In his best-selling 2011 book Thinking, Fast and Slow, Nobel Prize-winning economist Daniel Kahneman hypothesized that thinking could be broken down into two distinct processes -- aptly named fast and slow thought. The former is all about your gut, the initial automatic responses you have to things, while the later is calculated, reflective and time-consuming. A new algorithm from DeepMind is beginning to show us that so-called "slow" thinking may soon be within the reach of machine learning. In a new paper published in Nature, the Google subsidiary DeepMind explained a new approach to machine learning that uses something called a differentiable neural computer. Neural networks operate using what essentially amounts to a very sophisticated trial and error process, eventually arriving at an answer.

I've been studying and writing about DL for close to two years now, and it still amazes the misinformation surrounding this relatively complex learning algorithm. This post is not about how deep learning is or is not over-hyped, as that is a well documented debate. This discussion/rant is somewhat off the cuff, but the whole point was to encourage those of us in the machine learning community to think clearly about deep learning. Let's be bold and try to make some claims based on actual science about whether or not this technology will or will not produce artificial intelligence. After all, aren't we supposed to be the leaders in this field and the few that understand its intricacies and implications?

The hospitality industry has not always been at the forefront of high-tech innovation or implementation. Until recently, most of the bookings, transactions and administrative tasks at a hotel were handled manually. Revenue management – the process by which a revenue manager determines the best room rate at a given time, in order to maximise bookings and revenue – was a particularly difficult task. Revenue managers had to manually collect, review and analyse numerous data sets each time the rate needed to be updated, and then calculate the ideal room rate based on those variables. Even before the Internet, this was a very time-consuming task, which meant that revenue managers could not update rates as often as necessary (to ensure a property's continued financial success).

While the number of headlines about machine learning might lead one to think that we just discovered something profoundly new, the reality is that the technology is nearly as old as computing. It's no coincidence that Alan Turing, one of the most influential computer scientists of all time, started his 1950 treatise on computing with the question "Can machines think?" From our science fiction to our research labs, we have long questioned whether the creation of artificial versions of ourselves will somehow help us uncover the origin of our own consciousness, and more broadly, our role on earth. Unfortunately, the learning curve on AI is really damn steep. By tracing a bit of history, we should hopefully be able to get to the bottom of wtf machine learning really is.

Artifical intelligence is set to transform the marketing and communications world even more than social media has, according to 55 percent of CMOs surveyed by Weber Shandwick across five markets. The agency's latest study examines current consumer knowledge and attitudes toward AI in the US, UK, Brazil, China and Canada. Of the 150 senior executives surveyed, 68 percent said their brand is currently selling, using or planning for business in the AI era. Moreover, nearly six in 10 believe that within the next five years, companies will need to compete in the AI space to succeed. Weber Shandwick also polled 2,100 consumers across the five markets, and found that Chinese consumers (31 percent) report having the strongest knowledge of AI, while UK consumers report the weakest (10 percent).

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London: A computer "judge" using artificial intelligence has correctly predicted verdicts of the European Court of Human Rights with 79 per cent accuracy. Computer scientists devised a program that was able to weigh up legal evidence and moral questions of right and wrong. The algorithm scoured English language data sets for 584 cases relating to torture and degrading treatment, fair trials and privacy. In each case, the program analysed the information and arrived at its own judicial decision. In 79 per cent of cases, the artificial intelligence (AI) verdict matched the one that was delivered by the court.

An artificial intelligence system has correctly predicted the outcomes of hundreds of cases heard at the European Court of Human Rights, researchers have claimed. The AI predicted the verdicts to an accuracy of 79%, according to the scientists involved. AI is increasingly being used in fields such as journalism, law and accountancy. But critics said no AI would be able to understand the nuances of a legal case. The study, which was conducted by researchers at University College London and the universities of Sheffield and Pennsylvania, does not spell an end to lawyers, the researchers said.

The new effort by Toyota is also the latest indication of a changing of the guard in Silicon Valley's basic technology research. In September, when Dr. Pratt joined Toyota, the company announced an initial artificial intelligence research effort committing 50 million in funding to the computer science departments of both Stanford and M.I.T. In addition to focusing on navigation technologies, the new research corporation will also apply artificial intelligence technologies to Toyota's factory automation systems, Dr. Pratt said. A version of this article appears in print on November 6, 2015, on page B3 of the New York edition with the headline: Toyota Planning an Artificial Intelligence Research Center in California.

Self-driving cars, intelligent drones, robots and more: Artificial Intelligence, or AI, is expected to be the biggest revolution in the digital space since the dawn of the world wide web. And maybe the last one, as cynics like to say: From Stanley Kubrick (2001: A Space Odyssey) to Stephen Hawking, many have warned about the dangers of AI surpassing us. From a marketing perspective, AI looks like a great, rather than a gloomy, possibility. The future is full of promising applications for it. Tracking tools have become more and more sophisticated. They make it possible to track all the actions of your website visitors, from the first visit on -- but for the marketer there lies a challenge in actually reading the phenomenal amount of data they gather.