Elon Musk is develop artificial intelligence which will enable robots that can do housework, have conversations and play games. OpenAI's mission is to build safe AI, and ensure AI's benefits are as widely and evenly distributed as possible. OpenAI will measure intelligence using a metric which consists of a variety of OpenAI Gym environments with a unified action and observation space (so a single agent can run across all of them), including games, robotics, and language-based tasks. Their implementation will evolve over time, and they'll keep the community updated along the way They are working to enable a physical robot (off-the-shelf; not manufactured by OpenAI) to perform basic housework. There are existing techniques for specific tasks, but we believe that learning algorithms can eventually be made reliable enough to create a general-purpose robot.

A core methodology at Stitch Fix is blending recommendations from machines with judgments of expert humans. Our machines produce recommendations via algorithms operating over structured data, while our human stylists curate and modify these recommendations on the basis of unstructured data and knowledge that isn't yet reflected in our dataset (e.g., new fashion trends). This helps us choose the best 5 items to offer each client in each fix. The success of this strategy within our styling organization prompts consideration of how machines and humans might be brought together in the realm of fashion design. In this post we describe one implementation of such a system.

Let's go through an example of Credit Risk for Bank Loans: Decision trees create a model that predicts the class or label based on several input features. Decision trees work by evaluating an expression containing a feature at every node and selecting a branch to the next node based on the answer. A possible decision tree for predicting Credit Risk is shown below. The feature questions are the nodes, and the answers "yes" or "no" are the branches in the tree to the child nodes. Our data is from the German Credit Data Set which classifies people described by a set of attributes as good or bad credit risks.

In the last few months the Internet has been flooded with deep dreams: images augmented by neural networks which look incredibly trippy. Deep dreams have the potential to become the new fractals; beautifully backgrounds everyone knows are related to Maths, but no one knows really how. What are deep dreams, how are they generated and what can they teach us? A neural network gets an image as an input, and returns a classification result: yes, that's a face. It achieves this by recognising features in an hierarchical fashion.

She is currently in the NYC Data Science Academy 12 week full time Data Science Bootcamp program taking place between April 11th to July 1st, 2016. This post is based on her third class project - Web Scraping, due on the 6th week of the program. Oil prices have fallen sharply since the summer of 2014. Prices bottomed in February 2016, since then they have gradually increased. While the breakeven cost is a popular topic among investors, on the consumer side gasoline prices are very cheap.

Teachers are not ends-in-themselves, they are always a means to an end - improvements in the learner. Given this premise, could it be possible to eventually replace teachers with AI technology? This may not happen soon but let's, as a thought experiment, ask whether it could. Obvious points are that AI is 24/7, fast, scalable and cheaper. This gives it a head start.

So Brexit turned out to be the non-event for markets we expected with asset prices booming to record highs while support for remaining inside the single currency free-trade zone has risen within other European Union countries. As is often the case, the trade was to "fade" – or bet against – the apocalyptic hyperbole both before and after the referendum. Remarkably one boutique Australian quant shop did exactly this by leveraging academic studies of "blue green algae", which helped it generate enormous 32 per cent returns on the day of the vote. Taaffeite Capital Management (TCM) claim their "artificial intelligence" (aka computer code operating autonomously of humans) figured out that there were massive financial market mispricings that warranted shorts on equities and long positions on government bonds without actually knowing that a referendum was being held. The intellectual property underlying this "systematic" – or automated – trading strategy belongs to Massachusetts Institute of Technology PhD Desmond Lun, who is a 36-year old Australian professor of computer science at Rutgers University in the US, and a Melbourne University alumnus.

In the ever-changing world of high-tech gadgets and gizmos, a whole load of jargon is thrown our way that many of us don't necessarily understand. In our regular series, What is…, we'll tackle a tech term and explain what it means so you can understand it a bit more. Here we explain Artificial Intelligence, a developing computer science which is causing some controversy. In simple terms, Artificial Intelligence (AI) is enabling computers to do things like humans, especially the ability to think, learn and react like we do. AI was once something only imaginable in sci-fi movies, but in recent years it has increasingly become a reality. AI doesn't necessarily have to relate to robots either – it can be used as a tool within an array of IT systems.

If J.K. Rowling was ever concerned that artificial intelligence could do her job for her, she let out a sigh of relief this week. A fan of the Harry Potter series in San Francisco had an artificial intelligence computer algorithm write a few chapters for the iconic series, after teaching the computer to read earlier novels. The results are far from ideal. Max Deutsch, a product manager at Intuit, trained a a Long Short Term Memory (LSTM) Recurrent Neural Network computer by teaching it to read the first four Harry Potter novels. A LSTM computer is trained to notice patterns (say, in genomes or handwriting), which makes it a great test subject for writing patterns.

Legion Analytics is looking to make your sales team more productive with the help of artificial intelligence. We covered the startup last year when it was part of Y Combinator's fellowship program and offered to help companies find sales leads. CEO Jamasen Rodriguez told me that the company's vision has expanded -- it's not just focused on lead generation anymore, but rather becoming "a full-stack sales company." And part of that vision involves automating the mundane parts of the sales process. Bots are a pretty trendy topic right now, and Legion Analytics' claim of using natural language and machine learning technology is pretty familiar, too.