The drone turned slowly with a loud buzzing sound, red and blue lights flashing, and hit me right in the eyes with a spotlight. "Security has been notified," boomed a smooth voice from the aircraft's twin loudspeakers. Startup Aptonomy thinks this experience can keep intruders out of factories, warehouses, and other facilities more cheaply than human guards can and more effectively than cameras and alarms. I received the drone security guard treatment in a demonstration at the company's testing area on Treasure Island, an old naval base in San Francisco Bay. Cofounder Mihail Pivtoraiko says his drones will be ready to go on patrol next year.

ARTIFICIAL INTELLIGENCE: Tech Titans Join Forces to Stop AI From Behaving Badly Will Knight MIT Technology Review "A new organization called the Partnership on Artificial Intelligence to Benefit People and Society will seek to foster public dialogue and create guidelines for developing AI so that systems do not misbehave...'The positive impacts of AI will depend not only on the quality of our algorithms, but on the level of public engagement, of transparency, and ethical discussion that takes place around it.'" SPACE: SpaceX's Big Fucking Rocket – the Full Story Tim Urban Wait But Why "Yesterday, Elon Musk got on stage at the 2016 International Astronautical Congress and unveiled the first real details about the big fucking rocket they're making...Right now we're all on Earth, which means that if something terrible happens on Earth--caused by nature or by our own technology--we're done. That's like having a precious digital photo album saved only on one not-necessarily-reliable hard drive. If you were in that situation, you'd be smart to back the album up on a second hard drive. QUANTUM COMPUTING: Biggest Ever Quantum Chip Announced, but Scientists Aren't Buying It James Vincent The Verge '"There was only ever a hope that a quantum annealer would be better,' Matthias Troyer, who co-authored the 2014 Science paper, told The Verge.

There is an incredible amount of data in the world, and all that data is changing the way industries work. The talk is short, 12 minutes, and interesting to listen to as Mr. McHugh looks at autonomous cars and healthcare, talking about the impact of artificial intelligence on advancing these industries. There are examples showing how data and AI systems are already being used to change the way the transportation and medical fields can work. Whether you want to see more robot help in our world or not, I suspect some level of this is coming, and it's being driven by data. We have more and more data, and as companies have success in analyzing this data with various types of AI and machine learning systems, there is pressure for other companies to join the trend and build their own systems.

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.