Methylation definition at 5:05, 27:20 a lil about reprogramming, 32:00 q&a, 47:44 Aubrey chimes in, 57:00 Keith Comito(and other throughout)
Zoom transcription: https://otter.ai/u/AIIhn4i_p4DIXHAJx0ZaG0HUnAU
We will be joined by Morgan Levine, Yale University, to discuss the recent article “Underlying Features of Epigenetic Aging Clocks” she co-authored.
AI, Genetics, and Health-Tech / Wearables — 21st Century Technologies For Healthy Companion Animals.
Ira Pastor ideaXme life sciences ambassador interviews Dr. Angela Hughes, the Global Scientific Advocacy Relations Senior Manager and Veterinary Geneticist at Mars Petcare.
The global petcare industry is significantly expanding, with North America sales alone expected to hit US $300 billion by 2025. And while we may associate the Mars Corporation, the world’s largest candy company, with leading confectionary brands like Milky Way, M&M’s, Skittles, Snickers, Twix, etc. They also happen to be one of the world’s largest companies in pet care as well.
Computational molecular physics (CMP) aims to leverage the laws of physics to understand not just static structures but also the motions and actions of biomolecules. Applying CMP to proteins has required either simplifying the physical models or running simulations that are shorter than the time scale of the biological activity. Brini et al. reviewed advances that are moving CMP to time scales that match biological events such as protein folding, ligand unbinding, and some conformational changes. They also highlight the role of blind competitions in driving the field forward. New methods such as deep learning approaches are likely to make CMP an increasingly powerful tool in describing proteins in action.
Science, this issue p.
### BACKGROUND
The biggest computer chip in the world is so fast and powerful it can predict future actions “faster than the laws of physics produce the same result.”
That’s according to a post by Cerebras Systems, a startup company that made the claim at the online SC20 supercomputing conference this week.
Working with the U.S. Department of Energy’s National Energy Technology Laboratory, Cerebras designed what it calls “the world’s most powerful AI compute system.” It created a massive chip 8.5 inch-square chip, the Cerebras CS-1, housed in a refrigerator-sized computer in an effort to improve on deep-learning training models.
Code Unto Caesar
Durendal’s algorithm wrote scripture about three topics: “the plague,” “Caesar,” and “the end of days.” So it’s not surprising that things took a grim turn. The full text is full of glitches characteristic of AI-written texts, like excerpts where over half of the nouns are “Lord.” But some passages are more coherent and read like bizarre doomsday prophecies.
For example, from the plague section: “O LORD of hosts, the God of Israel; When they saw the angel of the Lord above all the brethren which were in the wilderness, and the soldiers of the prophets shall be ashamed of men.”
The feature could save YouTube creators from having to manually add chapters to the descriptions of their videos.
Whole-body positron emission tomography combined with computed tomography (PET/CT) is a cornerstone in the management of lymphoma (cancer in the lymphatic system). PET/CT scans are used to diagnose disease and then to monitor how well patients respond to therapy. However, accurately classifying every single lymph node in a scan as healthy or cancerous is a complex and time-consuming process. Because of this, detailed quantitative treatment monitoring is often not feasible in clinical day-to-day practice.
Researchers at the University of Wisconsin-Madison have recently developed a deep-learning model that can perform this task automatically. This could free up valuable physician time and make quantitative PET/CT treatment monitoring possible for a larger number of patients.
To acquire PET/CT scans, patients are injected with a sugar molecule marked with radioactive fluorine-18 (18 F-fluorodeoxyglucose). When the fluorine atom decays, it emits a positron that instantly annihilates with an electron in its immediate vicinity. This annihilation process emits two back-to-back photons, which the scanner detects and uses to infer the location of the radioactive decay.
Artificial intelligence is being developed that can analyze whether it’s own decision or prediction is reliable.
…An AI that is aware/determine or analyze it’s own weaknesses. Basically, it should help doctors or passengers of the AI know quickly the risk involved.
How might The Terminator have played out if Skynet had decided it probably wasn’t responsible enough to hold the keys to the entire US nuclear arsenal? As it turns out, scientists may just have saved us from such a future AI-led apocalypse, by creating neural networks that know when they’re untrustworthy.
These deep learning neural networks are designed to mimic the human brain by weighing up a multitude of factors in balance with each other, spotting patterns in masses of data that humans don’t have the capacity to analyse.
😃 KFC now has self driving 5G trucks? 😃
Autonomous 5G vehicles are getting more popular and closer to your neighborhood… Read about KFC’s trucks here.
