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.

AI developers are making amazing advances. Witness the excitement around AI's progress in search, cancer diagnosis, genomic medicine, autonomous vehicles, Go, smart homes, machine translation, and even lip reading. Progress in such complex problems raises hopes for the development of general-purpose AI that can be deployed in a wide range of intelligent, open-ended interactions with people like computer interface, customer service, planning and advice. It is easy to imagine an enhanced Apple Siri or Amazon Alexa that engages in conversations with people to answer questions, fulfill commands and even anticipate needs. In fact, unless you watch marketing videos with a very critical eye (like the latest one for Alexa shown below), you might even believe that AI has already reached this point.

AI can improve customer personalization, identify patterns or connections that a human couldn't find, and answer questions about banking relationships in real-time. Firms are already recording amazing success with AI in banking. What is amazing today, however, will be table stakes in the near future. Artificial intelligence (AI) is not new to banking. If we consider that the definition of AI is the ability for machines to interact and learn to do tasks previously done by humans, the history of AI goes back to the 50s in the banking industry.

We're unofficially past the "Happy New Year" stage of 2017; that new year smell has almost entirely worn off, people are back from their sunny vacations in the Caribbean, and many are hard at work. A lot of enterprise tech trends have been predicted to establish themselves this year, but none is as buzzy as artificial intelligence. It has been on the horizon for some time, but 2017 is poised to be the first time that bots at work are natural parts of our everyday workflow. Don't be mistaken, AI has a ways to go, and could accelerate at an unpredictable pace as bots gather more data. But people's hesitancy about committing to bots at work, and questions around the effectiveness of these tools, will gradually melt away.