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There is no shortage of attention lately on the "Internet of Things". As a case in point, see the "Developing Innovation and Growing the Internet of Things Act" or "DIGIT Act", i.e., S. 2607, a bill introduced in the Senate on March 1, 2016 and amended on September 28, 2016, "to ensure appropriate spectrum planning and inter-agency coordination to support the Internet of Things" – A companion bill, H.R. 5117, was introduced in the House of Representatives on April 28, 2016. However, since there is no "internet" dedicated to "things", it is fair to state that the Internet of Things does not exist as such. We are left with a definitional vacuum, but it is hammering the obvious to acknowledge that there is no dearth of attempts around the world to fill the gap. Perhaps as a helpful shortcut, we could view the expression as a metaphor that captures the arrival of almost anything and everything, until now out of scope, into the communications space.

Bayesian inference is a way to get sharper predictions from your data. It's particularly useful when you don't have as much data as you would like and want to juice every last bit of predictive strength from it. Although it is sometimes described with reverence, Bayesian inference isn't magic or mystical. And even though the math under the hood can get dense, the concepts behind it are completely accessible. In brief, Bayesian inference lets you draw stronger conclusions from your data by folding in what you already know about the answer.

Natural Language Processing (NLP) is a messy and difficult affair to handle. Word embeddings/representations – ever since they came in with great work of Mikolov et al, they have been revolutionary to say the least. The concept itself is very intuitive and motivates deeper understanding fora wide range of applications. The main advantage of the distributed representations is that similar words are close in the vector space, which makes generalization to novel patterns easier and model estimation more robust. Distributed vector representation is showed to be useful in many natural language processing applications such as Named Entity Recognition (NER), Word Sense Disambiguation (WSD), parsing, tagging and machine translation.

And as if all of this were not enough, we find ourselves at the dawn of a host of new technologies and applications. Many of us have become accustomed to the artificial intelligence (AI) within our devices. This can take the form of our increasingly accurate GPS systems, the voice recognition ability and immediate response of personal assistants such as Apple's Siri or Amazon's Echo. Now children's toys are being shipped in time for the holiday season with AI built in. From Hello Barbie to Dino the dinosaur, who happens to be connected to IBM's Watson, the world of kid's playthings is being radically reshaped.

Artificial intelligence inside Adobe Creative Cloud could soon help creatives, well, spend more time being creative. At Adobe's 2016 MAX Conference, which starts on November 2, the company announced a new framework for Creative Cloud using artificial intelligence, called Adobe Sensei. Along with the new AI additions, Adobe unveiled several new programs and updates that show where the design software giant is headed in the next several months. Highlights of the company's first-day announcements include enhanced virtual reality tools inside Adobe Premiere Pro, design tools for merging 2D and 3D design, expansion of the Adobe Stock platform, and several other Creative Cloud updates from Adobe Experience Design to Photoshop. Adobe Sensei is expected to take the artificial intelligence that Adobe is already using – like facial recognition and the content-aware tools inside Photoshop, for example – and leverage additional machine learning techniques to automate the more mundane tasks inside the Creative Cloud, Adobe Document Cloud, and Adobe Marketing Cloud.

Machine learning, which is the basis for most commercial artificial-intelligence systems, is intrinsically probabilistic. An object-recognition algorithm asked to classify a particular image, for instance, might conclude that it has a 60 percent chance of depicting a dog, but a 30 percent chance of depicting a cat. At the Annual Conference on Neural Information Processing Systems in December, MIT researchers will present a new way of doing machine learning that enables semantically related concepts to reinforce each other. So, for instance, an object-recognition algorithm would learn to weigh the co-occurrence of the classifications "dog" and "Chihuahua" more heavily than it would the co-occurrence of "dog" and "cat." In experiments, the researchers found that a machine-learning algorithm that used their training strategy did a better job of predicting the tags that human users applied to images on the Flickr website than it did when it used a conventional training strategy.

It wasn't done in a day or a week or over a few months. Almost since the day Apple introduced its voice assistant on October 4, 2011, Siri has undergone an almost continual series of brain transplants that shifted its silicon-powered mind from pure Artificial Intelligence to AI powered, in part, by machine learning. SEE ALSO: Here's how to use apps with Siri in iOS 10 Apple recently shared with me its perspectives on artificial intelligence, where it fits in the Apple ecosystem, which is, apparently, everywhere, and how it can be grown while respective users' privacy. In particular, though, they focused on how the introduction of machine learning transformed its now five-year-old digital assistant. Machine learning is considered a toolset within AI.

IBM Chief Executive Officer Ginni Rometty sees proprietary data combined with artificial intelligence technology as the competitive advantage for companies going forward. When IBM works with clients, it trains a unique version of its artificial intelligence technology, Watson, using proprietary data, and that information creates individual business insights that stay with the customer, Rometty said. That's how Watson is different from its competitors that offer similar services around data analytics and machine learning, she said last week in a speech at the company's World of Watson event in Las Vegas. "We made an important architectural decision for all our clients--all their data, it's their accumulated knowledge," she said. Rometty envisions a future in which all enterprises will add artificial intelligence to help automate business processes, improve productivity and increase sales--and she believes that they're likely to turn to IBM to provide that AI software.

Artificial intelligence can thank Hollywood for its bad rep. Movies like "Ex Machina" and the "Terminator" series conjure up futuristic doomsday scenarios where intelligent machines wreak havoc on humans. However, in real life, humans are already surrounded by artificial intelligence. Amazon and Netflix recommendation engines suggest books or movies based on previous selections, Google Now reroutes drivers around traffic accidents on the commute home, and smartphone digital assistants like Apple's Siri and Microsoft's Cortana answer questions about the weather and sports scores. Brands are using artificial intelligence to build better customer experiences and they're just getting started.