Self-driving cars, medical research, financial market predictions: The applications of machine learning technologies seem limitless. Data processing isn't a small task: Countless human work hours are spent collating and developing data, seeking patterns and information from seemingly unrelated tidbits. And there is a constantly growing number of data sources: Excel spreadsheets or SQL (or NoSQL) databases have long-since replaced the old-school filing cabinets of records, not to mention the growth in tracked website usage, which can improve web experience for users, as well as advertising options for companies. With all of these potential areas of growth, which industries can you expect to be most affected by advances in machine learning? Having personal exposure to emerging health-care tech, I can assure you we're in for some exciting times.

In 2011, IBM sent its supercomputer Watson onto the popular American TV quiz show Jeopardy where it succeeded in matching wits with and beating two of the TV show's most successful players. That was over five years ago, but if you ask members of the public to describe IBM Watson, those in the know will say that it's a huge great black mainframe computer that's incredibly smart. Yet according to IBM, that's where you'd be wrong – the computing giant is adamant that the future of artificial intelligence will not be one big scary digital brain, and technology is definitely not going to kill us off one day. "When IBM Watson first came out, we used to think about it as a giant brain in a jar, but it's not that," John Cohn, an IBM Fellow in the IBM Watson Internet of Things (IoT) division tells IBTimes UK while showing us around IBM's new global IoT headquarters in Munich, Germany. "It's a bunch of tools that you can use to compose systems that interact naturally with humans, learns from their situation, adapting and then applying that knowledge.

Physicists have developed a quantum machine learning algorithm that can handle infinite dimensions--that is, it works with continuous variables (which have an infinite number of possible values on a closed interval) instead of the typically used discrete variables (which have only a finite number of values). The researchers, Hoi-Kwan Lau et al., have published a paper on generalizing quantum machine learning to infinite dimensions in a recent issue of Physical Review Letters. As the physicists explain, quantum machine learning is a new subfield within the field of quantum information that combines the speed of quantum computing with the ability to learn and adapt, as offered by machine learning. One of the biggest advantages of having a quantum machine learning algorithm for continuous variables is that it can theoretically operate much faster than classical algorithms. Since many science and engineering models involve continuous variables, applying quantum machine learning to these problems could potentially have far-reaching applications.

When Amazon purchased Kiva Systems in 2012, the interest in Autonomous Mobile Robots (AMRs) for the warehouse soared. For a while, Kiva, now rebranded Amazon Robotics, continued to sell robots to other companies. But, after piloting the robots in some warehouses, and figuring out the optimal way to deploy them, Amazon stopped selling robots to other companies and took everything their robotic division could produce for their own distribution centers.

IBM is teaming up with Salesforce to make it easier for Salesforce customers to use data from IBM's Watson artificial intelligence platform. As part of the partnership, IBM has signed a deal to deploy the Salesforce Service Cloud for internal use there. The value of the deal and proposed pricing of the joint products were not disclosed. The deal has several parts. Both companies, like many others in the tech industry, are making investments in artificial intelligence and machine learning, by which computer programs attempt to connect data in new ways to provide new kinds of insights and assistance to users, and both frequently cite the importance of AI in their sales pitches to customers.

Sports analytics is routinely used to assign values to such things as shots taken or to compare player performance, but a new automated method based on deep learning techniques - developed by researchers at Disney Research, California Institute of Technology and STATS, a supplier of sports data - will provide coaches and teams with a quicker tool to help assess defensive athletic performance in any game situation. The innovative method analyzes detailed game data on player and ball positions to create models, or "ghosts," of how a typical player in a league or on another team would behave when an opponent is on the attack. It is then possible to visually compare what a team's players actually did during a defensive play versus what the ghost players would have done. "With the innovation of data-driven ghosting, we can now, for the first time, scalably quantify, analyze and compare detailed defensive behavior," said Peter Carr, research scientist at Disney Research. "Despite what skeptics might say, you can indeed measure defense."

Google's cryostats reach temperatures of 10 millikelvin to run its quantum processors. From aspects of quantum entanglement to chemical reactions with large molecules, many features of the world cannot be described efficiently with conventional computers based on binary logic. The solution, as physicist Richard Feynman realized three decades ago1, is to use quantum processors that adopt a blend of classical states simultaneously, as matter does. Many technical hurdles must be overcome for such quantum machines to be practical, however. These include noise control and improving the fidelity of operations acting on the quantum states that encode the information.

Three years after its founding, Seattle's Allen Institute for Artificial Intelligence is racking up recognition in the field of AI research – and some of its research will have an impact on the burgeoning AI market. The institute, known as AI2, was founded by Microsoft co-founder Paul Allen in 2014 with longtime computer science researcher Oren Etzioni as its CEO. Since its founding, AI2 has spawned two spin-offs: Kitt.ai, which was created a little more than a year ago; and Xnor.ai, which made its debut this month. AI2 has built its workforce up to 75 people, which Etzioni says makes it the largest nonprofit AI research center in North America. Etzioni said the institute is sharpening its focus on the moonshot challenges that artificial intelligence can address.

H2O.ai, an AI company that provides industry-leading data products for enterprise businesses, today announced it has been named by Gartner, Inc., the leading provider of research and analysis on the global machine learning industry, as a Representative Provider of deep learning platform provider that allows users to create their own deep learning solutions. Gartner's January 2017 Innovation Insight for Deep Learning report listed H2O's Deep Water as one provider of deep learning platforms, placing it alongside other offerings from Caffe, Ersatz Labs, Facebook's Torch, Google TensorFlow, Intel's Nervana, The Microsoft Cognitive Toolkit, Theano and Skymind's Deeplearning4j. "We believe our inclusion in this report validates and enforces our standing in the industry," said SriSatish Ambati, co-founder and CEO of H2O.ai. "We're excited to continue expanding our suite of deep learning solutions." H2O.ai launched in 2011 with the goal of democratizing data science by open sourcing deep learning and AI for everyone.

C3 IoT updated its Internet of Things platform with more machine learning and artificial intelligence technology, integrated more with Amazon Web Services and has more than 100 million sensors and devices under management. The tech revolution is spreading to every corner of the earth with the Internet of Things, and it's enabling data analytics and automation in ways never before imagined in business. Version 7 of C3 IoT's platform will help the company expand into new verticals. As noted previously, C3 IoT initially gained traction with utilities and then moved into new verticals. Houman Behzadi, chief product officer at C3 IoT, said the company is actively working on implementations at oil and gas, health care, and financial services companies.