I am guessing that the reason Apple and Musk are not involved is probably because they both have their own projects on the go. Or at least for sure Elon does, a consortium called OpenAI openai.com/blog/ . They are not afraid of going it alone, and they have the funds and other resources to do so. If its at all possible… and i think it is myself.. these are probably the ones who will find a way.. Which brings the next question, and most important .. SHOULD we be doing this sort of work? Thats one that should be asked before we go too far ahead a lot of people think.

Five of the world's leading tech companies this week unveiled a new nonprofit group that seeks to build public awareness of artificial intelligence and responsibly develop the groundbreaking technology. Amazon, Google, Facebook, IBM and Microsoft are the founding members of the Partnership on Artificial Intelligence to Benefit People and Society, and each will contribute financial and research resources to the group. Research published by the Partnership for AI will be made available on an open license; it's expected to focus on a wide range of topics, from the reliability and effectiveness of AI systems to questions about AI and ethics, transparency and privacy. "This partnership will ensure we're including the best and the brightest in this space in the conversation to improve customer trust and benefit society," Amazon machine learning director Ralf Herbrich said in a statement.

Will "Miss Peregrine's Home for Peculiar Children" have a fairy-tale ending at the box office? While its final chapter has yet to be written, Tim Burton's fantasy film is earning pretty good grades at the multiplex so far. The picture about a group of extraordinary children collected 9 million on Friday, according to an estimate from distributor 20th Century Fox. That means the movie is on track to gross around 27 million by weekend's end -- a so-so start, considering the picture cost the studio 110 million to make. The weekend's other big debut, "Deepwater Horizon," lagged slightly behind in ticket sales Friday, with 7.1 million.

The confusion matrix is great way to visualize the performance of a classifier and detect false positives and false negatives within your data. Now you can click on the confusion matrix and check out which samples are causing the confusions, making it much easier to clean and curate the training data to improve classifiers.

Machine learning is ubiquitous in our daily lives. Every time we talk to our smartphones, search for images or ask for restaurant recommendations, we are interacting with machine learning algorithms. They take as input large amounts of raw data, like the entire text of an encyclopedia, or the entire archives of a newspaper, and analyze the information to extract patterns that might not be visible to human analysts. But when these large data sets include social bias, the machines learn that too. If the source documents reflect gender bias – if they more often have the word'doctor' near the word'he' than near'she,' and the word'nurse' more commonly near'she' than'he' – then the algorithm learns those biases too, the researcher explains According to James Zou, Assistant Professor for Biomedical Data Science at Stanford University, machine systems are learning human biases when examples of such are included in the training set.

Claire Bretton is one of the co-founders of daco.io, a startup that is developing a unique tool to track competition thanks to deep learning. Earlier, she was a manager in a top strategy consulting firm based in Paris. She holds a master's degree from ESCP Europe. One of the biggest challenges of the 21st century is to make computers more similar to the human brain. We want them to speak, understand and solve problems -- and now we want them to see and recognize images.

We haven't designed fully sentient artificial intelligence just yet, but we're steadily teaching computers how to see, read, and understand our world. Last month, Google engineers showed off their "Deep Dream," software capable of taking an image and ascertaining what was in it by turning it into a nightmare fusion of flesh and tentacles. The release follows research by scientists from Stanford University, who developed a similar program called NeuralTalk, capable of analyzing images and describing them with eerily accurate sentences. First published last year, the program and the accompanying study is the work of Fei-Fei Li, director of the Stanford Artificial Intelligence Laboratory, and Andrej Karpathy, a graduate student. Their software is capable of looking at pictures of complex scenes and identifying exactly what's happening.

A simple Google image search for'women's professional hairstyles' returns the following: Momentum by TNW is our New York technology event for anyone interested in helping their company grow. That is, until you try searching for'unprofessional women's hairstyles' and find this: In it, you'll find a hodge-podge of hairstyles sported by black women, all of which seem, well, rather normal. In fact, Boing Boing spotted this back in April. In five years, 10 years, 25 years, you can imagine how much of our lives will be dictated by algorithms.

That has been its brand since the days of "It's not television, it's HBO." Television eventually caught up, which meant HBO had to swing bigger; it is not built for, or on, a slate of solid but not spectacular shows. It is a premium cable channel, which means it needs to bring the premium. So when an HBO series fails -- "Vinyl," "Luck," "John From Cincinnati" -- it fails hard. But when it connects, well, hits like "Game of Thrones" and "Veep," "Last Week Tonight With John Oliver" and "Olive Kitteridge" don't just sweep up Emmys, they change the nature of television.

For the 22nd year, Gartner has released its much-discussed hype cycle report on emerging technologies, "providing a cross-industry perspective on the technologies and trends that business strategists, chief innovation officers, R&D leaders, entrepreneurs, global market developers and emerging-technology teams should consider in developing emerging-technology portfolios." Reacting to last year's hype cycle report (see below), I made the following comment: Machine learning is making its first appearance on the chart this year, but already past the peak of inflated expectations. A glaring omission here is "deep learning," the new label for and the new generation of machine learning, and one of the most hyped emerging technologies of the past couple of years. This year, Gartner has moved machine learning back a few notches, putting it at the peak of inflated expectations, still with 2 to 5 years until mainstream adoption. Is machine learning an emerging technology and is there a better term to describe what most of the hype is about nowadays in tech circles?