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) …
At HPE Discover 2019, TechRepublic's James Sanders spoke with Aruba's Larry Lunetta about what is needed to deploy AI in security products. The following is an edited transcript of the interview. Larry Lunetta: So the AI washing is absolutely true--and you can reach back to the late '90s, it was web washing, right, as the internet became popular and then roll forward, it's cloud washing. There's a set of waves that the marketers ride, and AI is certainly one now. The interesting thing is as you go through those phases, the technologies get tougher and tougher to actually execute.
Recently I visited with the cybersecurity teams at NTT Communications, British Telecom (BT), and DBS Bank. Each has mature, useful and metrics-driven security solutions. Some of the subtleties of its threat management program are pretty amazing; it feels it can identify characteristics of not only groups of attackers, but actual individuals. BT has an incident response capability that is second to none, driven partly by its interest in combining red team and blue team tactics. These two security teams carefully hone their incident response steps and techniques.
Moore's Law, advocated by Gordon Moore of Intel fame, says that the computational capabilities will double every 18 to 24 months. And we've seen that really unfolding over the last 30 years (see chart). It's really stoked people's imagination, so much so that many believe that the promise of artificial intelligence (AI) could become reality, and computers could actually learn to think like humans. I believe it's still a number of years away, but it is fueling a lot of hype regarding AI. What it's truly capable of, where it can be effective, and what it takes to implement it, all of which have become somewhat inflated in the market today.
Well, what natural language processing that we use does is it actually goes in and identifies what's the thing that this it actually about. So, it's keeping track as it reads through and says, "Okay, I recognize that there a few things "that we're talking about here, "here are some ideas that are very similar." And it pulls all that together and says… "Okay, at the end, I think that this was really "about these three or four topics. "Like, that's what I'm going to put in front of you "and say this is what this piece "of content was actually about." And, so that's what we use the natural language processing for.
Anne is finishing up her work week with a few last-minute emails. She confirms a client meeting for next Wednesday, then replies to a lead, promising to send a proposal before the end of next week. The following Wednesday, Anne receives an Outlook alert that her meeting is in one hour, and traffic is a bit backed up. She should leave within the next five minutes to arrive on time. The next morning, another alert reminds her to finish up that proposal she promised to send.
There has been a lot of hype around AI to the point where some people are simply tuning it out. I think this is a mistake. While there are limits to what AI can do, there also are sophisticated attacks that we'd miss without it. The need for AI is driven by three fundamental yet significant changes in the enterprise computing environment. Taking all of these factors together leads me to believe that AI is not only a viable solution, but it may be the only solution.
Machine Learning (ML), the subset of artificial intelligence, is gaining fresh momentum. Powerful and affordable computational processing, growing volumes of huge data sets, affordable data storage options and the ability to automatically apply complex mathematical calculations to big data faster than before are the factors responsible for the resurging interest. ML is basically the idea of training machines to recognize patterns in data and apply them to particular problems. The iterative aspect of machine learning is important because when models are exposed to new data, they are able to independently adapt. They learn from previous computations and predictions to produce reliable, repeatable decisions and results with minimal or no human intervention.
Artificial intelligence is allowing us all to consider surprising new ways to simplify the lives of our customers. As a product developer, your central focus is always on the customer. But new problems can arise when the specific solution under development helps one customer while alienating others. We tend to think of AI as an incredible dream assistant to our lives and business operations, when that's not always the case. Designers of new AI services should consider in what ways and for whom might these services be annoying, burdensome or problematic, and whether it involves the direct customer or others who are intertwined with the customer.
Given our focus on the systems-level of AI machine building, storage was a big topic of discussion at the sold-out Next AI Platform event we hosted in May. It was difficult to leave out where NVMe over fabrics and other trends are fitting into AI training systems in particular, so we asked distributed NVM-Express flash storage upstart Excelero, which is a pioneer in creating pools of flash storage that look and behave as if they are directly attached storage for a server's applications, what is it about AI workloads that makes storage a challenge. The answer, according to Josh Goldenhar, vice president of products at Excelero, is revealed in some basic feeds and speeds that show the imbalance in many machine learning systems, either for training or inference. "The answer is pretty straightforward," explains Goldenhar. "If you look at the specs of the latest Nvidia DGX-2 and take the aggregate performance across the cards, the cards themselves can directly process from the memory 14 TB/sec, which is an amazing number. Even though that is an amazingly huge number, we have to count how the cards are hooked into NVLink and PCI-Express, and all of that is x16 to the server, when you add all of that up, it is actually around 256 GB/sec, and that is a considerably lower number. But that is still so much more than has been put into the box."
Many bloggers get very frustrated after they have been working for a couple of months. They initially are excited about the possibility of making a six-figure stream of passive income. After they get started though, they discovered that the legwork can be overwhelming. The good news is that machine learning is making it much easier for them to create a successful blogging career, as Jeff Bullas points out. One of the ways that machine learning has helped the most is with helping bloggers find profitable longtail keywords.