Demis Hassabis, the cofounder and CEO of DeepMind, a Google-owned AI lab in London, is planning to buy a Tesla 3 from Elon Musk -- one of the company's earliest investors. Hassabis congratulated Musk on Twitter after Musk tweeted that 276,000 Model 3 orders had been made by the end of Saturday, just two days after the electric car was launched. He also said he was planning to buy one of the new vehicles. "Really amazing to hear!! Just placing my order...," Hassabis wrote. At 35,000 ( 24,423), the five-seater Model 3 is the cheapest Tesla to date and is due to start shipping in late 2017.

Recently, the computer science field took a monumental leap forward when Google DeepMind's artificial intelligence (AI) program, AlphaGo, bested Go champion Lee Sedol, four matches to one. While programs that can master competitive games of logic and recall, such as chess, jeopardy and poker, have already been developed, the ancient Chinese game of Go has both enticed and frustrated computer scientists due to its complexity. With move combinations outnumbering the number of atoms that exist in the known universe, brute force algorithms that have been greatly successful in game mastery were deemed to be largely inapplicable for Go. However, through advanced tree searches and deep neural networks, researchers can narrow down Go's massive array of data trees into much more manageable combinations akin to that of a human brain's neural network. While the dream of conscious AI is still years away, there are still some potential investments in this fast-growing sector.

Ben is a CS/Computational Linguistics graduate, ex-Google intern, and python fanatic. In his past academic life his was in a deep learning lab studying character recognition, and a Natural Language Processing lab studying how to automate diagnosis of certain neurodegenerative diseases. Nowadays, he spends his days working as a data scientist, and his nights exploring data his finds online, learning new programming languages, and brewing beer.

To train glove model on text corpus put the data file in the data folder in parent folder. Currently text8 corpus (wikipedia's first 1B characters) is present for demo purpose. It lists the arguments which could be passed for training. All paramters except text path are optional and are initialized with default values. This will prompt for a string input.

Jeffrey Hawkins is the American founder of Palm Computing (where he invented the Palm Pilot) and Handspring (where he invented the Treo). He has since turned to work on neuroscience full-time, founded the Redwood Center for Theoretical Neuroscience (formerly the Redwood Neuroscience Institute) in 2002, founded Numenta in 2005 and published On Intelligence describing his memory-prediction framework theory of the brain. In 2003 he was elected as a member of the National Academy of Engineering "for the creation of the hand-held computing paradigm and the creation of the first commercially successful example of a hand-held computing device." Hawkins also serves on the Advisory Board of the Secular Coalition for America and offers advice to the coalition on the acceptance and inclusion of nontheism in American life. Andrew Yan-Tak Ng is Chief Scientist at Baidu Research in Silicon Valley.

Computing giant Nvidia has announced the world's first "supercomputer in a box" – the DGX-1. With a cool 170 teraflops of performance, the machine is designed to tackle the complex worlds of deep learning and artificial intelligence, areas of research requiring massive amounts of computing power. The DGX-1 uses the company's newly developed Pascal architecture that recently showed up in its beastly in-car supercomputer. The DGX-1 has eight Tesla GP100 GPUs, each with 16 gigabytes of memory. Alongside that, the knowledge hungry supercomputer contains 512 GB of RAM, and four 1.92 terabyte solid state hard drives.

In a major win for artificial intelligence, Google DeepMind's AlphaGo has beat European Go champion Fan Hui in the complex 2,500-year-old Chinese game of Go, touted the official Google blog. A victory in a Go game against a human champion has long been coveted among AI researchers, because the possible moves that a player can take can reach into the quadrillions and beyond. As a result, Go has proven a formidable challenge for artificial intelligence researchers. Microsoft and Facebook, for example, have been working on ways to win in the game over a human champion, but have had no luck to date, according to a BBC news report. Last October, Google DeepMind held a private, closed-door Go match in its London office between its AlphaGo system and Hui.

Deep-learning machines are conquering realm after realm of human expertise, but is there a difference between Them and Us? I think the only thing that distinguishes us from the machines is memory. It is what makes us human, says Rajeev Srinivasan. In the wake of the astonishing feat by Google's AlphaGo machine in defeating, nay thrashing 4-1 the world's best player of Go, it is time for us to wonder what it is that is truly human, that which distinguishes us from the machines. Deep-learning machines are conquering realm after realm of human expertise, from chess to natural language to Go to other domains, and there is no reason to imagine their progress will come to a halt any time soon.

I've never worked with audio data so I really have no idea on how to normalize it and feed it to my network, an example on github would be great, but its search engine returns nothing. I'm using theano and lasagne, but I just want to learn how to actually extract features from the speech samples. I found LITIS on the sub, but I heard it takes 16 gb of ram to process and I haven't found any implemented example.

In this paper, we design a Deep Dual-Domain ($\mathbf{D^3}$) based fast restoration model to remove artifacts of JPEG compressed images. It leverages the large learning capacity of deep networks, as well as the problem-specific expertise that was hardly incorporated in the past design of deep architectures. For the latter, we take into consideration both the prior knowledge of the JPEG compression scheme, and the successful practice of the sparsity-based dual-domain approach. We further design the One-Step Sparse Inference (1-SI) module, as an efficient and light-weighted feed-forward approximation of sparse coding. Extensive experiments verify the superiority of the proposed $D^3$ model over several state-of-the-art methods. Specifically, our best model is capable of outperforming the latest deep model for around 1 dB in PSNR, and is 30 times faster.