Today machines have the ability to interact with humans at a level that used to only seem possible in sci-fi movies. Amazon serves up personalized product recommendations, Facebook automatically tags photos and Google maps proactively reroutes you around traffic. AI is powering nearly every experience we have-- making it smarter, seamless and personalized-- and as a result our expectations as consumers are at an all-time high. The most indispensable consumer apps are powered by AI technologies, delivering real personalized value, in real-time. This seamless personalized, immediate and intelligent user experience will make its way to every business, across all industries.

New technologies are emerging and showing up in our everyday lives at a rapid rate. Voice-recognition, like Apple's Siri or Amazon's Alexa, and image recognition in our Facebook and Google accounts are just two mainstream applications that leverage artificial intelligence (AI)--one of the newest technologies gaining widespread momentum today. Artificial intelligence (AI) is defined by the Association for the Advancement of Artificial Intelligence as "the scientific understanding of the mechanism underlying thought and intelligent behavior and their embodiment in machines." It is one of the Top 10 Emerging Technologies of 2016 chosen by the World Economic Forum, based on the power to improve lives, transform industries, and safeguard the planet. Combining artificial intelligence with the advancements in natural language processing (NLP), social awareness algorithms, and big data enables its many applications.

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.

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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.

Salesforce is getting into the computer vision business with a new tool designed to let users easily train a custom image recognition system. Einstein Vision, as it's known, allows users to upload sets of images and classify them in a series of categories. After that, the system will create a recognizer based on machine learning technology that will identify future images fed into it. While Salesforce customers will have to wait a couple weeks before Vision is generally available, the company announced Tuesday that other Einstein features based on machine learning techniques are live. It's the latest step in a long journey for Salesforce, which began touting Einstein last year and demoed those capabilities at its Dreamforce conference. The company is facing heavy competition, and Einstein might give it an edge against the likes of Microsoft and Oracle.

Robotics and its ongoing impact on humanity, particularly the workforce, is a frequent topic of discussion for Constellation Research. Now, a team at MIT's Computer Science and Artificial Intelligence lab, along with Boston University, is developing a technology that targets an important sub-topic within the robotics debate: How humans may interact with them in the future. What if we could develop robots that were a more natural extension of us and that could actually do whatever we are thinking? A feedback system developed at MIT enables human operators to correct a robot's choice in real-time using only brain signals. Using data from an electroencephalography (EEG) monitor that records brain activity, the system can detect if a person notices an error as a robot performs an object-sorting task.

In Machine Learning, Cross-validation is a resampling method used for model evaluation to avoid testing a model on the same dataset on which it was trained. This is a common mistake, especially that a separate testing dataset is not always available. However, this usually leads to inaccurate performance measures (as the model will have an almost perfect score since it is being tested on the same data it was trained on). To avoid this kind of mistakes, cross validation is usually preferred. The concept of cross-validation is actually simple: Instead of using the whole dataset to train and then test on same data, we could randomly divide our data into training and testing datasets.

You didn't have to be following the coverage from this year's CES too closely to notice one name repeatedly cropping up, across what felt like every announcement at the show: Alexa. Alexa is an artificial intelligence, a virtual personal assistant developed by Amazon. And right now, it looks likely to be remembered as AI's first major foray into the mainstream. Developed by Amazon's Lab126 R&D division, and building on the eCommerce giant's acquisition of Cambridge AI startup Evi in 2012, Alexa made its debut – there's a temptation to say'her debut', given the gendered name and voice, but let's stay neutral for the moment – back in November 2014. It was first sold as part of the Amazon Echo, a smart speaker that enabled users to ask questions, select and listen to music and, of course, order shopping using just their voice. It wasn't until last year, however, that Alexa made a real splash.