You can always count on Google to have data -- tons of it, generated by the users who interact with and upload content to its services. Google uses that data to build intelligence for the company, but it's offered data for others to experiment with as well. These three data sets are abundantly large, have plenty of practical applications, and are guaranteed to be well-assembled, thanks to Google's imprimatur. The Open Images Dataset, unveiled at the end of last month, is a collection of 9 million URLs to images "that have been annotated with labels spanning over 6,000 categories," according to Google. All have a Creative Common Attributation license, so they can be reused readily, and the label assignments to the images have been verified by human eyes to ensure validity. Plus, plans are underway to "improve the quality of the annotations in Open Images the coming months."

This post was authored by Rimma Nehme, Technical Assistant, Data Group at Microsoft. Today, the business potential of Machine Learning and AI is real. Businesses can apply ML and AI to transform, optimize and automate their businesses having previously relied only on human intelligence. You may wonder, 'how can I apply AI to my business?' This blog post describes some of the ways you can do that using the resources in the Cortana Intelligence Gallery, without a PhD in Machine Learning or AI or even a deep expertise in these subjects.

Chinese search engine giant Baidu is launching a "conversational" medical chatbot designed to make diagnosing illnesses easier. The bot is named Melody and comes built into the company's iOS and Android Baidu Doctor app, which launched in China in 2015. Baidu Doctor allows users to contact local doctors, book appointments, and ask questions, with the chatbot intended to speed up this process. Melody builds on the Baidu Doctor app, which launched in China in 2015. Patients can open the app and ask a question, and Melody will respond with context-relevant questions to clarify information such as symptom frequency or duration.

The White House sees artificial intelligence as an increasingly critical technology that can fight cyberattacks, upgrade weapons of war, improve health care and even unclog traffic for the commute home. However, the Obama administration also expects that A.I. will reduce low-level jobs and create security and ethical issues. With critical pros and cons looming, the federal government wants to work with the private sector and academia to guide A.I. in a positive direction, according to a recently released report, Preparing for the Future of Artificial Intelligence. The report was prepared by the National Science and Technology Council's subcommittee on Machine Learning and Artificial Intelligence. "A.I. can be a major driver of economic growth and social progress, if industry, civil society, government, and the public work together to support development of the technology, with thoughtful attention to its potential and to managing its risks," the report states.

Every big company in Silicon Valley is chasing artificial intelligence, the increasingly powerful computer software that can learn from big sets of data almost like a human does. And Apple just hired one of the big guns of AI research, as the company continues to try to prove that it is not falling behind Google and its other rivals in the hot field. Ruslan Salakhutdinov announced on Twitter on Monday that he's joining Apple as a director of AI research. Salakhutdinov will continue to do work at Carnegie Mellon University, where he advises and does research on deep learning, a key AI technique. His hiring raises the question of whether Apple's growing AI team will take a more academic approach to AI research going forward.

Apple is once again looking to Asia to turbocharge its research and development process, this time to improve its artificial intelligence efforts. In an interview with Nikkei Asian Review, CEO Tim Cook said the future of the iPhone is AI, which will be supported by its new research and development center that will open by the end of the year in Yokohama, Japan. Cook seemed to suggest that AI in the iPhone would move beyond Siri and would actually help increase your battery life through resource management. It would also recommend music more skillfully, and perform other background tasks. As is typical with Apple, Cook stayed tight-lipped on specifics but said the Yokohama team will deal with "deep engineering" and be quite different from its planned R&D effort in China.

MediaGamma has announced the launch of a new Audience Prediction product, which is set to make a major impact on the ad tech market. By applying deep learning to unique data sets, coupled with MediaGamma's unique AI Decision Support Engine, the product is set to provide players in the ecosystem with over 90% certainty about a user's interests and demographic profile. The new product will help people to navigate uncertainty to make better decisions, and a major telecoms company has already signed up. The Audience Prediction product is the latest in a broad portfolio of products created by MediaGamma (http://www.mediagamma.com/), The start-up's world-renown team of data scientists deliver bespoke real time, prediction-based data science solutions focusing on online user behaviour.

Consider a personal assistant bot, a machine or application that you could ask to organise a meeting or arrange bookings just as you would a live, human assistant. As artificial intelligence (AI) becomes more prevalent and approaches advance, such a scenario is becoming increasingly realistic. This is according to DataProphet managing director, Frans Cronje who highlights that there will be a number of exciting developments and new approaches to AI in the short- and long-term future as the application matures and continues being explored. Machine learning extends AI "A subset of AI, machine learning has driven the majority of advances in recent years, many of which are improvements on or augmentations to existing processes." Cronje explains that there are multiple cases where improved machine learning models have extended the capability of AI.

Over the past few years AI has exploded, and especially since 2015. Much of that has to do with the wide availability of GPUs that make parallel processing ever faster, cheaper, and more powerful. It also has to do with the simultaneous one-two punch of practically infinite storage and a flood of data of every stripe (that whole Big Data movement) – images, text, transactions, mapping data, you name it.

Most traditional data management products aren't equipped to handle the increasingly complex and diverse information that is flowing into the corporate network these days. As a result, organizations are turning to new solutions like Neo4j. The widely used graph store, which sets itself apart by providing the ability to easily log the relationships among records, received a major update last week that promises to streamline large-scale analytics initiatives. The biggest change is in the way that Neo4j synchronizes queries and information across the servers on which it's deployed. Neo Technology Inc., the company behind the database, replaced the nearly 20-year-old PAXOS algorithm that was used for the task before with a much newer alternative called RAFT.