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Jan 26 2023

AI technology generates original proteins from scratch

Scientists have created an AI system capable of generating artificial enzymes from scratch. In laboratory tests, some of these enzymes worked as well as those found in nature, even when their artificially generated amino acid sequences diverged significantly from any known natural protein.

The experiment demonstrates that natural language processing, although it was developed to read and write language text, can learn at least some of the underlying principles of biology. Salesforce Research developed the AI program, called ProGen, which uses next-token prediction to assemble amino acid sequences into artificial proteins.

Scientists said the new technology could become more powerful than directed evolution, the Nobel-prize winning protein design technology, and it will energize the 50-year-old field of protein engineering by speeding the development of new proteins that can be used for almost anything from therapeutics to degrading plastic.

“The artificial designs perform much better than designs that were inspired by the evolutionary process,” said James Fraser, PhD, professor of bioengineering and therapeutic sciences at the UCSF School of Pharmacy, and an author of the work, which was published Jan. 26, in Nature Biotechnology.

“The language model is learning aspects of evolution, but it’s different than the normal evolutionary process,” Fraser said. “We now have the ability to tune the generation of these properties for specific effects. For example, an enzyme that’s incredibly thermostable or likes acidic environments or won’t interact with other proteins.”

To create the model, scientists simply fed the amino acid sequences of 280 million different proteins of all kinds into the machine learning model and let it digest the information for a couple of weeks. Then, they fine-tuned the model by priming it with 56,000 sequences from five lysozyme families, along with some contextual information about these proteins.

The model quickly generated a million sequences, and the research team selected 100 to test, based on how closely they resembled the sequences of natural proteins, as well how naturalistic the AI proteins’ underlying amino acid “grammar” and “semantics” were.

Out of this first batch of a 100 proteins, which were screened in vitro by Tierra Biosciences, the team made five artificial proteins to test in cells and compared their activity to an enzyme found in the whites of chicken eggs, known as hen egg white lysozyme (HEWL). Similar lysozymes are found in human tears, saliva and milk, where they defend against bacteria and fungi.

Two of the artificial enzymes were able to break down the cell walls of bacteria with activity comparable to HEWL, yet their sequences were only about 18% identical to one another. The two sequences were about 90% and 70% identical to any known protein.

Just one mutation in a natural protein can make it stop working, but in a different round of screening, the team found that the AI-generated enzymes showed activity even when as little as 31.4% of their sequence resembled any known natural protein.

The AI was even able to learn how the enzymes should be shaped, simply from studying the raw sequence data. Measured with X-ray crystallography, the atomic structures of the artificial proteins looked just as they should, although the sequences were like nothing seen before.

Salesforce Research developed ProGen in 2020, based on a kind of natural language programming their researchers originally developed to generate English language text.

They knew from their previous work that the AI system could teach itself grammar and the meaning of words, along with other underlying rules that make writing well-composed.

“When you train sequence-based models with lots of data, they are really powerful in learning structure and rules,” said Nikhil Naik, PhD, Director of AI Research at Salesforce Research, and the senior author of the paper. “They learn what words can co-occur, and also compositionality.”

With proteins, the design choices were almost limitless. Lysozymes are small as proteins go, with up to about 300 amino acids. But with 20 possible amino acids, there are an enormous number (20300) of possible combinations. That’s greater than taking all the humans who lived throughout time, multiplied by the number of grains of sand on Earth, multiplied by the number of atoms in the universe.

Given the limitless possibilities, it’s remarkable that the model can so easily generate working enzymes.

“The capability to generate functional proteins from scratch out-of-the-box demonstrates we are entering into a new era of protein design,” said Ali Madani, PhD, founder of Profluent Bio, former research scientist at Salesforce Research, and the paper’s first author. “This is a versatile new tool available to protein engineers, and we’re looking forward to seeing the therapeutic applications.”

Further information: https://github.com/salesforce/progen

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AI technology generates original proteins from scratch Republished from Source https://www.sciencedaily.com/releases/2023/01/230126124330.htm via https://www.sciencedaily.com/rss/computers_math/artificial_intelligence.xml

crowdsourcing week

Written by bizbuildermike · Categorized: AI · Tagged: AI

Jan 26 2023

Versatile robo-dog runs through the sandy beach at 3 meters per second

KAIST (President Kwang Hyung Lee) announced on the 25th that a research team led by Professor Jemin Hwangbo of the Department of Mechanical Engineering developed a quadrupedal robot control technology that can walk robustly with agility even in deformable terrain such as sandy beach.

Professor Hwangbo’s research team developed a technology to model the force received by a walking robot on the ground made of granular materials such as sand and simulate it via a quadrupedal robot. Also, the team worked on an artificial neural network structure which is suitable in making real-time decisions needed in adapting to various types of ground without prior information while walking at the same time and applied it on to reinforcement learning. The trained neural network controller is expected to expand the scope of application of quadrupedal walking robots by proving its robustness in changing terrain, such as the ability to move in high-speed even on a sandy beach and walk and turn on soft grounds like an air mattress without losing balance.

This research, with Ph.D. Student Soo-Young Choi of KAIST Department of Mechanical Engineering as the first author, was published in January in the Science Robotics. (Paper title: Learning quadrupedal locomotion on deformable terrain).

Reinforcement learning is an AI learning method used to create a machine that collects data on the results of various actions in an arbitrary situation and utilizes that set of data to perform a task. Because the amount of data required for reinforcement learning is so vast, a method of collecting data through simulations that approximates physical phenomena in the real environment is widely used.

In particular, learning-based controllers in the field of walking robots have been applied to real environments after learning through data collected in simulations to successfully perform walking controls in various terrains.

However, since the performance of the learning-based controller rapidly decreases when the actual environment has any discrepancy from the learned simulation environment, it is important to implement an environment similar to the real one in the data collection stage. Therefore, in order to create a learning-based controller that can maintain balance in a deforming terrain, the simulator must provide a similar contact experience.

The research team defined a contact model that predicted the force generated upon contact from the motion dynamics of a walking body based on a ground reaction force model that considered the additional mass effect of granular media defined in previous studies.

Furthermore, by calculating the force generated from one or several contacts at each time step, the deforming terrain was efficiently simulated.

The research team also introduced an artificial neural network structure that implicitly predicts ground characteristics by using a recurrent neural network that analyzes time-series data from the robot’s sensors.

The learned controller was mounted on the robot ‘RaiBo’, which was built hands-on by the research team to show high-speed walking of up to 3.03 m/s on a sandy beach where the robot’s feet were completely submerged in the sand. Even when applied to harder grounds, such as grassy fields, and a running track, it was able to run stably by adapting to the characteristics of the ground without any additional programming or revision to the controlling algorithm.

In addition, it rotated with stability at 1.54 rad/s (approximately 90° per second) on an air mattress and demonstrated its quick adaptability even in the situation in which the terrain suddenly turned soft.

The research team demonstrated the importance of providing a suitable contact experience during the learning process by comparison with a controller that assumed the ground to be rigid, and proved that the proposed recurrent neural network modifies the controller’s walking method according to the ground properties.

The simulation and learning methodology developed by the research team is expected to contribute to robots performing practical tasks as it expands the range of terrains that various walking robots can operate on.

The first author, Suyoung Choi, said, “It has been shown that providing a learning-based controller with a close contact experience with real deforming ground is essential for application to deforming terrain.” He went on to add that “The proposed controller can be used without prior information on the terrain, so it can be applied to various robot walking studies.”

This research was carried out with the support of the Samsung Research Funding & Incubation Center of Samsung Electronics.

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Versatile robo-dog runs through the sandy beach at 3 meters per second Republished from Source https://www.sciencedaily.com/releases/2023/01/230126100154.htm via https://www.sciencedaily.com/rss/computers_math/artificial_intelligence.xml

crowdsourcing week

Written by bizbuildermike · Categorized: AI · Tagged: AI

Jan 25 2023

Learning with queried hints

Posted by Sreenivas Gollapudi, Senior Staff Research Scientist, and Kostas Kollias, Staff Research Scientist, Google Research, Algorithms & Optimization Team

In many computing applications the system needs to make decisions to serve requests that arrive in an online fashion. Consider, for instance, the example of a navigation app that responds to driver requests. In such settings there is inherent uncertainty about important aspects of the problem. For example, the preferences of the driver with respect to features of the route are often unknown and the delays of road segments can be uncertain. The field of online machine learning studies such settings and provides various techniques for decision-making problems under uncertainty.

A navigation engine has to decide how to route this user’s request. The satisfaction of the user will depend on the (uncertain) congestion of the two routes and unknown preferences of the user on various features, such as how scenic, safe, etc., the route is.

A very well known problem in this framework is the multi-armed bandit problem, in which the system has a set of n available options (arms) from which it is asked to choose in each round (user request), e.g., a set of precomputed alternative routes in navigation. The user’s satisfaction is measured by a reward that depends on unknown factors such as user preferences and road segment delays. An algorithm’s performance over T rounds is compared against the best fixed action in hindsight by means of the regret (the difference between the reward of the best arm and the reward obtained by the algorithm over all T rounds). In the experts variant of the multi-armed bandit problem, all rewards are observed after each round and not just the one played by the algorithm.

An instance of the experts problem. The table presents the rewards obtained by following each of the 3 experts at each round = 1, 2, 3, 4. The best expert in hindsight (and hence the benchmark to compare against) is the middle one, with total reward 21. If, for example, we had selected expert 1 in the first two rounds and expert 3 in the last two rounds (recall that we need to select before observing the rewards of each round), we would have extracted reward 17, which would give a regret equal to 21 – 17 = 4.

These problems have been extensively studied, and existing algorithms can achieve sublinear regret. For example, in the multi-armed bandit problem, the best existing algorithms can achieve regret that is of the order √T. However, these algorithms focus on optimizing for worst-case instances, and do not account for the abundance of available data in the real world that allows us to train machine learned models capable of aiding us in algorithm design.

In “Online Learning and Bandits with Queried Hints” (presented at ITCS 2023), we show how an ML model that provides us with a weak hint can significantly improve the performance of an algorithm in bandit-like settings. Many ML models are trained accurately using relevant past data. In the routing application, for example, specific past data can be used to estimate road segment delays and past feedback from drivers can be used to learn the quality of certain routes. Models trained with such data can, in certain cases, give very accurate feedback. However, our algorithms achieve strong guarantees even when the feedback from the model is in the form of a less explicit weak hint. Specifically, we merely ask that the model predict which of two options will be better. In the navigation application this is equivalent to having the algorithm pick two routes and query an ETA model for which of the two is faster, or presenting the user with two routes with different characteristics and letting them pick the one that is best for them. By designing algorithms that leverage such a hint we can: Improve the regret of the bandits setting on an exponential scale in terms of dependence on T and improve the regret of the experts setting from order of √T to become independent of T. Specifically, our upper bound only depends on the number of experts n and is at most log(n).

Algorithmic Ideas

Our algorithm for the bandits setting utilizes the well known upper confidence bound (UCB) algorithm. The UCB algorithm maintains, as a score for each arm, the average reward observed on that arm so far and adds to it an optimism parameter that becomes smaller with the number of times the arm has been pulled, thus balancing between exploration and exploitation. Our algorithm applies the UCB scores on pairs of arms, mainly in an effort to utilize the available pairwise comparison model that can designate the better of two arms. Each pair of arms i and j is grouped as a meta-arm (i, j) whose reward in each round is equal to the maximum reward between the two arms. Our algorithm observes the UCB scores of the meta-arms and picks the pair (i, j) that has the highest score. The pair of arms are then passed as a query to the ML auxiliary pairwise prediction model, which responds with the best of the two arms. This response is the arm that is finally used by the algorithm.

The decision problem considers three candidate routes. Our algorithm instead considers all pairs of the candidate routes. Suppose pair 2 is the one with the highest score in the current round. The pair is given to the auxiliary ML pairwise prediction model, which outputs whichever of the two routes is better in the current round.

Our algorithm for the experts setting takes a follow-the-regularized-leader (FtRL) approach, which maintains the total reward of each expert and adds random noise to each, before picking the best for the current round. Our algorithm repeats this process twice, drawing random noise two times and picking the highest reward expert in each of the two iterations. The two selected experts are then used to query the auxiliary ML model. The model’s response for the best between the two experts is the one played by the algorithm.

Results

Our algorithms utilize the concept of weak hints to achieve strong improvements in terms of theoretical guarantees, including an exponential improvement in the dependence of regret on the time horizon or even removing this dependence altogether. To illustrate how the algorithm can outperform existing baseline solutions, we present a setting where 1 of the n candidate arms is consistently marginally better than the n-1 remaining arms. We compare our ML probing algorithm against a baseline that uses the standard UCB algorithm to pick the two arms to submit to the pairwise comparison model. We observe that the UCB baseline keeps accumulating regret whereas the probing algorithm quickly identifies the best arm and keeps playing it, without accumulating regret.

An example in which our algorithm outperforms a UCB based baseline. The instance considers n arms, one of which is always marginally better than the remaining n-1.

Conclusion

In this work we explore how a simple pairwise comparison ML model can provide simple hints that prove very powerful in settings such as the experts and bandits problems. In our paper we further present how these ideas apply to more complex settings such as online linear and convex optimization. We believe our model of hints can have more interesting applications in ML and combinatorial optimization problems.

Acknowledgements

We thank our co-authors Aditya Bhaskara (University of Utah), Sungjin Im (University of California, Merced), and Kamesh Munagala (Duke University).

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Learning with queried hints Republished from Source http://ai.googleblog.com/2023/01/learning-with-queried-hints.html via http://feeds.feedburner.com/blogspot/gJZg

crowdsourcing week

Written by Google AI · Categorized: AI · Tagged: AI

Jan 25 2023

Watch this person-shaped robot liquify and escape jail, all with the power of magnets

Inspired by sea cucumbers, engineers have designed miniature robots that rapidly and reversibly shift between liquid and solid states. On top of being able to shape-shift, the robots are magnetic and can conduct electricity. The researchers put the robots through an obstacle course of mobility and shape-morphing tests in a study publishing January 25 in the journal Matter.

Where traditional robots are hard-bodied and stiff, “soft” robots have the opposite problem; they are flexible but weak, and their movements are difficult to control. “Giving robots the ability to switch between liquid and solid states endows them with more functionality,” says Chengfeng Pan, an engineer at The Chinese University of Hong Kong who led the study.

The team created the new phase-shifting material — dubbed a “magnetoactive solid-liquid phase transitional machine” — by embedding magnetic particles in gallium, a metal with a very low melting point (29.8 °C).

“The magnetic particles here have two roles,” says senior author and mechanical engineer Carmel Majidi of Carnegie Mellon University. “One is that they make the material responsive to an alternating magnetic field, so you can, through induction, heat up the material and cause the phase change. But the magnetic particles also give the robots mobility and the ability to move in response to the magnetic field.”

This is in contrast to existing phase-shifting materials that rely on heat guns, electrical currents, or other external heat sources to induce solid-to-liquid transformation. The new material also boasts an extremely fluid liquid phase compared to other phase-changing materials, whose “liquid” phases are considerably more viscous.

Before exploring potential applications, the team tested the material’s mobility and strength in a variety of contexts. With the aid of a magnetic field, the robots jumped over moats, climbed walls, and even split in half to cooperatively move other objects around before coalescing back together. In one video, a robot shaped like a person liquifies to ooze through a grid after which it is extracted and remolded back into its original shape.

“Now, we’re pushing this material system in more practical ways to solve some very specific medical and engineering problems,” says Pan.

On the biomedical side, the team used the robots to remove a foreign object from a model stomach and to deliver drugs on-demand into the same stomach. They also demonstrate how the material could work as smart soldering robots for wireless circuit assembly and repair (by oozing into hard-to-reach circuits and acting as both solder and conductor) and as a universal mechanical “screw” for assembling parts in hard-to-reach spaces (by melting into the threaded screw socket and then solidifying; no actual screwing required.)

“Future work should further explore how these robots could be used within a biomedical context,” says Majidi. “What we’re showing are just one-off demonstrations, proofs of concept, but much more study will be required to delve into how this could actually be used for drug delivery or for removing foreign objects.”

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Watch this person-shaped robot liquify and escape jail, all with the power of magnets Republished from Source https://www.sciencedaily.com/releases/2023/01/230125121555.htm via https://www.sciencedaily.com/rss/computers_math/artificial_intelligence.xml

crowdsourcing week

Written by bizbuildermike · Categorized: AI · Tagged: AI

Jan 24 2023

Fish sensory organ key to improving navigational skills of underwater robots

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Fish sensory organ key to improving navigational skills of underwater robots Republished from Source https://www.sciencedaily.com/releases/2023/01/230124192629.htm via https://www.sciencedaily.com/rss/computers_math/artificial_intelligence.xml

crowdsourcing week

Written by bizbuildermike · Categorized: AI · Tagged: AI

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The National Digital Assets Research and Development Agenda is still being worked on by the administration of United States Vice President Joe Biden, who is still in office. The White House Office of Science and Technology Policy (OSTP) has issued a request for information (RFI) dated January 26 and posted by the Federal Register. The […]

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