societal benefits, and tackle ethical concerns, and make it easier for those in other fields to engage with everyone's work," said Mustafa Suleyman, Co-Founder and Head of Applied AI at DeepMind and Greg Corrado, Senior Research Scientist at Google in a statement.

Time Series prediction is a difficult problem both to frame and to address with machine learning. In this post you will discover how to develop neural network models for time series prediction in Python using the Keras deep learning library. The problem we are going to look at in this post is the international airline passengers prediction problem. This is a problem where given a year and a month, the task is to predict the number of international airline passengers in units of 1,000. Below is a sample of the first few lines of the file.

Arguably it is machine intelligence, along with a vast sea of data to apply it to. While you may never have as much data to process as Google does, you can use the very same machine learning and neural network library as Google. That library, TensorFlow, was developed by the Google Brain team over the past several years and released to open source in November 2015. TensorFlow does computation using data flow graphs. Google uses TensorFlow internally for many of its products, both in its datacenters and on mobile devices.

Visualize ML Python package to visualize some processes involved in Machine learning. If you have subscribed this blog, please make sure to change the feed address.

Google has a plan to speed up robotic learning, and it involves getting robots to share their experiences and collectively improve their capabilities. Sergey Levine from the Google Brain team, along with collaborators from Alphabet subsidiaries DeepMind and X, published a blog post on Monday describing an approach for "general-purpose skill learning across multiple robots." Teaching robots how to do even the most basic tasks in real-world settings like homes and offices has vexed roboticists for decades. To tackle this challenge, the Google researchers decided to combine two recent technology advances. The first is cloud robotics, a concept that envisions robots sharing data and skills with each other through an online repository.

Artificial intelligence can do remarkable things, like recognize faces on social networks, instantly translate speech from one language to another, and identify commands barked into a smartphone. But it also can do stupid things, like label an African-American couple "gorillas." The artificial intelligence underpinning Google Photos did just that last year. The platform uses deep neural networks to identify images in your photo collection. These networks of hardware and software, modeled after the network of neurons in your brain, learn to recognize objects, animals, and faces by analyzing many millions of pre-labeled photos.

"With deep learning in particular, you process all that data and you look for not the 100 percent conclusion, but you look for the 51 percent, the 60 percent opportunities that could send you in a new direction. And I think that's why it's so big," Cuban said. And while he thinks there is enough room for all of the major tech players like Facebook, Alphabet and Amazon to co-exist, the tough thing is that no one knows what the next big thing in technology is. Right now, executives are simply using their intuition to make moves in the space. "I could argue for precision medicine. I could argue that's why they are all acquiring artificial intelligence, deep learning companies, right, because they don't know where it will take them," Cuban said.

Have you ever been in a situation where knowing another language would have come in handy? I remember standing on the platform at Tokyo Station watching my train to Nagano -- the last train of the day -- pulling away without me on it. What ensued was a frustrating hour of gestures, confused smiles, and head-shaking as I wandered the station looking for someone who spoke English (my Japanese is unfortunately nonexistent). It would have been really helpful to have a bilingual pal along with me to translate. Bilingual pals can be hard to find, but Google's new translation software may be an equally useful alternative.

Neural attention has been applied successfully to a variety of different applications including natural language processing, vision, and memory. An attractive aspect of these neural models is their ability to extract relevant features from data, with minimal feature engineering.Brian Cheung is a PhD Student at UC Berkeley working with Professor Bruno Olshausen, as well as an Intern at Google Brain. By drawing inspiration from the fields of neuroscience and machine learning, he hopes to create systems which can solve complex vision tasks using attention and memory. At the Deep Learning Summit in Singapore, Brian will share expertise on the fovea as an emergent property of visual attention, ways we can extend this ability to learning interpretable structural features of the attention window itself, and finding conditions where these emergent properties are amplified or eliminated providing clues to their function. I asked him a few questions ahead of the summit to learn more.