Uber, according to its self-reported financials, said it lost (on a GAAP basis) $1.07 billion as it continues to invest in new areas, such as bicycles, scooters and freight shipments. The company is still growing however, as revenue rose 38 percent from a year ago to $2.95 billion. Albeit, those gains are down 51 percent from the previous quarter, meaning that overall the speed of growth is slightly down. Uber earned $12.7 billion from gross bookings, or the money it makes after paying commissions to drivers and delivery people, which is up 34 percent from the previous year. This comes ahead of the company's anticipated initial public offering (IPO) next year, to which some are valuing the company at $120 billion, nearly double more its last reported private valuation of $62 billion.
In a nondescript office tower a mile from the Las Vegas Strip, two women toil in a windowless room crammed with bikinis in every size, style, and hue. Down the hall, colleagues are working to break into China's red-hot market for surveillance software powered by artificial intelligence. Bikini.com is owned by Remark Holdings, a small public company with Hollywood producer Brett Ratner on its board and financial ties to TV's Dr. Mehmet Oz. Remark's leaders are trying to transform the unprofitable, debt-loaded website operator into a provider of corporate AI technology in Asia, particularly China. Remark's business and share price are struggling, but its peculiar AI project has made some progress.
Baidu has announced a strategic investment in Xinchao Media, a media company that specializes in elevator ads, according to the company's post on Bai Jiahao (in Chinese). While Baidu did not disclose the size of the new investment, other reports suggest Xinchao Media's latest financing round led by Baidu was totaled RMB 2.1 billion and Huaxing Capital was the exclusive financial advisor on the deal. Forming a strategic partnership with Xinchao Media is part of Baidu's offline advertising push. "In the age of AI, market environment along with the development of technology is pushing and renewing the vigor of offline advertising," the Chinese search engine giant said in the post. On one hand, the growth of mobile devices and the growth of online traffic are slowing.
A charitable assessment might have called it an experiment, really; an experiment to find out if the headwinds on the hardware and infrastructure services side of the house could be mitigated to some meaningful extent by perceived tailwinds on the software side of the house. A less charitable assessment might have called it a gamble... [...]
According to Global Market Insights, Artificial Intelligence (AI) in the Banking, Financial and Insurance (BFSI) Market is estimated to be worth over USD 2.5 billion in 2017 and is anticipated to grow at a Global CAGR of more than 30% from now through 2024. Not surprisingly, the Asia Pacific region driven by China is leading the way with an estimated CAGR of over 40%. AI has applications that vary widely in finance - from cost savings to improving customer experience and fraud detection - and right now there are already 2.5 million U.S. financial services workers whose jobs are directly impacted by AI. Finance was one of the first sectors to embrace AI. Sydney Swaine-Simon and Abhishek Gupta write: "The financial sector is one of the first domains to drive interest in using artificial intelligence, even before high computing machines were available. In the 1960s, a lot of research focused on Bayesian statistics, a method used heavily in machine learning. Some of its use cases included stock market prediction and auditing. It wasn't until the 1980s until the majority of commercialization opportunities were explored with expert systems. During that time, over two thirds of Fortune 1000 companies had at least one AI project being developed."
The starting point in Part 1 of this series was the fragmented "knowledge landscape" of most big companies. Information is everywhere but it mostly lives in autonomous silos, in different formats and suffers from "semantic incoherency". This is a huge problem for extending the use of AI in business, beyond the many "narrow" (i.e. To meet this challenge, I argued in Part 2 that we need to "connect up" different forms of enterprise knowledge, with the help of semantic technologies - such as ontologies and knowledge graphs - from the "Symbolic AI" tradition where meaning and reasoning take center stage. This concluding post will focus on business outcomes – the benefits that leading-edge companies around the world are already beginning to achieve, leveraging semantic graph technologies to integrate enterprise knowledge and transform knowledge work.
Mon 12 Nov 2018 08.21 EST Last modified on Mon 12 Nov 2018 10.30 EST The world of work is changing. Machine learning, the internet of things (IoT) and cloud computing are altering how society views employment and the tasks that are currently performed by humans. The World Economic Forum predicts that new technologies will create 133m jobs over the next three years – and these new roles may involve humans working closely with new technologies. As previously unconnected machines are hooked up to the internet, more data about their performance can be collected, resulting in the need for human jobs to adapt. The marine division of construction company Caterpillar, for instance, has been attaching sensors to generators, engines and fuel meters onboard ships.
The information age (the period starting in the year 2000) could not have gotten off to a better start. Digital platforms have become pivotal in gathering market information including consumer behavior and new shopping trends. Alphabet Inc. (NASDAQ:GOOG)(NASDAQ:GOOGL)'s Google has become smarter, Facebook Inc. (NASDAQ:FB) and Twitter Inc. (NYSE:TWTR) have disrupted social media and Amazon.com Inc. (NASDAQ:AMZN)'s shopping search engine has grown to top Google search. To capitalize on the data obtained from users of the internet including from the platforms mentioned above, businesses have employed the use of big data tools and artificial intelligence to convert the data into useful information.
The graph represents a network of 1,840 Twitter users whose tweets in the requested range contained "#FinServ", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Sunday, 11 November 2018 at 10:30 UTC. The requested start date was Sunday, 11 November 2018 at 01:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 5-day, 1-hour, 28-minute period from Monday, 05 November 2018 at 23:25 UTC to Sunday, 11 November 2018 at 00:53 UTC.
Two months into the launch of her dance studio, Natalie Borch needed a loan. The 34-year-old first-time business owner had opened the doors to The Pink Studio in February after she and her brother invested $40,000 of their own cash and took out a $100,000 loan from the Business Development Bank of Canada. "We just needed a small amount of money to expand our services for new teachers and classes," she said, from beginner Beyonce to Bollywood fusion. Finding no help from the main banks, she found Lendified Inc., a fintech startup that offers loans to small businesses based on artificial intelligence-powered screening assessments. After filling out a few online forms on cash flow and collateral, Borch received a $30,000 loan.