From 1958, since the invention of the first integrated circuit till 1965, the number of components or transistor density in an integrated circuit has doubled every year, marked Gordon Moore. So when Intel, the pioneer of chip developments adapted Moore's law as standard principle for advancing the computing power, the whole semi-conductor industry followed this outline on their chips. But then with the constant advancement, the electronics industry benefited from the Moore's standard method of designing processor chips till 50 years. The technology today is tending to design artificial intelligence technology that matches the super intelligence of human brain.

Human champion Ke Jie competes against AlphaGo at the Future of Go Summit. Two days ago in the Zhejiang Province of China, Google's Go-playing artificial intelligence AlphaGo bested current world Go champion Ke Jie in the first game of a three-part match, sliding by on a half-point victory. Now the second game has taken place -- and once again, AlphaGo has emerged the winner. The human gave it his all. "Incredible," wrote DeepMind founder and CEO Demis Hassabis on Twitter while the match was underway.

In a poem first published in 1967, Richard Brautigan imagined a world in which people were freed of all their labors and reunited with nature in a world "all watched over by machines of loving grace." In different words, this theme was a hot topic for attendees and presenters at the recent Dell EMC World conference in Las Vegas. Not so much about machines of loving grace, but it seemed that everywhere you went people were talking about machine learning and artificial intelligence, and what this new era means for all of us. In a "Guru Session," Sir Tim Berners-Lee, the inventor of the World Wide Web, explored some of the implications of the rise of AI, including the notion of machines taking over jobs long held by humans. He noted that there are many mundane jobs that humans really shouldn't be doing.

A new video shows off a dummy unit for the Galaxy Note 8, and reveals a dual-rear camera setup, along with the lack of a fingerprint scanner. This is leading to suggestions of an embedded fingerprint scanner.

A French startup company has developed an automated valet service operated by a robot. The robot, named Stan, doesn't require a customer's car keys - instead, it picks up your vehicle and takes it a secure car park. The system is also connected to the customer's flight details, so their car is ready to be picked up when they return, and it can also maximize space by double parking in front of vehicles that don't need to be picked up immediately. The robotic system, designed and developed by Stanley Robotics, has already been adopted by Paris' Charles de Gaulle airport. The company just raised $4 million (€3.6 million) from Elaia Partners, Bpifrance and Idinvest Partners.

But to keep its devices competitive, Apple is building a secondary mobile processor dedicated to powering AI. The tech titan's devices currently split AI tasks between two chips -- the main processor and a GPU -- but this new one, allegedly known internally as the Apple Neural Engine, has its own module dedicated to AI requests. That puts Apple further behind Qualcomm's latest Snapdragon mobile chips, which already have a dedicated AI module, and Google's Tensor Processing Units available in its Cloud Platform to do AI heavy lifting. Unlike the company's differential privacy methods protecting data sent to Apple's servers, the Neural Engine chip would let devices sift through data on their own, which would be faster and easier on the battery, just like the M7 processors did for motion back in 2013.

The third reason is not that important performance-wise, but it does offer an additional insight into GPUs' undeniable supremacy over CPUs. The first part of the process involves fetching memory from the main or RAM memory and transferring it over to on-chip memory, or the L1 cache (instruction memory) and registers. Registers are attached directly to the execution unit, which for GPUs is the stream processor and for CPUs the core. This is where all the computation happens. Normally, you'd want both L1 and register memory to be as close to the execution engine and allow for a quick access by keeping the memories small.

Artificial intelligence ("AI") is fast becoming the next great democratizer for services. In the medical field, 56% of consumers surveyed see its potential to lower cost and break down barriers in providing medical access to lower income adults. And the beginnings of that technology can already be seen: an AI system has successfully identified autism in babies with 81% accuracy, while a Stanford-led experiment used AI to identify skin cancer with 91% accuracy. But as much as these technologies develop and become more successful in application, the majority of consumers still want a human touch accompany cutting-edge tech. While consumers trust AI to make vital decisions on the back end in terms of data processing and analysis, they still prefer a human to deliver information to them or to help explain a result.

In the FBI's Crime in the United States, 2015 report, the intelligence and security organization revealed some startling facts about robberies in the U.S. According to the data provided, there were 327,374 robberies that took place in 2015 accounting for losses of $390 million (at least for those robberies that were reported). While that number feels shocking enough, it's the further elaboration on the weapons used during those robberies that provides a lot more insight into how these crimes are pulled off: With 46% of robbers relying on weapons to pull off their crimes, one way to quickly catch a threatening event is to catch the presence of an instrument commonly associated with aggression. Other key physical indicators, like the presence of a mask, or simply the presence of an unrecognized, unexpected visitor, can also be captured to quickly identify suspicious or immediately risky individuals on a business's premises. New Jersey-based, Deep Science AI has developed a technology that merges artificial intelligence with current business surveillance and security systems in an attempt to spot and prevent crime. While technology like AIsight–installed around Boston after the Marathon bombings–attempts to identify potential criminals through recognition of things like, "Casing the joint, poking around where he shouldn't be, going around looking at the back entrances to buildings", enhanced object identification technology is at the heart of Deep Science AI.

Artificial intelligence and machine learning (AI and ML) are so over-hyped today that I usually don't talk about them. But there are real and valid uses for these technologies in monitoring and performance management. Some companies have already been employing ML and AI with good results for a long time. VividCortex's own adaptive fault detection uses ML, a fact we don't generally publicize. AI and ML aren't magic, and I think we need a broader understanding of this.