The latest KPMG analysis revealed that China saw Venture Capital (VC) investments reach US$31 billion in 2016, a 19% tear-on-year growth and a new record high, despite a global slowdown. The consultant predicts that Artificial Intelligence (AI) will be an additional focus for investors. However, deal volumes declined 42% to 300 from 513 a year earlier, according to Venture Pulse, KPMG's quarterly global report on VC trends. The strong performance is attributed to a number of mega-deals recorded early in the year. The report highlights that artificial intelligence and cognitive learning are poised to transform almost every aspect of people's lives.

Could AI-powered bots fight hate speech by flooding the internet with love? As social media platforms have become ever more intrinsic to how we live our lives and begun to evolve into the primary medium through which we communicate and listen to the rest of the world, their rise has handed a megaphone to the world's hate and vitriol. In fact, it was Twitter who initially stepped forward to staunchly defend the rights of terrorists and their sympathizers to communicate via its platform before abruptly reversing itself in the face of fierce public criticism. Yet, despite myriad programs and policies designed on paper to fight abuse, in reality the platforms have done very little to curb the spread of hate speech, harassment and violent threats. This raises the question of whether the rise of deep learning-powered "bots" could offer a powerful solution to online hate speech, by deploying them en masse to report, counter and overwhelm hateful posts in realtime.

The legal profession is not far away from being exposed to the disruptive potential of AI. In India, a leading full service law firm, Cyril Amarchand Mangaldas has plans to deploy AI technology. This will help the firm to enhance its delivery model, in an attempt to provide certain legal services more efficiently, swiftly, and more accurately to its clients. The firm recently signed an agreement with a leading Canada-based machine learning software provider, called Kira Systems. Cyril Shroff, Managing Partner, Cyril Amarchand Mangaldas, remarks "The firm has been working on several interconnected initiatives since last year.

This photo made on Sunday Jan.1, 2017, shows a tenth grader attending a class how to investigate a computer network that has been hacked in Beit Shemesh, Israel. This photo made on Sunday Jan.1, 2017, shows a tenth grader attending a class how to investigate a computer network that has been hacked in Beit Shemesh, Israel. The training programs -- something of a boot camp for cyber defense -- are part of Israel's quest to become a world leader in cybersecurity and cyber technology by placing its hopes in the country's youth. Military service is compulsory for most Jewish high school graduates in Israel, giving military intelligence the power to enlist the country's best and brightest.

Robots are rapidly becoming more and more lifelike and unrecognizable from humans. Eerily human-like robots built by China's University of Science and Technology stole the show over at the World Robot Conference in Beijing last week. You may have already seen Jia Jia, a super realistic robot capable of micro facial expressions and basic conversation with humans. Jia Jia can also recognize faces, identify your gender and age, and detect your facial expressions. But a newcomer robot also made a debut: the university unveiled a male robot that can accurately paint calligraphy.

Last summer at the Black Hat cybersecurity conference, the DARPA Cyber Grand Challenge pitted automated systems against one another, trying to find weaknesses in the others' code and exploit them. "This is a great example of how easily machines can find and exploit new vulnerabilities, something we'll likely see increase and become more sophisticated over time," said David Gibson, vice president of strategy and market development at Varonis Systems. His company hasn't seen any examples of hackers leveraging artificial intelligence technology or machine learning, but nobody adopts new technologies faster than the sin and hacking industries, he said. "So it's safe to assume that hackers are already using AI for their evil purposes," he said. "It has never been easier for white hats and black hats to obtain and learn the tools of the machine learning trade," said Don Maclean, chief cybersecurity technologist at DLT Solutions.

Incuspaze, a Gurgaon-based co-working and incubation centre, will launch an incubation programme for startups in artificial intelligence, machine learning, IoT and big data, the company said in a statement on Monday. The 6,000 sq ft centre was launched by UK-based angel investor Sanjay Choudhary last week. Incuspaze will invest between Rs 1 -5 lakh in five to six startups in its batch, and will also offer parallel services such as marketing, accounting and legal. "We are building a global self-sustainable ecosystem for startups and entrepreneurs. We are not only providing a co-working space, but also investor support, mentoring, allied services, global partnerships and to ensure peak performance for all startup founders," said Choudhary in the statement.

NAGOYA – Toyota Motor Corp. and Suzuki Motor Corp. have agreed to form a tie-up in advanced technology development and other operations, sources close to the matter said Saturday. The two automakers will continue to discuss whether to expand the partnership to include a capital alliance, the sources said. The comprehensive tie-up comes after Toyota, Japan's biggest automaker by volume, and Suzuki, a small-car specialist, said in October they would start talks on how they could join forces. The deal is expected to be announced Monday, according to the sources. The global auto industry is facing soaring costs to meet stricter emissions regulations and develop advanced safety technology for electric vehicles and autonomous driving systems.

One of the key information pieces in improving energy efficiency of buildings is the appliance level breakdown of energy consumption. Energy disaggregation is the process of obtaining this breakdown from a building level aggregate data using computational techniques. Most of the current research focuses on residential buildings, obtaining this information from a single smart meter and often relying on high frequency data. This work is directed at commercial buildings equipped with building management and automation systems providing low frequency operational and contextual data. This paper presents a machine learning method to disaggregate energy consumption of the building using this operational data as input features. Experimental results on two publicly available datasets demonstrate the effectiveness of the approach, which surpasses existing methods. For all but one appliance of House 2 of the publicly available REDD dataset, improvements in normalized error in assigned power range between 20% (Lighting) and 220% (Stove). For another dataset from an educational facility in Singapore, disaggregation accuracy of 92% is reported for the facility's cooling system.