Lifeboat Foundation

Goldman Sachs said in a report late Thursday that Indian food delivery giant Zomato’s quick commerce arm Blinkit is now more valuable than its core food delivery business, as per the bank’s sum-of-the-parts analysis.

The investment bank estimates Blinkit’s implied value at 119 Indian rupees per share ($1.43) or about $13 billion, while Zomato’s food delivery business is valued at Rs 98 per share. Goldman previously pegged Blinkit’s valuation at $2 billion in March 2023.

Blinkit’s valuation surge is driven by its strong growth potential in India’s fast-growing quick commerce market. Goldman Sachs forecasts Blinkit’s gross order value (GOV) to grow at a compound annual growth rate (CAGR) of 53% between the financial years 2024 and 2027, outpacing the overall online grocery market’s projected CAGR of 38% during the same period.

Cyber attackers are experimenting with their latest ransomware on businesses in Africa, Asia, and South America before targeting richer countries that have more sophisticated security methods.

Hackers have adopted a “strategy” of infiltrating systems in the developing world before moving to higher-value targets such as in North America and Europe, according to a report published on Wednesday by cyber security firm Performanta.

“Adversaries are using developing countries as a platform where they can test their malicious programs before the more resourceful countries are targeted,” the company told Banking Risk and Regulation, a service from FT Specialist.

Successful implementation of GenAI hinges on three main factors: financial value to the organization, availability of key data and impact on the workforce.

Building new financial market infrastructure with ‘safety by design’ will require a spirit of endeavour and effective public-private collaboration.

Construction is the world’s largest industry, employing seven percent of the planet’s working-age adults, contributing 13 percent of the world’s GDP and completing floor space equivalent to the city of Paris every seven days.

The construction industry is also the most inefficient, least digitised and most polluting industry (37% of ALL emissions), so change is imperative from macro economic necessity alone. For the builders of the world faced with a jigsaw puzzle of partial digital solutions and chronic labor and supply chain issues, the margins are growing ever-thinner and the necessity is to change or perish.

British company Automated Architecture (AUAR) has a thoroughly ingenious solution and it has enlisted an all-star cast of financial backers in short order: Morgan Stanley, ABB Robotics, Rival Holdings (USA), Vandenbussche NV (Belgium) with VCs such as Miles Ahead and Bacchus Venture Capital (Jim Horowitz et al) helping to get the initial idea off the ground.

Researchers have produced, stored, and retrieved quantum information for the first time, a critical step in quantum networking.

The ability to share quantum information is crucial for developing quantum networks for distributed computing and secure communication. Quantum computing will be useful for solving some important types of problems, such as optimizing financial risk, decrypting data, designing molecules, and studying the properties of materials.

“Interfacing two key devices together is a crucial step forward in allowing quantum networking, and we are really excited to be the first team to have been able to demonstrate this.” —

A collaboration of scientists from various universities in the UK and Europe have stored and retrieved data from quantum computers, marking a “crucial connection for ‘quantum internet,’” in a global first.

This is an essential step in quantum networking as the world gears up for the next generation of computing.

With its ultrafast computational speeds, quantum computing is touted to solve the world’s problems in designing new drugs, understanding the properties of materials, and optimizing financial risk.

PRESS RELEASE — The full power of next-generation quantum computing could soon be harnessed by millions of individuals and companies, thanks to a breakthrough by scientists at Oxford University Physics guaranteeing security and privacy. This advance promises to unlock the transformative potential of cloud-based quantum computing and is detailed in a new study published in the influential U.S. scientific journal Physical Review Letters.

Quantum computing is developing rapidly, paving the way for new applications which could transform services in many areas like healthcare and financial services. It works in a fundamentally different way to conventional computing and is potentially far more powerful. However, it currently requires controlled conditions to remain stable and there are concerns around data authenticity and the effectiveness of current security and encryption systems.

Several leading providers of cloud-based services, like Google, Amazon, and IBM, already separately offer some elements of quantum computing. Safeguarding the privacy and security of customer data is a vital precursor to scaling up and expending its use, and for the development of new applications as the technology advances. The new study by researchers at Oxford University Physics addresses these challenges.

A UC San Diego team has uncovered a method to decipher neural networks’ learning process, using a statistical formula to clarify how features are learned, a breakthrough that promises more understandable and efficient AI systems. Credit: SciTechDaily.com.

Neural networks have been powering breakthroughs in artificial intelligence, including the large language models that are now being used in a wide range of applications, from finance, to human resources to healthcare. But these networks remain a black box whose inner workings engineers and scientists struggle to understand. Now, a team led by data and computer scientists at the University of California San Diego has given neural networks the equivalent of an X-ray to uncover how they actually learn.

The researchers found that a formula used in statistical analysis provides a streamlined mathematical description of how neural networks, such as GPT-2, a precursor to ChatGPT, learn relevant patterns in data, known as features. This formula also explains how neural networks use these relevant patterns to make predictions.