March 28, 2024

4 mins

The AI Corner

Hello, Niuralogists!

In the ever-evolving realm of artificial intelligence, this week's edition is committed to delivering the most recent breakthroughs. Our central focus is to analyze how these advancements impact different facets of our lives, including workplaces, businesses, policies, and personal experiences. In this issue, we will explore some updates such as the introduction of Apple's iOS 18 AI Vision and the enhancements made to extreme weather frequency forecasting through an algorithm developed by MIT.

For a more in-depth understanding, keep on reading…

Unveiling Apple's iOS 18 AI Vision

Apple's iOS 18 AI strategy is set to take center stage at Apple's upcoming Worldwide Developers Conference (WWDC), scheduled from June 10 to 14 at its Cupertino, CA, campus. Anticipation surrounds the event as Apple is expected to unveil significant advancements, including a revamped Siri, AI integration into iMessage, auto-generated playlists, and an AI health coach for Apple Watches. This marks a pivotal moment for Apple, with the potential to significantly expand AI accessibility to billions of users, making it a crucial milestone in the company's history.

MIT-Developed Algorithm Enhances Extreme Weather Frequency Forecasting

MIT researchers have developed a groundbreaking algorithm derived from machine learning and dynamical systems theory to enhance the accuracy of global climate models in predicting extreme weather events. By refining simulations produced by existing climate models, this innovative approach corrects for discrepancies at a larger scale, thus providing more precise forecasts for specific locations, such as Boston. Their method, which involves a combination of machine learning and climate modeling techniques, has shown promising results in predicting the frequency of extreme weather occurrences over the next few decades. This advancement holds significant implications for understanding and preparing for the impacts of climate change on various aspects of life, from biodiversity to infrastructure resilience.

Source: Pexels

IPPR Warns of 'Job Apocalypse' Threatening 8 Million UK Careers Due to AI

A recent report by the Institute for Public Policy Research (IPPR) warns of an impending "job apocalypse" in the UK due to the widespread adoption of AI. The study predicts that over eight million jobs are at risk unless urgent government action is taken. The report outlines two stages of AI adoption, with the first wave already affecting 11 percent of tasks, particularly routine cognitive and organizational work. However, the potential second wave could see AI handling up to 59 percent of tasks, impacting higher-earning jobs and non-routine cognitive work. IPPR emphasizes the need for a "job-centric" AI strategy, advocating for fiscal incentives, regulatory oversight, and support for green jobs less susceptible to automation. The report also highlights the disproportionate impact on certain demographics, such as women and young people, urging proactive measures to mitigate job displacement. Various scenarios presented in the report underscore the critical need for timely intervention to ensure a smooth transition in the face of advancing AI technologies.

Tech Giants Unite to Challenge Nvidia's Dominance

Nvidia faces mounting pressure from a coalition of tech giants, including Google, Intel, Qualcomm, and Arm, who have joined forces to challenge Nvidia's dominance in the AI chip market. Spearheaded by the UXL Foundation, this initiative aims to create an open-source software suite facilitating AI code to operate on any hardware, irrespective of chip architecture. Seeking broader industry support, the group is reaching out to additional chipmakers and cloud giants like Amazon and Microsoft to ensure compatibility across diverse hardware platforms. Leveraging Intel's OneAPI open standard, the project intends to eliminate dependencies on Nvidia's CUDA platform, which has traditionally kept developers locked into Nvidia's ecosystem. This concerted effort poses a significant threat to Nvidia's stronghold in the AI landscape, potentially paving the way for increased competition and innovation from new players in the industry.

Source: Reuters

Potential Revolution: Large Language Models in Finance Within Two Years

A recent report by The Alan Turing Institute suggests that large language models (LLMs) have the potential to revolutionize the finance sector within the next two years. LLMs, known for their ability to analyze vast amounts of data and generate coherent text, are predicted to enhance efficiency and safety in finance by detecting fraud, providing financial insights, and automating customer service. The report, based on research and a workshop involving professionals from major banks, regulators, insurers, and government agencies, highlights the current and potential future use of LLMs in various aspects of finance, such as regulatory review, investment research, and back-office operations. While participants anticipate widespread integration of LLMs into services like investment banking and venture capital strategy development, they also acknowledge the associated risks, particularly regarding regulatory compliance and safety. The report recommends collaboration among financial services professionals, regulators, and policymakers to address safety concerns and explore the potential of open-source models in the finance sector.


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Can Large Language Models Equip Household Robots with Common Sense?

MIT engineers have devised a groundbreaking method to imbue household robots with a sense of adaptability, leveraging insights from large language models (LLMs). By integrating robot motion data with LLMs' wealth of "common sense knowledge," these engineers enable robots to autonomously rectify errors and seamlessly proceed with tasks even in the face of disruptions. This innovative approach marks a significant stride in empowering robots to navigate household chores with enhanced efficiency and resilience, ultimately advancing the realm of domestic robotics.

Can Large Language Models Reveal What They Know About New Subjects?

Large language models (LLMs), like those powering popular AI chatbots, are complex systems whose inner workings remain somewhat mysterious. Seeking to shed light on this complexity, researchers at MIT and beyond have delved into how these models retrieve stored knowledge. Surprisingly, they discovered that LLMs often employ a remarkably simple linear function to decode stored facts, using the same function for similar types of information. By uncovering these decoding functions, researchers can probe the model's knowledge about various subjects and potentially correct false information it has stored. This research not only offers insights into the inner workings of LLMs but also lays the groundwork for refining their knowledge and improving their accuracy in providing information.


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