Asia
The impact of Artificial Intelligence and Machine Learning on major industries - Express Computer
Artificial Intelligence is no longer the technology of the future, but is the here and now as it underpins the digital transformation of various industries and businesses. No longer simply an intriguing concept encountered in science fiction films and books, AI is everywhere, especially where there are digital technologies involved. Some of its applications are quite close to our own daily lives and interactions with AI interfaces are far more common than we often seem to realise. But it's the application of AI on an industrial scale that are dominating conversations across various sectors, as the world seems to be hurtling towards a trend of large-scale automation. Identifying sources of data and optimising data to enhance productivity is at the heart of driving a true digital transformation and intensifying the scale of automation across various industries.
At Tokyo summit, diplomats say Japan can tap domestic stability to take on global leadership role
Despite the fraught global environment -- with U.S.-China animosity mounting alongside a bevy of regional security concerns -- Japan appears to be viewing the situation as a glass half-full scenario, according to leading experts, as well as current and former officials. That was the scene Wednesday, when pundits and diplomats from Japan and across the globe gathered for the Eurasia Group's inaugural G-Zero Summit in Tokyo. With China and the Trump administration posing potential headaches for Tokyo, many said the country's unique position and stable domestic politics could be an opportunity for it to break out of its diplomatic shell and play a larger leadership role in regional and global politics. "After World War II, the U.S. has shouldered much of the responsibility" in establishing and maintaining the international rules-based order, Foreign Minister Taro Kono said in the conference's keynote speech. Yet Kono believes Washington can't continue to go it alone and "has been getting a little tired … so someone else has to take up the responsibility."
docprime.com website goes live, to use AI for customer profiling
The website features include industry-first'free family doctor for life' service that will allow people to get instant and free consultation over chat and phone from in-house medical consultants for their health issues. Moreover, the website will provide easy access and navigation for people to find a doctor in their nearby area from a large network, book instant consultations and lab tests on special or discounted rates. The key focus is on improving the accessibility of doctors and labs thereby driving economies of scale. The goal is to ensure that the doctors have access to all patient information with the least effort so that they can focus more on diagnosing the ailment and prescribing the treatment. Our chat platform on the website also ensures that we assist the customers in understanding if they need to physically visit a doctor clinic and connect them to the most appropriate doctor."
UOB opens Singapore lab to improve digital banking experience ZDNet
United Overseas Bank (UOB) has opened a lab in Singapore to identify ways to better personalise engagement with digital customers and is planning open similar facilities across its Asean network. These would include markets such as Indonesia, Malaysia, Thailand, and Vietnam, the Singapore bank said in a statement Thursday. Its new Engagement Lab (eLab) would tap artificial intelligence (AI) and behavioural insights to improve its services and interaction with digital bank customers, according to UOB. Increased emphasis on building a cashless society to reflect country's success as a smart nation is misplaced, when the importance of getting the fundamentals right is overlooked. The bank in August said it was preparing to launch a digital bank in the region, designed to operate on a "data-centric business model" to enhance engagement across five stages in the banking journey: acquire, transact, generate data, insight, and engage.
AI is no silver bullet for cyber security
Business leaders put too much faith in using artificial intelligence (AI) to solve their cyber security problems, and should instead focus on educating users on cyber hygiene and managing risks, according to a cyber security expert. Security technologist Bruce Schneier's insights and warnings around the regulation of IoT security and forensic cyber psychologist Mary Aiken's comments around the tensions between encryption and state security were the top highlights of the keynote presentations at Infosecurity Europe 2017 in London. You forgot to provide an Email Address. This email address doesn't appear to be valid. This email address is already registered.
All hail our new robot overlords… and the jobs they will create
Huawei is not afraid to address the elephant in the room. When US mobile carrier AT&T pulled out of a deal to sell the Mate 10 Pro in January, the CEO of Huawei's consumer business unit, Yu Chengdong (Richard Yu), went off-script at the end of his CES 2018 keynote and tackled the topic head-on. Similarly, when Huawei made artificial intelligence the central theme of its annual Huawei Connect conference in Shanghai, rotating chairman Xu Zhijun (Eric Xu) was quick to bring up the topic of jobs in his opening speech. Concern that robots would replace human workers was a recurring topic throughout Huawei Connect 2018. Speakers addressed the issue during product launches, technical presentations, and panel discussions, offering varying perspectives around the fundamental impact AI will have on the nature of (human) work.
Spammers were behind recent Facebook data breach, company tentatively concludes
BENGALURU, INDIA – Facebook has tentatively concluded that spammers looking to make money, and not a nation-state, were behind the largest-ever data theft at the social media company, the Wall Street Journal reported on Wednesday. The people behind the attack were a group of Facebook and Instagram spammers who present themselves as a digital marketing company and whose activities were previously known to Facebook's security team, the Journal reported, citing people familiar with the company's internal investigation. Last week, Facebook said cyberattackers had stolen data from 29 million Facebook accounts using an automated program that moved from one friend to the next, adding that the data theft had hit fewer than the 50 million profiles it initially reported. Facebook said in an email that it was cooperating with the Federal Bureau of Investigation on this matter. The breach has left users more vulnerable to targeted phishing attacks and could deepen unease about posting to a service whose privacy, moderation and security practices have been called into question by a number of scandals, cybersecurity experts and financial analysts have said.
A giant new retail fulfillment center in China has only four employees
Automation angst: The news conjures images of rapid automation and job displacement. But it's important to note that the process of filling retail orders on a large scale is already heavily automated. Step inside one of Amazon's fulfillment centers, for instance, and you'll see robots moving around shelves of goods, as well as automated systems routing and tracking products and packages by the million.
First-order and second-order variants of the gradient descent: a unified framework
Pierrot, Thomas, Perrin, Nicolas, Sigaud, Olivier
In this paper, we provide an overview of first-order and second-order variants of the gradient descent methods commonly used in machine learning. We propose a general framework in which 6 of these methods can be interpreted as different instances of the same approach. These methods are the vanilla gradient descent, the classical and generalized Gauss-Newton methods, the natural gradient descent method, the gradient covariance matrix approach, and Newton's method. Besides interpreting these methods within a single framework, we explain their specificities and show under which conditions some of them coincide. Machine learning generally amounts to solving an optimization problem where a loss function has to be minimized.