"Too big to fail" strategy did not save banks from failing in financial crush in 2008. Recent news about content meets pipe by merging AT&T and Time warner or previous news Comcast was buying Timer warner that was not successful. Delta airlines almost bought southwest airlines that was blocked. It was not blocked when Delta bought northwest. JP Morgan Chase bought several Banks during financial crush and became one of the biggest financial institution ever. Continuous effort to grow bigger and making their stock price higher.

Enterprises around the world are increasingly investing in technologies for data innovation, including machine learning and even artificial intelligence (AI) as they look to close the gap with digital native companies such as Uber and Waze. But will these technologies really make a significant impact beyond these newer companies? New research from consulting firm Accenture says it will. A report from the company shows that these technologies are poised to exert an enormous impact on economic growth rates and workforce productivity. As IT organizations help their enterprises implement such technologies, they will also help those enterprises compete in this new era.

Artificial intelligence (AI) is becoming very real--and at an exponentially faster rate. Moreover, those organizations that leverage AI in sync with those hard and soft trends that are shaping the future stand to make the most of its extraordinary potential. On one level, artificial intelligence is poised to help anticipate and address such critical issues as cybersecurity, civil unrest and even outright acts of terrorism. For example, using technology such as automated smart detection, officials at the recent Olympics in Rio were successful in maintaining security in a wide array of venues and locations. Closer to home, the Central Intelligence Agency's deputy director for digital innovation Andrew Hallman recently addressed the issue of anticipatory intelligence at an event hosted by the government and technology website NextGov.

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 University of Sydney has established an on-campus innovation hub, aimed at fixing Australia's historic inability to turn ideas into "wealth generating businesses", which the university said is crucial to the country's economic future. According to James Meade, head of the Sydney Social Innovation Hub, universities have an essential role to play in fostering entrepreneurship and innovation in both the private and public sectors. "Innovation and entrepreneurship requires enthusiasm and we have lots of that. But it also requires targeted support. By leveraging informal knowledge networks within the university we can enable successful student innovation outcomes," Meade said.

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.