When it comes to staying on top of security threats, machine learning, unquestionably, must be part of the equation. The volume of data is simply too great to cope without it. But as it's currently being used, ML may be doing more harm than good, particularly when it comes to alarm fatigue. Alarm fatigue is a condition that occurs when an operator is overloaded with alarms; in many of these cases, the majority of alarms turn out to be false positives. With too many alarms to investigate in a limited amount of time–and the knowledge that most of them are false positives–the operator begins to ignore some alarms, which invariably leads to bad outcomes.

We all know what a professional is--or do we? For years, ACM has proclaimed that its members are part of a computing profession. But is it really a profession? Many people describe themselves as "professionals" in the colloquial sense of being paid to perform some specialized skill. Yet, only a few occupations are regarded as professions in the legal sense.

When we assess pros and cons in decision making, we weigh losses more than gains (Kahneman and Tversky (1979)). We are more frightened by a snake or a spider than by a passing car or an electrical shuffle. Such human assessments are qualified of biases, because they depart from physical measurements or objective statistical estimates. Thus, there is "bias" when a behavior is not aligned with a given "rationality benchmark" (like expected utility theory), as documented in the "heuristics and biases" literature (Kahneman et al. (1982); Gilovich et al. (2002)). However, if such biases are found consistently in human behavior, they must certainly have a reason. Some scholars (see (Gigerenzer (2004, 2008); Hutchinson and Gigerenzer (2005))) claim that those"so-called bias" were in

LinkedIn cofounder and Greylock Partners investor Reid Hoffman tells executives who are running startups that scale fast -- the kind who want to double in size every few months -- to build ethics into their businesses. As companies plan for the future and grow their engineering or sales ranks, they should consider what can go wrong, he said, and hire people whose job is dedicated to risk management. Next, he added, companies can develop a risk framework to sort risk levels. Anything that can be a catastrophic risk to individuals, a systemic risk to company systems, or a risk to a large number of users should be handled in a proactive way to stay competitive with other startups. Hoffman, who coauthored the book Blitzscaling, joined former White House chief data scientist DJ Patil and Stanford University political science professor Amy Zegart Tuesday at the Stanford Human-Centered AI Intelligence (HAI) fall conference on AI ethics, governance, and policy symposium at the Hoover Institution in Palo Alto.

Bias is everywhere in our society, it is well documented and when it comes to equality most people are in agreement that it's not a force for good. But bias can be useful. For instance, when we make the decision to not step in front of a bus, which most people are biased towards. However, when bias unfairly disadvantages one group over another, such as gender bias or racial bias, however, it's going to make the headlines in a bad way. This article isn't about that, but I wanted to put into context that bias itself is not good or bad, it is simply a decision (in the case of humans) made one way or another based on experience.

There is a mountain of hype around big data, artificial intelligence (AI), and machine learning. It's a bit like kissing in the schoolyard – everyone is talking about it, but few are really doing it, and nobody is doing it well (shoutout to my friend Steve Totman at Cloudera for that line). There is certainly broad consensus that organizations need to be monetizing their data. But with all the noise around these new technologies, I think many business leaders are left scratching their heads about what it all means. Given the huge diversity of applications and opinions on this topic, it may be folly, but I'd like to attempt to provide a practical, useful definition of artificial intelligence.

The short answer is yes. Most economists think the answer is no, because in the past automation hasn't caused lasting unemployment. They call it the Luddite Fallacy because the Luddites, the people who went around smashing up weaving machines during the Industrial Revolution, were wrong about the effect of automation – at least to the extent that they were making a broad economic argument. I think the economists are guilty of the Reverse Luddite Fallacy, which is to say that because automation hasn't caused lasting unemployment in the past it can't do so in the future. It's different this time because in previous rounds of unemployment machines have replaced our muscle jobs while in future rounds they're going to replace our cognitive skills.

The short answer is yes. Most economists think the answer is no, because in the past automation hasn't caused lasting unemployment. They call it the Luddite Fallacy because the Luddites, the people who went around smashing up weaving machines during the Industrial Revolution, were wrong about the effect of automation – at least to the extent that they were making a broad economic argument. I think the economists are guilty of the Reverse Luddite Fallacy, which is to say that because automation hasn't caused lasting unemployment in the past it can't do so in the future. It's different this time because in previous rounds of unemployment machines have replaced our muscle jobs while in future rounds they're going to replace our cognitive skills.

Elon Musk has a well-documented fear of evil artificial intelligence, so it's no surprise filmmaker Werner Herzog sought him out for Lo and Behold: Reveries of the Connected World. Instead, the tech mogul is worried about A.I. that does whatever it takes to accomplish its task. "The biggest risk is not that A.I. will develop a will of its own," Musk says in a short clip of the new film obtained by Fortune. "But rather it will follow the will of its utility function or optimization function." Herzog, the man who made Grizzly Man, is more attuned to nature than future technologies.