Years ago, Marvin Minsky coined the phrase “suitcase words” to refer to terms that have a multitude of different meanings packed into them. He gave as examples words like consciousness, morality and creativity.

“Artificial intelligence” is a suitcase word. Commentators today use the phrase to mean many different things in many different contexts. As AI becomes more important technologically, economically and geopolitically, the phrase’s use—and misuse—will only grow.

Like all suitcase words, artificial intelligence is notoriously elusive to define precisely. To make headway here, it is helpful to start by trimming one word and considering how to define the term “intelligence”.

There is no one dimension, metric or capability that encapsulates human intelligence. On the contrary, what we reductively call “intelligence” is in fact a constellation of capabilities spanning perception, memory, language skills, quantitative skills, planning, abstract reasoning, decision-making, creativity and emotional depth, among others.

Given that human intelligence is vastly multi-dimensional, it stands to reason that artificial intelligence likewise cannot be reduced to any one specific function or technology. After all, AI is ultimately nothing more than humanity’s effort to replicate its own cognitive capabilities in machines. This leads us to perhaps the best one-sentence definition that we can hope for: AI is best thought of as an entire field of study oriented around developing computing systems capable of performing tasks that otherwise would require human intelligence.

Over the years and to the present, initiatives falling under the broad auspices of artificial intelligence have included computer vision, speech recognition, natural language processing, language translation, manipulation of physical objects, navigation through physical environments, logical reasoning, game-playing, prediction, long-term planning and continuous learning, among many others.

An important related point is that the definition of artificial intelligence is a moving target. Practitioners refer to this phenomenon (sometimes with frustration) as the “AI effect”. In general, society finds it most natural to label a given capability as “AI” only when it has not yet been solved. Once researchers prove that a machine can accomplish a given feat, that feat begins to seem too pedestrian to be “real AI”. This has played out over and over again in recent years, with activities including language translation, chess, driving and Go.

As roboticist Rodney Brooks put it, “Every time we figure out a piece of [AI], it stops being magical; we say, ‘Oh, that’s just computation.’” Douglas Hofstadter summed it up even more succinctly: “AI is whatever hasn’t been done yet.”

The term “artificial intelligence”, then, defies straightforward definition by its very nature. No clear boundaries exist between AI and less exalted pursuits like statistics or computation. The entire inspiration for the field of AI is human intelligence, and just like human intelligence, artificial intelligence is multi-dimensional and cross-disciplinary. It is also dynamic across time: what industry observers consider “true AI” tends to be those capabilities that at that point lie just beyond the bleeding edge.

This definitional complexity inevitably invites overuse and abuse of the term—from entrepreneurs looking to attract venture capital, from reporters looking to attract clicks, from politicians looking to attract attention.

Yet in spite of all the hype, the term “artificial intelligence” remains useful, even essential (like all suitcase words). Even if its boundaries are blurry, there is value in having an overarching term to encompass and unify this family of conceptually related efforts. It facilitates communication and keeps us from getting bogged down every time we seek to discuss the topic.

Perhaps the most noteworthy difference between artificial intelligence and human intelligence is that there is no discernible upper bound to AI. Rather, its boundaries continue to expand with every passing year. No one knows with certainty where this will lead. Alan Turing, one of the first and greatest AI thinkers, had a thought-provoking point of view. “It is customary to offer a grain of comfort, in the form of a statement that some peculiarly human characteristic could never be imitated by a machine,” Turing said in 1951. “I cannot offer any such comfort, for I believe that no such bounds can be set.”

The post What Does “Artificial Intelligence” Really Mean? appeared first on Artificial Intelligence.

Artificial intelligence has transformed pretty much every industry in which it’s been embraced, including healthcare, the stock markets, and, increasingly, cybersecurity, where it’s being utilized to both enhance human work and strengthen defenses. As a result of recent improvements in machine learning, the dreary work that was once done by people, filtering through apparently unlimited amounts of information searching for threat indicators and anomalies would now be able to be automated. Present day AI’s ability to understand threats, risks and relationships enable it to sift through a generous amount of the noise burdening cybersecurity divisions and surface just the pointers destined to be legitimate.

Indeed, even as AI innovation changes some aspects of cybersecurity, the crossing point of the two remains significantly human. In spite of the fact that it’s maybe unreasonable, humans are upfront in all pieces of the cybersecurity triad: the terrible actors who look to do hurt, the gullible soft targets, and the great on-screen characters who retaliate.

Indeed, even without the approaching phantom of AI, the cybersecurity war zone is frequently hazy to average users and the technologically savvy alike. Including a layer of AI, which contains various innovations that can likewise feel unexplainable to many people, may appear to be doubly unmanageable as well as indifferent. That is on the grounds that in spite of the fact that the cybersecurity battle is once in a while profoundly personal, it’s once in a while pursued face to face.

With an expected 3.5 million cybersecurity positions expected to go unfilled by 2021 and with security ruptures increasing some 80% every year, infusing human knowledge with AI and machine learning tools gets critical to shutting the talent availability gap.

That is one of the recommendations of a report called Trust at Scale, as of late released by cybersecurity organization Synack and citing job and breach data from Cybersecurity Ventures and Verizon reports, individually. Indeed, when ethical human hackers were upheld by AI and machine learning, they became 73% increasingly proficient at identifying and evaluating IT risks and threats.

The advantages of this are twofold: Threats never again slip through the cracks because of fatigue or boredom, and cybersecurity experts are liberated to accomplish more strategic tasks, for example, remediation. Artificial intelligence can likewise be utilized to increase perceivability over the network. It can examine phishing by simulating clicks on email links and analyzing word choice and grammar. It can monitor network communications for endeavored installation of malware, command and control communications, and the presence of suspicious packets. What’s more, it’s changed virus detection from an exclusively signature-based framework which was entangled by issues with reaction time, proficiency, and storage requirements to the period of behavioral analysis, which can distinguish signatureless malware, zero-day exploits, and previously unidentified threats.

In any case, while the conceivable outcomes with AI appear to be unfathomable, the possibility that they could wipe out the role of people in cybersecurity divisions is about as unrealistic as the possibility of a phalanx of Baymaxes supplanting the nation’s doctors. While the ultimate objective of AI is to simulate human functions, for example, problem-solving, learning, planning, and intuition, there will consistently be things that AI can’t deal with (yet), as well as things AI should not handle.

The principal classification incorporates things like creativity, which can’t be viably instructed or customized, and therefore will require the guiding hand of a human. Anticipating that AI should viably and reliably decide the context of an attack may likewise be an unconquerable ask, at any rate for the time being, just like the idea that AI could make new solutions for security issues. At the end of the day, while AI can unquestionably add speed and exactness to tasks generally handled by people, it is poor at extending the scope of such tasks.

As it were, AI’s impact on the field of cybersecurity is the same as its effect on different disciplines, in that individuals frequently terribly overestimate what AI can do. They don’t comprehend that AI often works best when it has a restricted application, similar to anomaly detection, versus a broader one, like engineering a solution to a threat. In contrast to people, AI needs inventiveness. It isn’t inventive. It isn’t cunning. It regularly neglects to consider context and memory, leaving it incapable to decipher occasions like a human mind does.

In a meeting with VentureBeat, LogicHub CEO and cofounder Kumar Saurabh showed the requirement for human analysts with a kind of John Henry test for automated threat detection. “A few years ago, we did an examination,” he said. This included arranging a specific amount of information, a trifling sum for an AI model to filter through, yet a sensibly huge sum for a human analyst to perceive how teams utilizing automated frameworks would pass against people in threat detection.

The eventual fate of cybersecurity will be loaded with threats we can’t consider today. Yet, with vigilance and hard work, the blend of man and machine can do what neither can do alone, structure an integral team equipped for upholding order and fighting the forces of evil.

The post THE COMBINATION OF HUMANS AND ARTIFICIAL INTELLIGENCE IN CYBER SECURITY appeared first on Artificial Intelligence.