An iOS app has gone viral in post-Soviet states this past week, racking up over 650,000 downloads and the top spot in app stores around the region. Russian internet giant Mail.Ru even announced an investment into the product yesterday – a 10 percent stake that reportedly amounts to 2 million. The Russian-made photo app, Prisma, allows users to customize their images by feeding photos through an artificial intelligence that "repaints" them in the stye of great artists like Van Gogh, Munch, and Picasso. Unlike many other photo apps, Prisma doesn't simply slap a filter on top. Instead, the AI completely reinterprets the images using a deep learning method known as convolutional neural networks.

Intelligence, defined as the ability to acquire knowledge and skills. Intelligence for the longest time possible is associated with the human brain. Artificial intelligence is basically defined as intelligence that is originating from machines. Most computer applications only make existing processes and functions faster and maybe more efficiently but cannot create new duties altogether. However, artificial intelligence has already challenged this notion.

"Our research with DeepMind has the potential to revolutionise the way professionals carry out eye tests and could lead to earlier detection and treatment of common eye diseases such as age-related macular degeneration," said Professor Sir Peng Tee Kaw, the head of Moorfields' ophthalmology research centre. DeepMind, which Google paid 400 million to acquire two years ago, hopes to use artificial intelligence to advance medical and climate research after its software defeated the world champion at the ancient Chinese board game Go.

" If you have any interest in AI or programming, this book is a good start.

" If you have any interest in AI or programming, this book is a good start. It is really a solid guide and I have to recommend it. " – Sanjin, from Amazon.com " The author did a great job. " – Irvin J. Hoch, from Amazon.com " Props for the author for coming up with a lay man's illustration regarding swift programming to create AI. " – Lucinda, from Amazon.com * * INCLUDED BONUS: a Quick-start guide to Learning Swift in less than a Day! * * How would you like to Create the Next SIRI? It's not that complicated after all Does it require THAT much advanced math?

To inform the Open Philanthropy Project's investigation of potential risks from advanced artificial intelligence, and in particular to improve our thinking about AI timelines, I (Luke Muehlhauser) conducted a short study of what we should learn from past AI forecasts and seasons of optimism and pessimism in the field. In addition to the issues discussed on our AI timelines page, another input into forecasting AI timelines is the question, "How have people predicted AI -- especially HLMI (or something like it) -- in the past, and should we adjust our own views today to correct for patterns we can observe in earlier predictions?"1 We've encountered the view that AI has been prone to repeated over-hype in the past, and that we should therefore expect that today's projections are likely to be over-optimistic. To investigate the nature of past AI predictions and cycles of optimism and pessimism in the history of the field, I read or skim-read several histories of AI2 and tracked down the original sources for many published AI predictions so I could read them in context. I also considered how I might have responded to hype or pessimism/criticism about AI at various times in its history, if I had been around at the time and had been trying to make my own predictions about the future of AI. I can't easily summarize all the evidence I encountered that left me with these impressions, but I have tried to collect many of the important quotes and other data below. Then, in a final subsection, I summarize some questions I might have investigated if I had more time. I would be curious to learn whether people who read a set of sources similar to the set I consulted come away from that exercise with roughly the same impressions impressions I have. I would also be curious to hear how many AI scientists who were active during most of the history of the field share my impressions. The histories I read left me with the impression that some (but not all) of the earliest AI researchers -- starting around the time of the Dartmouth Conference in 1956 -- thought HLMI (or something like it) might only require a couple decades of work. For example, Moravec (1988) claims that John McCarthy founded the Stanford AI project in 1963 "with the then-plausible goal of building a fully intelligent machine in a decade" (p.

This is a recursive neural network (LSTM type) with 3 layers of 680 neurons each, trying to find patterns in audio and reproduce them as well as it can. It's not a particularly big network considering the complexity and size of the data, mostly due to computing constraints, which makes me even more impressed with what it managed to do. The audio that the network was learning from is voice actress Kanematsu Yuka voicing Hinata from Pure Pure. I used 11025 Hz, 8-bit audio because sound files get big quickly, at least compared to text files - 10 minutes already runs to 6.29MB, while that much plain text would take weeks or months for a human to read. I wrote a program that converts any data into UTF-8 text and vice-versa, and to my excitement, torch-rnn happily processed that text as if there was nothing unusual.

Here are the slides from my talk at July's Melbourne SEO Meetup. Thanks to the organisers for putting me on and to all those that attended. If you missed it or want a rerun, see the slides below!

A six-week-long research accelerator, championed by NASA's Office of the Chief Technologist and hosted at the SETI Institute, is engaging young researchers from around the world to take on one of the truly existential threats to our species. The NASA Frontier Development Lab (FDL) is bringing together a team of postgraduate researchers in data analytics and planetary science and challenging them to think outside the box on the threat of asteroid impacts. The initiative is under the aegis of experts from the space agency and the SETI Institute, with deep-learning expertise contributed by NVIDIA and Autodesk. Asteroids that collide with Earth are one cosmic danger that it's now possible to mitigate. In 2013, NASA's Asteroid Grand Challenge charged participants with identifying all possible asteroid threats, and determining what to do about them. FDL co-director, James Parr, describes the concept: "Grand challenges, such as detecting and characterizing the potentially hazardous asteroids we can't see, demand ingenious new applications of emerging technologies.

As Devin Coldeway of Tech Crunch puts it, "Just as you could supply the probable details of a pixelated face because you are familiar with how faces look, the AI can extrapolate as well, having examined on a pixel by pixel basis what certain features look like at various levels of detail."