If you follow the news on artificial intelligence, you'll find two diverging threads. The media and cinema often portray AI with human-like capabilities, mass unemployment, and a possible robot apocalypse. Scientific conferences, on the other hand, discuss progress toward artificial general intelligence while acknowledging that current AI is weak and incapable of many of the basic functions of the human mind. But regardless of where they stand in comparison to human intelligence, today's AI algorithms have already become a defining component for many sectors, including health care, finance, manufacturing, transportation, and many more. And very soon "no field of human endeavor will remain independent of artificial intelligence," as Harvard Business School professors Marco Iansiti and Karim Lakhani explain in their book Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World.

Let me start right off the bat: many incumbent organizations risk missing out on the strategic opportunity of AI -- instead focusing on tactical optimization initiatives. General purpose technologies like AI have the potential to change how companies operate and create value, disrupting entire industries along the way. History proves however that new paradigms are needed for new technologies to come to fruition. The good news is for AI that these paradigms are already emerging-- they are just unevenly distributed. Using AI for both short term gains and long term strategic value is not trivial and requires strong leadership skills: combining vision with experimentation and aiming for both optimization and strategic plays.

The year 2019 saw unprecedented growth in AI research, development and deployment. Great technical progress has been achieved in image recognition, image generation, natural language understanding and other fields; while challenges remain with data management, efficiency measurement, computational capacity and other issues. To welcome 2020 with some fresh AI perspectives, Synced spoke with global researchers from Google Brain, Sony AI, Alibaba affiliate Ant Financial (formerly known as Alipay), Israel-based AI processor company Habana (recently acquired by Intel), Russian tech giant Yandex, Vietnam's newly established research lab VinAI Research, French deep learning inference acceleration startup Mipsology, and China-based remote sensing data platform TerraQuanta. Colin Raffel, Senior Research Scientist, Google Brain In 2019 the community made huge progress on learning from limited labels. MixMatch, UDA, S4L, and ReMixMatch produced huge gains on standard semi-supervised learning benchmarks.

The year 2019 saw unprecedented growth in AI research, development and deployment. Great technical progress has been achieved in image recognition, image generation, natural language understanding and other fields; while challenges remain with data management, efficiency measurement, computational capacity and other issues. To welcome 2020 with some fresh AI perspectives, Synced spoke with global researchers from Google Brain, Sony AI, Alibaba affiliate Ant Financial (formerly known as Alipay), Israel-based AI processor company Habana (recently acquired by Intel), Russian tech giant Yandex, Vietnam's newly established research lab VinAI Research, French deep learning inference acceleration startup Mipsology, and China-based remote sensing data platform TerraQuanta. Colin Raffel, Senior Research Scientist, Google Brain In 2019 the community made huge progress on learning from limited labels. MixMatch, UDA, S4L, and ReMixMatch produced huge gains on standard semi-supervised learning benchmarks.

There are many reasons why Python has emerged as the number one language for data science. It's easy to get started and relatively forgiving for beginners, yet it's also powerful and extensible enough for experts to take on complex tasks. But there's one aspect of Python that has bedeviled developers in the big data age: Getting Python to scale past a single node. Solving that dilemma is the number one goal of Project Ray. The name "Ray" will ring a bell if you've been following the goings-on at RISELab, the advanced computing laboratory formed at UC Berkeley.

KPMG estimated the size of the automotive insurance is likely to shrink by 70 % because of the increase in need for the change and autonomous automobiles in liability subsequently being positioned on the automobile manufacturer. With the rise of AI in many sectors, it makes sense that AI would find the way of its into the motor vehicle insurance community. This particular article intends to offer business leaders of the automobile insurance room with a concept of what they are able to presently expect from Ai in the business of theirs. We wish this report allows business leaders in automobile insurance to garner insights they are able to confidently relay to the executive teams of theirs so they are able to make educated choices when thinking about AI adoption. At the minimum, this article intends to serve as a technique of decreasing the time industry leaders in automobile insurance invest researching AI businesses with whom they might (or might not) be keen on working.

Moven, which works with banks in eight regions, including Westpac New Zealand, provides behavioural models that help banks drive better engagement and retention – little "nudges" such as spending alerts and notifications, and savings prompts. Brett King says Australian banks will struggle to keep up with the likes of Chinese giant Ant Financial. King, who grew up in the Melbourne suburb of Berwick and was speaking at the FINSIA summit in Melbourne on Tuesday, says Moven's experience with TD Canada Trust – Canada's second-biggest bank – provides a good example of what the future might hold. Half of TD's customers use Moven's platform, called My Spend. This group has seen a 4 per cent to 8 per cent reduction in monthly spending thanks to the various "nudges" they receive compared with a controlled group.

The application of artificial intelligence is expected to make companies more efficient and add customer value, according to the latest study. Some 54 percent of Chinese consumers are willing to embrace digitalization and the use of artificial intelligence in customer service -- against an average of 43 percent at global level, according to a study by consultancy Accenture and Ant Financial. As many as 47 percent of Chinese customers are willing to let companies use their personal information to provide tailor-made services -- against the global level of 38 percent. Up to 80 percent of customers expect service staff to know about their previous dialogue and to understand their demands on the basis of protecting privacy while 55 percent are willing to pay extra for tailor-made services. Ant Financial has been improving its own service capabilities by analyzing consumer demand to allocate staff and resources at its call centers, and a number of industries, such as consumer finance, insurance, transport and online retailing, have adopted its capabilities and solutions.

Chinese AI firm Megvii Technology, backed by Alibaba, has filed in Hong Kong to conduct an IPO targeting proceeds of at least $500 million, two people said, just as the city faces political unrest and its first recession in a decade. Beijing-based Megvii, widely known for facial recognition platform Face, may raise as much as $1 billion in the initial public offering, said one of the people, who expect the share sale in the fourth quarter of the year. The filing comes as companies postpone or slow down listing plans in a recession-bound city blighted with nearly three months of anti-government protests, and where the benchmark Hang Seng share price index fell to seven-month lows this month. Reuters reported last week that China's biggest ecommerce firm, Alibaba Group, had delayed its up to $15 billion Hong Kong listing. Megvii has decided to press ahead with its IPO plans because it has little business in Hong Kong and expects the unrest to ease later this year, said a third person.

Megvii Technology, a Chinese company, founded in 2011 and widely known for its Face system, is one of the world leaders in facial recognition and artificial intelligence technology. While they might be best known for Face, Megvii uses artificial intelligence and machine vision in a variety of amazing ways. Megvii was the concept conceived by friends and Tsinghua University graduates Yin Qui, Yang Mu, and Tang Wenbin. After tremendous success in China (especially since they were able to train algorithms from China's vast pool of data) with clients such as Ant Financial, Vivo (smartphones), Didi Chuxing (ride-sharing) and investments from Bank of China, the State-Owned Venture Capital Fund, China-Russian Investment Fund and other private investors including Ant Financial (Alibaba's payment affiliate), Megvii is ready to go global. They have projects slated in the coming year for Japan, Europe, the Middle East, Southeast Asia, and the United States and have secured a distributor in Thailand.