Imagine yourself one hundred years from now. Yes, you're still alive, breathing through genetically engineered pig lungs, and having dinner at your favorite restaurant. A robot waiter rolls up to refill your glass of wine, which is equipped with a sensor that allows the restaurant to automatically deduct 10 from your Bitcoin account. Your companion, who doesn't actually speak the same language as you, is saying how much she loves her salad, which comes from a farm that uses precision agriculture techniques to boost productivity by effectively dividing fields into one-inch square plots that each receive customized fertilizer mixes based on their specific conditions. You understand her perfectly thanks to a small device in your ear that instantly translates her words and perfectly mimics her voice.

Scientists at Massachusetts Institute of Technology (MIT) claim they have created an AI that can detect 85% of cyber attacks -- albeit with the help of humans. The "AI2" algorithm, developed by MIT's Computer Science and Artificial Intelligence Lab (CSAIL) and machine learning startup PatternEx, can reportedly detect cyber attacks three times more effectively than today's current systems. AI2 has been tested on 3.6 billion pieces of data, known as "log lines," which were created over a three month period by millions of people. In order to predict attacks, AI2 scans sets of data and identifies suspicious activity. It does this by clustering the data into meaningful patterns using unsupervised machine-learning, according to MIT.

Computer scientists from the Michigan Institute of Technology (MIT) and a machine learning startup, PatternEx, have reportedly developed a new system that can correctly detect 85% of cyberattacks using artificial intelligence merged with input from human experts. At the moment, security systems are closely monitored by humans and programmed to pick up on cyberattacks that only follow very specific rules, as such missing any attacks that do not follow those rules. But, there are also systems autonomously run by computers that practice anomaly detection – i.e. the identification of items, events or observations – that do not conform to an expected pattern or other items in a dataset. 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. To improve this, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), in collaboration with PatternEx, have developed the AI2 artificial intelligent platform, which merges three different machine learning methods that enable computers to learn unsupervised.

Drones could be bringing parcels to your door within two years, thanks to a bill that left the Senate today. The bipartisan aviation policy bill, which passed the Senate 95-3 Tuesday, demands that the Federal Aviation Authority (FAA) authorize package deliveries by drones within two years. The bill, which must now be debated by the House, also makes changes to various regulations including those affecting airport security and airplane pricing. It says that the agency must create a small drone'air carrier certificate' for operators of delivery drone fleets, similar to the safety certificates granted to commercial airlines. These rules are needed for Amazon and other companies to deploy fleets of delivery drones.

Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have collaborated alongside the machine learning startup PatternEx to demonstrate how an artificial intelligence platform that makes use of continuous input from human experts would be able to predict cyber attacks better than the systems that exist today. CSAIL and PatternEx are calling their new AI platform AI² due to how it combines the intuition of analysts with artificial intelligence. Previously'analyst-driven solutions' relied on the rules created by security experts which resulted in attacks that did not correspond to their rules slipping through the cracks. The machine learning approach to cyber-security relies on'anomaly detection' to discover possible attacks but often results in false positives. CSAIL and PatternEx created AI² by combining machine learning's ability to detect anomalies with the knowledge of analysts to flag possible threats.

Malware has proven increasingly difficult to detect via signature or heuristic-based methods, which means most Antivirus (AV) programs are woefully ineffective against mutating malware, and especially ineffective against APT attacks (Advanced Persistent Threats). Typical malware consists of about 10,000 lines of code. Five to six years ago marked the beginning of the use of machine learning to solve non-linear problems such as facial recognition or understanding malware, and what features one needs to extract to uniquely identify such programs. Other techniques, such as sandboxing and machine-based techniques, are not as fast nor as accurate as Deep Learning. Deep Instinct, founded by Guy Caspi and Eli David, Israeli Defense Force Cybersecurity veterans, applies artificial intelligence Deep Learning algorithms to detect structures and program functions that are indicative of malware.

Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), and a machine-learning startup, PatternEx, have developed a virtual artificial intelligence analyst that can predict 85% of cyber-attacks using input from humans. As a way to address some of the challenges security analysts face, researchers from CSAIL and PatternEx presented a paper on a new artificial intelligence platform called AI2 They found that their system is able to detect cyberattacks more frequently because it is continuously incorporating input from human analysts. There are several problems that currently exist in today's state of cybersecurity. One major problem includes the lack of qualified security analysts in the market. It is critical to increase analyst efficiency, but existing tools generate too many false positives that create distrust and need to be investigated by humans in the end, said Ignacio Arnaldo Lucas, one of the researchers at PatternEx.

This time last year Steve Wozniak was sounding a cautionary note about the future of Artificial Intelligence (AI), warning that computers would one day take over from humans and joking that we might even end up as their pets. In a recent interview with Australia's ABC TV's Lateline the engineering genius appeared more sanguine about the future of self-aware, super-intelligent Artificial Intelligence and much more concerned with the real world killer robots that are all but with us: Lethal Autonomous Weapon Systems (LAWS). The Apple co-founder maintains that human-level Artificial Intelligence won't happen for "a very long time": It might take 200 years before they are really fully able to operate all of their needs in the world, until then they're going to need human beings … I'm not really worried at all. It's very scary to make autonomous weapons that are just following some programmed set of instructions … even when you're driving a car there is no one set of rules … if a lane is closed off you have to do something against the rules … I don't think it's a good idea at all. I don't think we can really stop it.

Researchers with MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have announced a breakthrough in cyber threat detection that promises to dramatically lower the amount of time human analysts spend sorting through information in search of evidence of cyber attacks. IT analysts spend countless hours scouring through mountains of data in order to find evidence of compromised systems. Often, the search for anomalies in data is so extensive, that overwhelmed human teams are forced to overlook potentially critical information for lack of time. That's where MIT's researchers believe they can help. In a world where most companies are under constant threat of cyber attack, having a system that never sleeps or takes a coffee break is a big help.

Maurice Stucke, a professor at the University of Tennessee College of Law and a former trial attorney with the US Department of Justice's Antitrust Division, recently received a 2016 Antitrust Writing Award for a legal article regarding artificial intelligence. The article, "Artificial Intelligence & Collusion: When Computers Inhibit Competition"--co-authored with Oxford University Faculty of Law Professor Ariel Ezrachi--discusses the challenging legal and ethical questions that are emerging as artificial intelligence development and implementation throughout society continues to develop at an accelerating rate. Artificial intelligence is "set to change the competitive landscape and the nature of competitive restraints," Stucke and Ezrachi write. "We are shifting from the world where executives expressly collude in smoke-filled hotel rooms to a world where pricing algorithms continually monitor and adjust to each other's prices and market data." Stucke and Ezrachi have frequently collaborated on intersectional issues concerning law and technology.