Elon Musk has built cars and rockets. OpenAI - the artificial-intelligence research nonprofit cochaired by Tesla Motors CEO Musk and Y Combinator President Sam Altman - wants to build a robot for your home. Building a robot, OpenAI's leadership explains in a blog entry on Monday, is a good way to test and refine a machine's ability to learn how to perform common tasks. By "build," the company means taking a current off-the-shelf robot and customizing it to do housework. "More generally, robotics is a good test bed for many challenges in AI," reads the blog entry.

Note from the Editor, Tricia Wang: Next up in our Co-designing with machines edition is Steven Gustafson (@stevengustafson), founder of the Knowledge Discovery Lab at the General Electric Global Research Center in Niskayuna, New York. In this post, he asked what is the role of humans in the future of intelligent machines. He makes the case that in the foreseeable future, artificially intelligent machines are the result of creative and passionate humans, and as such, we embed our biases, empathy, and desires into the machines making them more "human" that we often think. Steven is a former member of the Machine Learning Lab and Computational Intelligence Lab, where he developed and applied advanced AI and machine learning algorithms for complex problem solving. In 2006, he received the IEEE Intelligent System's "AI's 10 to Watch" award.

The nature of the visual representation for words has been fiercely debated for over 150 y. We used direct brain stimulation, pre- and postsurgical behavioral measures, and intracranial electroencephalography to provide support for, and elaborate upon, the visual word form hypothesis. This hypothesis states that activity in the left midfusiform gyrus (lmFG) reflects visually organized information about words and word parts. In patients with electrodes placed directly in their lmFG, we found that disrupting lmFG activity through stimulation, and later surgical resection in one of the patients, led to impaired perception of whole words and letters. Furthermore, using machine-learning methods to analyze the electrophysiological data from these electrodes, we found that information contained in early lmFG activity was consistent with an orthographic similarity space.

At Build 2016, Microsoft CEO, Satya Nadella, outlined our approach for the new era of conversational intelligence, based on a belief that the most impactful data-driven solutions will go beyond analytics, and utilize the best of big data, cloud, and intelligence capabilities. Microsoft Azure Machine Learning, now part of Cortana Intelligence Suite, is democratizing data and intelligence. Its best-in-class algorithms and simple drag-and-drop interface let data scientists quickly and easily go from idea to deployment. Since Build, I have been working with Azure Machine Learning and the Azure Machine Learning Studio, and thinking about the opportunities for partners to add more value to business intelligence, reporting, SharePoint, and data engagements. This is really a new monetary stream for your customer where they can provide their IP and domain expertise as a service to their customers. In this age of technologies, business decision makers are looking for ways to bring in other sources of revenue.

Twitter on Monday said it would buy Magic Pony Technology, a London-based firm that has developed novel machine learning techniques for visual processing. "Magic Pony's technology --based on research by the team to create algorithms that can understand the features of imagery -- will be used to enhance our strength in live and video and opens up a whole lot of exciting creative possibilities for Twitter," the San Francisco-based firm's co-founder and chief executive Jack Dorsey said in a post on the company' blog. "We are continuing to build strength into our deep learning teams with world-class talent to help Twitter be the best place to see what's happening and why it matters, first." Twitter's acquisition of Magic Pony builds on other investments the firm has made in machine learning, beginning with the acquisitions of Madbits in July 2014 and Whetlab in June 2015, Dorsey said. While Dorsey did not disclose the terms of the deal, sources told TechCrunch that Twitter is paying up to 150 million to buy Magic Pony, which also takes into account retention bonuses for the 11-member staff, including co-founders Zehan Wang and CEO Rob Bishop.

We have been hearing predictions for decades of a takeover of the world by artificial intelligence. In 1957, Herbert A. Simon predicted that within 10 years a digital computer would be the world's chess champion. That didn't happen until 1996. And despite Marvin Minsky's 1970 prediction that "in from three to eight years we will have a machine with the general intelligence of an average human being," we still consider that a feat of science fiction. The pioneers of artificial intelligence were surely off on the timing, but they weren't wrong; AI is coming.

Today, we're very excited to announce that we're expanding our capabilities in machine learning by acquiring Magic Pony Technology, a London-based technology company that has developed novel machine learning techniques for visual processing. Magic Pony's technology – based on research by the team to create algorithms that can understand the features of imagery – will be used to enhance our strength in live and video and opens up a whole lot of exciting creative possibilities for Twitter. The team includes 11 PhDs with expertise across computer vision, machine learning, high-performance computing, and computational neuroscience, who are alumni of some of the top labs in the world. We are continuing to build strength into our deep learning teams with world-class talent to help Twitter be the best place to see what's happening and why it matters, first.

Twitter today is taking another step to build up its machine learning muscle, and also potentially to improve how it delivers photos and videos across its apps: the company is acquiring Magic Pony Technology (that is really the name), a company based out of London that has developed techniques of using neural networks (systems that essentially are designed to think like human brains) and machine learning to provide expanded data for images -- used, for example, to enhance a picture or video taken on a mobile phone; or to help develop graphics for virtual reality or augmented reality applications. Terms of the deal are not being disclosed but we have two separate sources who tell us that Twitter is paying 150 million in all for the deal. This takes into account retention bonuses for the staff, which numbers about 11, including co-founders Zehan Wang and CEO Rob Bishop. "Machine learning is increasingly at the core of everything we build at Twitter," said Jack Dorsey, Twitter CEO and co-founder, in a statement. "Magic Pony's machine learning technology will help us build strength into our deep learning teams with world-class talent, so Twitter can continue to be the best place to see what's happening and why it matters, first. We value deep learning research to help make our world better, and we will keep doing our part to share our work and learnings with the community."

Machine learning is increasingly at the core of everything we build at Twitter. It's powering much of the work we're doing to make it easier to create, share, and discover the very best content so that every time you open Twitter you're immersed in the most relevant news, stories, and events for you. Today, we're very excited to announce that we're expanding our capabilities in machine learning by acquiring Magic Pony Technology, a London-based technology company that has developed novel machine learning techniques for visual processing. Our acquisition of Magic Pony builds on other investments we've made in machine learning, beginning with the acquisitions of Madbits in July 2014 and Whetlab in June 2015. Magic Pony's team will be joining Twitter Cortex, a team of engineers, data scientists, and machine learning researchers dedicated to building a product in which people can easily find new experiences to share and participate in.

Hi, I'm planning to make a 5/6 part series to reflect about my experience in University of Washington Machine Learning Specialization in Coursera while I take the five courses: Foundations, Regression, Classification, Clustering, Deep Learning and finish the capstone project. This is the first article in the series. I'll feature the first course Machine Learning Foundations: A Case Study Approach in this article and describe the philosophy behind the'case study approach' with a brief overview of the tools used and reflect on what I've learnt. I hope it will help people who want to use the same specialization.I'm also taking courses in Udacity, Edx and using other resources too, but experience those resources will be described in separate articles. I'm also planning to write a whole separate series on Udacity Machine Learning Nanodegree in recent future.