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.
American multinational bank Wells Fargo has informed two of its customers about a data breach.
The personal information involved includes names of clients and mortgage account numbers.
The financial services company claims to be responsible for safeguarding customer information and promptly responded to the incident as it arose.
Artificial Intelligence is making its presence felt in thousands of different ways. It helps scientists make sense of vast troves of data; it helps detect financial fraud; it drives our cars; it feeds us music suggestions; its chatbots drive us crazy. And it’s only getting started.
Are we capable of understanding how quickly AI will continue to develop? And if the answer is no, does that constitute the Great Filter?
The Fermi Paradox is the discrepancy between the apparent high likelihood of advanced civilizations existing and the total lack of evidence that they do exist. Many solutions have been proposed for why the discrepancy exists. One of the ideas is the ‘Great Filter.’
The third proof point is both the increase in manufacturing capacity investment and the change in how that investment will be managed. With the interest in governments to secure future semiconductor manufacturing for both supply security and economic growth, Mr. Gelsinger went on a spending spree with investment in expanding capacity in Oregon, Ireland, and Israel, as well as six new fabs in Arizona, Ohio, and Germany. Most of the initial investment was made without the promise of government grants, such as the US Chips Act. However, Intel has now secured more than $50B from US and European government incentives, customer commitments starting with its first five customers on the 18A process node, and its financial partners. Intel has also secured an additional $11B loan from the US government and a 25% investment tax credit.
In addition to it’s own investment in fab capacity, Intel is partnering with Tower Semiconductor and UMC, two foundries with long and successful histories. Tower will be investing in new equipment to be installed in Intel’s New Mexico facility for analog products, and UMC will partner with Intel to leverage three of the older Arizona fabs and process nodes, starting with the 12nm, to support applications like industrial IoT, mobile, communications infrastructure, and networking.
The second side of this investment is how current and future capacity will be used. As strictly an IDM, Intel has historically capitalized on its investments in the physical fab structures by retrofitting the fabs after three process nodes, on average. While this allowed for the reuse of the structures and infrastructure, it eliminated support for older process nodes, which are important for many foundry customers. According to Omdia Research, less than 3% of all semiconductors are produced on the latest process nodes. As a result, Intel is shifting from retrofitting fabs for new process nodes to maintaining fabs to support extended life cycles of older process nodes, as shown in the chart below. This requires additional capacity for newer process nodes.
