While the rise of artificial intelligence has caused more and more people to believe robots may one day make their jobs obsolete, those specializing in creative careers have always felt their skills could never be replicated by a mere computer program. Unfortunately, this feeling of assurance has taken a hit from IBM and a marketing company called The Drum, who have announced that Watson -- of Jeopardy! That's right, the brainy computer program that went toe-to-toe with Ken Jennings just edited an entire magazine all on its own. In other words, we're doomed. According to a press release published via The Drum, the magazine edited by Watson consists of a variety of features that cover Watson's different analytical functions, as well as how it can assist modern-day marketers.

New research suggests that computer models could help doctors achieve greater accuracy in the diagnosis of cancer and other diseases. A research team from Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School (HMS) have developed an artificial intelligence (AI) system which is able to train computers to analyse pathologic image data [PDF]. The scientists hope that the programme could one day aid in diagnosing disease. 'Our AI method is based on deep learning, a machine-learning algorithm used for a range of applications including speech recognition and image recognition,' explained Andrew Beck, director of bioinformatics at the Cancer Research Institute at BIDMC and associate professor at HMS. He added: 'This approach teaches machines to interpret the complex patterns and structure observed in real-life data by building multi-layer artificial neural networks, in a process which is thought to show similarities with the learning process that occurs in layers of neurons in the brain's neocortex, the region where thinking occurs.'

The future of artificial intelligence is now. After years of steady progress in making computers "smarter," AI prototypes are being incorporated into hundreds of day-to-day actions, such as self-driving cars, intelligent smartphone assistants and several applications in academia, government and industry. As the technology improves, it will be applied in ever more high-impact economic, social, political and cultural areas. Stanford's Russ Altman, left, and Fei-Fei Li will host a June 23 panel on artificial intelligence. In the face of this revolution, Stanford and the White House Office of Science and Technology Policy will host a panel of AI visionaries from academia, government and industry to discuss how to responsibly integrate the ever-evolving technology into the real world.

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.