Machine learning is, in essence, the process of applying algorithms to identify patterns in the data that correspond with the ground truth of that data. In our case, the ground truth is the reference values found in the GSL, and the patterns are the spectra for each sample. The regression model calculates a function to transform a spectrum into each of its reference values. For example, the presence of a significant peak in the spectrum could correspond with a high Potassium concentration. We also create regression models that allow us to predict how confident the predictions for a spectrum are.

Machine learning companies are being snapped up in droves by tech giants cognizant that these startups represent a new wave of technology innovation. This month alone, Intel announced plans to acquire deep learning startup Nervana Systems. And Apple confirmed it would acquire Turi inc. Earlier this year, Twitter acquired Magic Pony Technology, Salesforce acquired PredictionIO, ESI Group acquired Mineset, and Apple acquired Emotient, among other deals. PricewaterhouseCoopers LLP said 29 machine learning companies have been acquired so far this year by companies large and small, and total deals in 2016 will likely exceed the 37 such buyouts made last year.

The NASA-designed agent collects data on temperatures, gases and other vital signals - then crunches the stats to guide firefighters when they are tackling a blaze. Mark James, from NASA's Jet Propulsion Laboratory, said: "Because of all this data the sensor sees, firefighters won't run into the next room where the floor will collapse." The system uses sensors mounted on the firefighters' clothes - monitoring their GPS position, the heat level in surrounding areas, and whether any dangerous gases or chemicals are around. Audrey stands for Assistant for Understanding Data through Reasoning, Extraction, and sYnthesis. It is backed by the US Department of Homeland Security, and could also be used by police officers and other first responders. It has been in development for nine months and will be tested in the field next year.

A lot of chatter recently has been going around about how artificial intelligence will affect our lives for the better, understanding what are things that A.I. would take over in our lives. LinkedIn last week asked few influencers' the question "What is the first thing in your office AI will take over?" I've highlight below some of the replies they had on A.I. and how would it affect our life, starting of is: - "The microphone on your laptop or computer would be the first thing a A.I. will take over, it will listen to your ability in meeting and talking and will record things in order to help you take notes and suggestion action items for you" "First things that A.I. will take over is Scheduling meetings, where the language input is easy were they are contained (You say you want to schedule a meeting between Wednesday and Friday) it's a complex sentence but it's something companies are trying to figure out and fix." "It will be every aspect of my Scheduling and every aspect of my meetings and all the notes taking in meetings and the follow up that can be executing by A.I. cutting meeting massively and making it more efficient. I hope they are as kind as the executive assistance I had for the past decade" "Email that's the first thing A.I. will take in the office, I dictate most of the emails that I send every day because we can speak 3 times more then we can type its far more efficient" Majority of these great influencers kept going back to the same topic of Email and Scheduling because that's what we all do, it doesn't matter if you're a Developer, Sales, Account manager, Project manager at the end of the day you are 100% going to be checking your emails and you will always be on either side of a meeting whether its creating one or receiving an invite to one. Another Valid point was about having a Personal Assistant I think that's great and I wish we can all have one, life would be so much easier.

This week, Intel announced the acquisition of machine learning startups Nervana Systems for a rumored 400M. This move is intended to improve Intel's artificial intelligence(AI) and machine learning (ML) capabilities in its next generation architecture. The acquisition follows the news of Apple's acquisition of Seattle-based AI startup Turi for about 200M. These series of M&A events are signaling that we are entering a time of consolidation in the AI and ML markets.

In the musical "Hamilton," George Washington exclaims: "We are outgunned, outmanned, outnumbered, outplanned. We gotta make an all-out stand. Ayo, I'm gonna need a right-hand man." I recently saw this performance and the remark made me think to myself: Like George and his nascent collection of militia trying to defend New York City from a British armada and 32,000 troops, are clinicians about to face an overwhelming onslaught with respect to digital health and cognitive computing? Closer than ever I now wonder if these technologies have matured to a point where a convergence is happening.

Some people got a good laugh when a chatbot was given its own Twitter account – and was then transformed into a Holocaust-denying racist in 24 hours and swiftly taken offline. The Microsoft chatbot – called Tay – is a piece of software which can communicate with others with no human involvement. This bot was equipped with artificial intelligence but was easily manipulated by Twitter users. Teaching a robot works just like drilling an innocent, unknowing child, as artificial intelligence learns from the sum of its experiences, much like human beings. The more often a subject is talked about, opinions expressed, and certain wordings used, the more likely the software is to consider it normal and use it.

Two of today's major business and intellectual trends offer complementary insights about the challenge of making forecasts in a complex and rapidly changing world. Forty years of behavioral science research into the psychology of probabilistic reasoning have revealed the surprising extent to which people routinely base judgments and forecasts on systematically biased mental heuristics rather than careful assessments of evidence. These findings have fundamental implications for decision making, ranging from the quotidian (scouting baseball players and underwriting insurance contracts) to the strategic (estimating the time, expense, and likely success of a project or business initiative) to the existential (estimating security and terrorism risks). The bottom line: Unaided judgment is an unreliable guide to action. Consider psychologist Philip Tetlock's celebrated multiyear study concluding that even top journalists, historians, and political experts do little better than random chance at forecasting such political events as revolutions and regime changes.1 The second trend is the increasing ubiquity of data-driven decision making and artificial intelligence applications. Once again, an important lesson comes from behavioral science: A body of research dating back to the 1950s has established that even simple predictive models outperform human experts' ability to make predictions and forecasts. This implies that judiciously constructed predictive models can augment human intelligence by helping humans avoid common cognitive traps.