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.

Artificial intelligence is expected to transform a wide range of industries, as simple tasks are automated and carried out by machines. The IT sector is no different, with machine learning algorithms increasingly being targeted at automating and improving data centre operations. A notable example has been Google, which recently revealed that it is using its own DeepMind technology to manage power consumption at its huge server farms, reducing the amount of electricity needed by 40 percent. There is also potential for AI technology to automate functions carried out by IT operations teams. Machine learning offers a way to manage infrastructure and react quickly to faults without human intervention.

Artificial intelligence has become one of the hottest trends in Big Law, with a number of major U.S. and international law firms starting to use machine…

Google's Deep Dream software proved that computer imagination can be strange and hallucinogenic. But, given the right parameters, it can also be profoundly dirty. Just look at the AI-generated pictures above -- the top row of images all look fairly innocent (they're supposed to be towers); but the bottom row, well, has an unmistakeable penis-y feel to it. That's right: artificial intelligence has learned how to hallucinate genitals. This imagery is the work of computer scientist Gabriel Goh, who created a neural network that mashes together two existing programs. The first is a Deep Dream-like image generator from MIT that uses deep learning to look at libraries of pictures and create similar images, and the second is an open source program from Yahoo that automatically detects and filters pornography.

A team of researchers in the UK have developed an artificial intelligence (AI) program that can predict the outcome of human rights cases involving torture, degrading treatment, and privacy. The AI -- developed by researchers at University College London (UCL) and the University of Sheffield, alongside Dr Daniel Preo?iuc-Pietro from the University of Pennsylvania -- successfully predicted the verdicts for 79% of 584 cases at the European Court of Human Rights (ECtHR). In order to reach a decision, the AI analysed case text using a machine learning algorithm, the researchers said. The algorithm looked for patterns in the text and was able to classify each case either as a "violation" or a "non-violation". To prevent bias and mislearning, the team selected an equal number of violation and non-violation cases.

In the study, a team of British and American researchers said it had used an AI system to correctly predict the outcomes of hundreds of cases heard at the European Court of Human Rights. The AI, which analyzed 584 English language case texts related to Article 3, 6 and 8 of the European Convention on Human Rights using a machine learning algorithm, came to the same verdict as human judges in 79 percent of the cases. It could also be a valuable tool for highlighting which cases are most likely to be violations of the European Convention on Human Rights," lead researcher Nikolaos Aletras, also from UCL, noted in the statement. "It could also be a valuable tool for highlighting which cases are most likely to be violations of the European Convention on Human Rights."