If you are using data science for only one-time, ad-hoc analysis, then you are doing it wrong. There is no doubt that companies can benefit greatly from this type of one-time data science exercise and most start here. However, much more value is created when data science can be applied in real-time scenarios and in an ongoing manner. We can't just build a machine learning (ML) model and share the insights, we have to go to the next step and operationalize it, making it part of the fabric of our business processes and affecting outcomes in real-time. For example, what becomes possible when we can score human movement in real-time--like a system that can tell you that someone is currently running or moving at 30 MPH when they shouldn't be or just fell down on the floor.

Artificial Intelligence has the potential to revolutionize technology as we know it, although we're still a long ways away from Skynet (thank god). But with everyone from Google to Facebook to Elon Musk getting involved, how far away are we from that sci-fi future? We're getting closer, and whether that means robots eventually take over the world, or human and robot relations… your prediction is as good as mine. The potential for AI is practically limitless. What we are doing at Scope is trying to apply AI to revolutionize how we search for and share photos.

People learning new concepts can often generalize successfully from just a single example, yet machine learning algorithms typically require tens or hundreds of examples to perform with similar accuracy. People can also use learned concepts in richer ways than conventional algorithms--for action, imagination, and explanation. We present a computational model that captures these human learning abilities for a large class of simple visual concepts: handwritten characters from the world's alphabets. The model represents concepts as simple programs that best explain observed examples under a Bayesian criterion. On a challenging one-shot classification task, the model achieves human-level performance while outperforming recent deep learning approaches.

If you ask many people to name the technology categories that are creating sweeping change right now, cloud computing and Big Data analytics would probably be top of mind for a lot of them. However, there is an absolute renaissance goind on right now in the field of artifical intelligence and the closely related field of machine learning. Some of the biggest tech companies are helping to drive the trend, and Google added to the momentum on this front this week. Specifically, Sundar Pichai, Google's CEO, said on a conference call, "I do think in the long run we will evolve in computing from a mobile-first to an A.I.-first world." In this post, you'll find a collection of the most notable A.I. tools that have recently been open sourced.

Humans have a terrible track record of predicting financial crises in time to fend them off. Some computer scientists think that algorithms might help. Given the right information, some crises can be foreseen. In "The Big Short," Michael Lewis told the story of the scattered few who saw the imbalance growing in the mortgage market and profited as a result. Over decades, academic research has shown that many banking crises come with early warning signals, such as rapidly increasing debt and leverage.

Leading investment crowdfunding platform OurCrowd has registered its 6th successful exit as portfolio company Crosswise has been purchased by Oracle. The transaction was revealed in Israeli media on April 15th revealing that Oracle paid 50 million for the "machine learning based cross-device data" company. The transaction closed on April 14th. OurCrowd, and its registered investors, participated in a Series A funding round that provided 670,392 to the young company. Co-investors on the funding round included Giza Venture Capital, Horizons Ventures and a "high profile angel group."

Perhaps the most important news of our day is that datasets--not algorithms--might be the key limiting factor to development of human-level artificial intelligence. At the dawn of the field of artificial intelligence, in 1967, two of its founders famously anticipated that solving the problem of computer vision would take only a summer. Now, almost a half century later, machine learning software finally appears poised to achieve human-level performance on vision tasks and a variety of other grand challenges. What took the AI revolution so long? A review of the timing of the most publicized AI advances over the past thirty years suggests a provocative explanation: perhaps many major AI breakthroughs have actually been constrained by the availability of high-quality training datasets, and not by algorithmic advances.

Narratives are made by the artful omission of facts. Never was this maxim more evident than in a gullible feature story that landed on the front page of the New York Times last fall, about a young woman's last-ditch bid for life extension as she succumbed to the ravages of brain cancer. A sober look at the case would have revealed it to be but the latest botched mortuary procedure conducted by a gang of creepy scam artists. Instead, through the good graces of the Times, this grim tale was spun into an inspirational saga of one person's courageous quest for a second chance at life, aided by medical visionaries on the verge of miraculous technological breakthroughs. Kim Suozzi died at age twenty-three in January 2013. After her first diagnosis, twenty-one months earlier, Suozzi chose to become one of the youngest people ever[*] to undergo an expensive form of ritualistic corpse mutilation called cryonic preservation. In pop culture, cryonics is perhaps best known as the plot device that transports the schlubby pizza delivery guy in Matt Groening's animated series Futurama into the thirty-first century. The decades-old quack procedure, which involves freezing corpse parts for later resuscitation, was for a long time apocryphally associated with such wealthy eccentrics as Walt Disney. It then caused a scandal in 2002 when it was widely reported that the body of baseball great Ted Williams had gone into deep freeze against the wishes of some in his family. In recent years, cryonics has regained an entirely undue aura of respectability as the thought leaders of Silicon Valley have trained their enterprising, disruptive vision on the conquest of disease and death.[**] Suozzi, an agnostic libertarian and aspiring neuroscientist, began taking cryonics seriously after discovering the work of the futurologist Ray Kurzweil through a cognitive science class at Truman State University in Missouri. After surgery and other treatments failed to stop the growth of her brain tumor, Suozzi determined that upon death she--or rather, her head--would be frozen and stored for decades, centuries, or millennia in the hope that one day, diligent, wonder-working doctors would transplant her consciousness into a new, healthy body, or perhaps onto a high-capacity hard drive. As a tech-savvy millennial, Suozzi turned to the chat website Reddit for help in raising the 80,000 she needed to fulfill her last wish. That got her well on her way, with about 7,000 reportedly raised.

The Gundersons want a robot that can solve simple tasks, like asking the bartender for a beer and then bringing it back to the table. To do this, they think that robots must solve the kind of problem that humans do instinctively when dealing with the world.

Video Optimization With Machine Learning is now a reality and publishers are intelligently making the most out of their O&O digital assets. The digital video industry is undergoing a transformation and machine learning is advancing the video user experience. Mobile, combined with video, is truly the definitive on-demand platform making it the fastest growing sector in digital content distribution. Video machine learning is a new field. The ability to crowd source massive human interactions on video content has created a new data-set.