In an earlier blog, "Need for DYNAMICAL Machine Learning: Bayesian exact recursive estimation", I introduced the need for Dynamical ML as we now enter the "Walk" stage of "Crawl-Walk-Run" evolution of machine learning. First, I defined Static ML as follows: Given a set of inputs and outputs, find a static map between the two during supervised "Training" and use this static map for business purposes during "Operation". I made the following points using IoT as an example. Dynamical ML solution involves State-Space data model (more below). What more does a Dynamical ML solution offer?

Microsoft has released an updated version of Microsoft Cognitive Toolkit, a system for deep learning that is used to speed advances in areas such as speech and image recognition and search relevance on CPUs and NVIDIA GPUs. The toolkit, previously known as CNTK, was initially developed by computer scientists at Microsoft who wanted a tool to do their own research more quickly and effectively. It quickly moved beyond speech and morphed into an offering that customers including a leading international appliance maker and Microsoft's flagship product groups depend on for a wide variety of deep learning tasks. "We've taken it from a research tool to something that works in a production setting," said Frank Seide, a principal researcher at Microsoft Artificial Intelligence and Research and a key architect of Microsoft Cognitive Toolkit. The latest version of the toolkit, which is available on GitHub via an open source license, includes new functionality that lets developers use Python or C programming languages in working with the toolkit.

Udacity, the education platform focused on helping workers gain skills they need for great careers in tech, has partnered with IBM Watson, Didi Chuxing and Amazon Alexa to offer a new nanodegree in artificial intelligence, the companies announced today at the IBM World of Watson conference. IBM Watson is co-developing the curriculum of the course with Udacity. Chinese ride-hailing company Didi Chuxing intends to hire students who successfully complete the nanodegree, as does IBM. And Amazon Alexa is serving as an advisor to Udacity in developing the new AI nanodegree. According to Udacity's founder Sebastian Thrun, who previously started Google's innovation shop Google X and its self-driving car initiative, the new AI nanodegree will be for students who already have a level of mastery in software development.

Much of our current understanding about Artificial Intelligence is informed by what we see in popular culture. Due to ignorance and popular cultural portrayals of A.I. and machine learning, many people fear machines that are capable of computing complex tasks. There exists the belief that artificial intelligence could operate in a way that is counter to humanity's best interests, conjuring images of The Terminator and other similar films. As we explore the boundaries of the technology with consumer-facing technology like self-driving cars, and machine-learning algorithms such as recommendation engines, the general public is learning more and more about how these things work. But will we get to a point where people accept AI? A.I. refers to software that models its programming on human behaviour, mimicking rational and logical decision making processes.

AI can be a big boon to HR if we get past problems with data. Last week I attended the #Dreamforce conference, the annual conference of Salesforce.com, while in Chicago the #HRTech Conference was held. At #Dreamforce I watched demonstration of Salesforce.com's new Artificial Intelligence component called Einstein. As I sat their watching what Einstein could do for sales and marketing I was wondering if anyone has anything similar underway in HR. Last year I was at a IBM conference and learned about Watson, IBM's version of AI.

Udacity is partnering with IBM Watson, Didi Chuxing, and Amazon for a new "Nanodegree" program. Artificial intelligence, the machine learning technology that allows "smart" machines to take over human tasks like driving cars or ordering pizza, is quickly becoming the go-to technology for many industries to hire talent for, including health care, auto, and finance. Research firm Markets and Markets estimates the AI market will grow to more than $5 billion by 2020, given the rising adoption of AI across these industries. That's why online education company Udacity is debuting a new way for workers to learn skills needed to be experts in developing artificial intelligence for the likes of IBM and others. Udacity originally launched "Nanodegrees" to train people hoping to land technical jobs, such as software developing.

Clarifai, a startup providing an application programming interface (API) that offers a type of artificial intelligence (A.I.) known as "deep learning," is announcing a $30 million round of funding today. Beyond its core application programming interface (API) for image and video recognition, Clarifai has launched the Forevery photo storage app for iOS and recently introduced Custom Training and Visual Search services. To date, Clarifai has raised $41.25 million, including the $10 million round from last year.

The descendants of Alexa and Siri might come with a surprisingly good sales pitch. I met an early version of such a persuasive chatbot at a tech conference in Pittsburgh recently. After some small talk and jokes, the bot, called Sara, recommended some other people for me to meet. The suggestions were in fact excellent, and if I hadn't just met with them already, I would've followed her lead. Sara was developed by Justine Cassell, director of human-computer interaction at Carnegie Mellon University, who is studying ways for virtual agents to use subtle cues in conversation to build rapport with people and become more effective at conveying information or persuading them to do something.

TensorFlow is a framework created by Google for creating Deep Learning models. Deep Learning is a category of machine learning models that use multi-layer neural networks. The idea of deep learning has been around since 1943 when neurophysiologist Warren McCulloch and mathematician Walter Pitts wrote a paper on how neurons might work and they modeled a simple neural network using electrical circuits. Many, many developments have occurred since then. These highly accurate mathematical models are extremely computationally expensive.

SINCE THE EARLY 1800s, several waves of technological change have transformed how we work and live. Each new technological marvel -- the steam engine, railroad, ocean steamship, telegraph, harvester, automobile, radio, airplane, TV, computer, satellite, mobile phone, and now the Internet -- has changed our home lives, communities, workplaces, schools, and leisure time. For two centuries we've asked whether ever-more-powerful machines would free us from drudgery or would instead enslave us. The question is becoming urgent. IBM's Deep Blue and other chess-playing computers now routinely beat the world's chess champions.