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It is impossible to ignore the critical role that artificial intelligence (AI) and its subset, machine learning, play in the stock market today.

While AI refers to machines that can perform tasks that would normally require human intelligence, machine learning (ML) involves learning patterns from data, which enhances the machines’ ability to make predictions and decisions.

One of the main ways the stock market uses machine learning is in algorithmic trading. The ML models recognize patterns from vast amounts of financial data, then make trades based on these patterns — thousands upon thousands of trades, in small fractions of a second. These algorithmic trading models learn continually, adjusting their predictions and actions in a process that occurs continuously, which can sometimes lead to phenomena like flash crashes, when certain patterns instigate a feedback loop, sending certain segments of the market into a sudden freefall.

Algorithmic trading, despite its occasional drawbacks, has become indispensable to our financial system. It has enormous upside; which is another way of saying that it makes some people an awful lot of money. According to the technology services company Exadel, banks stand to save $1 trillion by 2030 thanks to algorithmic trading.

Such reliance on machine learning models in finance is not without risks, however — risks beyond flash crashes, even.

One significant and underappreciated threat to these systems is what’s known as adversarial attacks. These occur when malevolent actors manipulate the input data that is fed to the ML model, causing the model to make bad predictions.

One form of this adversarial attack is known as “data poisoning,” wherein bad actors introduce “noise” — or false data — into the input. Training on this poisoned data can then cause the model to misclassify whole datasets. For instance, a credit card fraud system might wrongly attribute fraudulent activity where there has been none.

Such manipulations are not just a theoretical threat. The effects of data poisoning and adversarial attacks have broad implications across different machine learning applications, including financial forecast models. In a study conducted by researchers at the University of Illinois, IBM and other institutions, they demonstrated the vulnerability of financial forecast models to adversarial attacks. According to their findings, these attacks could lead to suboptimal trading decisions, resulting in losses of 23 percent to 32 percent for investors. This study highlights the potential severity of these threats, and underscores the need for robust defenses against adversarial attacks.

The financial industry’s reaction to these attacks has often been reactive — a game of whack-a-mole in which defenses are raised only after an attack has occurred. However, given that these threats are inherent in the very structure of ML algorithms, a more proactive approach is the only way of meaningfully addressing this ongoing problem.

Financial institutions need to implement robust and efficient testing and evaluation methods that can detect potential weaknesses and safeguard against these attacks. Such implementation could involve rigorous testing procedures, employing “red teams” to simulate attacks, and continually updating the models to ensure they’re not compromised by malicious actors or poor data.

The consequences of ignoring the problem of adversarial attacks in algorithmic trading are potentially catastrophic, from significant financial losses to damaged reputations for firms, or even widespread economic disruption. In a world increasingly reliant on ML models, the financial sector needs to shift from being reactive to proactive to ensure the security and integrity of our financial system.

Joshua Steier is a technical analyst, and Sai Prathyush Katragadda is a data scientist, at the nonprofit, nonpartisan RAND Corporation.

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Money, markets and machine learning: Unpacking the risks of adversarial AI - The Hill

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Stocks connected to machine learning are synonymous with those connected to artificial intelligence. Machine learning falls under the umbrella of AI and relates to the use of data and algorithms to imitate human learning to improve accuracy. Kinda scary? Sure. However, machine learning is also proving to be revolutionary in 2023. The emergence of generative AI and its promise to improve our world has created a lot of value. This has led to the rise of machine learning stocks to buy.

While the companies discussed in this article might not be truly up-and-coming as they are established, they certainly are improving. That makes them must-buy stocks that any investor ought to consider.

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There are 13.5 billion reasons Nvidia (NASDAQ:NVDA) why stock should be on every investors list. Im of course referring to Nvidias $13.5 billion in second-quarter revenues. That far exceeded the $11 billion mark, perceived as incredibly ambitious, that Nvidia had given as guidance.

Those blowout earnings lend credence to the notion that AI and machine learning will be much more than a bubble. Instead, it is crystal clear that companies are clamoring for Nvidias leading AI chips and that the pace of things is increasing, not slowing.

Nvidias data center revenues alone at $10.32 billion nearly reached that $11 billion figure. Cloud firms are scrambling to secure their supply of chips that are used for machine learning purposes among other things.

NVDA shares can absolutely run higher from their current position. Their price-to-earnings ratio has temporarily fallen given how unexpectedly high earnings were. Nvidia is predicting $16 billion in revenues for the coming quarter. I dont believe theres any real reason to back off from its shares currently.

Source: T. Schneider / Shutterstock.com

Crowdstrike (NASDAQ:CRWD) is another machine learning stock to consider. The company utilizes machine learning to help it better understand how to stop breaches before they can occur. Its an AI-powered cybersecurity firm that is strongly rated on Wall Street and offers a lot of upside on that basis.

Crowdstrike is getting better and better at thwarting cyber attacks probably by the second. Machine learning allows the company to more intelligently prevent cyber attacks with each piece of data it gathers from an attack.

The company has been growing at a rapid pace over the last few years and has seen year-over-year increases above 40% in each of those periods. However, it has simultaneously struggled to find profitability which likely explains the disconnect between prices and expected prices.

Crowdstrike has several opportunities in front of it. First, if it can address profitability concerns its certain to appreciate in price. Second, theres a general rush toward securing systems that also benefit the company and should provide it fertile ground for future gains.

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AMD (NASDAQ:AMD) is the runner-up in the battle for machine learning supremacy at this point.

The stock has boomed in 2023 alongside Nvidia but not to the same degree. It is going to continue to crop up in the machine learning/AI conversation and absolutely makes sense as an investment now.

Lets try to understand AMD in relation to machine learning and its strengths and weaknesses vis-a-vis Nvidia. By now, everyone knows that Nvidia wins the overall battle hands down. When it comes to CPUs, AMD has a lot to offer. Its CPUs, along with those from Intel (NASDAQ:INTC), are the highest rated for machine learning purposes.

However, GPUs outperform CPUs when it comes to machine learning and Nvidia is the king of GPU. It has the highest-rated machine learning GPUs for at least the top five spots according to this source.

As bad as that sounds AMD is roughly 80% as capable as Nvidia overall in relation to AI and machine learning. Therefore, it has a massive opportunity at hand in closing that gap. Its also one of those machine learning stocks to buy.

On the date of publication, Alex Sirois did not have (either directly or indirectly) any positions in the securities mentioned in this article. The opinions expressed in this article are those of the writer, subject to the InvestorPlace.com Publishing Guidelines.

Alex Sirois is a freelance contributor to InvestorPlace whose personal stock investing style is focused on long-term, buy-and-hold, wealth-building stock picks. Having worked in several industries from e-commerce to translation to education and utilizing his MBA from George Washington University, he brings a diverse set of skills through which he filters his writing.

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3 Up-and-Coming Machine Learning Stocks to Put on Your Must-Buy List - InvestorPlace

Researchers have improved deep learning by selecting the most efficient overall path to the output, leading to a more effective AI without added layers.

Like climbing a mountain via the shortest possible path, improving classification tasks can be achieved by choosing the most influential path to the output, and not just by learning with deeper networks.

Deep Learning (DL) performs classification tasks using a series of layers. To effectively execute these tasks, local decisions are performed progressively along the layers. But can we perform an all-encompassing decision by choosing the most influential path to the output rather than performing these decisions locally?

In an article published today (August 31) in the journal Scientific Reports, researchers from Bar-Ilan University in Israel answer this question with a resounding yes. Pre-existing deep architectures have been improved by updating the most influential paths to the output.

Like climbing a mountain via the shortest possible path, improving classification tasks can be achieved by training the most influential path to the output, and not just by learning with deeper networks. Credit: Prof. Ido Kanter, Bar-Ilan University

One can think of it as two children who wish to climb a mountain with many twists and turns. One of them chooses the fastest local route at every intersection while the other uses binoculars to see the entire path ahead and picks the shortest and most significant route, just like Google Maps or Waze. The first child might get a head start, but the second will end up winning, said Prof. Ido Kanter, of Bar-Ilans Department of Physics and Gonda (Goldschmied) Multidisciplinary Brain Research Center, who led the research.

This discovery can pave the way for better enhanced AI learning, by choosing the most significant route to the top, added Yarden Tzach, a PhD student and one of the key contributors to this work.

This exploration of a deeper comprehension of AI systems by Prof. Kanter and his experimental research team, led by Dr. Roni Vardi, aims to bridge between the biological world and machine learning, thereby creating an improved, advanced AI system. To date they have discovered evidence for efficientdendriticadaptationusingneuronal cultures, as well as how toimplement those findingsin machine learning, showing howshallow networkscan compete with deep ones, and finding themechanism underlying successful deep learning.

Enhancing existing architectures using global decisions can pave the way for improved AI, which can improve its classification tasks without the need for additional layers.

Reference: Enhancing the accuracies by performing pooling decisions adjacent to the output layer 31 August 2023, Scientific Reports. DOI: 10.1038/s41598-023-40566-y

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Smarter AI: Choosing the Best Path to Optimal Deep Learning - SciTechDaily

Steffi Diem (middle) participating in a panel at the White House Summit on Developing a Bold Decadal Vision for Commercial Fusion Energy. Diem has joined a collaboration across multiple institutions that will use machine learning to better understand magnetic fusion energy.

Researchers at the University of WisconsinMadison are taking part in a new collaboration built on open-science principles that will use machine learning to advance our knowledge of promising sources of magnetic fusion energy.

The U.S. Department of Energy has selected the collaboration, led by researchers at the Massachusetts Institute of Technology, to receive nearly $5 million over three years. The teams including researchers at UWMadison, William & Mary, Auburn University and the HDF group (a non-profit data management technology organization) are tasked with creating a platform to publicly share data they glean from several unique fusion devices and optimize that data for analysis using artificial intelligence tools. Student researchers from each institutionwill also have an opportunity to participate ina subsidized summer program that will focus on applying data science and machine learning to fusion energy.

The data sources will include UWMadisons Pegasus-III experiment, which is centered around a fusion device known as a spherical tokamak. Pegasus-III is a new Department of Energy funded experiment that began operations in summer 2023 and represents the latest generation in a long-running set of tokamak experiments at UWMadison. A primary goal of the experiment is to study innovative ways to start up future fusion power plants.

Im incredibly excited to be a part of projects like this one as we continue to push innovation both in the analysis and development of experimental devices and diverse workforce development initiatives, says Steffi Diem, a professor of nuclear engineering and engineering physics, who leads the Pegasus-III experiment.

Diem is an emerging leader in the fusion research world. In 2022, she was invited to present at the White Houses Bold Decadal Vision for Commercial Fusion Energy that launched several efforts focused on commercializing fusion energy. In a field traditionally dominated by men, Diem is also one of four women leading the new collaboration.

UWMadison researchers are using the new Pegasus-III experiment to study innovative techniques for starting a plasma. Joel Hallberg

Throughout much of my career, I have often been one of the few women in the room, so it is great to be a part of a collaboration where four out of the five principal investigators are women, Diem says.

The collaboration is based around the principles of open science Diem and her colleagues will make the wealth of data coming from Pegasus-III and other fusion experiments more accessible and usable to others, particularly for machine learning platforms.

While this approach is designed to accelerate knowledge of magnetic fusion devices, its also aimed at providing a more accessible path into fusion research programs for students with wider skillsets and backgrounds, particularly in data sciences. Building a more diverse fusion workforce will be tantamount going forward, says Diem.

Fusion isnt just plasma physicists anymore, she says. As fusion moves out of the lab and toward the goal of providing clean energy to communities, it requires an interdisciplinary approach with engineers, data scientists, skilled technical staff, community members and more.

UWMadison is supporting a broader push to diversify the fusion field. Some of the student researchers who will be participating in the new collaboration are part of the student-led Solis group, which provides gender-inclusive support for students studying plasma physics on campus.

The new collaboration fits well with Diems other research, funded through the Wisconsin Alumni Research Foundation, focused on reimagining fusion energy system design. That work centers energy equity and environmental justice early in the design phase to support a just and equitable energy transition.

While there are still many challenges that lie ahead for fusion, the potential benefits are huge as we drive towards a cleaner, more sustainable, equitable and just future, says Diem.

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UWMadison part of effort to advance fusion energy with machine ... - University of Wisconsin-Madison

One of the worlds leaders in computational psychiatry will kick off the upcomingMaury Strauss Distinguished Public Lecture Seriesat the Fralin Biomedical Research Institute at VTC in September.

The public lectures bring innovators and thought leaders in science, medicine, and health from around the globe to the Health Sciences and Technology campus in Roanoke.

Leading the series with a discussion of machine learning and human thought is Read Montague, the Virginia Tech Carilion Vernon Mountcastle Research professor anddirector of theCenter for Human Neuroscience Researchat the Fralin Biomedical Research Institute at VTC.

Montagues research led to the development of the prediction error reward hypothesis among the most influential ideas at the basis for human decision-making in health and in neuropsychiatric disorders and recently to first-of-their-kind observations in the human brain of how the neurochemicals dopamine and serotonin shape peoples perceptions of the world around them.

He will share details of his data-driven neuroscience applications to machine learning to better identify and treat diseases of the brain at 5:30 p.m. on Sept. 28 at the institute.

Montague, who is working with clinicians and research centers worldwide to gather data on brain signaling, is also a professor in the department of physics at Virginia Techs College of Science.

Next in the series is J. Marshall Shepherd, who started his career as a meteorologist and became a leading international expert in weather and climate. He is an elected member of three of the nations influential scientific academies: the National Academy of Sciences, the National Academy of Engineering, and the American Academy of Arts and Sciences.

How is his work part of a series on health? The World Health Organization recognizes climate change as the single biggest health threat facing humanity. Shepherd will address the intersection of climate, risk and perception.

Bookending the series in May 2024 is Rick Woychik, director of the National Institute of Environmental Health Sciences at the National Institutes of Health. The molecular geneticist oversees federal funding for biomedical research related to environmental influences, including climate change, on human health and disease.

Other lectures in the series address Alzheimers disease, infant nutrition, dementia, COVID-19 and cardiovascular outcomes, and locomotor learning in children with brain injury.

We look forward to joining with members of the wider community to better understand these exciting new innovations and insights that are germane to health, said Michael Friedlander, Virginia Techs vice president for health sciences and technology and executive director of the Fralin Biomedical Research Institute. This is an incredible collection of speakers who represent some of the best thinking in science, medicine, and policy in the context of improving health. We are also proud that our own Read Montague is among them, and we look forward to sharing this research with the wider community.

The free public lectures are named for Maury Strauss, a Roanoke businessman and longtime community benefactor who recognized the value of welcoming leaders in science, medicine, and health to share their work. The 2023-24 series, which began in 2011, highlights the research institutes commitment to the community.

The full 2023-24 Maury Strauss Distinguished Public lectures include:

The public is invited to attend the lectures, which begin with a 5 p.m. reception. Presentations begin at 5:30 p.m. in 2 Riverside at the Fralin Biomedical Research Institute.All are free, in person, and open to the public. Community attendance is encouraged. To make the lectures accessible to a wider audience, most are streamed live via Zoom and archived.

In addition to the Maury Strauss Distinguished Public Lectures, the Fralin Biomedical Research Institute also hostsPioneers in Biomedical Research Seminars, theTimothy A. Johnson Medical Scholar Lecture Series, as well as other conferences, programs, lectures, and special events.

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Machine learning and thought, climate impact on health, Alzheimer's ... - Virginia Tech