Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and Boston University collaborated to design a system that combines neuroscience and machine learning to create a mind-reading robot. This robot can read your mind to learn if it is right or wrong. According to Forbes, "The researchers created a system that allows a robot to correct its mistakes in real time when a human observer notices that a mistake is being made. The observer just sits there and watches; she doesn't physically interact with the robot or anything else. If the observer recognizes that a mistake is occurring, the robot changes course and does the right thing."

Phillip De Leon has been named associate dean of research and doctoral studies for the New Mexico State University College of Engineering. LAS CRUCES -- Through the use of New Mexico State University's High Performance Computing system, a supercomputer known as Joker, Phillip De Leon, associate dean for research in the College of Engineering, not only conducted research but students in his graduate electrical engineering course also used the system. In the Pattern Recognition and Machine Learning course, which is a data science class De Leon taught in the fall, graduate students used Joker on projects that included developing machine learning codes and evaluating the models with standard datasets. "These projects including identifying a song much like the Shazam app, recognizing handwritten digits like ZIP codes, classifying email as ham or spam, analyzing Twitter feeds, etc.," De Leon said. "Being able to use this system allowed the students to experiment and tune their codes much faster since everything ran much faster. It also allowed for big datasets to be used in training and evaluation."

Machine learning is a trending topic today and for good reason. It has enormous potential to transform entire markets and industries. Successful machine-learning startups will be the ones targeting vertical applications with a clear need for the technology. The consumer packaged goods industry is a good example. Machine learning can more accurately predict inventory levels to better manage the supply chain, reduce inventory costs, minimize excess capacity requirements, and eliminate stockouts.

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More investors are setting their sights on the financial technology (Fintech) arena. According to consulting firm Accenture, investment in Fintech firms rose by 10 percent worldwide to the tune of $23.2 billion in 2016. China is leading the charge after securing $10 billion in investments in 55 deals which account for 90 percent of investments in Asia-Pacific. The US came second taking in $6.2 billion in funding. Europe, also saw an 11 percent increase in deals despite Britain seeing a decrease in funding due to the uncertainty from the Brexit vote.

A group of financial technology startups under Dayli Financial Group, a startup alliance in Seoul, have joined hands to offer AI services that could give "practical help" to financial firms. "Unlike Google's AlphaGo, IBM's Watson and Facebook's AI Research lab, which are more like pure research, our AI-based services are focused on addressing issues facing businesses," said Sophie Eom, who heads machine learning-based analytics solution firm Solidware, at a press conference in Yeouido, Seoul on Feb. 22. Solidware is one of the seven startups that have joined forces to form the startup group, called Dayli Intelligence. Each of the seven startups under the group, including Leevi and Heenam, is specialized in different fintech segments, but together they can offer better services, according to Eom, who also serves as a chief of the group's AI division. "After a series of meetings with corporate customers, I have found there is demand for a bundle of solutions, not just a single service," she said. The group will provide AI-based solutions for data analysis, natural language processing, and data scraping, which can be tailored for banks, insurance firms, and credit rating agencies.

Here's an analogy using a concept that we can all relate to: a supermarket. Picture the scene: Shopping list in one hand, shopping basket in the other, you're ready to tackle your weekly shopping in your local supermarket. Your items range from fruit and vegetables to washing detergent, perhaps with some free-range eggs thrown in for good measure. Quite the eclectic mix, but you know that you'll be able to find all you need under one roof. The fact that this is possible is in itself quite remarkable.

In this article, I present a few modern techniques that have been used in various business contexts, comparing performance with traditional methods. The advanced techniques in question are math-free, innovative, efficiently process large amounts of unstructured data, and are robust and scalable. Implementations in Python, R, Julia and Perl are provided, but here we focus on an Excel version that does not even require any Excel macros, coding, plug-ins, or anything other than the most basic version of Excel. It is actually easily implemented in standard, basic SQL too, and we invite readers to work on an SQL version. In short, we offer here an Excel template for machine learning and statistical computing, and it is quite powerful for an Excel spreadsheet.

According to one of Maurice Conti's TED presentations, we arrived to a new, augmented age, in which Artificial Intelligence gains a new role. The future holds a generated era in which artificial intelligence comes up with its own designs, its own ideas, its own products instead of all our tools being passive, us telling them what to do and them doing it. From now on, tasks will be executed by robots, AI and humans cooperating, robots providing perfect execution, AI the design and humans making the necessary decisions. But in order to get to this point it is crucial to build a "nervous system" for robots through sensors. Industry 4.0 has introduced the concept of data exchange withing manufacturing, opening countless business opportunities, some that were previously unimaginable. By implementing sensors experts will be able to make use of data of all type which were not at hand before, making products become even services as well.

The famous cartoon series "The Jetsons" predicted 53 years ago how our lives would look like in 2063 when all our "daily devices" such as home appliances or cars would be connected to the internet. In 2016, the Internet of Things (IoT) is still evolving to a convergence of multiple technologies, including wireless communication, real-time analytics, machine learning, commodity sensors, embedded systems, automation, and more. On October 21, a series of Distributed Denial of Service (DDoS) attacks caused widespread disruption of internet activity across Europe and the US. This attack was also unique because it targeted IoT devices due to the fact they have soft security profiles. As the awareness of what impact connected objects would have on our lives and on our security.