HANOVER, GERMANY – President Barack Obama delivered a strong defense of international trade deals Sunday in the face of domestic and foreign opposition, saying it's "indisputable" that such agreements strengthen the economy and make U.S. businesses more competitive worldwide. Obama, on a farewell visit to Germany as president, is trying to counter public skepticism about a trans-Atlantic trade deal with Europe, while also facing down criticism from the 2016 presidential candidates of a pending Asia-Pacific trade pact. Despite all that, Obama said: "the majority of people still favor trade. They still recognize, on balance, that it's a good idea. "It is indisputable that it has made our economy stronger," Obama said about international trade. He said he was confident the trans-Atlantic trade deal could be completed by the end of year, to be presented for ratification.

They continue to target computer networks and damage their infrastructure. Now, a combined team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and machine-learning startup PatternEx have developed a powerful artificial intelligence system called AI2 which works significantly better than any existing cyber-attack detection system. The system has been tested on 3.6 billion log lines or pieces of data that reveal major system activities triggered by millions of users over a period of three months. Researchers have found that new tool can detect cyber-attacks with 85% accuracy which is roughly three times better than the previous benchmark. Moreover, it reduces the number of'false positives' – an event wrongly identified as threat – by a factor of 5. Conventional security systems are either virtual machine-based or humanly operated but none of them has proven overwhelmingly successful at encountering cyber-attacks.

Researchers from the Massachusetts Institute of Technology have created an AI system that can predict a cyberattack before it happens in 85% of incidents. Analyst-driven systems rely on rules created by people and consequently can't detect attacks that don't adhere to those rules, whereas machine-learning systems rely on anomaly detection, which tends to generate false positives that have to be investigated by people.MIT researchers have announced that they've concocted a new artificial intelligence system capable of successfully detecting 85% of cyber-attacks. Part of the challenge of merging human- and computer-based threat detection has been the manual labeling of data for algorithms.The system has been tested on 3.6 billion log lines or pieces of data that reveal major system activities triggered by millions of users over a period of three months. It then reports this activity to a human analyst who can then judge if there's an actual attack.With that feedback, it takes on board whether or not it should be classifying the events as attacks or not, then refines its internal models.According to Engadget, Kaylan Veermachaneni, co-creator of the system, said that one should think of the new system as a virtual analyst. In the near future the industry and federal regulators will need to figure out a balance between the need of cyber security and protecting consumers' privacy. This method often leads to false positives, meaning that humans doubt the reliability of the system and are forced to go back and check all the results anyway.And the more data it analyses, the more accurate it becomes.

Machine learning and the artificial intelligence that it promises to deliver are clearly here to stay. The only remaining question is what will these technologies conquer next? The algorithms and techniques that have been exciting researchers and practitioners over the last few years are being dramatically improved, tuned for perfection, and in some cases completely replaced by a new generation of increasingly powerful algorithms. The investments in areas such as deep learning and the promise of building multi-layer perceptron (or artificial neurons) to solve a host of challenging problems has started to move out of dusty offices and laboratories toward the center of our economy in areas such as healthcare, marketing, communications, finance, energy, education, and even public safety. The number of useful applications is growing rapidly and the benefits of early investments by technology giants and influential research institutions are paying off nicely.

A car's driver doesn't necessarily have to be human: The artificial intelligence behind Google Inc.'s self-driving system could count, according to federal highway safety officials. In a letter posted on the National Highway Traffic Safety Administration's website, the agency responded to Google's request for interpretation of several federal safety standards as they apply to the tech giant's self-driving cars. As a premise of the interpretation, "NHTSA will interpret'driver' in the context of Google's described motor vehicle design as referring to the [self-driving system], and not to any of the vehicle occupants," Chief Counsel Paul Hemmersbaugh said in the letter. "We agree with Google its [self-driving vehicle] will not have a driver in the traditional sense that vehicles have had drivers during the last more than 100 years." Google's not-so-secret special projects lab, Google X, is housed in an old shopping mall near Mountain View, Calif.

Cybersecurity threats are among the most pressing concerns for businesses and institutions that need to protect information, but today's security systems are limited. Most security systems fall into two categories: human analyst or machine learning. Now, a new research paper from MIT shows that a combination of human experts with a machine learning system--in other words, supervised machine learning--provides better results than either human or machine alone. "AI squared," which uses a system developed by PatternEx, is 10 times better at catching threats than machine learning alone, and reduces false positives by a factor of five. This, said MIT's researchers, is three times better than current benchmarks.

I'm often asked about my thoughts on the future. What will transportation be like? What new forms of entertainment will we enjoy? While I have covered these topics in previous articles, it's important to understand that they're all forms of work. So today I want to discuss the future of work -- how technological advancements, namely robotic assistants and tools, as well as tech-enhanced globalization, will affect our daily work flow and the labor market in general.

Peter Singer is a professor of bioethics at Princeton University and Laureate Professor at the University of Melbourne His books include Animal Liberation, The Life You Can Save, The Most Good You Can Do, and, most recently, Famine, Affluence and Morality. Last month, AlphaGo, a computer program specially designed to play the game Go, caused shock waves among aficionados when it defeated Lee Sedol, one of the world's top-ranked professional players, winning a five-game tournament by a score of 4-1. Why, you may ask, is that news? Twenty years have passed since the IBM computer Deep Blue defeated world chess champion Garry Kasparov and we all know computers have improved since then. But Deep Blue won through sheer computing power, using its ability to calculate the outcomes of more moves to a deeper level than even a world champion can.

A group of MIT researchers has sketched out a way to address a gap in cybersecurity that exists between human and machine. Human-made rules, which are meant to alert the system of an attack, don't work unless an attack exactly matches one of those rules. Machine-learning measures typically rely on anomaly detection. Consequently, false alarms aren't uncommon and the system starts to distrust itself. Combine these two forces - man and machine - and that's when magic can happen, according to a group of researchers out of MIT's Computer Science and Artificial Intelligence Lab (CSAIL).

It's fun to ponder a future filled with self-driving cars, a world with breezy commutes where robot navigators have made deadly crashes a thing of the past. But how far off is that future, really? Last month, Google suggested that this driverless utopia may actually be much further away than many people may realize. In a speech at SXSW in Austin, Google's car project director Chris Urmson explained that the day when fully autonomous vehicles are widely available, going anywhere that regular cars can, might be as much as 30 years away. There are still serious technical and safety challenges to overcome. In the near term, self-driving cars may be limited to more narrow situations and clearer weather.