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.

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.

According to Elon Musk, driverless car technology is a problem that's pretty much solved -- the regulators just need to catch up. And they might want to start moving faster, because Musk isn't slowing down. The chief executive of electric car maker Tesla said Wednesday that all the cars the company produces going forward will be equipped with the hardware needed to transform them into self-driving cars, as soon as the software and road rules are ready. On Thursday, the company posted a video with a Rolling Stones soundtrack that shows a Tesla Model S driving itself around highways and streets in Silicon Valley, pulling into a Tesla parking lot, searching for a spot and parking itself. It even spins its front wheels to the left so the passenger-side tire properly kisses the curb.

Verdigris, a San Francisco, CA-based provider of an artificial intelligence and IoT platform for smart buildings, raised an additional 6.7m in Series A funding. The round was led by Jabil – which also plans to roll out Verdigris' AI energy sensor platform in a number of its largest manufacturing sites – with participation from Verizon Ventures, Stanford StartX Fund and existing angels. The company, which has raised 16m in total funding to date, plans to use the funds to scale manufacturing and customer operations. Led by co-founder and CEO Mark Chung, Verdigris is an artificial intelligence and IoT platform which combines proprietary hardware sensors, machine learning, and software to make buildings smarter and more connected while reducing energy consumption and costs. The software produces reports including energy forecasts, alerts about faulty equipment, maintenance reminders, and energy usage information for each and every device and appliance.

Israeli startup Faception made headlines this year by claiming it could predict how likely people are to be terrorists, pedophiles, and more by analyzing faces with deep learning. Experts and research in the field, however, suggest that it is more fantasy than reality. Faception assigns ratings after training artificial intelligence on faces of terrorists, pedophiles, Mensa members, professional poker players, and more. Through deep learning--that emerging technique found in everything from Alpha Go to Siri to Netflix--the AI can supposedly predict how likely a new face is to belong to any given group. While this may sound believable, there's no evidence that face-based personality predictions are more than a tiny bit accurate.