RISC-V is an industry standard instruction set architecture (ISA) born in UC Berkeley. RISC-V is the fifth iteration in the lineage of historic RISC processors. The core value of the RISC-V ISA is the freedom of usage it offers. Any organization can leverage the ISA to design the best possible core for their specific needs, with no regional restrictions or licensing costs. It attracts a massive ecosystem of developers and companies building systems using the RISC-V ISA. To support these efforts and grow the ecosystem, the brains behind RISC decided to form RISC-V International—a non-profit foundation that governs the ISA and guides the ecosystem.

We had the privilege of talking with Andrea Gallo, Vice President of Technology at RISC-V International. Andrea oversees the technological advancement of RISC-V, collaborating with vendors and institutions to overcome challenges and expand its global presence. Andrea's career in technology spans several influential roles at major companies. Before joining RISC-V International, he worked at Linaro, where he pioneered Arm data center engineering initiatives, later overseeing diverse technological sectors as Vice President of Segment Groups, and ultimately managing crucial business development activities as executive Vice President. During his earlier tenure as a Fellow at ST-Ericsson, he focused on smartphone and application processor technology, and at STMicroelectronics he optimized hardware-software architectures and established international development teams.

When OpenAI announced its GPT-4 model, it first became a part of ChatGPT, behind the paywall for premium users. The GPT-4 is the latest installment in the Generative Pretrained Transformer (GPT) Large Language Models (LLMs). The GPT-4 aims to be a more capable version than the GPT-3.5 that powered ChatGPT at first, which was capable once it launched. However, it seems like the performance of GPT-4 has been steadily dropping since its introduction. Many users noted the regression, and today we have researchers from Stanford University and UC Berkeley, who benchmarked the GPT-4 performance in March 2023, and the model's performance in June 2023 in tasks like solving math problems, visual reasoning, code generation, and answering sensitive questions.

The results? The paper shows that GPT-4 performance has been significantly degraded in all the tasks. This could be attributed to improving stability, lowering the massive compute demand, and much more. What is unexpected, GPT-3.5 experienced a significant uplift in the same period. Below, you can see the examples that were benchmarked by the researchers, which also compare GTP-4 and GPT-3.5 performance in all cases.

For weeks, researchers at IBM Quantum and UC Berkeley were taking turns running increasingly complex physical simulations. Youngseok Kim and Andrew Eddins, scientists with IBM Quantum, would test them on the 127-qubit IBM Quantum Eagle processor. UC Berkeley's Sajant Anand would attempt the same calculation using state-of-the-art classical approximation methods on supercomputers located at Lawrence Berkeley National Lab and Purdue University. They'd check each method against an exact brute-force classical calculation.

Eagle returned accurate answers every time. And watching how both computational paradigms performed as the simulations grew increasingly complex made both teams feel confident the quantum computer was still returning answers more accurate than the classical approximation methods, even in the regime beyond the capabilities of the brute force methods. "The level of agreement between the quantum and classical computations on such large problems was pretty surprising to me personally," said Eddins. "Hopefully it's impressive to everyone."