A team of scientists has used artificial intelligence (AI) to gain insight into the biophysics of cancer with their machine-learning platform predicting a trio of reagents that generated a cancer-like phenotype in tadpoles. The research, reported in journal Scientific Reports, showed that during these extensive experiments, the biologists observed that all the melanocytes -- a mature melanin-forming cell -- in a single frog larva either converted to the cancer-like form or remained completely normal. In their study, the researchers asked their AI-derived model to answer the question of how to achieve partial melanocyte conversion within the same animal using one or more interventions. "We wanted to see if we could break the concordance among cells, which would help us understand how cells make group decisions and determine complex body-wide outcomes," said Tufts University's Michael Levin, who is the paper's corresponding author. The AI model ultimately predicted that a precise combination of three reagents -- altanserin, a 5HTR2 inhibitor; reserpine, a VMAT inhibitor, and VP16-XlCreb1, mRNA encoding constitutively active CREB -- would achieve that outcome.

Artificial intelligence is changing the face of SEO, with advancing algorithms which enable machines to make connections and "learn" how to process data based on human requests more effectively. When you search for something, the results displayed take into account a bucket of considerations like your location, search history, favorite websites, and what other users have previously clicked on for a similar query. AI improvements mean that ranking factors can change from query to query, as the algorithm learns from how people are clicking on the search results and decides on the most relevant factors to take into account for each individual search. So what does this mean for marketers? How does it change our jobs?

I wouldn't call PixelRNN to be a direct application of dilated convolutions. They do mention dilation, but I don't think they apply it for their Gated PixelCNN architecture, which I believe is SOTA for image generation (at least in terms in NLL). The other important difference is that the authors don't have a dilated convolution LSTM model for 1-dimensional data i.e wavenet and bytenet. They did explore such a structure in their work on conditional image generation - PixelRNN, Pixel Bi-LSTM etc.

John McCarthy coined the term Artificial Intelligence in 1955. Since then, the AI industry at large has seen dramatic ups and downs -- progress and promise mixed with disappointment and disillusion. But now with the convergence of Megatrends on massive data, lightning fast processing speeds, and renewed competitive fever from the American MAFIA (Microsoft, Alphabet, Facebook, IBM, Amazon), AI is poised to cause disruption on a scale that could surpass the Internet itself. As we prepare for a wave of AI first companies (@sundarpichai) and AI natives (Ryan Hoover), every person in the innovation economy will need to understand how AI will (or will not) change their industry and their lives. These titans shape the conversation and have the most ability to move the entire AI industry.

Do you think you can tell a piece of music composed by artificial intelligence (AI) from one created by a human composer? Before you read any further, let's find out. The following audio consists of two fragments, one written by AI, the other by a human. If you didn't get it right the first time, no worries--we'll have a couple more mini-quizzes like this below. The AI that wrote the fragment above has been programmed by Jukedeck, a UK-based startup working on machine-made music that won the competition at TechCrunch Disrupt London in 2015.

Dematerialization is going to continue. We'll see hardware that is thinner and lighter. New materials are coming on the market, like stretchable electronics. Imagine what that could do for wearable technologies. Everything is going to be computing in the future, whether it's your shoes, clothes, or the temporary tattoo that you wear to monitor your health.

The power of AI is all about the data. AI that does something based on your data, which Reese calls transferred learning. More importantly is when AI can help businesses make more informed decisions and actions on data sets that you may not have. Reese believes that AI use in business will be ubiquitous. The answer is that every line of business, from HR to sales, services and marketing can use AI to make better decisions, execute faster and bolster competitive advantage.

The European Union is currently considering the need to redefine the legal status of robots, with a draft report last week suggesting that autonomous bots might, in the future, be granted the status of "electronic persons" -- a legal definition that confers certain "rights and obligations." It sounds like science fiction and that's because it is: any engineer will tell you we're a long way from seeing robot marches for civil rights. For a start, this is only a draft report. It's not actual legislation, and is only a series of recommendations for the EU's law-making body -- they could always ignore it completely. And although parts of the report are a bit odd (Frankenstein's monster, the Greek myth of Pygmalion, and the Golem of Prague are all referenced in the first paragraph alone), at its core it's interested in the rights of people, not the rights of robots.

Almost a year ago, we published our now-annual landscape of machine intelligence companies, and goodness have we seen a lot of activity since then. This year's landscape has a third more companies than our first one did two years ago, and it feels even more futile to try to be comprehensive, since this just scratches the surface of all of the activity out there. As has been the case for the last couple of years, our fund still obsesses over "problem first" machine intelligence--we've invested in 35 machine intelligence companies solving 35 meaningful problems in areas from security to recruiting to software development. At the same time, the hype around machine intelligence methods continues to grow: the words "deep learning" now equally represent a series of meaningful breakthroughs (wonderful) but also a hyped phrase like "big data" (not so good!). We care about whether a founder uses the right method to solve a problem, not the fanciest one.

Airlines and airports can save an estimated USD 25 billion annually from flight disruptions by harnessing artificial intelligence, cognitive computing, predictive analytics and other progressive technical capabilities, says an industry report. According to a report by Sita, a technology services provider to air transport industry, predictive tools using artificial intelligence and cognitive computing are likely to be adopted by half of airlines and airports over the next decade, which can help them save up to USD 25 billion. Predictive technologies can provide passengers with more relevant information about their journey to help them create more seamless and personal experiences, Nigel Pickford, director market insight at Sita said. "Airlines and airports are focusing on technologies that will make them more responsive to issues in their operations. This will enable them to improve their performance and customer services," Pickford said.