Artificial intelligence has moved into the world of corporate legal matters: A Tokyo-based start-up founded by young lawyers is using AI to check for omissions and mistakes in contracts, sometimes taking only one second. There have been calls lately for greater legal compliance, and the service is drawing attention over whether it can help streamline corporate legal practices. LegalForce Inc. was established in April last year, and is led by 31-year-old lawyer Nozomu Tsunoda, who quit a leading law firm to go into business for himself. Even with only seven employees, LegalForce checks contract documents such as a confidentiality agreement between companies. The service uses AI to automatically check such things as provisions or phrasing that could be detrimental to a company, as well as omissions or incomplete elements.

Artificial intelligence (AI) is coming to education. And LAIX (LAIX) hopes to be at the forefront. LAIX (pronounced "LIKES") is a Chinese AI that creates and delivers products and services to promote English learning. "We want everyone to become global citizens," said the company's Chairman & CEO Yi Wang when we spoke to him in the floor of the NYSE right after its stock began trading for the first time. The company, recently named to the list of "50 Most Innovative Companies" for 2018 by Forbes China, hopes to teach a multitude of languages someday, although it will begin by teaching English to the Chinese.

One of China's most popular video games is testing the use of facial recognition to check users' ages. Honour of King's publisher Tencent announced the move at the weekend. It said the trial would initially be limited to "thousands" of new players based in Beijing and Shenzhen. The title has been criticised in local media over claims children have become addicted to it. But one expert questioned whether the test could be scaled up.

Andrew Ng helped create two of Silicon Valley's leading artificial intelligence labs. First, he built Google Brain, now the hub of AI research inside the internet giant. Then he built a lab in the Valley for Baidu, the company known as the Google of China. Ng was one of the primary figures behind the enormous and rapid rise of AI over the last five years as everyone from Facebook to Microsoft rebuilt themselves around deep learning. And on Tuesday night, he announced his departure from Baidu.

Artificial Intelligence (AI) has evolved to be a crucial tool in transforming businesses from followers to leaders. AI promises to take business operations and customer experiences to a new level. The dependence on Artificial Intelligence has made nations to go data hungry. For a majority of businesses in the Asia-Pacific, data dependence has become the main hurdle in adopting AI. Increased data dependency has led nations struggling to gather and integrate big data into their operations.

As the saying goes: "Beauty is in the eye of the beholder." But this egalitarian sentiment is easy to forget in our image-conscious age of photoshopped social media stars. Many feel pressure to look "perfect" and apps like Snapchat only help to filter out the "ugly" bits -- also known as reality. My own reflection was scrutinised this week in an uncomfortably public social experiment. A "biometric mirror" set up in Melbourne's CBD scanned my mug and used an algorithm to assess my level of "perfection".

Understanding the powerful Chinese government should be the top priority for Australian brands looking to move into the lucrative Chinese market, according to Blackmores executive director, Marcus Blackmore. The vitamin king told Which-50 prospective brands could "do well to copy some of the things we've done".

A screen shows a demonstration of the cognitive level of a facial recognition software at the Ericsson AB booth at the Mobile World Congress Shanghai in Shanghai, China, on Thursday, June 28, 2018. The global videos surveillance market is expected to post a compound annual growth rate of close to 11% during the period 2018-2022 according to Technavio. The potential benefits of leveraging artificial intelligence (A) in the physical security industry have pros and cons on both sides, but the debate over the ethical ways to leverage AI and surveillance continues as more and more surveillance systems are getting the brains to match what they see. AI startups like Boulder AI, which offers a vision-as-a-service and IC Realtime, which lets you search and analyze your video feeds from CCTV system; are gaining traction. Alongside the Chinese facial recognition startups like Megvii's Face with $600 million in private equity; SenseTime with $62o million from a series C; and, Yitu Technology with $300 million from a series C, the potential uses of facial recognition technology are well funded.

We present a study on predicting the factuality of reporting and bias of news media. While previous work has focused on studying the veracity of claims or documents, here we are interested in characterizing entire news media. These are under-studied but arguably important research problems, both in their own right and as a prior for fact-checking systems. We experiment with a large list of news websites and with a rich set of features derived from (i) a sample of articles from the target news medium, (ii) its Wikipedia page, (iii) its Twitter account, (iv) the structure of its URL, and (v) information about the Web traffic it attracts. The experimental results show sizable performance gains over the baselines, and confirm the importance of each feature type.

Stochastic gradient descent (\textsc{Sgd}) methods are the most powerful optimization tools in training machine learning and deep learning models. Moreover, acceleration (a.k.a. momentum) methods and diagonal scaling (a.k.a. adaptive gradient) methods are the two main techniques to improve the slow convergence of \textsc{Sgd}. While empirical studies have demonstrated potential advantages of combining these two techniques, it remains unknown whether these methods can achieve the optimal rate of convergence for stochastic optimization. In this paper, we present a new class of adaptive and accelerated stochastic gradient descent methods and show that they exhibit the optimal sampling and iteration complexity for stochastic optimization. More specifically, we show that diagonal scaling, initially designed to improve vanilla stochastic gradient, can be incorporated into accelerated stochastic gradient descent to achieve the optimal rate of convergence for smooth stochastic optimization. We also show that momentum, apart from being known to speed up the convergence rate of deterministic optimization, also provides us new ways of designing non-uniform and aggressive moving average schemes in stochastic optimization. Finally, we present some heuristics that help to implement adaptive accelerated stochastic gradient descent methods and to further improve their practical performance for machine learning and deep learning.