Machine learning is a subset of the field of computer science known as artificial intelligence (AI), which seeks to simulate the workings of a human brain. Machine learning is a form of programming, where the software improves its response after learning from previous responses, rather than following only scripted responses. Nascent forms of machine learning first emerged some 20 years ago. The most modern form is "deep learning", which tries to model the neural networks of the human brain by identifying millions of patterns in large datasets to predict the next outcome. It plays an important role in big data analytics and is currently the rage in machine-learning research, given the accessibility of big data and computational power.

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.

Over the last several decades, the digital revolution has changed nearly every aspect of our lives. The pace of progress in computers has been accelerating, and today, computers and networks are in nearly every industry and home across the world. Many observers first noticed this acceleration with the advent of modern microchips, but as Ray Kurzweil wrote in his book The Singularity Is Near, we can find a number of eerily similar trends in other areas too. According to Kurzweil's law of accelerating returns, technological progress is moving ahead at an exponential rate, especially in information technologies. This means today's best tools will help us build even better tools tomorrow, fueling this acceleration.

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.

High performance DAQMAG2A Rugged Display Computer: High performance DAQMAG2A Rugged Display Computer from GE's Intelligent Platforms business are designed to minimize cost, risk and time-to-market for prime contractors, systems integrators and OEMs developing sophisticated video capture, processing and transmission applications with multiple inputs and outputs, it is qualified to the DO-160G Environmental Conditions and Test Procedures for Airborne Equipment standard. The DAQMAG2A Line Replaceable Unit (LRU) has already been successfully deployed by AgustaWestland on the AW189 and AW101 helicopters and by FLIR Systems Inc.GE's Intelligent Platforms business (NYSE: GE) is headquartered in Charlottesville, VA and part of GE Energy Management. The company's work in the military/aerospace segment, headquartered in Huntsville, AL, and Towcester, England, provides one of the industry's broadest ranges of high performance, rugged, SWaP-optimized embedded computing platforms. Backed by programs that provide responsive customer support and minimize long term cost of ownership for multi-year programs, GE's solutions are designed to help customers minimize program risk and cost, and to speed time-to-market. The high TRL (Technology Readiness Level 9) of the DAQMAG2A means systems integrators can select it with confidence to concentrate on solving more important challenges such as integration into the higher level system, application development/port and so on.

Almost 30 years ago I stood on the steps of a converted Armenian dance hall in Cambridge, Massachusetts, about to start my first day at an MIT startup called Gold Hill Computers. They were the first company to develop software that enabled companies to build expert systems applications on a PC platform. The focus in the 1980s for AI was on industries such as aerospace and defense, computer-aided design and engineering, software engineering, financial services, manufacturing, medicine and science. Never at that point in time did I consider that algorithms could be applied to my own profession in communications. And yet that is just what has happened.

A recent article by a Foreign-Exchange Journalist suggests the'Skynet' of Finance is not too far away. In particular, Transfer/Payments business expect to lose 28% of their business to FinTech in the next 5 years, and Banks expect to lose 24% of their business. The silver lining to this takeover could however be, the article points out, the greater emphasis on the'human touch' in key customer interfacing areas. For example, a human hand at the wheel to prevent another'flash crash' or a human interpreter of the decisions of an Artificial Intelligence made lending / investment decision. Whatever happens, we are likely to see more automation, lower costs for the customer, and smarter decision making – albeit in the near term.

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.

"Sales and revenue generation are as a result of human interaction; it is that human interaction that is changing" – when asked to keynote speeches on the subject of technology in sales and marketing this is often a quote that gets a lot of heads nodding- it is very pertinent in this discussion too. This is due to the fact that I believe, ultimately, there will be little human interaction in certain real estate transactions in the future; some and future being the key points. Could there really be a possibility that artificial intelligence and real estate could work together? Which elements and when are pivotal to the debate and I hope to cover a few here. Ultimately it is my belief that in the near future the real estate industry could be automated and run algorithmically. However, it is important, from the outset to distinguish between automation and the more challenging theory of machine learning which is a term often used to describe Artificial Intelligence.

Business documents written in foreign languages are no longer the problem they once were thanks to technologies like Google Translate, but what about contracts written in legalese? That's where LawGeex hopes to help with an AI-based online tool. LawGeex offers what it calls the world's first contract review platform based on artificial intelligence. The goal, it says, is to help businesses and individuals "get a fair deal" before signing an agreement. Toward that end, it combines machine-learning algorithms with crowdsourced data, text analytics, and the knowledge of expert lawyers to make in-depth contract reviews accessible to everyone.