If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
About 4,000 people listened to Cuban as he kicked off his shoes--literally--and explained how AI will change the game for companies, educators, and future developments. He's also keeping his eyes peeled for smaller companies in machine learning and AI, and already has at least three companies in his investment portfolio. "[Software writing] skill sets won't be nearly as valuable as being able to take a liberal arts education … and applying those [skills] in assisting and developing networks." But in order for the country to advance to that future, AI and robotics need to become core competencies in the U.S., and not just in the business world, Cuban said.
The forecast is not all gloomy – artificial intelligence (AI), machine learning (ML) and automation are also expected to create jobs that will likely be much more interesting and creative than the repetitive tasks of the industrial age. According to Andrew McAfee, principal research scientist at MIT and co-director of the university's Initiative on the Digital Economy (IDE), AI amounts to, "the largest disruption in labor and the way we work," in generations. But as Joi Ito, director of the MIT Media Lab and moderator of a panel titled, "Putting AI to Work," put it, the fear that machines will become smarter than humans and take over the world is tempered by the reality that "they're stupid and they've already taken over the world." Seth Earley, CEO of Earley Information Science, while agreeing there will be, "an enormous amount of disruption," from AI, was more optimistic about retraining for the jobs of the future.
With the rise of cloud-based apps and the proliferation of mobile devices, information security is becoming a top priority for both the IT department and the C-Suite. Businesses ranging from startups to large corporations are increasingly looking to new technologies, like artificial intelligence (AI) and machine learning, to protect their consumers. For cybersecurity, AI can analyze vast amounts of data and help cybersecurity professionals identify more threats than would be possible if left to do it manually. But the same technology that can improve corporate defences can also be used to attack them.
With the rise of cloud-based apps and the proliferation of mobile devices, information security is becoming a top priority for both the IT department and the C-Suite. Businesses ranging from startups to large corporations are increasingly looking to new technologies, like artificial intelligence (AI) and machine learning, to protect their consumers. For cybersecurity, AI can analyze vast amounts of data and help cybersecurity professionals identify more threats than would be possible if left to do it manually. But the same technology that can iimprove corporate defences can also be used to attack them.
It's because of the concept's many possibilities: object recognition in pictures and videos, anticipating cybersecurity threats, finding specific kinds of people amid thousands or millions in a data set. There's also a fundamental need for all AI algorithms: training. They all need to run data to learn what it is they're looking for. What if there isn't enough data to make the algorithms as good as they need to be? Or what if it takes too long to collect and prepare that data?
While technological advances say they are on the brink of achieving that perfect artificial intelligence, we are not quite there yet. Fortunately for us, an AI does not need to be irreproachable, just better than a human. Take connected cars, for instance. An AI-based driver may not be mistake-proof, but it is certainly less imperfect than a human driver. This is very much the case in cybersecurity where IT experts are changing the rules of the game using Machine Learning.
I've been watching this space for some time now, and I continue to be bullish on the prospects of ML/AI in the healthcare industry. Here, I'm going to write about my views on The Why of ML/AI, some examples of The Who in this space, and, finally, some thoughts on The How these practices are going to disrupt processes in healthcare. Oh, and I'll also provide some thoughts on the infrastructure that's needed to make this all happen, because, those who know me will know my thoughts on infrastructure, viz. Let's start by looking at this clever map of the most well-funded AI startups in each state. Upon tallying the results, you'll note that 21% of the most well-funded AI startups across the US focus exclusively on healthcare.
It's common knowledge that healthcare organizations are prime – and relatively easy – targets for ransomware attacks. So it is no surprise that those attacks have become rampant in the past several years. The term "low-hanging fruit" is frequently invoked. But according to at least one report, and some experts, it doesn't have to be that way. ICIT – the Institute for Critical Infrastructure Technology – contends in a recent whitepaper that the power of artificial intelligence and machine learning (AI/ML) can "crush the health sector's ransomware pandemic."
Machine learning (ML) is routinely cited by post-truth vendors as their biggest selling point, their main advantage. But ML – if it's done properly – comes with problems and limitations. ESET has spent years perfecting automated detections, our name for ML in the cybersecurity context. Here are some of the biggest challenges we have observed and overcome in the course of implementing this technology in our business and home solutions. First, to use machine learning you need a lot of inputs, every one of which must be correctly labeled.
The problem with this simple AI algorithm is that it is easily bypassed by new and unknown threats. As such, more sophisticated mechanisms have been designed to complement it, including behavioral analysis. In this method, the file's actions are inspected, rather than its appearance. Continuing our horse analogy, imagine standing outside a horse training ground. Although you can't see the horse, you can hear it running and smell manure, so your mind will conclude that there's a horse inside.