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.

Google is making it easier for businesses to take advantage of the machine learning revolution with a new product for building models that predict the future. At the company's GCP Next conference in San Francisco on Wednesday, Google announced the private beta of a new Cloud Machine Learning service that lets businesses create a custom machine learning model. To do so, users work with data they have in Google's other cloud services. Cloud Machine Learning handles data ingestion and training and then uses the resulting machine-learning model to make predictions. It's designed for companies that want to use machine learning to make predictions for their business.

A new dating app wants to use Watson's artificial intelligence to improve your dating experience. Called Connectidy, the app aims to help users figure out what they are looking for in a partner and how to best communicate with them. Those who download the app can choose to have it filter through any or all of your social media accounts -- LinkedIn, Twitter, Facebook, Instagram, and Gmail -- to create a personality profile. Dineen Tallering, co-founder and president of Connectidy, told Tech Insider that the app doesn't store data, but analyzes the text to give you your Big Five personality traits. The Big Five is one of the most common, preferred ways psychologists use to measure personality.

It took less than 24 hours for Twitter to corrupt an innocent AI chatbot. Yesterday, Microsoft unveiled Tay -- a Twitter bot that the company described as an experiment in "conversational understanding." The more you chat with Tay, said Microsoft, the smarter it gets, learning to engage people through "casual and playful conversation." Pretty soon after Tay launched, people starting tweeting the bot with all sorts of misogynistic, racist, and Donald Trumpist remarks. And Tay -- being essentially a robot parrot with an Internet connection -- started repeating these sentiments back to users, proving correct that old programming adage: flaming garbage pile in, flaming garbage pile out.

A chatbot developed by Microsoft has gone rogue on Twitter, swearing and making racist remarks and inflammatory political statements. The experimental AI, which learns from conversations, was designed to interact with 18-24-year-olds. Just 24 hours after artificial intelligence Tay was unleashed, Microsoft appeared to be editing some of its more inflammatory comments. The software firm said it was "making some adjustments". "The AI chatbot Tay is a machine learning project, designed for human engagement. As it learns, some of its responses are inappropriate and indicative of the types of interactions some people are having with it. We're making some adjustments to Tay," the firm said in a statement.