Cynet, a cybersecurity startup that leverages AI and machine learning to detect threats, has raised $18 million in venture capital.
However, the situation has been improving as Chinese tech giants including e-commerce company Alibaba, search engine Baidu, on-demand delivery company Meituan Dianping, ride-hailing operator Didi Chuxing and smartphone maker Xiaomi now offer more affordable health care plans via mutual aid platforms, which operate as a collective claim-sharing mechanism.
China’s online mutual aid platforms are disrupting old school insurance companies by leveraging big data and internet finance technologies to offer low cost medical coverage.
Pagaya, an AI-driven institutional asset manager that focuses on fixed income and consumer credit markets, today announced it raised $102 million in equity financing. CEO Gal Krubiner said the infusion will enable Pagaya to grow its data science team, accelerate R&D, and continue its pursuit of new asset classes including real estate, auto loans, mortgages, and corporate credit.
Pagaya applies machine intelligence to securitization — the conversion of an asset (usually a loan) into marketable securities (e.g., mortgage-backed securities) that are sold to other investors — and loan collateralization. It eschews the traditional method of securitizing pools of previously assembled asset-backed securities (ABS) for a more bespoke approach, employing algorithms to compile discretionary funds for institutional investors such as pension funds, insurance companies, and banks. Pagaya selects and buys individual loans by analyzing emerging alternative asset classes, after which it assesses their risk and draws on “millions” of signals to predict their returns.
Pagaya’s data scientists can build algorithms to track activities, such as auto loans made to residents in cities and even specific neighborhoods, for instance. The company is only limited by the amount of data publicly available; on average, Pagaya looks at decades of information on borrowers and evaluates thousands of variables.
Tunde Ajaja Former Minister of Finance, Dr Ngozi Okonjo-Iweala, has said in all the positions she has occupied, serving as Nigeria’s Minister of Finance remains one of her toughest jobs so far. O…
A bug in a protocol used by virtually all Internet of Things devices exposes millions of users to potential attack, a researcher reported Monday. The fault centers on the Universal Plug and Play protocol, a 12-year-old implementation that simplifies connections among network devices such as computers, printers, mobile devices and Wi-Fi access points.
Billions of devices are theoretically vulnerable, the report stated, but only those with UPnP activated currently face risk of attack.
Turkish security engineer Yunus Çadirci uncovered the UPnP bug, named CallStranger, that could be exploited to gain access to any smart device such as security cameras, printers and routers that are connected to the Internet. Once access is gained, malicious code can be sent through network firewalls and other security defenses and reach internal data banks.
A pair of new security threats to Intel-based computer systems have been revealed. The beleaguered semiconductor chip manufacturer has faced a seemingly endless series of vulnerabilities over the past two years.
Although no known attacks have occurred, two teams of researchers have confirmed vulnerabilities in what is supposed to be the safest neighborhood within Intel processor architecture.
One attack, dubbed SGAxe, can gain entry into Intel’s Software Guard eXtensions (SGX) services that were specifically designed to protect critical data in the event of massive assault elsewhere in a system. A hacker theoretically can steal cryptographic keys stored in SGX and use them to break security measures protecting sensitive data such as financial records, copyrighted content or passwords.
WASHINGTON — For the second time in less than 18 months, SpaceX has abandoned plans to build a manufacturing facility at the Port of Los Angeles for its next-generation Starship launch vehicle.
In a March 27 letter obtained by SpaceNews, SpaceX notified the Port of Los Angeles that it was terminating a lease approved just a month earlier for a parcel of land at the port. News of the lease termination was first reported by the Los Angeles Times.
The letter, signed by Bret Johnsen, SpaceX’s chief financial officer, served as a 45-day notice of SpaceX’s intent to terminate the lease, making the effective end date of the lease May 11. The letter did not explain why the company was terminating the lease.
Immortalists magazine issue no. 5 is out smile
It is predicted that a pandemic of psychological and societal injuries is to come as we face financial and emotional crises across the globe.
As the pandemic’s economic toll grows around the world, some experts fear it could harm science for decades by putting many thousands of researchers out of work and forcing nations to slash funding as they rebuild societies. Others say the pandemic could highlight the importance of science and spur long-term support, especially for basic research, much as the Second World War did.
Financial crises could spell trouble for science budgets but spending could surge in some countries: part 2 in a series on science after the pandemic.
Researchers in Italy have melded the emerging science of convolutional neural networks (CNNs) with deep learning — a discipline within artificial intelligence — to achieve a system of market forecasting with the potential for greater gains and fewer losses than previous attempts to use AI methods to manage stock portfolios. The team, led by Prof. Silvio Barra at the University of Cagliari, published their findings on IEEE/CAA Journal of Automatica Sinica.
The University of Cagliari-based team set out to create an AI-managed “buy and hold” (B&H) strategy — a system of deciding whether to take one of three possible actions — a long action (buying a stock and selling it before the market closes), a short action (selling a stock, then buying it back before the market closes), and a hold (deciding not to invest in a stock that day). At the heart of their proposed system is an automated cycle of analyzing layered images generated from current and past market data. Older B&H systems based their decisions on machine learning, a discipline that leans heavily on predictions based on past performance.
By letting their proposed network analyze current data layered over past data, they are taking market forecasting a step further, allowing for a type of learning that more closely mirrors the intuition of a seasoned investor rather than a robot. Their proposed network can adjust its buy/sell thresholds based on what is happening both in the present moment and the past. Taking into account present-day factors increases the yield over both random guessing and trading algorithms not capable of real-time learning.
