International Business Machines Corp. showed off its artificial intelligence software this week for use as a foundation for programmers, as it welcomed about 1,300 software developers to the first Watson Developer Conference in San Francisco. Watson in 2011 famously beat human contestants on the television game show "Jeopardy," and IBM went on to apply the technology to helping doctors diagnose and treat cancer. Since then, the technology has evolved into a collection of software that dispenses human-like conversation,...

Employing machine learning, RadarCat uses Google's Soli radar sensors to recognize and distinguish between objects. Researchers from St Andrews University in Scotland have used Google's mini radar for mobile gesture control to create RadarCat, a classification machine that can accurately distinguish between objects in real time. Rather than use cameras to capture objects in view, the RadarCat machine combines recognition algorithms with radio waves beamed from Google's Soli radars to identify objects based on unique traits reflected back to its sensor. The mini radar was developed by Google's ATAP team and unveiled at the 2015 IO as Project Soli. The Soli sensors are capable of recognizing fine gestures, such as rubbing two fingers together, when held in front of the radar.

It is the end of polling as we know it. But all of these pundits and pollsters, who with their fancy mathematical models and surveys and assumptions pontificated endlessly in this, their finest hour, only to get it absolutely, disastrously wrong, seem to be facing not just widespread opprobrium, but maybe even their demise. In an era where self-driving cars promise to soon swarm down roads and Black Mirror is the hottest show on Netflix, it is only appropriate that an algorithm created by a Mumbai-based company MogIA called the election correctly on October 28. It did so with both the Democratic and Republican primaries this year and with three of the last US Presidential elections (although there's been no public verification of those results). As MogIA's creator Sanjiv Rai explained it to CNBC when he sent them his results in late October, MogIA (named after Rudyard Kipling's Mowgli) plumbs the vast and endless terrain offered by social media to make sense of national sentiments.

Microsoft has an array of components in its cloud for data management and analysis. There are the obvious ones like Azure SQL Database, Machine Learning and HDInsight; more infrastructural ones, like Azure Data Factory and Azure Stream Analytics; there are IoT-related services like Azure IoT Hub and there are several others you may not even know much about. So, while it's easy to see how Microsoft has its bases covered, it's less obvious whether and how these services fit together. And testimonials from customers using a great many of these services in an integrated fashion may seem few and far between. Poster child I found one though: MediaBrix, a five-year-old mobile advertising technology (AdTech) firm, based right near me in New York City, and focused on "moment-based targeting." I spoke with MediaBrix's CTO, Christopher Beach, who explained in nerdy detail which parts of the Azure data stack his company is using, and how they're tied together.

One of the latest trends in the world of technology and engineering is "machine learning" -- in fact, all of the big technology companies today have invested in artificial intelligence and machine learning projects. The term "machine learning" was first defined by Arthur Samuel, way back in 1959. He defined it as "the ability to learn without being explicitly programmed," which basically means that a machine could learn from its own mistakes and reprogram itself to improve its performance over time. The idea gained popularity in the 90s when the concept of data mining came into existence. Data mining uses algorithms to look for patterns in a given set of information, which led to data-driven predictions and decision making.

Decision trees can suffer from high variance which makes their results fragile to the specific training data used. Building multiple models from samples of your training data, called bagging, can reduce this variance, but the trees are highly correlated. Random Forest is an extension of bagging that in addition to building trees based on multiple samples of your training data, it also constrains the features that can be used to build the trees, forcing trees to be different. This, in turn, can give a lift in performance. In this tutorial, you will discover how to implement the Random Forest algorithm from scratch in Python.

Revenue was up 54% from last year and beat analysts' expectations by over $300 million. Nvidia used to be a little company making graphics chips for PCs, but it's well on the way to transforming into one of the leading computing platforms for cloud servers, machine learning, and artificial intelligence. Fortunately for Nvidia, it turns out that the kinds of tasks graphics chips are good at--like processing many, many simple calculations at the same time--are just what's needed to run analysis programs in a cloud data center, steer a self-driving car, or pilot an automated drone. Thursday brought more evidence that the company's successful transition is in full swing. Nvidia NVDA reported third quarter results that blew through Wall Street expectations, and its stock price, which had already doubled this year, rose another 15% in after-hours trading.

In this talk we will present some techniques that we use on a day to day basis in our research, where we combine our internet-wide data scanning and acquisition platform with ML/Data science techniques which allows us to find things faster or extract results in a more automated way. We will focus on practical cases and examples that even our audience at home will be able to use if they want. A couple of examples we will look at is how to classify images such as VNC screenshots, we will look at network scans and using machine learning to classify them and also the use of natural language processing to analyze CVEs. We will also talk a bit about a data analysis and classification pipeline architecture, we will look at the different technologies and what they do and how they can be used. We will start by giving a very brief entry to the data science world and talk about: Technologies Techniques How these relate to infosec Algorithms and how they can be used How people can come into the world of data and machine learning Data visualization techniques and what are the best choices for different types of data A couple of examples we will look at is how to classify images such as VNC or x11 screenshots, OCR, we will look at network scans and using machine learning to classify them and also the use of natural language processing to analyze CVEs.

Machine learning is the idea that there are generic algorithms that can tell you something interesting about a set of data without you having to write any custom code specific to the problem. Instead of writing code, you feed data to the generic algorithm and it builds its own logic based on the data. For example, one kind of algorithm is a classification algorithm. It can put data into different groups. The same classification algorithm used to recognize handwritten numbers could also be used to classify emails into spam and not-spam without changing a line of code. It's the same algorithm but it's fed different training data so it comes up with different classification logic. "Machine learning" is an umbrella term covering lots of these kinds of generic algorithms.

Artificial intelligence and its machine learning subset are all the rage these days. That was evident when I spoke this week at the AI World event, which was packed with vendors and users seeking to understand what the hell AI and machine learning are--and wanting to know how they could use this old but revitalized technology effectively. Amazon Web Services, Google, IBM, Microsoft, and the other major cloud providers all have machine learning services in their clouds now. But most enterprises have no clue on what the heck to do with machine learning systems, whether cloud or on-premises. It is critical to find the right uses for machine learning.