(The Conversation is an independent and nonprofit source of news, analysis and commentary from academic experts.) (THE CONVERSATION) As with weeds in a garden, it is a challenge to fully get rid of cancer cells in the body once they arise. They have a relentless need to continuously expand, even when they are significantly cut back by therapy or surgery.
If You’re Down He’ll Pick You Up! Dr. Robot Scientists have lifted the lid on a new project that involves a robot with “human consciousness”. Researchers at Columbia University announced that a prototype is in the making, which could be a scientific breakthrough “bigger than curing cancer”.
Future smartphones, sensors, solar panels and wind turbines will contain electronics in which graphullerene is present.
All mammals hold a backup copy of cellular youth, a new study says. All we have to do is trigger the switch to turn back the clock, researchers say.
Watch this Hyundai crab walk. Imagine the ease getting in and out of curbside parking.
Hyundai Mobis presents a IONIQ 5 with the e-Corner Module (In-wheel System).
You can see the real-life motions of crab driving and zero turn.
Whalen et al. couple deep learning with functional assays in chimpanzee and human cells to interrogate the neurodevelopmental enhancer potential of 2,645 human accelerated regions (HARs). Activity is dominated by cis rather than trans effects, and compensatory changes are identified as a driver of rapid HAR evolution.
Natbot is a Python program that enables GPT-3 to browse the web to perform a specified objective. It works by supplying GPT-3 descriptions of what is shown on the page, such as links and buttons, and then telling GPT-3 to perform an…
Could the advancement of ‘Non Player Character’ tech give us a glimpse of being able to bersion ourselves?
Over the last decade, the landscape of machine learning software development has undergone significant changes. Many frameworks have come and gone, but most have relied heavily on leveraging Nvidia’s CUDA and performed best on Nvidia GPUs. However, with the arrival of PyTorch 2.0 and OpenAI’s Triton, Nvidia’s dominant position in this field, mainly due to its software moat, is being disrupted.
This report will touch on topics such as why Google’s TensorFlow lost out to PyTorch, why Google hasn’t been able to capitalize publicly on its early leadership of AI, the major components of machine learning model training time, the memory capacity/bandwidth/cost wall, model optimization, why other AI hardware companies haven’t been able to make a dent in Nvidia’s dominance so far, why hardware will start to matter more, how Nvidia’s competitive advantage in CUDA is wiped away, and a major win one of Nvidia’s competitors has at a large cloud for training silicon.
Join Our Discord to enter the giveaway (and comment with your username (without the at!)): https://discord.gg/learnaitogether.
Attend WAICF with a 20% discount: https://www.worldaicannes.com/pass/executive-pass/?utm_campa…ICF23-LOBO
References:
►Read the full article: https://www.louisbouchard.ai/vall-e/
►Link for the audio samples: https://valle-demo.github.io/
►Wang et al., 2023: VALL-E. https://arxiv.org/pdf/2301.02111.pdf.
►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/newsletter/
Chapters:
