LONDON: Using Artificial intelligence (AI) or machine learning technology, a team of researchers has predicted outcomes in judicial decisions at the European Court of Human Rights (EctHR) with 79 per cent accuracy. The AI method, developed by researchers from University College London (UCL), University of Sheffield and US-based University of Pennsylvania is the first to predict the outcomes of a major international court by automatically analysing case text using a machine learning algorithm. "We don't see AI replacing judges or lawyers but we think they will find it useful for rapidly identifying patterns in cases that lead to certain outcomes," said Nikolaos Aletras, who led the study at UCL's computer science department. "It could also be a valuable tool for highlighting which cases are most likely to be violations of the European Convention on Human Rights," Aletras added. In developing the method, the team found that judgements by the ECtHR are highly correlated to non-legal facts rather than directly legal arguments, suggesting that judges of the Court are'realists' rather than'formalists'.

The rise of artificial intelligence (AI) and the growing maturity of cloud-based business software promise to dramatically change the role of the accountant over the next five to 10 years. Savvy professionals should already be reskilling themselves in anticipation of the shift in the market. The Finance Indaba is taking place today and tomorrow – it's the perfect platform for finance professionals to gear up and learn about the changing accounting world from leaders in the industry He says that the arrival of smart software bots, paired with the affordability of cloud-based business applications, will change the way that accountants work as vividly as the first spreadsheet and accounting software packages did. "Financial software is getting smarter, more affordable and easier to use, so more and more of the admin accountants typically do for the business is becoming automated," Cohen says. "What's more, intuitive software paired with AI and other new developments, could empower small business owners do more of the tasks they used to entrust to an accountant."

The question is heard more and more often, both from those who think that machines cannot become moral, and who think that to believe otherwise is a dangerous illusion, and from those who think that machines must become moral, given their ever-deeper integration into human society. In fact, the question is a hard one to answer, because, as typically posed, it is beset by many confusions and ambiguities. Only by sorting out some of the different ways in which the question is asked, as well as the motivations behind the question, can we hope to find an answer, or at least decide what an adequate answer might look like. For some, the question is whether artificial agents, especially humanoid robots, like Commander Data in Star Trek: The Next Generation, will someday become sophisticated enough and enough like humans in morally relevant ways so as to be accorded equal moral standing with humans. This would include holding the robot morally responsible for its actions and according it the full array of rights that we confer upon humans.

London, Oct 24 (IANS) Using Artificial intelligence (AI) or machine learning technology, a team of researchers has predicted outcomes in judicial decisions at the European Court of Human Rights (EctHR) with 79 per cent accuracy. The AI method, developed by researchers from University College London (UCL), University of Sheffield and US-based University of Pennsylvania is the first to predict the outcomes of a major international court by automatically analysing case text using a machine learning algorithm. "We don't see AI replacing judges or lawyers but we think they will find it useful for rapidly identifying patterns in cases that lead to certain outcomes," said Nikolaos Aletras, who led the study at UCL's computer science department. "It could also be a valuable tool for highlighting which cases are most likely to be violations of the European Convention on Human Rights," Aletras added. In developing the method, the team found that judgements by the ECtHR are highly correlated to non-legal facts rather than directly legal arguments, suggesting that judges of the Court are'realists' rather than'formalists'.

Imagine receiving a phone call from your aging mother seeking your help because she has forgotten her banking password. The voice on the other end of the phone call just sounds deceptively like her. It is actually a computer-synthesized voice, a tour-de-force of artificial intelligence technology that has been crafted to make it possible for someone to masquerade via the telephone. Such a situation is still science fiction -- but just barely. It is also the future of crime.

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).