In the 1960s, the moral philosopher Philippa Foot devised a thought experiment that would revolutionize her field. This ethical puzzle, today known as the "trolley problem," has become so influential--not just in philosophy but also in neuroscience, behavioral economics, evolutionary psychology, and meme culture--that it's garnered its own tongue-in-cheek sub-discipline, called "trolleyology." That body of commentary, wrote one philosopher, "makes the Talmud look like Cliffs Notes." The person largely responsible for popularizing the trolley problem was the philosopher Judith Jarvis Thomson. Her 1976 paper, "Killing, Letting Die, and The Trolley Problem," tweaked the original scenario.

Push it, pull it, break it, maybe even give it a lick. Children experiment this way to learn about the physical world from an early age. Now, artificial intelligence trained by researchers at Google's DeepMind and the University of California, Berkeley, is taking its own baby steps in this area. "Many aspects of the world, like'Can I sit on this?' or'Is it squishy?' are best understood through experimentation," says DeepMind's Misha Denil. In a paper currently under review, Denil and his colleagues have trained an AI to learn about the physical properties of objects by interacting with them in two different virtual environments.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. This RHex dance contest was filmed in 2013, as part of the Philadelphia Science Festival. More isn't always better, but with glowy flying drones, it definitely is: If you do the math (and the math is simple and exponential), by 2020 Intel will be flying 312,500 drones, which is a VGA display.

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,...

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.

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.

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.

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.