DeepMind is an artificial intelligence lab in London that creates what are known as general purpose self-learning algorithms. The company, acquired by Google in 2014 for a reported 400 million, is best-known for creating software "agents" that have mastered games like Go and Space Invaders but it also wants to apply its technology to healthcare. Mustafa Suleyman, DeepMind cofounder and head of DeepMind Health, gave a talk at the King's Fund in London this week where he explained how the company is working with the NHS and what kind of benefits patients can expect to see in the long run. The company operates independently of Google and creates software that can think for itself. In order to create this kind of AI software, DeepMind draws on huge data sets that can help to teach DeepMind's AI how to perform certain tasks.

During the peak of the dot-com bubble, you'd be forgiven for thinking prefix investing was a legitimate tactic. A company could receive a nice jump in valuation by adding an "e-" prefix or ".com" suffix. Just being awake to the potential of the World Wide Web was enough to indicate to investors that a company might take advantage of it. What many of those suffixes and prefixes missed however was a detailed plan of attack. The internet was young and full of promises that were either technically or logistically impossible to fulfill.

We are looking for an intern (m/f) who would like to work on Artificial Intelligence with focus on Big Data Analytic starting in July 2016 for approx. The tasks will be: · Install, learn and setup tool chain for data analysis (e.g. R Studio or Matlab Simulink) · Learn and develop scripts for preparing and analyzing data (e.g. in R, Matlab or Python) · Learn and investigate machine learning algorithms for data processing · Document results and present them to other students and development team · Student (m/f) in the field of Engineering, Mathematics, Information Technology or comparable disciplines · Confident in handling script languages like R, Python or Matlab · Experience with Hadoop / Spark is advantageous · Fluent written and spoken English and good knowledge in German · Experience with basic statistical concepts and machine learning algorithms is advantageous · Eagerness to learn and develop new technologies, specially related to Artificial Intelligence and Big Data analytics · High motivation, team-, organizational- and communication skills

According to TechSci Research report, "United States Artificial Intelligence Market, By Application, By Region, By End User Competition Forecast & Opportunities, 2011-2021", the artificial intelligence market in the US is projected to grow at a CAGR of 75% during 2016 - 2021 on account of growing artificial intelligence technology adoption in consumer electronic devices, research and developmental activities in healthcare industry, unmanned aerial vehicles, autonomous cars, etc. Moreover, venture capital investments in this sector, are in full swing, especially in the US. The country is witnessing numerous start-ups sprouting every year, backed by various angel investors and venture capitalists. Major venture capitalist active in the United States artificial intelligence market include Accel, General Catalyst Partners, GV, Work-Bench, Promus Ventures, Kleiner Perkins Caulfield & Byers, Khosla Ventures, Samsung Electronics, Wipro Technologies, Samsung Global Innovation Centre, Goldman Sachs, Bank of America Merrill Lynch, and Formation 8, among others. In 2015, western region of the United States dominated the artificial intelligence market of the country, on account of presence of major end users such as cyber security solution providers, healthcare institutes, government headquarters, etc., in the region.

If you even remotely follow tech news, it is very likely that you have read outlandish claims like "chatbots are the new apps." But the fact is, there are way too many misconceptions around conversational products. Facebook Messenger already has over 11,000 bots. Let's add Kik, Telegram, Line, and Slack bots on top of that. Now that Apple has joined the race as well, expect the bot explosion to get even more intense.

As the country reels from the spasm of gun violence that killed two black men and five police officers this week, a prominent digital vigilante is using an online tool he hacked together to keep an eye on hotspots that seem at risk of boiling over into bloodshed. The Web app, which is powered partly by artificial intelligence, analyzes posts on social media as well as police radio chatter and feeds of the local airspace in virtually any region. To detect rumblings of unrest and alert the public. At the moment, the tool has its gaze trained on Baton Rouge, where protesters backed by the New Black Power Party have gathered for a rally. "I'm looking for any indication they are coordinating skirmishes … I guess I'm expecting trouble in that location, so [I] have it trained on Baton Rouge preemptively," said the creator of the site -- who goes solely by his Internet pseudonym, the Jester -- in an interview with The Washington Post.

This post was first published on my Linkedin page and posted here as a contributed post. In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I've observed that some actually lack the necessary mathematical intuition and framework to get useful results. This is the main reason I decided to write this blog post. Recently, there has been an upsurge in the availability of many easy-to-use machine and deep learning packages such as scikit-learn, Weka, Tensorflow etc. Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results.

Soon, the glow of hundreds of screens illuminates each face in every classroom. Inside Skye Templeton's seventh-grade Social Studies class, students are enthralled by online documents and videos about the casualties of World War II. Nearby, in Sara Sharpe's sixth-grade math class, a small group of students works through computer drills covering ratios and percents. And, across the hallway, English and Language Arts teacher Lori Meyer expresses amazement at how much her eighth graders enjoyed doing their final project: a research paper and iMovie on the 1960s. With their MacBooks, students researched topics, wrote their papers, and submitted to their teacher via email. "This is the first time in my 12 years of teaching that students said writing the research paper was their favorite assignment," Meyer said, "and I know it was due to the laptops."

Researchers are developing a personalised privacy assistant app that can simplify the task of setting permissions for your smartphone applications. That is a job that requires well over a hundred decisions, an unmanageable number for the typical user, researchers from Carnegie Mellon University (CMU) in the US said. The privacy assistant can learn the user's preferences and quickly recommend the most appropriate settings, such as with which app to share the user's location, or contact list. In the field test, people accepted almost 80 per cent of the recommendations made by the privacy assistant and, at the end of the study, these people indicated they were more comfortable with their privacy settings than users who did not have a privacy assistant, researchers said. "It is clear that people just cannot cope with the complexities of privacy settings associated with the apps they have on their smartphones," said Norman Sadeh from CMU.

Ever since the late 1950s, when pioneering IBM researcher Arthur Samuel trained the world's first self-learning computer to play a mean game of checkers, the future has promised a widespread emergence of intelligent machines. Machine learning, the computing methodology Samuel introduced to the world, is now seen as mature, effective and -- thanks to a variety of new offerings -- readily accessible to the channel. "For consulting companies like ours, this is the opportunity of a lifetime," said Dj Das, CEO of Third Eye Consulting Services and Solutions, a big data and analytics solution provider in Santa Clara, Calif., that partners with four major hyper-scale cloud providers: IBM, Microsoft, Google and Amazon Web Services. That opportunity has presented itself in a diverse spectrum of use cases, from optimizing supply chains, predicting customer buying patterns, diagnosing illnesses, detecting fraud, recognizing text and images, and improving IT performance and security. "The clients I talk to, the partners I talk to, they understand that this is going to be a disrupter," Ed Harbour, IBM's vice president for implementations for Watson, Big Blue's cloud-based cognitive computing platform, told CRN. "And whether they choose to embrace it to their advantage or whether they don't is probably going to determine the outcome of how their businesses go forward." IBM is staking much of its future on cognitive computing, an analytic approach that mimics human thought processes of which machine learning is an essential component.