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High-resolution meteorology with climate change impacts from global climate model data using generative machine ... - Nature.com

COMMENTARY

The days of large, monolithic apps are withering. Today's applications rely on microservices and code reuse, which makes development easier but creates complexity when it comes to tracking and managing the components they use.

This is why thesoftware bill of materials (SBOM)has emerged as an indispensable tool for identifying what's in a software app, including the components, versions, and dependencies that reside within systems. SBOMs also deliver deep insights into dependencies, vulnerabilities, and risks that factor into cybersecurity.

An SBOM allows CISOs and other enterprise leaders to focus on what really matters by providing an up-to-date inventory of software components. This makes it easier to establish and enforce strong governance and spot potential problems before they spiral out of control.

Yet in the age of artificial intelligence (AI), the classic SBOM has some limitations. Emerging machine learning (ML) frameworks introduce remarkable opportunities, but they also push the envelope on risk and introduce a new asset to organizations: the machine learning model. Without strong oversight and controls over these models, an array ofpractical, technical, and legal problemscan arise.

That's where machine learning bills of materials (MLBOMs) enter the picture. The framework tracks names, locations, versions, and licensing for assets that comprise an ML model. It also includes overarching information about the nature of the model, training configurations embedded in metadata, who owns it, various feature sets, hardware requirements, and more.

CISOs are realizing that AI and ML require a different security model and the underlying training data and models that run them are frequently not tracked or governed. An MLBOM can help an organization avoid security risks and failures. It addresses critical factors like model and data provenance, safety ratings, and dynamic changes that extend beyond the scope of SBOM.

Because ML environments are in a constant state of flux and changes can take place with little or no human interaction, issues related to data consistency including where it originated, how it was cleaned, and how it was labeled are a constant concern.

For example, if a business analyst or data scientist determines that a data set is poisoned, the MLBOM simplifies the task of findingall the various touch points and models that were trained with that data.

Transparency, auditability, control, and forensic insight are all hallmarks of an MLBOM. With a comprehensive view of the "ingredients" that go into an ML model, an organization is equipped to manage its ML models safely.

Here are some ways to build a best practice framework around an MLBOM:

Recognize the need for an MLBOM:It's no secret that ML fuels business innovation and even disruption. Yet it also introduces significant risks that can extend to reputation, regulatory compliance, and legal issues. Having visibility into ML models is critically important.

Conduct essential due diligence:An MLBOM should integrate withthe CI/CD pipelineand deliver a high level of clarity. Support for standard frameworks like JSON or OWASP'sCycloneDXcanunify SBOM and MLBOM processes.

Analyze policies, processes, and governance:It's essential to sync an MLBOM with an organization's workflows and business processes. This increases the odds that ML pipelines will work as intended, while minimizing risks related to cybersecurity, data privacy, compliance, and other risk-associated areas.

Use an MLBOM with machine learning gates:Rigorous controls and gateways lead to essential AI and ML guardrails. In this way, the business and the CSO can build on successes and harness ML to unlock greater cost savings, performance gains, and business value.

Machine learning is radically changing the business and IT landscape. By extending proven SBOM methodologies to ML through MLBOMs, it's possible to take a giant step toward boosting machine learning performance and protecting data and assets.

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Why MLBOMs Are Useful for Securing the AI/ML Supply Chain - Dark Reading

Blog Article

Published April 8, 2024

By Nick Fetty

Yann LeCun, a Turing Award winning computer scientist, had a wide-ranging discussion about artificial intelligence (AI) with Nicholas Dirks, President and CEO of The New York Academy of Sciences, as part of the first installment of the Tata Series on AI & Society on March 14, 2024.

LeCun is the Vice President and Chief AI Scientist at Meta, as well as the Silver Professor for the Courant Institute of Mathematical Sciences at New York University. A leading researcher in machine learning, computer vision, mobile robotics, and computational neuroscience, LeCun has long been associated with the Academy, serving as a featured speaker during past machine learning conferences and also as a juror for the Blavatnik Awards for Young Scientists.

As a postdoc at the University of Toronto, LeCun worked alongside Geoffrey Hinton, whos been dubbed the godfather of AI, conducting early research in neural networks. Some of this early work would later be applied to the field of generative AI. At this time, many of the fields foremost experts cautioned against pursuing such endeavors. He shared with the audience what drove him to pursue this work, despite the reservations some had.

Everything that lives can adapt but everything that has a brain can learn, said LeCun. The idea was that learning was going to be critical to make machines more intelligent, which I think was completely obvious, but I noticed that nobody was really working on this at the time.

LeCun joked that because of the fields relative infancy, he struggled at first to find a doctoral advisor, but he eventually pursued a PhD in computer science at the Universit Pierre et Marie Curie where he studied under Maurice Milgram. He recalled some of the limitations, such as the lack of large-scale training data and limited processing power in computers, during those early years in the late 1980s and 1990s. By the early 2000s, he and his colleagues began developing a research community to revive and advance work in neural networks and machine learning.

Work in the field really started taking off in the late 2000s, LeCun said. Advances in speech and image recognition software were just a couple of the instances LeCun cited that used neural networks in deep learning applications. LeCun said he had no doubt about the potential of neural networks once the data sets and computing power was sufficient.

Large language models (LLMs), such as ChatGPT or autocomplete, use machine learning to predict and generate plausible language. While some have expressed concerns about machines surpassing human intelligence, LeCun admits that he takes an unpopular opinion in thinking that he doesnt think LLMs are as intelligent as they may seem.

LLMs are developed using a finite number of words, or more specifically tokens which are roughly three-quarters of a word on average, according to LeCun. He said that many LLMs are developed using as many as 10 trillion tokens.

While much consideration goes into deciding what tunable parameters will be used to develop these systems, LeCun points out that theyre not trained for any particular task, theyre basically trained to fill in the blanks. He said that more than just language needs to be considered to develop an intelligent system.

Thats pretty much why those LLMs are subject to hallucinations, which really you should call confabulations. They cant really reason. They cant really plan. They basically just produce one word after the other, without really thinking in advance about what theyre going to say, LeCun said, adding that we have a lot of work to do to get machines to the level of human intelligence, were nowhere near that.

LeCun argued that to have a smarter AI, these technologies should be informed by sensory input (observations and interactions) instead of language inputs. He pointed to orangutans, which are highly intelligent creatures that survive without using language.

Part of LeCuns argument for why sensory inputs would lead to better AI systems is because the brain processes these inputs much faster. While reading text or digesting language, the human brain processes information at about 12 bytes per second, compared to sensory inputs from observations and interactions, which the brain processes at about 20 megabytes per second.

To build truly intelligent systems, theyd need to understand the physical world, be able to reason, plan, remember and retrieve. The architecture of future systems that will be capable of doing this will be very different from current large language models, he said.

As part of his work with Meta, LeCun uses and develops AI tools to detect content that violates the terms of services on social media platforms like Facebook and Instagram, though he is not directly involved with the moderation of content itself. Roughly 88 percent of content removed is initially flagged by AI, which helps his team in taking down roughly 10 million items every three months. Despite these efforts, misinformation, disinformation, deep fakes, and other manipulated content continue to be problematic, though the means for detecting this content automatically has vastly improved.

LeCun referenced statistics stating that in late 2017, roughly 20 to 25 percent of hate speech content was flagged by AI tools. This number climbed to 96 percent just five years later. LeCun said this difference can be attributed to two things: first the emergence of self-supervised, language-based AI systems (which predated the existence of ChatGPT); and second, is the transformer architecture present in LLMs and other systems. He added that these systems can not only detect hate speech, but also violent speech, terrorist propaganda, bullying, fake news and deep fakes.

The best countermeasure against these [concerns] is AI. AI is not really the problem here, its actually the solution, said LeCun.

He said this will require a combination of better technological systems, The AI of the good guys have to stay ahead of the AI of the bad guys, as well as non-technological, societal input to easily detect content produced or adapted by AI. He added that an ideal standard would involve a watermark-like tool that verifies legitimate content, as opposed to a technology tasked with flagging inauthentic material.

LeCun pointed to a study by researchers at New York University which found that audiences over the age of 65 are most likely to be tricked by false or manipulated content. Younger audiences, particularly those who grew up with the internet, are less likely to be fooled, according to the research.

One element that separates Meta from its contemporaries is the formers ability to control the AI algorithms that oversee much of its platforms content. Part of this is attributed to LeCuns insistence on open sourcing their AI code, which is a sentiment shared by the company and part of the reason he ended up at Meta.

I told [Meta executives] that if we create a research lab well have to publish everything we do, and open source our code, because we dont have a monopoly on good ideas, said LeCun. The best way I know, which I learned from working at Bell Labs and in academia, of making progress as quickly as possible is to get as many people as possible contributing to a particular problem.

LeCun added that part of the reason AI has made the advances it has in recent years is because many in the industry have embraced the importance of open publication, open sourcing and collaboration.

Its an ecosystem and we build on each others ideas, LeCun said.

Another advantage is that open sourcing lessens the likelihood of a single company developing a monopoly over a particular technology. LeCun said a single company simply does not have the ability to finetune an AI system that will adequately serve the entire population of the world.

Many of the early systems have been developed using English, where data is abundant, but, for example, different inputs will need to be considered in a country such as India, where 22 different official languages are spoken. These inputs can be utilized in a way that a contributor doesnt need to be literate simply having the ability to speak a language would be enough to create a baseline for AI systems that serve diverse audiences. He said that freedom and diversity in AI is important in the same way that freedom and diversity is vital to having an independent press.

The risk of slowing AI is much greater than the risk of disseminating it, LeCun said.

Following a brief question and answer session, LeCun was presented with an Honorary Life Membership by the Academys President and CEO, Nick Dirks.

This means that youll be coming back often to speak with us and we can all get our questions answered, Dirks said with a smile to wrap up the event. Thank you so much.

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Yann LeCun Emphasizes the Promise AI - NYAS - The New York Academy of Sciences

The minor initially was suggested by leaders and external advisory board members in Georgia Techs biomedical engineering department to provide more AI and ML curriculum for their students.

AI is so rampant in so many disciplines, and biomedical engineering students need to have that background before and after graduation, said Jaydev Desai, associate chair for undergraduate studies in the Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University. Once we talked to the rest of the College about the minor, it quickly became clear that other schools had the same goals. This minor will truly be a transformative experience for our undergraduate students and make them competitive after graduation, be it in industry or pursuit of further education.

Desai quickly found a partner in IAC when he spoke with Shatakshee Dhongde, the Colleges associate dean for academic affairs. Together they built a program that teaches both the technical and policy aspects of AI.

This program is designed to address how AI and machine learning models are applied to solve some of the world's most pressing and complex problems," said Dhongde, who is alsoassociate professor in theSchool of Economics. Using AI/ML models with an understanding of the ethical issues around the technology is the real strength of this minor.

Students on both tracks are required to take three core courses including a philosophy course in AI ethics and policy and two electives.

Engineering courses are offered by six of the Colleges eight schools: biomedical, chemical, electrical and computer, industrial systems, materials, and mechanical engineering. Subjects range from robotics to biomedical AI to signal processing and more.

IAC courses cover topics that include machine learning for economics, language and computers, race and gender and digital media, and public policy.

Organizers developed several new courses to create the minor. Desai has already heard from other Georgia Tech colleges and schools about adding more classes, and hes excited to see many more undergraduate students at Georgia Tech benefit as the initiative expands.

We wanted to create something that will improve the educational experience of our undergraduate students and make them more competitive in the marketplace, Desai said. The current collection of courses also will make them stronger if they have a goal of starting their own businesses or creating devices. The minor really has a nice structure that welcomes other disciplines around the campus, and we look forward to them joining us in the future.

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Starting this Summer, Students Can Minor in Applications of Artificial Intelligence and Machine Learning - Georgia Tech College of Engineering

Please join the Data Science Seminar this Wednesday for a talk by Dr. Timothy Li. Tim currently serves as principal data scientist and vice president at the Central Modeling Team of Citizens Bank. He will present two of his earlier AI projects in finance, using innovative machine learning models for stock price predication and auto-trading, outperforming conventional approaches.

Li will present two of his earlier AI finance projects. The first used a deep-learning long short-term memory model to forecast the alpha returns of approximately 1000 stocks on both the Chinese A-stock market and the U.S. stock market. Based on predicted returns, the research team devised a new portfolio strategy that outperformed a traditional model in both markets significantly.

In the second project, Li and his fellow researchers developed an optimal portfolio execution system (OPEX), using a tree-based ensemble machine-learning model for automated stock trading to reduce the trading cost. The study demonstrated that the OPEX system could effectively reduce trading costs, with an estimated savings of approximately $35 million per year compared to a legacy linear model.

Dr. Timothy (Minghai) Li currently serves as a principal data scientist and vice president at the central modeling team of Citizens Bank. Prior to this role, he held positions as a senior data scientist at Fidelity Investments, Netbrain Tech Inc., and FIS. He earned his Ph.D. in physics from Boston University and conducted post-doctoral research in Professor Sharon Huos group at Clark. He has authored or co-authored more than 20 publications covering topics in physics, material science, chemistry, biology, and computer modeling and simulation. Presently, his focus lies in the application of advanced machine learning algorithms and generative AI to finance.

Read more here:
AI in Finance: Machine Learning Models for Stock Price Predication and Auto-Trading - Clark University