Nanoscale transistors could enable more efficient electronics

 

Silicon transistors, which are used to amplify and switch signals, are a critical component in most electronic devices, from smartphones to automobiles. But silicon semiconductor technology is held back by a fundamental physical limit that prevents transistors from operating below a certain voltage.

This limit, known as “Boltzmann tyranny,” hinders the energy efficiency of computers and other electronics, especially with the rapid development of artificial intelligence technologies that demand faster computation.

In an effort to overcome this fundamental limit of silicon, MIT researchers fabricated a different type of three-dimensional transistor using a unique set of ultrathin semiconductor materials.

Their devices, featuring vertical nanowires only a few nanometers wide, can deliver performance comparable to state-of-the-art silicon transistors while operating efficiently at much lower voltages than conventional devices.

“This is a technology with the potential to replace silicon, so you could use it with all the functions that silicon currently has, but with much better energy efficiency,” says Yanjie Shao, an MIT postdoc and lead author of a paper on the new transistors.

The transistors leverage quantum mechanical properties to simultaneously achieve low-voltage operation and high performance within an area of just a few square nanometers. Their extremely small size would enable more of these 3D transistors to be packed onto a computer chip, resulting in fast, powerful electronics that are also more energy-efficient.

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OpenAI Unveils ChatGPT App Store: Integrations, Hardware, and AI Future

In the rapidly evolving world of artificial intelligence, OpenAI is pushing boundaries that could redefine how we interact with software. The company’s recent launch of an app store integrated into ChatGPT marks a pivotal shift, blending conversational AI with traditional app functionalities. Design executives from major players like Canva, Figma, Adobe, and Target have already begun integrating their tools, raising profound questions about the very nature of applications in an AI-driven era. What happens when an app isn’t a standalone entity but part of an ongoing dialogue with a machine?

This transformation isn’t just theoretical. OpenAI’s Apps SDK, introduced earlier this year, empowers developers to build apps that users can summon mid-conversation. Imagine drafting a design in Figma without leaving your ChatGPT window or ordering from DoorDash while brainstorming meal ideas. According to insights from industry leaders, this could streamline workflows, reducing the friction of switching between apps. Yet, it also challenges long-held assumptions about user interfaces, where static screens give way to dynamic, context-aware interactions.

The implications extend beyond convenience. As AI becomes more agentic—capable of independent actions—the line between user and system blurs. Early adopters report that these integrations feel like having a digital assistant that anticipates needs, pulling in tools seamlessly. But this raises concerns about data privacy and control, as conversations weave through multiple services. OpenAI’s move is bold, positioning ChatGPT not just as a chatbot, but as a central hub for digital life.

 

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Generative AI’s environmental impact

 

Rapid development and deployment of powerful generative AI models comes with environmental consequences, including increased electricity demand and water consumption.

In a two-part series, MIT News explores the environmental implications of generative AI. In this article, we look at why this technology is so resource-intensive. A second piece will investigate what experts are doing to reduce genAI’s carbon footprint and other impacts.

The excitement surrounding potential benefits of generative AI, from improving worker productivity to advancing scientific research, is hard to ignore. While the explosive growth of this new technology has enabled rapid deployment of powerful models in many industries, the environmental consequences of this generative AI “gold rush” remain difficult to pin down, let alone mitigate.

The computational power required to train generative AI models that often have billions of parameters, such as OpenAI’s GPT-4, can demand a staggering amount of electricity, which leads to increased carbon dioxide emissions and pressures on the electric grid.

Furthermore, deploying these models in real-world applications, enabling millions to use generative AI in their daily lives, and then fine-tuning the models to improve their performance draws large amounts of energy long after a model has been developed.

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A new AI-powered tool goes beyond detecting genetic mutation

A new AI-powered tool goes beyond detecting genetic mutations by accurately predicting the diseases they are likely to cause. By integrating genomic data with clinical knowledge, functional annotations, and machine learning models, this technology helps researchers and clinicians better interpret variant pathogenicity, accelerate diagnosis of rare and complex diseases, and support more precise, personalized treatment strategies.

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New Machine Learning Method Refines Peach Fruit Quality Trait Analysis

 

Newswise — Peach (Prunus persica) is an economically important fruit, and understanding the genetic basis of its quality traits is crucial for breeding. Recent advances in genome sequencing have led to the construction of detailed genetic maps, enabling deeper insights into the inheritance of traits. However, complex traits like fruit color remain challenging due to the multiple factors involved. Traditional methods for measuring color, such as colorimetric systems, often fail to provide consistent results. These challenges highlight the need for advanced genomic and computational approaches to improve trait analysis. Based on these challenges, or due to these issues, deeper research is needed to refine breeding strategies and enhance genetic mapping.

In a new study published (DOI: 10.1093/hr/uhaf087) in Horticulture Research (May 2025), researchers from the Chinese Academy of Agricultural Sciences and the Institute of Agrifood Research and Technology (IRTA) applied whole-genome resequencing and machine learning to map key fruit quality traits in peaches. This collaborative study identifies multiple genetic loci and introduces a novel approach for phenotyping fruit color, opening new pathways for precision breeding in peaches and potentially other fruit crops.

The study focused on analyzing eight fruit-related traits in peaches using a high-density genetic map constructed from 134,277 segregating SNPs in the progeny of two genetically distant peach cultivars, ‘Zhongyou Pan #9’ and ‘September Free’. Researchers identified major genes for fruit shape and flesh adhesion to the stone, alongside nine QTLs for important traits such as fruit weight, soluble solids concentration, titratable acidity, and maturity date. One of the major innovations was the use of machine learning for phenotyping peach fruit color, specifically for grading yellow to orange flesh. Traditional methods, based on physical colorimetric parameters like the L, a*, and b* scales, were ineffective in detecting certain QTLs. The machine learning approach identified two new QTLs that were previously undetectable, demonstrating that machine learning can refine the accuracy of complex trait phenotyping. The study also provides valuable insights into the genetic architecture of peach fruit quality, contributing to better breeding practices by pinpointing genetic hotspots for fruit color and other quality traits. This approach marks a significant advancement in the genetic analysis of crops with complex traits.

Dr. Jinlong Wu, one of the senior authors, comments, “This study demonstrates the power of combining genomic sequencing with machine learning to address the complexities of phenotyping in peach breeding. By refining the accuracy of trait measurement, we are not only improving our understanding of fruit quality traits but also setting the stage for more precise, efficient breeding programs. This method can be extended to other crops, potentially accelerating the development of new varieties with improved quality and resilience.”

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