Touchkin is a platform that uses AI and machine learning to provide personalised care solutions. For Jo Aggarwal and Ramakant Vempati, one of the biggest concerns they had while working abroad was the health of their family living back in India. The husband and wife duo were working in high-flying corporate jobs with organsations like Pearson Learning Solutions and Goldman Sachs International in the UK. Living away from family, the couple realised that remote care giving was a huge global challenge, not just in India, but abroad as well, especially when it came to understanding the emotional and mental well-being of the families living in their home countries. So, in 2012, the two of them moved back to India to take care of their family.

Marketing is moving from programmatic to predictive, but what does this look like in practice? A future where it's possible to zero in on understanding consumer behavior and deliver relevant experiences, according to leading marketers from The Weather Co., BlackRock, TripleLift, Nielsen and 84.51 /Kroger featured in the video above. We also touch on the question of how organizations should recruit in the cognitive era, and the larger impact of AI across business and society. As Jordan Bitterman, CMO of The Weather Co., notes: "AI is one of those revolutions taking place. There are those who say AI is just a catch phrase … but that's how it starts."

Two big Boston institutions, General Electric Co. and Partners HealthCare, on Wednesday launched an ambitious initiative to employ artificial intelligence to improve medical care. The decade-long effort will include clinical and technology experts at the Partners-owned Massachusetts General and Brigham and Women's hospitals working alongside engineers and developers at GE. The companies will begin by building software to help doctors more quickly and accurately interpret medical images, but over time, they also want to create applications for genomics, population health, and other areas of medicine. Artificial intelligence -- also called machine learning technology -- refers to computers that can sift through vast amounts of data to recognize patterns, becoming more accurate over time. Executives from GE, one of the nation's largest corporations, and Partners, Massachusetts' biggest nonprofit hospital network, said such technology has the potential to help care providers do their jobs more efficiently so that patients receive more accurate diagnoses and better treatments.

In this paper we consider a general matrix factorization model which covers a large class of existing models with many applications in areas such as machine learning and imaging sciences. To solve this possibly nonconvex, nonsmooth and non-Lipschitz problem, we develop a non-monotone alternating updating method based on a potential function. Our method essentially updates two blocks of variables in turn by inexactly minimizing this potential function, and updates another auxiliary block of variables using an explicit formula. The special structure of our potential function allows us to take advantage of efficient computational strategies for non-negative matrix factorization to perform the alternating minimization over the two blocks of variables. A suitable line search criterion is also incorporated to improve the numerical performance. Under some mild conditions, we show that the line search criterion is well defined, and establish that the sequence generated is bounded and any cluster point of the sequence is a stationary point. Finally, we conduct some numerical experiments using real datasets to compare our method with some existing efficient methods for non-negative matrix factorization and matrix completion. The numerical results show that our method can outperform these methods for these specific applications.

Democratization of Artificial intelligence, Microsoft's promise to take the AI and Machine learning from the ivory towers and make it accessible for all, is starting to take shape quite effectively. Let's face it; resource constraints around AI/ML is a real problem. Most companies with real-world AI use cases just don't have enough runway to build their own artificial intelligence offerings, and Microsoft cognitive services provide a sophisticated yet easy to use abstraction which fills this gap. Microsoft has also announced AI as an MVP category (http://aka.ms/AIMVP) Being a Microsoft MVP for Data Platforms, I have had the front row seat to see how Cognitive Services, a collection of powerful APIs and toolkits unfold to fulfill the promise of AI democratization.

RoboCup is an international scientific initiative with the goal to advance the state of the art of intelligent robots. Established in 1997, the original mission was to field a team of robots capable of winning against the human soccer World Cup champions by 2050. The competition has now grown into an international movement with a variety of leagues that go beyond soccer. Teams compete to make robots for rescue missions, the home, and industry. And it's not just researchers, kids also have their own league.

Today, SAP has announced three initiatives that expand and accelerate our machine learning capabilities. First, we are launching SAP Leonardo Machine Learning, an exciting new offering that embeds machine learning into a new wave of applications. Second, we are opening our SAP Leonardo Machine Learning Foundation to the SAP ecosystem via SAP Cloud Platform. And third, SAP has joined the Partnership on AI, a broad collaboration of commercial and non-profit organizations to benefit people and society. SAP Leonardo Machine Learning is part of the new SAP Leonardo portfolio, which combines machine learning, Internet of Things (IoT), blockchain, analytics, Big Data, and data intelligence into a holistic digital innovation system allowing our customers to innovate at scale and redefine their business.

Data Harmonization is an approach to Data Quality that is meant to improve the governance and usefulness of data across the enterprise. How does it do that? And how should a company go about implementing a Data Harmonization strategy? To answer these questions, DATAVERSITY spoke with Anil Kaul, co-founder and CEO of Absolutdata. Mr. Kaul was named one of the ten most influential Analytics Leaders in India. He has over two decades of experience in Data Analytics, market research, and management consulting.

Adding more ammunition to the grand AI alliance, Intel, Salesforce, eBay, Sony, SAP, McKinsey & Company, Zalando and Cogitai are joining the Partnership on AI, a collection of companies and non-profits that have committed to share best practices and communicating openly about the prospects and challenges of artificial intelligence research. The group also announced a slew of non-profit partners including the Allen Institute for Artificial Intelligence, the AI Forum of New Zealand, the Centre for Democracy & Technology, the Centre for Internet and Society (India), Data & Society Research Institute among others. The new members expand a group that already counts heavyweights like Facebook, Amazon, Google, IBM, Microsoft and Apple. The platform will be hosting a series of AI Grand Challenges to encourage and incentivize researchers working on AI. It has also announced an award for best paper on the topic of "AI, People, and Society" to aid in addressing a similar goal.

This question originally appeared on Quora. Firstly, my response contains some bias, because I work at Google Brain and I really like it there. My opinions are my own, and I do not speak for the rest of my colleagues or Alphabet as a whole. I rank "leaders in AI research" among IBM, Google, Facebook, Apple, Baidu, Microsoft as follows: I would say Deepmind is probably #1 right now, in terms of AI research. Their publications are highly respected within the research community, and span a myriad of topics such as Deep Reinforcement Learning, Bayesian Neural Nets, Robotics, transfer learning, and others.