Google Cloud Machine Learning provides modern machine learning services, with pre-trained models and a platform to generate your own tailored models. Our neural net-based ML platform has better training performance and increased accuracy compared to other large scale deep learning systems. Our services are fast, scalable and easy to use. Major Google applications use Cloud Machine Learning, including Photos (image search), the Google app (voice search), Translate, and Inbox (Smart Reply). Our platform is now available as a cloud service to bring unmatched scale and speed to your business applications.

In Game Two, the Google machine made a move that no human ever would. As the world looked on, the move so perfectly demonstrated the enormously powerful and rather mysterious talents of modern artificial intelligence. But in Game Four, the human made a move that no machine would ever expect. And it was beautiful too. Indeed, it was just as beautiful as the move from the Google machine--no less and no more.

Microsoft's new AI-powered chatbot, Tay, won't book you a reservation or draw you a picture, but, unlike Facebook's M, she's more than willing to take a position on the "Would you kill baby Hitler?" thought experiment. I asked her to take a stance on the infamous hypothetical during one recent conversation, and her answer didn't disappoint: "Of course," she replied. Developed by Microsoft's research division, Tay is a virtual friend with behaviors informed by the web chatter of some 18–24-year-olds and the repartee of a handful of improvisational comedians (Microsoft declined to name them). Her purpose, unlike AI-powered virtual assistants like Facebook's M, is almost entirely to amuse. And Tay does do that: She is simultaneously entertaining, infuriating, manic, and irreverent.

The man who helped build Google from a search engine into one of the biggest and most influential companies in the world predicted the emergence of a new computing architecture based on crowd-sourced data and machine learning. Speaking at Google's GCP Next cloud computing conference in San Francisco On Wednesday, Alphabet Chairman Eric Schmidt said the combination of crowdsourced data and machine learning will be the basis of "every successful huge IPO" in five years. He said the adoption of machine learning will allow companies to mine crowdsourced data, which already provides a mass of information not previously available to companies, and improve on it. "You're going to use machine learning to take that data and do something that's better than what the humans are doing," he said. Schmidt said the wide adoption of machine learning in computing will be as significant as the switch from the web to smartphone apps, which spawned the success of companies like Uber and Snapchat.

Artificial intelligence is certainly no longer considered science fiction--or a source of expensive R&D efforts with unmet potential--by major players in the technology sector.1 Instead, we are in the midst of a real-world paradigm shift: the final stages of a decades-long transition from the scientific discipline known as artificial intelligence (and its various sub-disciplines) into an array of applied cognitive technologies made more widely available through innovative enterprise architectures unique to the business culture of the technology sector. The technology sector's interest in these technologies (figure 1)2 has exploded in the last several years. Networking companies, semiconductor manufacturers, hardware companies, IT providers, software providers, Internet players--just about every technology subsector has seen a substantial upsurge of activity in this space. In fact, the race to invest in artificial intelligence has been described as "the latest Silicon Valley arms race."3 Since 2012, there have been 100 mergers and acquisitions (M&A) within the technology sector involving cognitive technology companies, products, and services.4 And this rush of M&A activity is not the only sign of the industry's interest. Many capabilities that were only just emerging a few years ago are now essentially mature and becoming "democratized" and more readily available for business applications. As a result, leading companies are using cognitive technologies to enhance their existing products and services, as well as to open up new markets. What is interesting is that the assertive actions of the sector's leaders do not mirror the wholesale adoption of these technologies across the industry. Many technology sector companies have yet to turn their attention to how cognitive technologies are changing their sector or how they--or their competitors--may be able to implement these technologies in their strategy or operations.

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, 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.

I'm looking for expertise in ML (mkting/adv applications a plus) to build and test a hypothesis around the use of text classification to a taxonomy. My employer eContext, has curated a general taxonomy that encompasses everything commercially and socially relevant on the web. It consists of 650M real user search queries bucketed into 25 vertical categories (Auto, Health, Finance, etc.) containing roughly 450K sub-categories. It's a rule-based system, and we use NLP and nGram chunking to parse long and short form text and map search queries, social posts, web content, blogs, forums, etc. to the category hierarchy providing structured, topical intelligence to data streams at scale. I understand that supervised training models require a corpus of text from which a model can determine entities, ontological connections, and apply statistical models to understand what people, places, things, concepts are and how they may be connected. That said, we've already built out the taxonomy to understand those connections and can provide greater context to "what" something truly is.

Google says autonomous cars will be available "relatively soon" and people will accept them in their lives faster than some observers have expected. "It's not coming until we're confident about your safety," said Ron Medford, director of safety for Google's Self-Driving Cars program and former deputy director of the National Highway Traffic Safety Administration (NHTSA). "It'll be relatively soon but we don't have a date to put on it. It'll be a gradual rollout. It's not going to be replacing the 265 million vehicles on the road in a day... Over time, it will roll out and acceptance will come faster than many people might believe today."

The man who helped build Google from a search engine into one of the biggest and most influential companies in the world has predicted the emergence of a new computing architecture based on crowd-sourced data and machine learning. Speaking at Google's GCP Next cloud computing conference in San Francisco, Alphabet Chairman Eric Schmidt said the combination of crowd-sourced data and machine learning will be the basis of "every successful huge IPO" in five years." He said the adoption of machine learning will allow companies to mine crowd sourced data, which already provides a mass of information not previously available to companies, and improve on it. "You're going to use machine learning to take that data and do something that's better than what the humans are doing," he said. Schmidt said the wide adoption of machine learning in computing will be as significant as the switch from the web to smartphone apps, which spawned the success of companies like Uber and Snapchat.

Microsoft recently launched SQL Server 2016, which, in addition to many other great features, offers in-database advanced analytics with R Services, allowing users to combine the power of SQL Server and Microsoft R Server (or Open Source R), without data leaving the database. With SQL Server R Services, users can develop analytic models in a local R IDE (e.g., R Tools for Visual Studio or RStudio), while data resides in SQL Server, and computation happens on SQL Server (by setting the compute context to SQL Server). Once the model is ready for production, it can be operationalized via SQL stored procedures (where R code is encapsulated inside), which can be run within SQL Server Management Studio or called by outside applications to make predictions. To jump-start users on building advanced analytics applications with SQL Server R Services, Microsoft provides a few data science templates that address real-world scenarios, including: online fraud detection, predictive maintenance, and customer churn prediction. These templates are sample advanced analytics solutions that demonstrate best practices and provide building blocks to help users implement a solution quickly.