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The study is part of the PsychENCODE Consortium, which brings together multidisciplinary teams to generate large-scale gene expression and regulatory data from human brains across several major psychiatric disorders and stages of brain development. (From left: first authors Sean Whalen and Chengyu Deng, and senior authors Katie Pollard and Nadav Ahituv.)

Credit: Gladstone Institutes / Michael Short

SAN FRANCISCOMay 24, 2024In a scientific feat that broadens our knowledge of genetic changes that shape brain development or lead to psychiatric disorders, a team of researchers combined high-throughput experiments and machine learning to analyze more than 100,000 sequences in human brain cellsand identify over 150 variants that likely cause disease.

The study, from scientists at Gladstone Institutes and University of California, San Francisco (UCSF), establishes a comprehensive catalog of genetic sequences involved in brain development and opens the door to new diagnostics or treatments for neurological conditions such as schizophrenia and autism spectrum disorder. Findings appear in the journal Science.

We collected a massive amount of data from sequences in noncoding regions of DNA that were already suspected to play a big role in brain development or disease, says Senior Investigator Katie Pollard, PhD, who also serves as director of the Gladstone Institute for Data Science and Biotechnology. We were able to functionally test more than 100,000 of them to find out whether they affect gene activity, and then pinpoint sequence changes that could alter their activity in disease.

Pollard co-led the sweeping study with Nadav Ahituv, PhD, professor in the Department of Bioengineering and Therapeutic Sciences at UCSF and director of the UCSF Institute for Human Genetics. Much of the experimental work on brain tissue was led by Tomasz Nowakowski, PhD, associate professor of neurological surgery in the UCSF Department of Medicine.

In all, the team found 164 variants associated with psychiatric disorders and 46,802 sequences with enhancer activity in developing neurons, meaning they control the function of a given gene.

These enhancers could be leveraged to treat psychiatric diseases in which one copy of a gene is not fully functional, Ahituv says: Hundreds of diseases result from one gene not working properly, and it may be possible to take advantage of these enhancers to make them do more.

Organoids and Machine Learning Take the Spotlight

Beyond identifying enhancers and disease-linked sequences, the study holds significance in two other key areas.

First, the scientists repeated parts of their experiment using a brain organoid developed from human stem cells and found that the organoid was an effective stand-in for the real thing. Notably, most of the genetic variants detected in the human brain tissue replicated in the cerebral organoid.

Our organoid compared very well against the human brain, Ahituv says. As we expand our work to test more sequences for other neurodevelopmental diseases, we now know that the organoid is a good model for understanding gene regulatory activity.

Second, by feeding massive amounts of DNA sequence data and gene regulatory activity to a machine learning model, the team was able to train the computer to successfully predict the activity of a given sequence. This type of program can enable in-silico experiments that allow researchers to predict the outcomes of experiments before doing them in the lab. This strategy enables scientists to make discoveries faster and using fewer resources, especially when large quantities of biological data are involved.

Sean Whalen, PhD, a senior research scientist in the Pollard Lab at Gladstone and a co-first author of the study, says the team tested the machine learning model using sequences held out from model training to see if it could predict the results already gathered on gene expression activity.

The model had never seen this data before and was able to make predictions with great accuracy, showing it had learned the general principles for how genes are impacted by noncoding regions of DNA in developing brain cells, Whalen says. You can imagine how this could open up a lot of new possibilities in research, even predicting how combinations of variants might function together.

A New Chapter for Brain Discoveries

The study was completed as part of the PsychENCODE Consortium, which brings together multidisciplinary teams to generate large-scale gene expression and regulatory data from human brains across several major psychiatric disorders and stages of brain development.

Through the consortiums publication of multiple studies, it seeks to shed light on poorly understood psychiatric conditions, from autism to bipolar disorder, and ultimately jumpstart new treatment approaches.

Our study contributes to this growing body of knowledge, showing the utility of using human cells, organoids, functional screening methods, and deep learning to investigate regulatory elements and variants involved in human brain development, says Chengyu Deng, PhD, a postdoctoral researcher at UCSF and a co-first author of the study.

About the Study

The study, Massively Parallel Characterization of Regulatory Elements in the Developing Human Cortex, appears in the May 24, 2024 issue of Science. Authors include: Chengyu Deng, Sean Whalen, Marilyn Steyert, Ryan Ziffra, Pawel Przytycki, Fumitaka Inoue, Daniela Pereira, Davide Capauto, Scott Norton, Flora Vaccarino, PsychENCODE Consortium, Alex Pollen, Tomasz Nowakowski, Nadav Ahituv, and Katherine Pollard.

The work was funded in part by the National Institute of Mental Health, the New York Stem Cell Foundation, the National Human Genome Research Institute, and Coordination for the Improvement of Higher Education Personnel. The data generated was part of thePsychENCODE Consortium.

About Gladstone Institutes

Gladstone Institutesis an independent, nonprofit life science research organization that uses visionary science and technology to overcome disease. Established in 1979, it is located in the epicenter of biomedical and technological innovation, in the Mission Bay neighborhood of San Francisco. Gladstone has created a research model that disrupts how science is done, funds big ideas, and attracts the brightest minds.

Massively parallel characterization of regulatory elements in the developing human cortex

24-May-2024

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Scientists leverage machine learning to decode gene regulation in the developing human brain - EurekAlert

AI

The bill increases regulatory requirements for machine learning systems in California.

May 24, 2024 In a Tuesday press release, Haltia AI, an artificial intelligence startup based in Dubai, warned leaders in machine learning that Californias new AI bill will cripple innovation with overly burdensome regulations.

Haltia said that the bill throws a wrench into the growth of AI startups with its unrealistic requirements and stifling compliance costs.

The legislation, titled the Understanding the Safe and Secure Innovation for Frontier Artificial Intelligence Act, was introduced in February and passed the California State Senate on Tuesday. The act mandates that developers of AI tools comply with various safety requirements and report any safety concerns.

AI systems are defined by the act as machine-based systems that can make predictions, recommendations, decisions, and formulate options. Safety tests include ensuring that an AI model does not have the capability to enable harms, such as creation of chemical and biological weapons or cyberattacks on critical infrastructure. Third party testers will be required to determine the safety of these systems.

Haltia said that on the surface, the act aims for responsible AI development. However, its implementation creates a labyrinth of red tape that disproportionately impacts startups. Because the bill requires ongoing annual reviews, Haltia argues that it adds significant technical and financial burdens.

Arto Bendiken, co-founder and CTO at Haltia, said that the act is a prime example of how well-intentioned regulations can morph into a bureaucratic nightmare. He added that the financial penalties for non-compliance only exacerbate the issue, potentially deterring groundbreaking ideas before they even take flight.

Haltia called for other AI startups to follow its lead and move operations to the United Arab Emirates where its thriving ecosystem, coupled with its commitment to the future of AI, makes it the ideal launchpad for the next generation of groundbreaking AI technologies in the Silicon Valley of the East.

In 2023, California Governor Gavin Newson signed an executive order that announced new directives aimed at understanding the risks of machine learning technologies in order to ensure equitable outcomes when used and to prepare the states workforce for its use.

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AI Startup Says California AI Bill Will Hamper Innovation - BroadbandBreakfast.com

PHILADELPHIA (KYW Newsradio) With the help of machine learning technology, Airbnb says it will be cracking down on parties this summer.

Its really important that those spaces are respected and treated with care, and that, you know, people are not showing up and taking advantage of that, said Airbnbs Global Director of Corporate and Policy Communications Christopher Nulty.

The best part about staying in an Airbnb is often that you're staying in a neighborhood, and the only way to continue staying in a neighborhood is to be a good neighbor.

Nulty says the company will be using the technology to prevent any disruptive parties, paying close attention to bookings on Memorial Day, Fourth of July and Labor Day. It looks at how long guests are staying, past rental ratings, distance from home, and the number of guests.

So far, it has resulted in a 50% reduction in unauthorized parties. In 2023, more than 67,000 people across the U.S., including 950 in Philadelphia, were deterred from booking entire home listings over those weekends.

Those who are flagged, but arent actually planning on throwing a party, can call Airbnbs customer service line.

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Airbnb using machine learning technology to prevent parties - KYW

TAMPA, Fla. Amazon Web Services is busy positioning its cloud infrastructure business to capitalize on the promise of generative artificial intelligence for transforming space and other industries.

More than 60% of the companys space and aerospace customers are already using some form of AI in their businesses, according to AWS director of aerospace and satellite Clint Crosier, up from single digits around three years ago.

Crosier predicts similar growth over the next few years in space for generative AI, which uses deep-learning models to answer questions or create content based on patterns detected in massive datasets, marking a major step up from traditional machine-learning algorithms.

Mathematical advances, an explosion in the amount of available data and cheaper and more efficient chips for processing it are a perfect storm for the rise of generative AI, he told SpaceNews in an interview, helping drive greater adoption of cloud-based applications.

In the last year, AWS has fundamentally reorganized itself internally so that we could put the right teams [and] organizational structure in place so that we can really double down on generative AI, he said.

He said AWS has created a generative AI for space cell of a handful of people to engage with cloud customers to help develop next-generation capabilities.

These efforts include a generative AI laboratory for customers to experiment with new ways of using these emerging capabilities.

Crosier sees three main areas for using generative AI in space: geospatial analytics, spacecraft design and constellation management.

Earth observation satellite operators such as BlackSky and Capella Space already use these tools to help manage search queries and gain more insights into their geospatial data.

Its early days in the manufacturing sector, but Crosier said engineers are experimenting with how a generative AI model fed with design parameters could produce new concepts by drawing from potentially overlooked data, such as from the automotive industry.

Whether youre designing a satellite, rocket or spacecraft, youre letting the generative AI go out and do that exploratory work around the globe with decades of data, he said, and then it will come back and bring you novel design concepts that nobody has envisioned before for your team to use as a baseline to start refining.

He said generative AI also has the potential to help operators manage increasingly crowded orbits by helping to simulate testing scenarios.

If I have a constellation of 600 satellites, I want to model how that constellation will behave under various design parameters, he said.

Well, I can get a model of two concepts, which leaves me woefully inadequate but it costs time and money to model them, or I can model an infinite number. Gen AI will tell me what are the top 25 cases I should model for my modeling simulation capability that will give me the best design optimization, and so were seeing it used that way.

AWS efforts to accelerate the adoption of these emerging computing capabilities also include scholarships and a commitment announced in November to provide free AI training for two million people worldwide before the end of 2025.

Continue reading here:
Bringing generative artificial intelligence to space - SpaceNews

In the latest episode of Lexicon, the podcast by Interesting Engineering (IE), we sit down with Jaroslaw Rzepecki, Ph. D., Monumos chief technology officer (CTO).

Our mission is to improve the efficiency of motor systems in a way that has never been possible before and, in doing so, help us use precious resources more sustainably, Monumo explains. Monumo is working hard to get there using a unique set of data and machine learning techniques to build one of the worlds first large engineering models (LEM).

Once matured, this model will work like an engineering R&D Midjourney or Dall-E to help engineers with components or entire machine plans on demand. A quantum leap in computer-aided design (CAD), if you like.

While the interface wont be as dumbed down as you might expect from large-language models (LLMs) like ChatGPT, it will leverage an engineers time to make the best kit they can imagine. And the potential is enormous.

Jaroslaw Rzepecki leads the companys technological development, oversees the hardware and software development pipelines, and directs machine learning (ML) research.

Before joining Monumo, Jaroslaw was an integral part of the Codemasters team behind the racing video games Grid and Dirt 2; he has also worked as a software engineer at Siemens and held senior roles at Microsoft Research and ARM.

As he told Interesting Engineering during our interview, he also spends some of his spare time in martial arts, specifically kickboxing. We asked him if martial arts had helped his professional life.

Yeah, so its a bit similar to my professional journey, so I tried several different disciplines as I moved around. You know I was also changing clubs, and obviously, then you also change the styles a little bit, Jaroslaw told us.

I did quite a few different ones. I would say that my favorite sport is kickboxing. Ive done that for probably the longest out of all of them, and whether it helps, it does. I think it helps with focus. It helps with clearing your mind, he added.

Afterwards, you probably feel physically exhausted; youre quite invigorated. You have more energy that day to do something than if you would skip that training the previous day. So yes, I would say that it does help, Jaroslaw said.

After his extensive and diverse career, including academia, computer game design, and software engineering at Siemens and ARM, Jaroslaw saw the potential for Monumo and jumped ship to become its second-ever employee. He has since worked up the ranks to become its head tech honcho.

When asked if this was a big risk for him, Jaroslaw said, Um, there is always some risk involved when you change, right? But you know no risk, no fun, right? So, yes, I think a bit of a risk was involved. But um, as I said, I calculated that risk and thought, thats okay.

The main thrust of Monumos work is to combine physics and engineering knowledge with machine learning (ML) and artificial intelligence (AI) to build a computer model that can help sketch out new models for machines. The idea is that, with enough data and training, such a model could conceivably be used to make novel designs never dreamed up before.

And it will be data and professional-driven to boot. Not just any Tom, Dick, and Harry will be able to pick it up and run with it. This is mainly because Monumo plans to keep its software proprietary but also because, at its heart, the software is a complicated multidisciplinary physics model.

It combines data and understanding of many different engineering fields and physics and can conceivably integrate many other diverse fields. This could encompass nuclear physics, nanotechnology, biology, and geology. It could be integrated into the model if it can be measured or modeled.

One sentence headline here, and Im sure that everybody in the engineering community listening to this podcast will appreciate how difficult it is to find the right balance of different components of a complex engineering system if you want to design it, right? Jaroslaw said.

Its a difficult problem. So I like a challenge, I like difficult problems, and applying deep tech to engineering also automatically makes it a multidisciplinary problem because obviously, you have to combine, you know, the latest developments in computer science algorithms optimization, math, and physics, he added.

But the LEM is the long-term goal. For now, they are building an Anser model that can generate models but, crucially, provide the training data for the LEM later down the line. Monumo is focusing on making electric motors as energy-efficient as possible.

When pressed about problems of LLMs and hallucinations, Jaroslaw explained that Anser and the eventual LEM would be immune to this. He explained this because the generated designs are then sense checked using mechanical engineering tools to assess their viability.

If they dont pass the muster, the software flags issues, and the user will go back to the drawing board to amend the design accordingly. The entire design process is the same as in real life, with multiple stages yielding the final piece.

It is a collaborative approach, like tweaking parameters in Midjourney or Dall-E to get the picture you want. Anser can also integrate certain customer considerations or constraints into the design based on their needs.

Since many aspects of our modern world use energy in some form or another, even a marginal increase in energy efficiency could provide enormous energy savings around the world. Less energy wasted is a bonus for the planet as a whole.

And so any kind of improvements that we can make to electric motors will have a huge positive impact on ecology and our movement of the society to towards a more and more green way of life, Jaroslaw said.

The company chose the electric motor as it is a simple and complex enough problem. If Anser can be proven with something like this, it can be used for basically anything (within reason) with enough data and training.

The techniques that were applying and the simulation that we build is a multiphysics simulation so that it could be applied to other branches of engineering we are indeed. Yes, we are laying the foundations and building the simulation that is flexible enough, he explained.

LLMs (Large Language Models) drive todays AI models to mimic human ability with words and pictures. Tomorrow, LEMs (Large Engineering Models) will create solutions that surpass anything humans have previously achieved. Our ability to run and store large volumes of simulations, combined with our optimization intelligence, means that we are already on the way to building these precious data sets and training new models, Monumo explains.

And dont worry about such a model taking your engineering job. Jaroslaw explained that Anser and its progeny should be considered a new, competent computer-aided (CAD) design software.

I dont think that Engineers have to worry about losing their jobs. I will always need engineers. You know, all of this is, um, its a tool, and weve seen in the past that each time a new tool is developed in principle, he said.

Humankind has an option: either Im going to use this great new tool and do the same thing that I did before but with fewer humans being involved, or I can use this new tool and all the humans that I have just to do more, and we always go for Lets just do more, he added.

So, it may be time to brush up on your AI and ML expertise.

NEWSLETTER

Stay up-to-date on engineering, tech, space, and science news with The Blueprint.

Christopher McFadden Christopher graduated from Cardiff University in 2004 with a Masters Degree in Geology. Since then, he has worked exclusively within the Built Environment, Occupational Health and Safety and Environmental Consultancy industries. He is a qualified and accredited Energy Consultant, Green Deal Assessor and Practitioner member of IEMA. Chris’s main interests range from Science and Engineering, Military and Ancient History to Politics and Philosophy.

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Here's how AI and ML are shaping the future of machine design - Interesting Engineering