What better way to create a movie trailer about an artificially enhanced human than to use the reality behind the premise; artificial intelligence (AI). That's just what a partnership between IBM Research and 20th Century Fox recently set out to do, when they used machine learning techniques to produce what they described as the "first ever cognitive movie trailer." You'll have to judge the merits of the result yourself, but what is beyond doubt is this is just one example of the many ways AI and machine learning techniques are already changing the face of the entertainment industry. It's only makes sense that creative industries are leading the pack when it comes to the adoption of and experimentation with AI. Media, entertainment, and advertising are all the on the cutting edge when it comes to the adoption of AI and machine learning.

I define creativity as the association between two ideas that one would not ordinarily consider a reasonable one to make but that works. I think that this is a rather simple thing to do for computers. I've been working on writing novels computationally for well over 10 years now and I'm still trying it, although I believe that within the next two to three years I will have broken its back and will produce 100,000-word novels in half an hour or so, novels that I think most people would consider to be creative. I can do uncreative short stories at this point, ones where the associations are routine and expected; they have occurred in many thousands of previous books. But I believe that in the near future my programs will be able to do creative ones. We are used to machines being used as tools that do not have a high level of cognitive ability, so it's difficult for people to think of them as being able to exhibit truly creative behaviour.

As we are all fond of saying, innovation follows pain points. Are we missing something in our uber-critical search capabilities that needs to be resolved? A colleague recently pointed me to a slim volume "Structured Search for Big Data" by Mikhail Gilula (published by Elsevier and available on Amazon) that argues that not only are our search tools deficient but that a complete revamp of the underlying key-word NoSQL DB structure is what's required. Use Google, Amazon, or any of the other life-critical search tools we've become so reliant upon and you are using key-word search on NoSQL. The pain that Gilula identifies is the length of time it takes the consumer to research and select complex merchandise for best deals resulting from the imprecision of the search results.

While this skill is not unique to humanity (Wikipedia has helpfully documented other cases of "Tool use by animals"), human civilization is suffused with tool use. We use tools to help us eat, tools to help us build homes and other structures, tools for transportation, even tools to assist us in getting exercise. Awash in the many tools of our technological society, we are seeing a new type of tool on the horizon, one which is similar in some ways to what has come before and in other ways quite different. These are tools that augment human creativity. We are increasingly, as a society, building tools to help assist human creativity, whether in art, design, or even scientific discovery.

Today I'm announcing a $100M commitment to Kernel in an effort to enhance human intelligence and reimagine our future. Unlocking our brain is the most significant and consequential opportunity in history -- and it's time sensitive.

Today I'm announcing a 100M commitment to Kernel in an effort to enhance human intelligence and reimagine our future. Unlocking our brain is the most significant and consequential opportunity in history -- and it's time sensitive. We're starting to identify the mechanisms underlying neural code and make them programmable. Our biology and genetics have become increasingly programmable; our neural code is next in line. Programming our neural code will enable us to author ourselves and our existence in ways that were previously unimaginable. I started Kernel in 2016 (read more at the Washington Post) to build the world's first neural prosthetic for human intelligence enhancement.

To build the feature libraries for auto-featurization, we leverage algorithms from decades of Microsoft research in natural language processing, machine learning, computer vision, speech, big data and much more – the same algorithms that power products such as Bing, Cortana and Microsoft Office. Today we have started with a basic set of text featurizers, and we will be continually expanding the selection overtime. For example, coming soon, we will be adding support for deep neural network based featurizers. The power of these feature libraries is that they are usually trained on a large amount of data that is not available to most users (e.g. DNN image featurizer, trained on tens of millions of annotated images, or DSSM, trained on years of click data from Bing Ads and web search), and they save users weeks or months relative to training their own complex models.

Bryan Johnson is the founder and chief executive officer of the neuroprosthesis developer Kernel and the founder of OS Fund and Braintree. Through the past few decades of summer blockbuster movies and Silicon Valley products, artificial intelligence (AI) has become increasingly familiar and sexy, and imbued with a perversely dystopian allure. What's talked about less, and has also been dwarfed in attention and resources, is human intelligence (HI). In its varied forms -- from the mysterious brains of octopuses and the swarm-minds of ants to Go-playing deep learning machines and driverless-car autopilots -- intelligence is the most powerful and precious resource in existence. Our own minds are the most familiar examples of a phenomenon characterized by a great deal of diversity.

By integrating scalable human judgment into your analytics solution, you can augment your lower-confidence machine learning predictions with accurate labels, and these labels can be used as additional training data to help improve your model. In this session, we'll demonstrate how to build a real-world text analytics workflow using machine learning with humans in the loop.

While this skill is not unique to humanity (Wikipedia has helpfully documented other cases of "Tool use by animals"), human civilization is suffused with tool use. We use tools to help us eat, tools to help us build homes and other structures, tools for transportation, even tools to assist us in getting exercise. Awash in the many tools of our technological society, we are seeing a new type of tool on the horizon, one which is similar in some ways to what has come before and in other ways quite different. These are tools that augment human creativity. We are increasingly, as a society, building tools to help assist human creativity, whether in art, design, or even scientific discovery.