Imagine you're sitting in a self-driving car that's about to make a left turn into on-coming traffic. One small system in the car will be responsible for making the vehicle turn, one system might speed it up or hit the brakes, other systems will have sensors that detect obstacles, and yet another system may be in communication with other vehicles on the road. Each system has its own goals -- starting or stopping, turning or traveling straight, recognizing potential problems, etc. -- but they also have to all work together toward one common goal: turning into traffic without causing an accident. Harvard professor and FLI researcher, David Parkes, is trying to solve just this type of problem. Parkes told FLI, "The particular question I'm asking is: If we have a system of AIs, how can we construct rewards for individual AIs, such that the combined system is well behaved?"

Google, Facebook, Amazon, Microsoft and IBM have joined forces to explore the future of Artificial Intelligence (AI) and agree on basic rules on privacy and ethics for the development of the industry. AI is considered the next big technological breakthrough and many firms are excited about the prospect of using it to gain an advantage in the marketplace. But the potential for misuse is clear so it is sensible that the main players with a big stake in the development of technology get together to set the boundaries. However, technology giant Apple is not currently at the table but has made noises it intends to join in the near future. DeepMind, which is part of Google's artificial intelligence division, shocked the world when they recently beat champion Go player Lee Sedol.

Learn to compete in the Kaggle leaf detection challenge! All exercises are designed to be run from a CPU on a laptop, but can be accelerated with GPU resources. Labs 1, 2, 3 and 5 have been translated from Theano/Lasagne with minor modifications from the following repositories: Nvidia Summer Camp and 02456 deep learning. Guides for downloading and installing TensorFlow on Linux, OSX and Windows using Docker can be found here. Optional reading material from Michael Nielsen chapters 1-4 (Do 3-5 of the optional exercises).

To get an idea of why this was a common thought among researchers, it may be useful to understand how programs that play chess work (a game where machines have vastly surpassed humans), and then see why the same approach couldn't be used for the game of Go. In chess, a procedure known as minimax (along with several other clever tricks that help optimize it) is a common strategy to write programs that play the game (a.k.a. The most sophisticated of these programs use this approach at their core, including popular open source programs such as GNU Chess and Crafty. Minimax, which performs what is known in game theory as a "game tree search," can be explained in simple terms as a simulation of the game that takes into account all possible moves of one player and all counter moves of the opponent, until either the end of the game is reached or a certain prefixed number of moves has been simulated (more on this later). In essence, it's a way of simulating all possible futures of a game, and then figuring out, from the current position, which of the best futures can be forced by the player in turn to get the best possible outcome.

A significant chunk of the data that we encounter on a daily basis is available in an unstructured, free text format. Hence, the ability to glean useful bits of information from this unstructured pile can be quite valuable. In this post, we will attempt a basic analysis of the text from the first Presidential debate between Clinton and Trump. A good part of this post involves data manipulation steps to convert the raw transcript text (of the debate) into a more structured/ ordered form, which you can then start analyzing – This initial data manipulation process to transform the raw text into a more structured form suitable for further analysis/modelling, is a key step in any text analytics effort, and hence a key focus point of this post. Post data transformation and structuring, we attempt to answer a few simple questions from the data (such as Who spoke more, Who interrupted more, Key discussion points etc).

In the previous article (Part I), we introduced the general ideas behind model evaluation in supervised machine learning. We discussed the holdout method, which helps us to deal with real world limitations such as limited access to new, labeled data for model evaluation. Using the holdout method, we split our dataset into two parts: A training and a test set. First, we provide the training data to a supervised learning algorithm. The learning algorithm builds a model from the training set of labeled observations.

Benjamin Graham, Director of Communication at personalised marketing app Clemmie, looks at how marketers can make the most of AI. If you believe the blogs, magazine features and tabloid news stories, AI is a revelation, and it's going to change everything. From op-eds predicting humanity's end at the hands of superior intelligence to scientists feverishly discussing the future of medical care, AI is on everyone's lips. Reporting on AI often seems to focus on the worst-case scenario, but AI can change our lives for the better and revolutionise the way we interact with technology, the world, and even each other. The ways in which we perceive the world around us is in a constant flux as technology develops.

The vision that computers could emulate human reasoning and decision making arose in the 1940s--soon after the development of modern computers themselves. There have been long periods when progress was scant, but Artificial Intelligence is now poised to take off. To understand why now is the time, let's look for a minute at then. The challenge with AI has always been to understand how humans represent knowledge and how they apply it to make decisions. The idea was to capture the knowledge of experts along with a set of rules that governed how to apply it.

Technology has been good to 1-800-Flowers. The company has long pioneered new ways of retailing, a toll-free number, direct sales via the internet. So when, in 2009, it opened its online store on Facebook the company was expecting another tech-based success. Like many others they found Facebook was a tough sell. "We were one of the first to actually have a Facebook store, and we did have big expectations, but it turned out to be not very successful," recalled Jon Mandell, vice-president of marketing at the flower and gift seller.

Big Data: A Startup Thriller Novel by Lucas Carlson My rating: 5 of 5 stars Big Data: A Startup Thriller Novel is a new ingenious creation by Lucas Carlson, a fiction and non-fiction author and entrepreneur, who already got my attention and won me over with his first thrilling startup novel The Term Sheet. Big Data is a maddening ride through our near future where artificial intelligence is incorporated in our lives to the point that people rely on its services more than on their natural instincts, reasoning and decision making. It serves us, it helps us, it cures us, and then it kills us... This is exactly what happens when Luna Valencia's most-advanced supercomputer Ancien in history starts to refine and improve on its own code which can "solve many problems in the world of artificial intelligence without human assistance, interpretation, or intervention." It is the holy grail in the world of computers, but it is also the weapon for mass murder in the world of humans.