Cloud Hosting

This article has been reviewed according to ScienceX's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility:

fact-checked

peer-reviewed publication

trusted source

proofread

Artificial intelligence has exploded across our news feeds, with ChatGPT and related AI technologies becoming the focus of broad public scrutiny. Beyond popular chatbots, biologists are finding ways to leverage AI to probe the core functions of our genes.

Previously, University of California San Diego researchers who investigate DNA sequences that switch genes on used artificial intelligence to identify an enigmatic puzzle piece tied to gene activation, a fundamental process involved in growth, development and disease. Using machine learning, a type of artificial intelligence, School of Biological Sciences Professor James T. Kadonaga and his colleagues discovered the downstream core promoter region (DPR), a "gateway" DNA activation code that's involved in the operation of up to a third of our genes.

Building from this discovery, Kadonaga and researchers Long Vo ngoc and Torrey E. Rhyne have now used machine learning to identify "synthetic extreme" DNA sequences with specifically designed functions in gene activation.

Publishing in the journal Genes & Development, the researchers tested millions of different DNA sequences through machine learning (AI) by comparing the DPR gene activation element in humans versus fruit flies (Drosophila). By using AI, they were able to find rare, custom-tailored DPR sequences that are active in humans but not fruit flies and vice versa. More generally, this approach could now be used to identify synthetic DNA sequences with activities that could be useful in biotechnology and medicine.

"In the future, this strategy could be used to identify synthetic extreme DNA sequences with practical and useful applications. Instead of comparing humans (condition X) versus fruit flies (condition Y) we could test the ability of drug A (condition X) but not drug B (condition Y) to activate a gene," said Kadonaga, a distinguished professor in the Department of Molecular Biology.

"This method could also be used to find custom-tailored DNA sequences that activate a gene in tissue 1 (condition X) but not in tissue 2 (condition Y). There are countless practical applications of this AI-based approach. The synthetic extreme DNA sequences might be very rare, perhaps one-in-a-million if they exist they could be found by using AI."

Machine learning is a branch of AI in which computer systems continually improve and learn based on data and experience. In the new research, Kadonaga, Vo ngoc (a former UC San Diego postdoctoral researcher now at Velia Therapeutics) and Rhyne (a staff research associate) used a method known as support vector regression to train machine learning models with 200,000 established DNA sequences based on data from real-world laboratory experiments. These were the targets presented as examples for the machine learning system. They then fed 50 million test DNA sequences into the machine learning systems for humans and fruit flies and asked them to compare the sequences and identify unique sequences within the two enormous data sets.

While the machine learning systems showed that human and fruit fly sequences largely overlapped, the researchers focused on the core question of whether the AI models could identify rare instances where gene activation is highly active in humans but not in fruit flies. The answer was a resounding "yes." The machine learning models succeeded in identifying human-specific (and fruit fly-specific) DNA sequences. Importantly, the AI-predicted functions of the extreme sequences were verified in Kadonaga's laboratory by using conventional (wet lab) testing methods.

"Before embarking on this work, we didn't know if the AI models were 'intelligent' enough to predict the activities of 50 million sequences, particularly outlier 'extreme' sequences with unusual activities. So, it's very impressive and quite remarkable that the AI models could predict the activities of the rare one-in-a-million extreme sequences," said Kadonaga, who added that it would be essentially impossible to conduct the comparable 100 million wet lab experiments that the machine learning technology analyzed since each wet lab experiment would take nearly three weeks to complete.

The rare sequences identified by the machine learning system serve as a successful demonstration and set the stage for other uses of machine learning and other AI technologies in biology.

"In everyday life, people are finding new applications for AI tools such as ChatGPT. Here, we've demonstrated the use of AI for the design of customized DNA elements in gene activation. This method should have practical applications in biotechnology and biomedical research," said Kadonaga. "More broadly, biologists are probably at the very beginning of tapping into the power of AI technology."

More information: Long Vo ngoc et al, Analysis of the Drosophila and human DPR elements reveals a distinct human variant whose specificity can be enhanced by machine learning, Genes & Development (2023). DOI: 10.1101/gad.350572.123

Journal information: Genes & Development

Read more:
Artificial intelligence catalyzes gene activation research and uncovers rare DNA sequences - Phys.org

by Pawan Jain, West Virginia University, [This article first appeared in The Conversation, republished with permission]

Artificial Intelligence-powered tools, such as ChatGPT, have the potential to revolutionize the efficiency, effectiveness and speed of the work humans do.

And this is true in financial markets as much as in sectors like health care, manufacturing and pretty much every other aspect of our lives.

Ive been researching financial markets and algorithmic trading for 14 years. While AI offers lots of benefits, the growing use of these technologies in financial markets also points to potential perils. A look at Wall Streets past efforts to speed up trading by embracing computers and AI offers important lessons on the implications of using them for decision-making.

In the early 1980s, fueled by advancements in technology and financial innovations such as derivatives, institutional investors began using computer programs to execute trades based on predefined rules and algorithms. This helped them complete large trades quickly and efficiently.

Back then, these algorithms were relatively simple and were primarily used for so-called index arbitrage, which involves trying to profit from discrepancies between the price of a stock index like the S&P 500 and that of the stocks its composed of.

As technology advanced and more data became available, this kind of program trading became increasingly sophisticated, with algorithms able to analyze complex market data and execute trades based on a wide range of factors. These program traders continued to grow in number on the largey unregulated trading freeways on which over a trillion dollars worth of assets change hands every day causing market volatility to increase dramatically.

Eventually this resulted in the massive stock market crash in 1987 known as Black Monday. The Dow Jones Industrial Average suffered what was at the time the biggest percentage drop in its history, and the pain spread throughout the globe.

In response, regulatory authorities implemented a number of measures to restrict the use of program trading, including circuit breakers that halt trading when there are significant market swings and other limits. But despite these measures, program trading continued to grow in popularity in the years following the crash.

Fast forward 15 years, to 2002, when the New York Stock Exchange introduced a fully automated trading system. As a result, program traders gave way to more sophisticated automations with much more advanced technology: High-frequency trading.

HFT uses computer programs to analyze market data and execute trades at extremely high speeds. Unlike program traders that bought and sold baskets of securities over time to take advantage of an arbitrage opportunity a difference in price of similar securities that can be exploited for profit high-frequency traders use powerful computers and high-speed networks to analyze market data and execute trades at lightning-fast speeds. High-frequency traders can conduct trades in approximately one 64-millionth of a second, compared with the several seconds it took traders in the 1980s.

These trades are typically very short term in nature and may involve buying and selling the same security multiple times in a matter of nanoseconds. AI algorithms analyze large amounts of data in real time and identify patterns and trends that are not immediately apparent to human traders. This helps traders make better decisions and execute trades at a faster pace than would be possible manually.

Another important application of AI in HFT is natural language processing, which involves analyzing and interpreting human language data such as news articles and social media posts. By analyzing this data, traders can gain valuable insights into market sentiment and adjust their trading strategies accordingly.

These AI-based, high-frequency traders operate very differently than people do.

The human brain is slow, inaccurate and forgetful. It is incapable of quick, high-precision, floating-point arithmetic needed for analyzing huge volumes of data for identifying trade signals. Computers are millions of times faster, with essentially infallible memory, perfect attention and limitless capability for analyzing large volumes of data in split milliseconds.

And, so, just like most technologies, HFT provides several benefits to stock markets.

These traders typically buy and sell assets at prices very close to the market price, which means they dont charge investors high fees. This helps ensure that there are always buyers and sellers in the market, which in turn helps to stabilize prices and reduce the potential for sudden price swings.

High-frequency trading can also help to reduce the impact of market inefficiencies by quickly identifying and exploiting mispricing in the market. For example, HFT algorithms can detect when a particular stock is undervalued or overvalued and execute trades to take advantage of these discrepancies. By doing so, this kind of trading can help to correct market inefficiencies and ensure that assets are priced more accurately.

But speed and efficiency can also cause harm.

HFT algorithms can react so quickly to news events and other market signals that they can cause sudden spikes or drops in asset prices.

Additionally, HFT financial firms are able to use their speed and technology to gain an unfair advantage over other traders, further distorting market signals. The volatility created by these extremely sophisticated AI-powered trading beasts led to the so-called flash crash in May 2010, when stocks plunged and then recovered in a matter of minutes erasing and then restoring about $1 trillion in market value.

Since then, volatile markets have become the new normal. In 2016 research, two co-authors and I found that volatility a measure of how rapidly and unpredictably prices move up and down increased significantly after the introduction of HFT.

The speed and efficiency with which high-frequency traders analyze the data mean that even a small change in market conditions can trigger a large number of trades, leading to sudden price swings and increased volatility.

In addition, research I published with several other colleagues in 2021 shows that most high-frequency traders use similar algorithms, which increases the risk of market failure. Thats because as the number of these traders increases in the marketplace, the similarity in these algorithms can lead to similar trading decisions.

This means that all of the high-frequency traders might trade on the same side of the market if their algorithms release similar trading signals. That is, they all might try to sell in case of negative news or buy in case of positive news. If there is no one to take the other side of the trade, markets can fail.

That brings us to a new world of ChatGPT-powered trading algorithms and similar programs. They could take the problem of too many traders on the same side of a deal and make it even worse.

In general, humans, left to their own devices, will tend to make a diverse range of decisions. But if everyones deriving their decisions from a similar artificial intelligence, this can limit the diversity of opinion.

Consider an extreme, nonfinancial situation in which everyone depends on ChatGPT to decide on the best computer to buy. Consumers are already very prone to herding behavior, in which they tend to buy the same products and models. For example, reviews on Yelp, Amazon and so on motivate consumers to pick among a few top choices.

Since decisions made by the generative AI-powered chatbot are based on past training data, there would be a similarity in the decisions suggested by the chatbot. It is highly likely that ChatGPT would suggest the same brand and model to everyone. This might take herding to a whole new level and could lead to shortages in certain products and service as well as severe price spikes.

This becomes more problematic when the AI making the decisions is informed by biased and incorrect information. AI algorithms can reinforce existing biases when systems are trained on biased, old or limited data sets. And ChatGPT and similar tools have been criticized for making factual errors.

In addition, since market crashes are relatively rare, there isnt much data on them. Since generative AIs depend on data training to learn, their lack of knowledge about them could make them more likely to happen.

For now, at least, it seems most banks wont be allowing their employees to take advantage of ChatGPT and similar tools. Citigroup, Bank of America, Goldman Sachs and several other lenders have already banned their use on trading-room floors, citing privacy concerns.

But I strongly believe banks will eventually embrace generative AI, once they resolve concerns they have with it. The potential gains are too significant to pass up and theres a risk of being left behind by rivals.

But the risks to financial markets, the global economy and everyone are also great, so I hope they tread carefully.

Pawan Jain, Assistant Professor of Finance, West Virginia University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Here is the original post:
ChatGPT-powered Wall Street: The benefits and perils of using ... - Cobb County Courier

Artificial intelligence (AI)

Labour for the Long Term says UK risks falling even further into dependence on US tech firms

Keir Starmer should pledge 11bn towards building BritGPT and a national artificial intelligence (AI) cloud in the next Labour manifesto or risk the UK falling ever further into dependence on American tech companies, an affiliated thinktank has said.

Labour for the Long Term, which campaigns within the party for it to adopt long-termist policies that mitigate dangers such as pandemics, climate breakdown, and AI extinction, argues in a report that the 1bn pledged by the government in the 2023 budget is not enough to protect Britains future independence.

The report calls for the creation of BritGPT, a homemade system with a remit to focus on market failures rather than simply trying to compete with Silicon Valley to build the biggest models.

Private profit-seeking companies arent going to invest enough in AI for good or AI safety, so the UK government should step in to correct this market failure and provide more public goods such as medical research, clean energy research, and AI safety research, it said. They suggested some of the budget could even come out of Labours 28bn annual climate investment pledge as a result.

This is a hugely important technology, arguably the most transformative in the next few decades, and the UK risks being left behind, said Haydn Belfield, associate fellow at the University of Cambridges Leverhulme Centre for the Future of Intelligence, said.

The government has pledged 100m to train new foundation models, similar to the GPT-4 system that underpins ChatGPT, and a further 900m on a new exascale supercomputer for similar work. But, Belfield warns, those numbers are an order of magnitude too small.

Building up datacentres to make a new cloud region, the sort of investment a company such as Amazon or Google makes to launch their services, costs in the region of 10bn. And GPT-4 alone probably costs about $100m, and if you look at the cost trends, these are increasing rapidly: we should expect GPT-5 or GPT-6 to cost in the hundreds of millions of pounds, even before you account for salary costs. Thats what it takes to compete at this level, to support British companies and the British ecosystem.

At the physical infrastructure level, a 10bn Great British cloud would mirror Labours pledges to establish Great British energy and to bring private rail franchise back into public ownership, and be comparable to the creation of the BBC and Channel 4.

Labour for the Long Term is not alone in calling for more state-backed investment in AI. In an interview with the Guardian earlier this month, Geoffrey Hinton, the co-inventor of deep learning, warned that AI development could doom humanity if it was pursued for purely commercial motivations.

Google is the leader in this research, the core technical breakthroughs that underlie this wave came from Google, and it decided not to release them directly to the public, Hinton said. Google was worried about all the things we worry about, it has a good reputation and doesnt want to mess it up. And I think that was a fair, responsible decision.

The problem is, in a capitalist system, if your competitor then does do that, theres nothing you can do but do the same.

{{topLeft}}

{{bottomLeft}}

{{topRight}}

{{bottomRight}}

{{.}}

Read more:
Labour should pledge 11bn to build BritGPT AI, thinktank says - The Guardian

A laboratory in Lawrenceville is harnessing the intellectual talent of Pittsburgh's research institutions to target cancer. We speak with a man on a mission to help cancer patients by using artificial intelligence. It starts with the cryogenically frozen tumor. Predictive Oncology CEO Raymond Vennare doesn't like the term tumor to refer to the cancer they study.I refer to them as human beings. These human beings are repurposing their lives for us for a purpose, to be able to find cures to help their descendants; that's their legacy, Vennare said. Vennare is not a scientist. He's a businessman who builds biotech companies. He's had a bullseye on cancer for 15 years.What's different about this venture? The mission: to get cancer drugs that work to market, years faster.And what would have taken three to five years and millions of dollars, we were able to do in a couple of cycles in 11, 12, 13 weeks, Vennare said.In pre-trial drug development tumor heterogeneity, patient heterogeneity isn't introduced early enough, said Amy Ewing, a senior scientist at Predictive Oncology.Translation: Predictive Oncology's scientists are focusing on cell biology, molecular biology, computational biology and bioinformatics to determine how cancer drugs work on real human tumor tissue.A bank of invaluable tumor samples allows them to crunch that data faster.Remember, those samples are people.When I think about cancer, I see their faces, Vennare said. I don't see cells on a computer screen.Vennare sees his brother, Alfred.He was my first best friend. I grew up, Al, Alfred was always there. And whenever I needed something, Alfred was always there.He also thinks of his parents.In my case, my mother and my father and my brother sequentially died of cancer, which means I was the caregiver. My family was the caregiver, my siblings and my sister were caregivers for five consecutive years, he said.Ewing thinks of her father.I lost my father to prostate cancer about a year ago, she said. So to me, I have a deeper understanding now of what it means to have another day, or another month, or another year. I think that's really what gets me up in the morning now is to say that I want to carry on his legacy and help somebody else have more time with their family members.With a board of scientific advisors that includes an astronaut and some of the top scientists in the country, Vennare says ethics is part of the ongoing artificial intelligence conversation."The purpose is to make the job of the scientist easier, so they can expedite the process of discovery, he said. It's not AI that's going to do that, it's the scientists that are going to do that.Venarre says Predictive Oncology is agnostic, meaning this company seeks to help drug companies quickly zero in on effective drugs for all kinds of cancer.

A laboratory in Lawrenceville is harnessing the intellectual talent of Pittsburgh's research institutions to target cancer. We speak with a man on a mission to help cancer patients by using artificial intelligence.

It starts with the cryogenically frozen tumor. Predictive Oncology CEO Raymond Vennare doesn't like the term tumor to refer to the cancer they study.

I refer to them as human beings. These human beings are repurposing their lives for us for a purpose, to be able to find cures to help their descendants; that's their legacy, Vennare said.

Vennare is not a scientist. He's a businessman who builds biotech companies. He's had a bullseye on cancer for 15 years.

What's different about this venture? The mission: to get cancer drugs that work to market, years faster.

And what would have taken three to five years and millions of dollars, we were able to do in a couple of cycles in 11, 12, 13 weeks, Vennare said.

In pre-trial drug development tumor heterogeneity, patient heterogeneity isn't introduced early enough, said Amy Ewing, a senior scientist at Predictive Oncology.

Translation: Predictive Oncology's scientists are focusing on cell biology, molecular biology, computational biology and bioinformatics to determine how cancer drugs work on real human tumor tissue.

A bank of invaluable tumor samples allows them to crunch that data faster.

Remember, those samples are people.

When I think about cancer, I see their faces, Vennare said. I don't see cells on a computer screen.

Vennare sees his brother, Alfred.

He was my first best friend. [When] I grew up, Al, Alfred was always there. And whenever I needed something, Alfred was always there.

He also thinks of his parents.

In my case, my mother and my father and my brother sequentially died of cancer, which means I was the caregiver. My family was the caregiver, my siblings and my sister were caregivers for five consecutive years, he said.

Ewing thinks of her father.

I lost my father to prostate cancer about a year ago, she said. So to me, I have a deeper understanding now of what it means to have another day, or another month, or another year. I think that's really what gets me up in the morning now is to say that I want to carry on his legacy and help somebody else have more time with their family members.

With a board of scientific advisors that includes an astronaut and some of the top scientists in the country, Vennare says ethics is part of the ongoing artificial intelligence conversation.

"The purpose is to make the job of the scientist easier, so they can expedite the process of discovery, he said. It's not AI that's going to do that, it's the scientists that are going to do that.

Venarre says Predictive Oncology is agnostic, meaning this company seeks to help drug companies quickly zero in on effective drugs for all kinds of cancer.

Link:
Pittsburgh researchers using artificial intelligence to help cancer patients - WTAE Pittsburgh

DORAL, FLORIDA, UNITED STATES, May 20, 2023 /EINPresswire.com/ -- UTMStack, a leading innovator in cybersecurity solutions, has announced a significant breakthrough in the field of cybersecurity - an Artificial Intelligence (AI) system that performs the job of a security analyst, promising to transform cybersecurity practices forever.

In an era marked by an explosion of cyber threats and the requirement for 24/7 monitoring, cybersecurity personnel often find themselves overwhelmed by a deluge of alerts. Recognizing the need for a solution to mitigate alert fatigue and empower security analysts to focus on value-added tasks, UTMStack has developed a revolutionary AI technology. This AI system is context-aware, capable of learning from previous alerts and company activities, enhancing its ability to discern false positives and detect genuine incidents over time.

Leveraging a blend of advanced Machine Learning, Threat Intelligence, Correlation Rules, and cutting-edge GPT 3.5 Turbo, UTMStack's AI not only responds to real-time data but also correlates this with threat intelligence to identify indicators of compromise swiftly. This capability positions UTMStack at the forefront of cybersecurity development, marking a significant stride in the incorporation of AI into real-time threat detection and response.

"This is a major milestone for us at UTMStack and the broader cybersecurity community," said Rick Valdes. "Our AI system is poised to change the landscape of cybersecurity operations by effectively managing routine tasks and allowing security personnel to concentrate on strategic initiatives. We're excited about the potential this holds for organizations looking to streamline their cybersecurity processes and enhance their overall security posture."

By introducing AI into the heart of cybersecurity operations, UTMStack reaffirms its commitment to continually innovate and equip organizations with advanced, cost-effective, and efficient security solutions. The launch of this AI system marks a new era in cybersecurity, promising not only a significant reduction in alert fatigue for security personnel but also a substantial elevation in threat detection and response capabilities.

About UTMStack: UTMStack is a leading provider of comprehensive, integrated cybersecurity solutions. Our mission is to deliver advanced security tools and platforms that help organizations effectively manage cyber threats, achieve compliance, and create a secure digital environment.

Raul Gomez UTMStackraul.gomez@utmstack.com

Visit link:
UTMStack Unveils Free Ground-breaking Artificial Intelligence to Revolutionize Cybersecurity Operations - EIN News