Artificial intelligence has captured public imagination, dominated media coverage, and driven furious volumes of investment and acquisition activity. In the midst of this hype cycle, spotting the difference between phony wannabes and true investments can be a challenge. We interviewed seasoned VCs from top firms like CRV, IA Ventures, Two Sigma, and more to find how these successful investors evaluate artificial intelligence startups. If you're a founder thinking of starting an artificial intelligence company, be sure to have solid answers for all of these key questions. "Many companies who can't raise money try to shoehorn themselves as AI companies," warns Varun Jain of Qualcomm Ventures.

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Education has mostly followed the same structure for centuries -- e.g., the "sage on a stage" and "assembly line" models. As AI continues to disrupt industries like consumer electronics, ecommerce, media, transportation, and healthcare, is education the next big opportunity? Given that education is the foundation that prepares people to pursue advancements in all the other fields, it has the potential to be the most impactful application of AI. The three segments of the education market -- K-12, higher education, and corporate training -- are going through transitions. In the K-12 market, we are seeing the effect of the newer, more rigorous academic standards (Common Core, Next Generation Science Standards) shifting the focus toward measuring students' critical thinking and problem-solving skills and preparing them for college and career success in the 21st century.

Any video gamer knows how boring NPCs (non-playable characters) in digital worlds are. Their behavior is simple and predictable and their words entirely scripted by a staff of writers. This makes them uninteresting opponents and unsatisfying companions. We're far more likely to emotionally attach to lifelike characters, like the emo robot sidekicks in the Star Wars franchise, but crafting believable, autonomous entities you can actually interact with is no easy feat. Character models built by artificial intelligence aim to break out of the uncanny valley and imbue inanimate objects and digital characters with an aura of realism and life.

First it was checkers (draughts to you and me), then chess, then Jeopardy!, then Go and now poker. One after another, these games, all of which require significant amounts of intelligence and expertise if they are to be played well, have fallen to the technology we call artificial intelligence (AI). And as each of these milestones is passed, speculation about the prospect of "superintelligence" (the attainment by machines of human-level capabilities) reaches a new high before the media caravan moves on to its next obsession du jour. Never mind that most leaders in the field regard the prospect of being supplanted by super-machines as exceedingly distant (one has famously observed that he is more concerned about the dangers of overpopulation on Mars): the solipsism of human nature means that even the most distant or implausible threat to our uniqueness as a species bothers us. The public obsession with the existential risks of artificial superintelligence is, however, useful to the tech industry because it distracts attention from the type of AI that is now part of its core business. This is "weak AI" and is a combination of big data and machine-learning – algorithms that ingest huge volumes of data and extract patterns and actionable predictions from them.

You might not expect to find a player named Libratus around a poker table in a high-stakes game of no-limit Texas Hold'em. Yet it was Libratus – an artificial intelligence (AI) – that emerged triumphant from a gruelling 20-day tournament that culminated late last Monday in a dramatic victory over four of the world's top players. The victory – which saw Libratus pocket $1.7m in fake chips at the expense of the quartet of serious pros – stunned the generally unshockable world of poker. But more than that, it reopened the increasingly urgent debate about the potential – and possible dangers – of AI, or intelligent machines. If machines are clever enough to beat humans at a game that requires intuition, bluffing skills, intelligence as well as a capacity to retain data – then what else is possible?

A major change is coming, over unknown timescales but across every segment of society, and the people playing a part in that transition have a huge responsibility and opportunity to shape it for the best. What will trigger this change? Recently, some of the top minds in AI and related fields got together to discuss how we can ensure AI remains beneficial throughout this transition, and the result was the Asilomar AI Principles document. The intent of these 23 principles is to offer a framework to help artificial intelligence benefit as many people as possible. But, as AI expert Toby Walsh said of the Principles, "Of course, it's just a start. The Principles represent the beginning of a conversation, and now that the conversation is underway, we need to follow up with broad discussion about each individual principle. The Principles will mean different things to different people, and in order to benefit as much of society as possible, we need to think about each principle individually. As part of this effort, I interviewed many of the AI researchers who signed the Principles document to learn their take on why they signed and what issues still confront us. Today, we start with the Value Alignment principle. Value Alignment: Highly autonomous AI systems should be designed so that their goals and behaviors can be assured to align with human values throughout their operation. Stuart Russell, who helped pioneer the idea of value alignment, likes to compare this to the King Midas story. When King Midas asked for everything he touched to turn to gold, he really just wanted to be rich. He didn't actually want his food and loved ones to turn to gold. We face a similar situation with artificial intelligence: how do we ensure that an AI will do what we really want, while not harming humans in a misguided attempt to do what its designer requested? "Robots aren't going to try to revolt against humanity," explains Anca Dragan, an assistant professor and colleague of Russell's at UC Berkeley, "they'll just try to optimize whatever we tell them to do.

This is a guest post written by Scott Middleton, founder and CEO of stratejos as well as part-time sausage maker. Will you still be doing your job in 5-10 years or will a robot do it for you? This is a question knowledge workers have started asking themselves as AI is becoming more capable and widely adopted. Atlassian co-founder and CEO, Mike Cannon-Brookes, has said AI will play a major role in the future of team productivity. Bank of America Merrill Lynch predicts that AI will have a $9 trillion impact on knowledge work over the coming decade.

These and many other insights are from the McKinsey Global Institute's study The Age of Analytics: Competing In A Data-Driven World published in collaboration with McKinsey Analytics this month. You can get a copy of the Executive Summary here (28 pp., free, no opt-in, PDF) and the full report (136 pp., free, no opt-in, PDF) here. Five years ago the McKinsey Global Institute (MGI) released Big Data: The Next Frontier For Innovation, Competition, and Productivity (156 pp., free no opt-in, PDF), and in the years since McKinsey sees data science adoption and value accelerate, specifically in the areas of machine learning and deep learning. The study underscores how critical integration is for gaining greater value from data and analytics. Posted in Featured Posts, Technology / Software Tagged analytics, enosix, enosix integration, enosiX Salesforce integration, enosix SAP integration, Louis Columbus' blog, machine learning, McKinsey 2016 Study, McKinsey Analytics, McKinsey Global Institute', McKinsey's 2016 Analytics Study