The family of the driver killed in a US drone strike that targeted Taliban leader Mullah Akhtar Mansoor have registered a case against US officials seeking murder charges. The case, filed by the family of Mohammad Azam who was killed last week along with Mansoor in the Pakistani town of Ahmad Wal near Afghan border, said the father of four was innocent. US officials described the car's driver as a "second male combatant" but according to Pakistani security officials he was a chauffeur named Mohammad Azam who worked for the Al Habib rental company based out of Quetta, the region's main city. "US officials whose name I do not know accepted the responsibility in media for this incident, so I want justice and request legal action against those responsible for it," Mohammad Qasim, Azam's brother said in a police report, a copy of which was seen by the AFP news agency. "My brother was innocent and he was very poor who has left behind four small children and he was the lone bread earner in the family," he added.

Deep Learning is now of the hottest trends in Artificial Intelligence and Machine Learning, with daily reports of amazing new achievements, like doing better than humans on IQ test. In 2015 KDnuggets Software Poll, a new category for Deep Learning Tools was added, with most popular tools in that poll listed below. I haven't used all of them, so this is a brief summary of these popular tools based on their homepages and tutorials. Theano and Pylearn2 are both developed at University of Montreal with most developers in the LISA group led by Yoshua Bengio. Theano is a Python library, and you can also consider it as a mathematical expression compiler.

Machine learning is being used in a variety of domains to restrict or prevent undesirable behaviors by hackers, fraudsters and even ordinary users. Algorithms deployed for fraud prevention, network security, anti-money laundering belong to the broad area of adversarial machine learning where instead of ML trying to learn the patterns of benevolent nature, it is confronted with a malicious adversary that is looking for opportunities to exploit loopholes and weaknesses for personal gain. To evade these models an attacker needs to arm themselves with knowledge of the algorithm, feature space and the training data. Attackers have to obtain this information through a limited number of probing opportunities. Designing the feature space for adversarial models is highly dependent on the use case and what limitations you wish to place on the adversary.

A DNA test has confirmed that Afghan Taliban leader Mullah Akhtar Mansour was killed in a U.S. drone strike, Pakistan's interior ministry said Sunday, as the family of a driver killed in the strike sought legal action. A DNA sample from one of the men killed in the U.S. drone attack was successfully matched with a close relative of Mansour, the interior ministry statement said. American and Afghan officials had already confirmed Mansour's death, but Islamabad had declined to do so before the DNA test results. Mansour had entered Pakistan from Iran using a false name and fake Pakistani identity documents on May 21, when his car was hit by the U.S. missile. On Sunday, the family of his driver -- identified as Mohammed Azam -- filed a police case against unknown U.S. officials, seeking to press murder charges against them, police officer Abdul Wakil Mengal said.

You've probably heard about OpenAI -- a new nonprofit to focus on artificial intelligence research that is good for humanity. So here we have a case of knowledgeable people recognizing a threat and deciding that the way forward is to create a nonprofit organization. This moment has some historical precedent. In 1955, Albert Einstein, Bertrand Russell and several other scientists got together and issued a manifesto about the dangers of nuclear technologies. It would launch decades of Pugwash conferences and the anti-nuclear movement. The founders of OpenAI also issued a manifesto.

Identifying critical information out of a sea of unstructured data, or customizing real-time human interaction are a couple of examples of how clients utilize our technology at Idibon--a San Francisco startup focusing on Natural Language Processing (NLP). The machine learning libraries in Spark ML and MLlib have enabled us to create an adaptive machine intelligence environment that analyzes text in any language, at a scale far surpassing the number of words per second in the Twitter firehose. Our engineering team has built a platform that trains and serves thousands of NLP models, which function in a distributed environment. This allows us to scale out quickly and provide thousands of predictions per second for many clients simultaneously. In this post, we'll explore the types of problems we're working to resolve, the processes we follow, and the technology stack we use.

Apple now has the tech in place to give its digital assistant a big boost thanks to a UK-based company called VocalIQ it bought last year. "Wipes the floor" remains to be proven and we'll likely see something announced at WWDC in a couple of weeks but this article points out that Apple develops "in private" for the most part. Those who doubt Apple's AI efforts seem to forget that fact.

On the heels of such great discoveries as the germ theory, X-rays, DNA, and Penicillin, the next stage of our growth in healthcare will come from Artificial Intelligence (A.I.). This includes effective machine learning, neural networks, and a promise of greater pattern recognition, data analysis, 'general thinking', decision-making and efficiency. Up until recently, a large percentage of our data was not digitized, nor did we have the storage and processing power. Now that we have this capability, we will soon see significant steps forward in A.I. innovation, growth of neural networking, and deep-learning technology including: Take a moment and see what's on the horizon. Venture funding will continue to increase in the digitial health sector, with respct to dollars invested and deals made.

This painting appears in Angela Gilmour's "Boolean Logic" series, which went on display in Cork, Ireland, last fall. Long before the smartwatch or Google search, there was George Boole. Born in 1815 in the U.K., the mathematician invented a system of logic distilling complicated actions into simpler values -- "true" or "false," "on" or "off" -- thereby inventing the foundations of the digital age. This system, dubbed "Boolean logic" after the mathematician, was used in the development of the first electrical circuits, the parent technology to computers. Artist Angela Gilmour, who is based in Ireland, saw these principles up close while working as a process and product development engineer in the computer manufacturing industry.

As with most recent developments in AI, the web erupted with outlandish storylines. Many described the move as bold despite the fact that (Torch), which is maintained by Ronan Collobert of Facebook AI Research, already offers categorically similar open-source deep learning tools and that Yoshua Bengio's lab has long maintained Theano, the revolutionary software package which pioneered the category in the first place, making deep learning easy for the masses. In an article at Wired, Cade Metz described TensorFlow as Google's "Artificial Intelligence Engine". Even this headline stands out as hyperbolic for an article describing an open-source library for performing linear algebra and taking derivatives. A number of other news outlets marveled that Google made the code open source.