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.

Some executives may wring their hands over the impact of bot automation, machine learning and AI on job roles. But others see the potential of combining software with people-centric tasks to drive efficiency and enhance human work. At KLM Royal Dutch Airlines, customer service agents deal with a high volume of requests for flight information or help with trip cancellations, particularly during bad weather. In 2015, KLM began experimenting with a hybrid approach -- chatbots combined with human customer service -- to deal with high-volume customer interaction on social media platforms. Bot automation is just one of the many types of artificial intelligence (AI) that major software providers have touted during the past year as the next great thing in technology.

This post is the third in a three part series. In the first post 2 weeks ago, we gave some insight and context into why your CEO is asking this question, why now, and why you the CIO. In the second post last week, we gave you a foundational framework to think about AI so you could give your CEO a thoughtful response. This week we will discuss how you can engage the business on the topic of AI and consider important criteria when evaluating AI vendors. So now you have the AI TD ML HITL conceptual model to apply to your business, it's time to start engaging with your executive counterparts in Marketing, Product, Sales and Customer Support.

ANI has never been easy to build or test. The incumbent technologies are too simple and limited (like decision trees), and the newer technologies are non trivial to use, and deeply scientific (like machine learning). What the world has been missing is a new, more powerful rules-based technology that can be used by business people. A platform without the one-dimensional limitations of decision trees where you can build human models of knowledge that can solve multiple problems. A platform that can be connected to existing data in order to make nuanced judgements at scale, with an audit trail for each and every decision.

In this episode of Planet Lex, host Daniel Rodriguez speaks with Northwestern Pritzker School of Law George C. Dix Professor in Constitutional Law John McGinnis and Northwestern University's McCormick School of Engineering Professor of Electrical Engineering and Computer Science Larry Birnbaum about emergent technology and its effects on the law. Dan opens the interview by reminding everyone that it has been 10 years since the publication of Raymond Kurzweil's book, "The Singularity is Near: When Humans Transcend Biology," and poses the question of exactly how close we are to the day when computer intelligence surpasses human intelligence. Larry shares his belief that the singularity is coming, though the time table is unknown. John agrees and states that the victory of IBM's computer system Watson over its human competition on Jeopardy shows the accelerating technology and that software and connectivity are improving, in addition to hardware computation. Both guests analyze how this technology might significantly impact intermediary positions within the workforce and consider the implications on the practice of law.

Computing has evolved through a series of distinct architectural eras: mainframes, distributed computing, client-server, Internet, and cloud. Each offered a new way of organizing information and connecting people. However, the volume (and velocity and variety) of information is exploding. Today a PC generates only about 90 megabytes of network data per day, but in the not too distant future, a connected autonomous vehicle will produce many terabytes and a connected automated factory will generate over a petabyte. We need more than simply new ways to organize and connect to information, we need new ways to uncover the hidden insights within and to harness the full potential of machines.

LONDON Jaguar Land Rover, Ford and Tata Motors are testing connected cars which can communicate with each other using technology designed to speed up journeys and cut accidents, the first such trials in Britain.

Elon Musk has little patience for people who question the safety of Tesla Motors' technologies. The CEO insists that Tesla's Autopilot software and newly unveiled self-driving hardware will make its electric vehicles remarkably safer than conventional cars. Musk bristles during press calls at the mention of Autopilot-related accidents or broader anxieties about abdicating the wheel to a computer. Yet Musk might be undermining his own safety claims by glossing over the cars' vulnerabilities and branding the vehicles as more advanced than they really are, transportation experts told Mashable. SEE ALSO: Elon Musk to the media: 'You're killing people' Consider Wednesday's announcement that all future Model S, X and 3 Teslas will come equipped with Level 5 capabilities.

If there is anything that chip giant Intel has learned over the past two decades as it has gradually climbed to dominance in processing in the datacenter, it is ironically that one size most definitely does not fit all. As the tight co-design of hardware and software continues in all parts of the IT industry, we can expect fine-grained customization for very precise – and lucrative – workloads, like data analytics and machine learning, just to name two of the hottest areas today. Software will run most efficiently on hardware that is tuned for it, although we are used to thinking of that process in a mirror image, where programmers tweak their code to take advantage of the forward-looking features a chip maker conceives of four or five years before they are etched into its transistors and delivered as a product. The competition is fierce these days, and Intel has to move fast if it is to keep its compute hegemony in the datacenter. That is why at the Intel Developer Forum in San Francisco the company put a new path on the Knights family of many-core processors that will see the company deliver a version of this chip specifically tuned for machine learning workloads.

In the past half decade, artificial intelligence and machine learning have made significant leaps into the mainstream and into our daily lives. According to research firm Markets and Markets, the artificial intelligence market is set to grow to 5.05 billion by 2020 thanks to the increased applicability of various AI technologies into everything from finance to healthcare to retail. Today, doctors can diagnose Sepsis with an AI algorithm, for instance, and researchers can track endangered species through AI-enhanced photo capture systems. Clearly, these new self-learning and ever-improving technologies have limitless potential in a number of innovative industries. The U.S. Chamber of Commerce's Technology Engagement Center (C_TEC) recently hosted a panel discussion during its TecNation 2016 event that focused on where we stand with Artificial Intelligence and how it will affect our lives and unlock our potential in the long run.