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Category: biological – Page 56

The Schwartz Reisman Institute for Technology and Society and the Department of Computer Science at the University of Toronto, in collaboration with the Vector Institute for Artificial Intelligence and the Cosmic Future Initiative at the Faculty of Arts \& Science, present Geoffrey Hinton on October 27, 2023, at the University of Toronto.

0:00:00 — 0:07:20 Opening remarks and introduction.
0:07:21 — 0:08:43 Overview.
0:08:44 — 0:20:08 Two different ways to do computation.
0:20:09 — 0:30:11 Do large language models really understand what they are saying?
0:30:12 — 0:49:50 The first neural net language model and how it works.
0:49:51 — 0:57:24 Will we be able to control super-intelligence once it surpasses our intelligence?
0:57:25 — 1:03:18 Does digital intelligence have subjective experience?
1:03:19 — 1:55:36 Q\&A
1:55:37 — 1:58:37 Closing remarks.

Talk title: “Will digital intelligence replace biological intelligence?”

Abstract: Digital computers were designed to allow a person to tell them exactly what to do. They require high energy and precise fabrication, but in return they allow exactly the same model to be run on physically different pieces of hardware, which makes the model immortal. For computers that learn what to do, we could abandon the fundamental principle that the software should be separable from the hardware and mimic biology by using very low power analog computation that makes use of the idiosynchratic properties of a particular piece of hardware. This requires a learning algorithm that can make use of the analog properties without having a good model of those properties. Using the idiosynchratic analog properties of the hardware makes the computation mortal. When the hardware dies, so does the learned knowledge. The knowledge can be transferred to a younger analog computer by getting the younger computer to mimic the outputs of the older one but education is a slow and painful process. By contrast, digital computation makes it possible to run many copies of exactly the same model on different pieces of hardware. Thousands of identical digital agents can look at thousands of different datasets and share what they have learned very efficiently by averaging their weight changes. That is why chatbots like GPT-4 and Gemini can learn thousands of times more than any one person. Also, digital computation can use the backpropagation learning procedure which scales much better than any procedure yet found for analog hardware. This leads me to believe that large-scale digital computation is probably far better at acquiring knowledge than biological computation and may soon be much more intelligent than us. The fact that digital intelligences are immortal and did not evolve should make them less susceptible to religion and wars, but if a digital super-intelligence ever wanted to take control it is unlikely that we could stop it, so the most urgent research question in AI is how to ensure that they never want to take control.

About Geoffrey Hinton.

Geoffrey Hinton received his PhD in artificial intelligence from Edinburgh in 1978. After five years as a faculty member at Carnegie Mellon he became a fellow of the Canadian Institute for Advanced Research and moved to the Department of Computer Science at the University of Toronto, where he is now an emeritus professor. In 2013, Google acquired Hinton’s neural networks startup, DNN research, which developed out of his research at U of T. Subsequently, Hinton was a Vice President and Engineering Fellow at Google until 2023. He is a founder of the Vector Institute for Artificial Intelligence where he continues to serve as Chief Scientific Adviser.

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A critical molecule for the metabolism of living organisms has been synthesized for the first time by University of Hawaiʻi at Mānoa researchers at low temperatures (10 K) on ice coated nanoparticles mimicking conditions in deep space, marking a “cool” step in advancing our understanding of the origins of life.

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Engineers have unveiled an encodable multifunctional material that can dynamically tune its shape and mechanical properties in real time. Inspired by the remarkable adaptability observed in biological organisms like the octopus, a breakthrough has been achieved in soft machines. A research team, led by Professor Jiyun Kim in the Department of Materials Science and Engineering at UNIST has successfully developed an encodable multifunctional material that can dynamically tune its shape and mechanical properties in real-time. This groundbreaking metamaterial surpasses the limitations of existing materials, opening up new possibilities for applications in robotics and other fields requiring adaptability.

Current soft machines lack the level of adaptability demonstrated by their biological counterparts, primarily due to limited real-time tunability and restricted reprogrammable space of properties and functionalities.

In order to bridge this gap, the research team introduced a novel approach utilizing graphical stiffness patterns.

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Organic materials discovered on Mars may have originated from atmospheric formaldehyde, according to new research, marking a step forward in our understanding of the possibility of past life on the Red Planet.

Scientists from Tohoku University have investigated whether the early atmospheric conditions on Mars had the potential to foster the formation of biomolecules – organic compounds essential for biological processes.

Their findings, published in Scientific Reports, offer intriguing insights into the plausibility of Mars harboring life in its distant past.

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Drawing inspiration from the extraordinary adaptability seen in biological entities such as the octopus, a significant advancement in the field of soft robotics has been made. Under the guidance of Professor Jiyun Kim from the Department of Materials Science and Engineering at UNIST, a research team has successfully developed an encodable multifunctional material that can dynamically tune its shape and mechanical properties in real-time.

This groundbreaking metamaterial surpasses the limitations of existing materials, opening up new possibilities for applications in robotics and other fields requiring adaptability.

Current soft machines lack the level of adaptability demonstrated by their biological counterparts, primarily due to limited real-time tunability and restricted reprogrammable space of properties and functionalities. In order to bridge this gap, the research team introduced a novel approach utilizing graphical stiffness patterns. By independently switching the digital binary stiffness states (soft or rigid) of individual constituent units within a simple auxetic structure featuring elliptical voids, the material achieves in situ and gradational tunability across various mechanical qualities.

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The FIT4NANO project has mapped out the expansive applications and future directions of focused ion beam technology, emphasizing its critical role in advancing research and development across multiple disciplines, from microelectronics to life sciences.

Processing materials on the nanoscale, producing prototypes for microelectronics, or analyzing biological samples: The range of applications for finely focused ion beams is huge. Experts from the EU collaboration FIT4NANO have now reviewed the many options and developed a roadmap for the future. The article, published in Applied Physics Review, is aimed at students, users from industry and science as well as research policymakers.

Discovery and Applications.

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The motions within the molecule provide a new way to compare the structures and functions of similar proteins.

Proteins play a central role in nearly every biological process, and they often change shape as they function. Over the past decade, a research team has developed a method of analysis that can help make sense of the available atomic-scale structural data and reveal the key physical distortions that underlie protein functions. Now the team has shown that the technique provides a consistent way of comparing proteins from different species, demonstrating similar structural changes in many of them [1]. The researchers believe that the technique will help biologists better understand the cross-species variations among proteins.

Proteins are linear chains of amino acids that fold up into specific three-dimensional shapes. Although there are lots of atomic-scale data on the structural changes that protein molecules execute as they function, researchers have had few quantitative methods to extract insights from these data, says biophysicist Pablo Sartori of the Gulbenkian Institute of Science in Portugal. One challenge, he says, is the arbitrary choice one makes when comparing two similar protein structures, such as the structures of a protein in two different conformations. “If you align region A of the protein, then region B shows displacement. If you align region B, then region A shows displacement. If you align the average, then both are displaced a bit.” Another problem is that the relative displacement is often not the quantity that best reflects the structural changes associated with protein function.

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The perplexing phenomenon of homochirality in life, where biomolecules exist in only one of two mirror-image forms, remains unexplained despite historical attention from scientific figures like Pasteur, Lord Kelvin, and Pierre Curie. Recent research suggests the combination of electric and magnetic fields might influence this preference through experiments showing enantioselective effects on chiral molecules interacting with magnetized surfaces, offering indirect evidence towards understanding this mystery.

The phenomenon known as homochirality of life, which refers to the exclusive presence of biomolecules in one of their two possible mirror-image configurations within living organisms, has intrigued several prominent figures in science. This includes Louis Pasteur, who first identified molecular chirality, William Thomson (also known as Lord Kelvin), and Pierre Curie, a Nobel Laureate.

A conclusive explanation is still lacking, as both forms have, for instance, the same chemical stability and do not differ from each other in their physicochemical properties. The hypothesis, however, that the interplay between electric and magnetic fields could explain the preference for one or the other mirror-image form of a molecule – so-called enantiomers – emerged early on.

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Researchers have discovered a mechanism steering the evolution of multicellular life. They identify how altered protein folding drives multicellular evolution.

In a new study led by researchers from the University of Helsinki and the Georgia Institute of Technology, scientists turned to a tool called experimental evolution. In the ongoing Multicellularity Long Term Evolution Experiment (MuLTEE), laboratory yeast are evolving novel multicellular functions, enabling researchers to investigate how they arise.

The study, published in Science Advances, puts the spotlight on the regulation of proteins in understanding evolution.

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Structured light, which encompasses various spatial patterns of light like donuts or flower petals, is crucial for a myriad of applications from precise measurements to communication systems.

The many properties of light allow it to be manipulated and used for applications that range from very sensitive measurements to communications and intelligent ways to interrogate objects. A compelling degree of freedom is the spatial pattern, called structured light, which can resemble shapes such as donuts and flower petals. For instance, patterns with different numbers of petals can represent letters of the alphabet, and when observed on the other side, deliver the message.

Unfortunately, what makes these patterns sensitive for measurements also make them susceptible to unwanted environmental factors such as air turbulence, aberrated optics, stressed fibers, or biological tissues doing their own “patterning” and distorting the structure. Here the distorted pattern can deteriorate to the point that the output pattern looks nothing like the input, rendering them ineffective.

Conventional methods to correct this have needed one to reapply the same distortion—this can take the form of measuring the distorting and applying the reverse or reversing the distortion in the beam and resending it back into the aberration, allowing this to “undo” itself in the process.

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