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Category: robotics/AI – Page 1,112

Aubrey De Grey discusses the progress and potential of therapies related to his ideas on anti-aging medicine, including the four therapies that will be tested in a mouse rejuvenation trial. He also shares his thoughts on partnering with organizations and individuals in the field, integrating AI into his work, and the importance of structure in maximizing impact. Aubrey de Grey discusses the potential for Yamanaka factors to be used in organ rejuvenation, and the role of transcription factors in creating induced pluripotent stem cells. He also provides advice for those interested in getting involved in the field and shares his views on time management and productivity. Aubrey De Grey discusses the potential for reversing the pathology of aging to address mechanical issues and mentions promising research being conducted by MAIA Biotechnology on cancerous cells that express telomerase. He also expresses his optimism about the possibility of reaching “longevity escape velocity” within the next 15 years.

Youtube:
Aubrey De Grey links.
https://twitter.com/aubreydegrey?ref_src=twsrc%5Egoogle%7Ctw…r%5Eauthor.
https://www.levf.org/
https://www.linkedin.com/in/aubrey-de-grey-24260b/

PODCAST INFO:
The Learning With Lowell show is a series for the everyday mammal. In this show we’ll learn about leadership, science, and people building their change into the world. The goal is to dig deeply into people who most of us wouldn’t normally ever get to hear. The Host of the show – Lowell Thompson-is a lifelong autodidact, serial problem solver, and founder of startups.
LINKS
Youtube: https://www.youtube.com/channel/UCzri06unR-lMXbl6sqWP_-Q
Youtube clips: https://www.youtube.com/channel/UC-B5x371AzTGgK-_q3U_KfA
Linkedin: https://www.linkedin.com/in/lowell-thompson-2227b074
Twitter: https://twitter.com/LWThompson5
Website: https://www.learningwithlowell.com/
Podcast email: [email protected].
Timestamps.
00:00 Start.
01:00 Mark Hamaleinen write in question: Why we still don’t have any therapies based on his ideas published 20 years ago in his breakout paper.
04:30 update on escape velocity. 50% in 15 years.
08:30 Experiments on mice.
12:00 Yamanaka factor thoughts, current research on mice continued.
14:30 Lev foundation research expanded and explained.
16:30 Lev foundation thesis compared to others.
21:00 Hardships being at the tip of the innovation field of longevity.
23:45 Open source, non profits, lev foundation.
26:00 Ideas from previous organizations (ie. SENS) applied in LEV
27:15 Ichor therapeutics, and partnership process of LEV
29:30 Next generation mentorship.
30:30 Summer internship program.
32:45 Bullish on longevity.
33:30 AI role in longevity.
35:55 Anything fundamental making longevity therapies only for the rich.
38:30 Longevity surgery therapies.
39:30 Advice for people.
40:30 Bottlenecks of longevity.
42:00 Books!
43:05 Staying on cutting edge/ learning.
44:15 Current curiosity and fascination.
46:45 What happiness means to him and how he optimizes for it.
48:30 how he stays healthy over the years, longevity practices he uses.
51:45 How much money does he and the space need.
52:55 Anything stopping him from getting Jeff Bezos and other high network people to invest or donate.
51:55 Thoughts on altos labs & calico labs.
56:35 up and coming people that inspire him (crypto people, michael levin)
59:00 Finding up and coming people to work with and for (advice)
60:30 Things people get wrong and what people ask him about longevity.
1:01:35 Aubrey newsletter and updates (news suggestions)
1:03:30 Question he wonders about that doesn’t have answer to.
1:04:15 How he maximizes his day and stays productive.
1:05:15 Thoughts on MAIA Biotechnology, telomerase, short vs long.
1:09:30 Final thoughts on lev foundation.

#longevity #aubreydegrey #LEVF

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The self-driving vehicle features a bubble-like dome that resembles the space car from The Jetsons.

The Asahi Kasei AKXY2 concept car is designed to envision a future of sustainable and community-focused transportation.

The Tokyo-based firm hopes its concept will light up imaginations and reposition the automobile as a social device rather than a soulless commuting machine.

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This past year, global attention has been focused on geo-strategic issues, such as the devastating war in Ukraine, which has dislocated many and caused immense suffering. Attention has also been focused on the recovery from the COVID pandemic, which was the overriding concern over the past three years. And finally, the economic destruction wrought by rapidly ramped interest rates which have targeted all sectors of the economy, particularly technology. But despite all this negativity, the business of building the future continues. There has been progress across major axes of computing, from visualization to AI and new types of processors (quantum).

With immense progress in technology, what might we look forward to in 2023?

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Nearly a year after Sony and Honda shared plans to jointly make and sell electric vehicles, the two companies revealed a prototype under the brand name Afeela.

The four-door sedan was driven onstage at CES Wednesday as Kenichiro Yoshida, the CEO of Sony, talked through the company’s mobility philosophy, which prioritizes building vehicles that have autonomous capabilities and are transformed into “moving entertainment space[s].”

The first preorders of the Afeelas are scheduled in the first half of 2025, with sales to begin the same year, said Yoshida. Initial shipments will be delivered to customers in North America in the spring of 2026.

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Though it is a cornerstone of virtually every process that occurs in living organisms, the proper folding and transport of biological proteins is a notoriously difficult and time-consuming process to experimentally study.

In a new paper published in eLife, researchers in the School of Biological Sciences and the School of Computer Science have shown that AF2Complex may be able to lend a hand.

Building on the models of DeepMind’s AlphaFold 2, a machine learning tool able to predict the detailed three-dimensional structures of individual proteins, AF2Complex—short for AlphaFold 2 Complex—is a deep learning tool designed to predict the physical interactions of multiple proteins. With these predictions, AF2Complex is able to calculate which proteins are likely to interact with each other to form functional complexes in unprecedented detail.

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Artificial Intelligence is the buzzword of the year with many big giants in almost every industry trying to explore this cutting-edge technology. Right from self-checkout cash registers to AI-based applications to analyse large data in real-time to advanced security check-ins at the airport, AI is just about everywhere.

Currently, the logistics industry is bloated with a number of challenges related to cost, efficiency, security, bureaucracy, and reliability. So, according to the experts, new age technologies like AI, machine learning, the blockchain, and big data are the only fix for the logistics sector which can improve the supply chain ecosystem right from purchase to internal exchanges like storage, auditing, and delivery.

AI is an underlying technology which can enhance the supplier selection, boost supplier relationship management, and more. When combined with big data analytics AI also helps in analysing the supplier related data such as on-time delivery performance, credit scoring, audits, evaluations etc. This helps in making valuable decisions based on actionable real-time insights.

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The first important generative models for images used an approach to artificial intelligence called a neural network — a program composed of many layers of computational units called artificial neurons. But even as the quality of their images got better, the models proved unreliable and hard to train. Meanwhile, a powerful generative model — created by a postdoctoral researcher with a passion for physics — lay dormant, until two graduate students made technical breakthroughs that brought the beast to life.

DALL·E 2 is such a beast. The key insight that makes DALL·E 2’s images possible — as well as those of its competitors Stable Diffusion and Imagen — comes from the world of physics. The system that underpins them, known as a diffusion model, is heavily inspired by nonequilibrium thermodynamics, which governs phenomena like the spread of fluids and gases. “There are a lot of techniques that were initially invented by physicists and now are very important in machine learning,” said Yang Song, a machine learning researcher at OpenAI.

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