MENLO PARK, Calif.--If you've never seen a robot climb straight up an entirely flat vertical wall, I dare you not to be impressed the first time you do. That was my certainly experience when I watched a wall-climbing robot do its thing at SRI International here the other day. Indeed, my host, who had been with me through several product and project demonstrations over about three hours, noticed how excited I was by watching this little device go straight up the wall, and, I think, began to wonder if I was actually interested in any of the other things I'd seen. In fact, she shouldn't have worried. I was at SRI as part of my ongoing Road Trip at Home series and was getting a rapid-fire lay of the land at this Silicon Valley research and development--and incubation--powerhouse.

At a panel discussion at the Consumer Electronics Show this week, Mike Masnick of TechDirt noted that we typically don't recognize disruptive technologies until after the fact. He's probably right, but sometimes you really can see a technology rocking an industry in real time. These aren't new technologies, but judging from CES, they are finally poised to metastasize. Microsoft's Kinect motion sensors, of which more than 18 million have been sold, have prompted industries far removed from video games to rethink how people will use their products and services. Similarly, Apple's Siri virtual assistant has taught manufacturers and software developers that voice recognition has moved beyond recognition and into comprehension.

For three decades, Mr. Phillips had focused on writing software to allow computers to understand human speech. In 2006, he had co-founded a voice recognition company, and eventually executives at Apple, Google and elsewhere proposed partnerships. Mr. Phillips's technology was even integrated into Siri itself before the digital assistant was absorbed into the iPhone. But in 2008, Mr. Phillips's company, Vlingo, had been contacted by a much larger voice recognition firm called Nuance. "I have patents that can prevent you from practicing in this market," Nuance's chief executive, Paul Ricci, told Mr. Phillips, according to executives involved in that conversation. Mr. Ricci issued an ultimatum: Mr. Phillips could sell his firm to Mr. Ricci or be sued for patent infringements. When Mr. Phillips refused to sell, Mr. Ricci's company filed the first of six lawsuits. Soon after, Apple and Google stopped returning phone calls. The company behind Siri switched its partnership from Mr. Phillips to Mr. Ricci's firm. And the millions of dollars Mr. Phillips had set aside for research and development were redirected to lawyers and court fees. When the first lawsuit went to trial last year, Mr. Phillips won.

Voice-activated assistants are playing an increasingly prominent role in the technology world, with Apple's introduction of Siri for the iPhone 4S and Google's (rumored) work on a Siri competitor for Android phones. Voice-activated technology isn't new--it's just getting better because of increasingly powerful processors and cloud services, advancements in natural language processing, and improved algorithms for recognizing voice. We spoke with Nuance Communications, maker of Dragon software and one of the biggest names in voice recognition technologies, about why voice is becoming more popular and what advancements we can expect in the future. Peter Mahoney, Nuance chief marketing officer and general manager of the Dragon desktop business, told Ars one of the most significant improvements coming in the next few years is a far more conversational voice-activated assistant that remembers everything you say. This should create better responses to casual questions.

There was much ado about artificial intelligence (AI) platforms in 2016. Major developments and offerings came out of Microsoft (Cognitive Services), Google (TensorFlow), Amazon (Rekognition, Polly, Lex), IBM (Watson), Salesforce (Einstein), and many more. AI and machine learning (ML) are the hammers that turn just about every business' data problem into a nail. But if 2016 was the year of the platform, 2017 will be the year of data. Looking at AI through the lens of platforms is a bit like looking through the wrong end of a set of binoculars.

When I was a young boy I saw the classic movie "2001 A Space Odyssey" with HAL, the voice interactive computer system that bordered on AI, and that sparked in me, a lifelong interest and career in IT. Today we are seeing devices that are starting to provide the beginnings of that same functionality like the Amazon Echo, Dot, or Google Home. It's one thing to sit in your living room and call out to the air "Alexa, how old is Matt Damon" or "Alexa, play'The Logical Song' by Supertramp", it's another when your 6 year old is having a conversation with Alexa and orders a bunch of things from Amazon and it is quite another when you are trying to find ways to use it in the office to make your company more productive. The difference between Alexa and HAL is pretty dramatic, but at the core of them both, and AI in general, is Machine Learning. As Guy Levy-Yurista, Sisense Head of Product, described in this recent blog post: "Sisense employs machine learning as a core element of its In-Chip data processing algorithms….We call it query recycling – breaking queries into smaller blocks that are later reassembled to answer future queries: if user A asks a completely new question such as'what was our average deal size last year?' and user B later asks'what is our year-over-year growth in sales?', This isn't OLAP, it is a learning algorithm that grows smarter and more efficient over time and as more unique queries accumulate. It learns to identify the reusable chunks within each query, and to use these as a knowledge base for future reference".

"Hey, Alexa"--a phrase that millions of people call out at home just before telling Amazon their desires at that moment. All those people asking Alexa to order kitchen supplies, turn on the lights, or play music gives Amazon a valuable stockpile of data that it could use to fend off competitors and make breakthroughs in what voice-operated assistants can do. "There are millions of these in households, and they're not collecting dust," Nikko Strom, a speech-recognition expert and founding member of the team at Amazon that built Alexa and Echo, said at the AI Frontiers conference in Santa Clara, California, last week. "We get an insane amount of data coming in that we can work on." Strom said that data had already helped the company make progress on a longstanding challenge in speech recognition known as the cocktail party problem, where the challenge is to pick out a single voice from a hubbub of many people talking.

The First Amendment may protect speech by strong Artificial Intelligence (AI). In this Article, we support this provocative claim by expanding on earlier work, addressing significant concerns and challenges, and suggesting potential paths forward. This is not a claim about the state of technology. Whether strong AI -- as-yet-hypothetical machines that can actually think -- will ever come to exist remains far from clear. It is instead a claim that discussing AI speech sheds light on key features of prevailing First Amendment doctrine and theory, including the surprising lack of humanness at its core.

Summary: There are five basic styles of recommenders differentiated mostly by their core algorithms. You need to understand what's going on inside the box in order to know if you're truly optimizing this critical tool. In our first article, "Understanding and Selecting Recommenders" we talked about the broader business considerations and issues for recommenders as a group. In this article we'll cover the five basic types of recommenders and their strengths and weaknesses. Given that Recommenders add 10% to 25% of incremental income to your ecommerce business you need to know exactly how these are working.

From artificial intelligence (AI) that can recognize faces, choose music for you based on your mood, and even drive your car, the technology has come a long way in recent years. Although the concept of intelligent machines has been around since ancient history as part of Greek mythology, only in recent decades has the scientific community made significant advances in practical uses of AI. Consumer applications for AI only took off in the past few years, with the introduction of Amazon's Alexa, the AI that resides in the handy little Bluetooth speaker, Echo. The business world, too, is just starting to explore AI's possible applications in big data analysis for customer relationship management, marketing, and more. Most of the world's tech giants are taking note, exemplified through both internal investments in AI research and development and the acquisition of AI startups.