SoftBank Group Corp. fell as much as 10 percent after a provider of satellite-based internet service that it invested in filed for bankruptcy, ceding some gains from an unprecedented plan to sell assets and buy back shares. OneWeb made the filing late Friday U.S. time after raising about $3.3 billion in debt and equity financing from shareholders including SoftBank, Airbus SE and Qualcomm Inc. since its inception. At least $1 billion of that came from SoftBank, which said it first invested in December 2016 and declined to give a total amount. It is the latest blow to SoftBank founder Masayoshi Son, who last week unveiled a plan to raise $41 billion to buy back shares and slash debt. The announcement sent the shares soaring more than 50 percent in just a few days.
In the past decade, artificial intelligence (AI) has made it to mainstream society from academic journals. The technology has achieved numerous milestones when it comes to digital transformation across society including businesses, education, and healthcare as well. Today people can do the tasks which were not even possible ten years back. The proportion of organizations using AI in some form rose from 10 percent in 2016 to 37 percent in 2019 and that figure is extremely likely to rise further in the coming year, according to Gartner's 2019 CIO Agenda survey. While the breakthroughs in surpassing human ability at human pursuits, such as chess, make headlines, AI has been a standard part of the industrial repertoire since at least the 1980s.
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This is the first part of a series by Levon Paradzhanyan that demystifies data science, machine learning, deep learning, and artificial intelligence down while explaining how they all tie into one another. Artificial Intelligence emerged in our lives many years ago. First, as science fiction and today embedded in real products. It has since been followed by newer buzzwords such as data science, machine learning, and deep learning. Yet there are many misconceptions related to these terms.
It's time for big business to embrace the public chain – as a coordination tool, rather than as a place to carry out large-scale financial transactions. The group has come up with a new way of using the public ethereum mainnet to connect firms' internal systems for resource planning. Generative adversarial networks (GANs) -- two-part AI models consisting of a generator that creates samples and a discriminator that attempts to differentiate between the generated samples and real-world samples -- have been applied to tasks ranging from video, artwork, and music synthesis to drug discovery and misleading media detection. They've also made their way into ecommerce, as Amazon revealed in a blog post this morning. Frontline healthcare employees -- nurses, call center agents, among others -- have one of the toughest jobs in America.
As AI, image quality and cloud-based services improve, the insurance industry is slowly moving closer to an era where many claims may soon be processed without any human touch at all. One area where fully automated claims could bring new efficiencies and benefits is in commercial auto insurance. In a market where losses and claims are high, improving operational efficiencies is one of the most viable ways to gain profitability. Experts say most of the technology is already here, and while it must be refined to be ready for widespread adoption, it could be a mainstream reality within the next few years. Commercial auto insurance has been a tough market for many carriers in recent years.
From product development, underwriting and claims, to customer service chatbots, risk assessments, and quotations, technology are being deployed across the sector to provide faster, more accurate services. What is intriguing, however, is that while some major insurance companies are investing aggressively in AI, many are moving slowly, unsure of how best to deploy these technologies. In a recent AI survey, it says only half of the insurance executives consider AI technologies to be'extremely' or'very important' to their company's success, lower than for any other industry, such as financial services, healthcare, and manufacturing. Looking ahead three years, only 36% felt AI would be very important, again lower than any other industry. This lack of awareness around the importance of AI is worrying as new entrants to the market start making an impression.
Intel makes processors that act as the main computing brains for PCs and servers. Nomura Instinet chip analyst David Wong initiated coverage on Intel on Tuesday with a Buy rating, predicting long-term sales growth of 8% to 10% annually for the technology giant. "Intel is the world leader in processors for artificial intelligence and autonomous driving," he wrote. "We think that microprocessor growth could well be above overall semiconductor industry growth over the next decade, fueling long-term top-line growth for Intel." The analyst started his price target for Intel at $65, representing 17% upside to the current stock price.
From automating the most menial and repetitive tasks to free up the time to focus on higher level objectives, to assisting with customer service management and reducing the risk of frauds, AI is employed from back-office tasks to the frontend with nimbleness and agility. According to the Alan Turing Institute, with $70 billion USD spent by banks on compliance each year just in the U.S., the amount of money spent on fraud is staggering. And when the number of reported cases of payments-related fraud has increased by 66% between 2015 and 2016 in the United Kingdom, it's clear how this problem is much more than a momentary phenomenon. AI is a groundbreaking technology in the battle against financial fraud. ML algorithms are able to analyze millions of data points in a matter of seconds to identify anomalous transactional patterns.