Software's immersion within businesses, process and objects has expanded the scope of what could be automated. When you add in rapid advances in artificial intelligence, you start seeing a lot more interest in technologies that are increasing how well machine sense, learn and act. Investors are taking notice--investments in artificial intelligence start-ups by venture capitalists have increased roughly 20x in the last four years. What's your Intelligent Automation play?

Editors at the MIT Technology Review recently weighed in with their annual review of the world's 50 Smartest Companies. This list celebrates the most effective pairing of innovation and business across the globe. For the first time, more than 20% of MIT's picks rely on artificial intelligence to support their business at a fundamental level, somewhat redefining what it means to be a truly "smart" company today. How many of these 13 artificial intelligence leaders are you already using? It's working on speech recognition intelligence called Deep Speech 2. This reduces the chance of accidents on autopilot by 50% relative to the safety record of human drivers, according to CEO Elon Musk.

Google unleashed a new group of online artificial-intelligence experiments and one in particular stands out as both an addictive game and a fascinating window on machine learning. Quick, Draw challenges you to create a series of drawings in under 20 seconds apiece. The neural network tries to figure out what you're drawing as you go. It learns from its mistakes and seeks to improve its recognition skills. I'm no great visual artist, but the AI racked up an impressive 5-out-of-6 score on my first round, correctly identifying grass, a helicopter, a penguin, a remote control and scissors.

This'aware' AI knows what you want to buy and how to make you buy it Google's Translation App Is About To Get Much Better Why machine learning could change customer engagement, as we know it. Is Machine Learning Making Your Data Scientists Obsolete? Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

Recently we wrote about how the'popular' Watson of Jeopardy fame still lingers in the memories of our non-data scientist colleagues and perhaps misleads them about the capabilities of AI. It's time we got in tune with the modern Watson, or more correctly IBM's Watson Group and its Watson platform and took a look at all there is to offer. There are three broad capabilities in today's AI and they are: Image and video processing: Largely driven by Convolutional Neural Nets (CNNs) this field has been getting most of the press with capabilities like facial and object recognition. Text and speech processing: This is generally known as Natural Language Processing (NLP), the capability to not only intake speech and text but to understand the nuanced context of strings of words, to perform search or translation on them, and output words and blocks of text and speech. Siri, Alexa, and Cortana represent examples of this field that used to be the butt of jokes but are now more than commercially acceptable. This field has been largely driven by Recurrent Neural Nets (RNNs).

One of the hot topics on Machine Learning is, with no doubts, feature engineering. In fact, it comes before the buzz on this topic, simple when we talk about Data Mining. Remembering the CRISP-DM process, feature engineering (and, consequently, feature selection) is the core of a great data mining project – it comes to life on the Data Preparation phase, that is the task to have constructive data preparation operations such as the production of derived attributes or entire new records, or transformed values for existing attributes. A very good definition, elegant in its simplicity, is that feature engineering is the process to create features that make machine learning algorithms work. And what makes it so important?

Almost a year ago, we published our now-annual landscape of machine intelligence companies, and goodness have we seen a lot of activity since then. This year's landscape has a third more companies than our first one did two years ago, and it feels even more futile to try to be comprehensive, since this just scratches the surface of all of the activity out there. As has been the case for the last couple of years, our fund still obsesses over "problem first" machine intelligence -- we've invested in 35 machine intelligence companies solving 35 meaningful problems in areas from security to recruiting to software development. At the same time, the hype around machine intelligence methods continues to grow: the words "deep learning" now equally represent a series of meaningful breakthroughs (wonderful) but also a hyped phrase like "big data" (not so good!). We care about whether a founder uses the right method to solve a problem, not the fanciest one.

There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). There certainly is a massive uptick of articles about AI being a competitive game changer and that enterprises should begin to seriously explore the opportunities. The distinction between AI, ML and DL are very clear to practitioners in these fields. AI is the all encompassing umbrella that covers everything from Good Old Fashion AI (GOFAI) all the way to connectionist architectures like Deep Learning. ML is a sub-field of AI that covers anything that has to do with the study of learning algorithms by training with data.

Don't know where to start? If you are looking for something more, you could look here for an overview of MOOCs and online lectures from freely-available university lectures. Of course, nothing substitutes rigorous formal education, but let's say that isn't in the cards for whatever reason. Not all machine learning positions require a PhD; it really depends where on the machine learning spectrum one wants to fit in. Check out this motivating and inspirational post, the author of which went from little understanding of machine learning to actively and effectively utilizing techniques in their job within a year.

A collection of minimal and clean implementations of machine learning algorithms. This project is targeting people who want to learn internals of ml algorithms or implement them from scratch. The code is much easier to follow than the optimized libraries and easier to play with. All algorithms are implemented in Python, using numpy, scipy and autograd.