When artificial intelligence technology intersects with abundant oil and gas seismic data, the outcome could yield a more accurate depiction of what lies beneath the surface, enabling cash-strapped drillers to better target sweet spots and maximize returns. It's all based on algorithms that essentially teach computers how to solve complex problems--in this instance, how to quickly and accurately find subsurface faults that lead to lucrative hydrocarbon discoveries. Naveen Rao, the CEO of two-year-old startup Nervana Systems, compared the concept to the brain and its network of neurons. "Each neuron does a little bit of information processing. It combines that with the output of many other neurons, and the whole stack basically processes information that comes in through our sensors," Rao told Hart Energy.

Researchers from MIT and machine learning startup PatternEx combined machine learning algorithms-based systems and human expertise in new cybersecurity AI platform AI2. This innovation is said to detect cyberattacks 85 percent of the time. Cybersecurity today is executed by either humans or machines, which could have their own challenges – missed attacks because the parameters don't match rules set by human experts, or systems mistakenly zeroing in on non-threats. So why not combine humans and AI to get the best of both worlds? This is what researchers from the Massachusetts Institute of Technology accomplished with its new artificial intelligence system called AI2.

As a reminder, all participants' lines will be in the listen-only mode. There will be an opportunity for you to ask questions after the presentation concludes. I would now like to hand the conference over to Mr. Aravind Viswanathan. Thank you and over to you, sir. We will begin the call with business highlights and overview by Abid, the Chief Executive Officer and Member of the Board, followed by the financial overview by our CFO, Jatin Dalal. Afterwards, the operator will open the bridge for Q&A with our management team. Before Abid starts, let me draw your attention to the fact that during this call, we may make certain forward-looking statements within the meaning of Private Securities Litigation Reform Act 1995. These statements are based on management's current expectations and are associated with uncertainties and risks, which may cause the actual results to differ materially from those expected. The uncertainties and risk factors are being explained in our detailed filings with the SEC. Wipro does not undertake any obligation to update the forward-looking statements to reflect events and circumstances after the date of filing thereof. The conference call will be archived and the transcript will be available on our website. Ladies and gentlemen, let me now hand it over to Mr. Abid. Today is the first opportunity for me to interact with all of you since I've taken over as the Chief Executive Officer of Wipro, and it's a special moment for me. While I will speak about the performance of our full quarter and the full fiscal year, I thought I will take this opportunity to begin by speaking about our ambition, our strategy and how we are going to execute this strategy. Since I got announced within two days, I was able to define and announce my structure and I had already preselected my leadership team which I announced on 6th of January, effective February 1. Over the past 80 days after I have taken over as CEO, I've had the opportunity to go around the globe and meet about 70 of our top 100 clients. And both with my leadership team and with the customers, I've had the opportunity to validate the strategy that we have been working on and this gives me a high level of confidence on the relevance of our overall strategy. Our ambition is to double our revenues to 15 billion by fiscal 2020 with a 23% operating margin.

The team from the university's Computer Science and Artificial Intelligence Laboratory (CSAIL) and machine-learning startup PatternEx developed the new platform that can identify cyberattacks 85% of the time and even reduce the amount of false positives by a factor of five. AI2 goes through data and then spots suspicious activity through unmanned machine learning. From there, human reviewers check for signs of a security breach, a solution that can predict attacks with precision and eliminate the need to pursue bogus intelligence leads. AI2 uses three machine learning algorithms for detecting suspicious events, but just like other AI systems it also needs human feedback to verify its findings, so the system is constantly being enhanced through the team's so-called'continuous active learning system'. For computer science professor Nitesh Chawla of University of Notre Dame, the research is a potential'line of defense' against fraud, account takeover, service abuse, and other attacks faced by consumer-oriented systems today.

At a time when there is high pressure and scrutiny around security, innovative new technologies are helping to track and predict crime. Artificial intelligence (AI) is key in this new way of fighting crime, providing analysis that can give real support to law enforcement services as they make difficult decisions about how best to use their resources. CCTV cameras have been in use in Europe since the Second World War. However, they have sometimes come under scrutiny about their effectiveness when it comes to tackling crime, as they tend to only provide retrospective support for law enforcement (such as placing a suspect at the scene of the crime in their trial). With new cameras able to send and receive data via a computer network and the Internet (IPTV), as well as the huge amount of data collected every second using traditional CCTV, AI can collate this data extremely quickly ready for analysis.

Madeline MacDonald says she was an 18-year-old freshman at Brigham Young University when she was sexually assaulted by a man she met on an online dating site. She reported the crime to the school's Title IX office. That same day, she says, BYU's honor code office received a copy of the report, triggering an investigation into whether MacDonald had violated the Mormon school's strict code of behavior, which bans premarital sex and drinking, among other things. Now MacDonald is among many students and others, including a Utah prosecutor, who are questioning BYU's practice of investigating accusers, saying it could discourage women from reporting sexual violence and hinder criminal cases. Tens of thousands have signed an online petition calling on the university to give victims immunity from honor code violations committed in the lead-up to a sexual assault.

Norwegian authorities have violated mass killer Anders Behring Breivik's human rights by holding him in solitary confinement in a three-cell complex where he can play video games, watch TV and exercise, a court in Oslo ruled Wednesday. In a written decision, the Oslo district court said Breivik's solitary confinement for killing 77 people in 2011 bomb-and-gun massacres breached the European Convention on Human Rights' ban on inhuman treatment. "The prohibition of inhuman and degrading treatment represents a fundamental value in a democratic society," the court said. "This applies no matter what -- also in the treatment of terrorists and killers." The court ordered the government to pay Breivik's legal costs of 331,000 kroner, about 41,000.

MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), together with researchers from security firm PatternEx, has revealed a new AI (Artificial Intelligence) system called AI2, which can detect 85 percent of cyber-attacks, with false positives rates five times smaller than existing solutions. The new system doesn't rely entirely on artificial intelligence (AI), but also on user input, something that researchers call analyst intuition (AI), hence its name of AI2. Researchers said they fed AI2 with over 3.6 billion lines of log files, allowing the system to scan the content with unsupervised machine-learning techniques. At the end of each day, the system presents its findings to a human operator, who then confirms or dismisses security alerts. This human feedback is then incorporated into AI2's learning system and used the next day for analyzing new logs. After their tests had concluded, MIT and PatternEx researchers said AI2 achieved an 85 percent accuracy rate in detecting cyber-attacks, which is 2.92 times better than similar automated cyber-attack detection systems used today.

An AI created by scientists at the Massachusetts Institute of Technology (MIT) uses machine learning to detect suspicious activity - getting it right 85% of the time. The system uses an algorithm called "AI2", that detects anomalies, in conjunction with a human expert, because AI2 on its own can lead to false positives, according to MIT News. "The more attacks the system detects, the more analyst feedback it receives, which, in turn, improves the accuracy of future predictions," said one of the researchers behind the project, Kalyan Veeramachaneni. "That human-machine interaction creates a beautiful, cascading effect." The merging of artificial intelligence and what researchers call "analyst intuition" has allowed for this new system to be successful in its early development, Veeramachaneni and fellow scientist Ignacio Arnaldo said.

MIT scientists have built a hybrid human/artificial intelligence (AI) machine that they claim can learn how to detect 85% of cyber attacks – that's roughly three times better than previous benchmarks – while reducing false positive rates by a factor of 5. Nitesh Chawla, professor of computer science at Notre Dame University, said in a statement from MIT that the machine "has the potential to become a line of defense against attacks such as fraud, service abuse and account takeover." Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the machine-learning startup PatternEx demonstrated the platform, called AI2, in a paper titled "AI2: Training a big data machine to defend". As the researchers describe the current state of the art, today's security systems are typically driven by either humans – so-called "analyst-driven solutions" – or by machine. The problem with security systems based on fixed rules is that they miss attacks that don't match those rules. Machine-learning approaches, as the name suggests, rely on an adaptive process that can trigger annoying numbers of false positives.