A software platform that mines data and uses machine learning to boost household energy efficiency has taken out top honours at Energy Hack 2016 – a two-day energy and technology brainstorm held in Melbourne over the weekend. The event, hosted by the University of Melbourne's Energy Institute and upstart online electricity retailer Powershop, brought together 80 participants to form 20 teams to unlock ideas and stimulate entrepreneurship in the energy industry. Eight judges saw pitches for 13 creative technology ideas, ranging from an energy management chatbot, to a matchmaking service for renewable energy project owners and investors, to an app to educate primary school children, and an energy load matching algorithm. The winners, a team of PhD students called Planet Lovers, were chosen for their design of a platform that uses data mining techniques and machine learning to help consumers use energy more efficiently. "We believe that existing energy services don't use the full potential of big data to provide deep insights for consumers," said Planet Lovers co-founder Zahra Ghafoori.

For this setup we need adjusted data for all Dow-30 stocks since 01/2000. The DLPAL p-indicator workspace setup is shown below. We have applied a profit target and stop-loss of 2% because we are interested in short-term directional price action. We also marked "Show All results" because we then would like to calculate the P-Dow indicator value. The longer-term trend is removed from the results by checking "Detrend All results.

With the proliferating "smart" products such as smartphones, smart home devices, machines are currently being made "smarter" than humans. While this is the next "it" subject for the future generations, as with all aspects, there are risks that come with it. There are over 8,000 leading researchers and scientists including Stephen Hawking, Elon Musk, and Bill Gates, who signed an open letter insinuating the AI's possible detrimental effects to humanity. Tech Crunch writes, "Their main concern is that an existential risk faces humanity: an AI in control of autonomous weapons." In addition to that, Stephen Hawking says, "Success in creating AI would be the biggest event in human history," but he adds, "Unfortunately, it might also be the last, unless we learn how to avoid the risks." In an Observer Opinion report, there are U.S. scientists who are already using algorithms on computers, which can predict military strategies of terrorists.

A team of researchers has used an artificial intelligence system to correctly predict the outcome of hundreds of human rights cases. The AI, developed in collaboration between University College London, the University of Sheffield and the University of Pennsylvania, analysed a variety of cases heard in the European Court of Human Rights. It was then able to predict the correct verdict with an accuracy of 79%. While the results make a convincing case for the use of machine learning in legal settings, the researchers don't believe it will mark the end of lawyers and judges. "There is a lot of hype about AI but we don't see it replacing judges or lawyers any time soon," said Dr Nikolaos Aletras, leader of the study at UCL. "What we do think is they'd find it useful for rapidly identifying patterns in cases that lead to certain outcomes."

In recent months, security researchers have shown that machine learning algorithms can be reverse-engineered and made to expose user data, like personal photos or health data. So how can we protect that information? New research from OpenAI and Google shows a way to build AI that never sees personal data, but is able to function as if it had. Ian Goodfellow, a researcher at OpenAI, compares the system to medical school. "The doctors who teach in medical school have learned everything they know from decades of experience working with specific individual people, and as a side effect they know a lot of private medical histories," Goodfellow says.

Elon Musk's new plan to go all-in on self-driving vehicles puts a lot of faith in the artificial intelligence needed to ensure his Teslas can read and react to different driving situations in real time. AI is doing some impressive things--last week, for example, makers of the AlphaGo computer program reported that their software has learned to navigate the intricate London subway system like a native. Even the White House has jumped on the bandwagon, releasing a report days ago to help prepare the U.S. for a future when machines can think like humans. But AI has a long way to go before people can or should worry about turning the world over to machines, says Oren Etzioni, a computer scientist who has spent the past few decades studying and trying to solve fundamental problems in AI. Etzioni is currently the chief executive officer of the Allen Institute for Artificial Intelligence (AI2), an organization that Microsoft co-founder Paul Allen formed in 2014 to focus on AI's potential benefits--and to counter messages perpetuated by Hollywood and even other researchers that AI could menace the human race.

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 system was developed by researchers at University College London (UCL), the University of Sheffield and the University of Pennsylvania. The study, led by Dr. Nikolaos Aletras, was published in the PeerJ Computer Science journal today. The team developed artificial intelligence software capable of detecting patterns in complex decisions. The researchers tasked the computer with weighing up legal evidence and moral questions of right and wrong, enabling it to act as a judge in court cases. The technology is more commonly applied to engagement analysis for films and music but it proved to be adept at reaching legal verdicts.

When is it appropriate for police to conduct a face recognition search? To figure out who's who in a crowd of protesters? To monitor foot traffic in a high-crime neighborhood? To confirm the identity of a suspect -- or a witness -- caught on tape? According to a new report by Georgetown Law's Center on Privacy & Technology, these are questions very few police departments asked before widely deploying face recognition systems.

With the growth of Machine Learning in the past few years, many tasks are being done with the help of machine learning algorithms.Unfortunately or fortunately, there has been little work done on machine learning and cyber security. So I thought of presenting some at Fsecurify. A few days ago, I had this idea about what if we could detect a malicious URL from a non-malicious URL using some machine learning algorithm. There has been some research done on the topic so I thought that I should give it a go and implement something from scratch. The first task was gathering data.