Microsoft is pausing the Twitter account of Tay--a chatbot invented to sound like millennials--after the account sent messages with racist and other offensive statements. The company quickly deleted the tweets but not before internet users captured the messages in screenshots. In a statement to the Washington Post, Microsoft said the Tay account was baited into the questionable remarks by folks hoping to stir controversy. "Unfortunately, within the first 24 hours of coming online, we became aware of a coordinated effort by some users to abuse Tay's commenting skills to have Tay respond in inappropriate ways," the statement said. "As a result, we have taken Tay offline and are making adjustments."

It's not certain how Microsoft will teach Tay better manners, although it seems like word filters would be a good start. The company tells Business Insider that it's making "adjustments" to curb the AI's "inappropriate" remarks, so it's clearly aware that something has to change in its machine learning algorithms. Frankly, though, this kind of incident isn't a shock -- if we've learned anything in recent years, it's that leaving something completely open to input from the internet is guaranteed to invite abuse. Update: A Microsoft spokesperson has provided the statement that BI received. You can read the whole thing below.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

Employees across the company are creating new technology, refining existing products, enhancing business operations and developing their own machine learning expertise as applied scientists, data scientists, software engineers and program managers, says Amanda Papp, a senior machine learning and data science recruiter. "Machine learning is the DNA of Microsoft. "You can apply machine learning to search, advertising, security, gaming -- and that's just the tip of the iceberg." Many employees participate in quarterly machine learning hackathons, attend twice-yearly conferences and learn from others in the company's 4,300-member Machine Learning and Data Science community, according to Alex Blanton, a senior program manager who helps manage the community and its events. Papp says she looks for candidates who have experience with researching or applying machine learning algorithms to solve real-world problems and the "passion for making a difference on a global scale."

Build and consume machine learning models for language processing from your Python apps.

Nearly everything at Google has an acronym. Machine learning, the artificial intelligence method for processing reams of data, currently all the rage across Google, is just "ML" inside the company. On Wednesday, Google presented its newly assertive push for the enterprise, hosting its inaugural cloud developer conference in San Francisco. In fact, Google unveiled a handful of new offerings that, in essence, pour ML all over the cloud. "I've become convinced that there's a new architecture emerging," an exuberant Eric Schmidt, chairman of Google parent Alphabet, said from the stage.

Tamr first brought its data unification technology to the market as a general purpose solution to help companies in their quest to become truly data- and analytics-driven enterprises, providing a next-generation means for them to clean and connect disparate data in an automated and scalable way. When DATAVERSITY spoke to Tamr co-founder Andy Palmer in late 2014 for an article on data curation, he discussed how using Machine Learning and semantic triple stores to address the enterprise data unification issue offered a great opportunity for businesses to gain 360-degree views of suppliers, customers, products, or whatever their needs might be to inform analytics and address hard business questions. At the time, he pointed to one unnamed enterprise that was putting the technology to work to optimize spending, making sure to get the best price for all products it buys across the entire company. Now, spend analytics for the cause of strategic sourcing is a primary use case that Tamr has settled on as giving businesses, large and small, the biggest opportunities for success from the holistic Data Management enabled by its technology. "Data is in the forefront for our customers like GE, Toyota Motors Europe, GSK and Thomson Reuters," says Nidhi Aggarwal, Global Lead of Strategy and Marketing at Tamr. "They understand the importance of data preparation and how that lets them become data-driven."

IBM is probably the lower hanging fruit in this case, but Google is now investing resources into a new Cloud Machine Learning product that it hopes will make it competitive in the Cloud industry against the likes of Amazon, Microsoft, and IBM. The company has slowly lost pace with the rest of the industry in the last few years. There's no good reason for losing ground other than focus. Now, that the company has reorganized under Alphabet, it appears Google is back to being interested in innovating and cutting off the slough that has been weighing it down. If Google can minimize distractions and retain focus long enough, it could still have a shot somewhere in the Cloud industry.

Google announced two services today, one new and one out of preview. They are part of the company's ongoing push to fashion itself as a provider of not only tools for building machine learning resources, but also APIs for accessing premade ones. Cloud Machine Learning (CML) can plug into Google's other storage, querying, and data-handling products to generate machine learning models. Among the data sources is Google Cloud Dataproc, the managed Hadoop and Spark platform that was previously announced but is now in general availability. You may have been wondering when machine learning as a service would arrive in Google Cloud, considering it has been available on Amazon for months and on Azure for a year. TensorFlow, Google claims, was used to build and deliver many existing Google products with machine intelligence aspects, such as its speech-recognition API, newly available to the public.

A few years ago, a breakthrough in machine learning suddenly enabled computers to recognize objects shown in photographs with unprecedented--almost spooky--accuracy. The question now is whether machines can make another leap, by learning to make sense of what's actually going on in such images. A new image database, called Visual Genome, could push computers toward this goal, and help gauge the progress of computers attempting to better understand the real world. Teaching computers to parse visual scenes is fundamentally important for artificial intelligence. It might not only spawn more useful vision algorithms, but also help train computers how to communicate more effectively, because language is so intimately tied to representation of the physical world.