Archive for the ‘Machine Learning’ Category

The Surprising Synergy Between Acupuncture and AI – WIRED

I used to fall asleep at night with needles in my face. One needle shallowly planted in the inner corners of each eyebrow, one per temple, one in the middle of each eyebrow above the pupil, a few by my nose and mouth. Id wake up hours later, the hair-thin, stainless steel pins having been surreptitiously removed by a parent. Sometimes theyd forget about the treatment, and in the morning wed search my pillow for needles. My very farsighted left eye gradually became only somewhat farsighted, and my mildly nearsighted right eye eventually achieved a perfect score at the optometrists. By the time I was six, my glasses had disappeared from the picture albums.

The story of my recovered eyesight was the first thing Id think to mention when people found out that my parents are specialists in traditional Chinese medicine (TCM) and asked me what I thought of the practice. It was a concrete and rather miraculous firsthand experience, and I knew what it meantto begin to see the world more clearly while under my mother and fathers care.

Otherwise, I rarely knew what to say. I would recall hearing TCM mentioned in relation to poor evidence or badly designed studies and feel challenged to providesome defense for a line of work seen as illegitimate. I would feel a pull of obligation to defend Chinese medicine as a way to protect my parents, their care and toils, but also an urge to resist shouldering that obligation for the sake of someone elses fleeting curiosity and perhaps entertainment.

Mostly, I wished I had a better understanding of TCM, even just for myself. Now that I work in machine learning (ML), Im often struck by the parallels between this cutting-edge technology and the ancient practice of TCM. For one, I cant quite explain either satisfactorily.

Its not that there arent explanations for how the field of Chinese medicine works. I, and many others, just find the theories dubious. According to both classical and modern theory, blood and qipronounced chi, variously interpreted to mean something like vapormove around and regulate the body, which itself is not considered separate from the mind.

Qi flows through channels called meridians. The anatomical charts hanging on the walls of my parents clinics feature meridians scoring the body in neat, straight linesfrom chest to finger, or from the waist to the inner thighoverlaid on diagrams of the bones and organs. At various points along these meridians, needles can be inserted to remove blockages, improving the flow of qi. All TCM treatments ultimately revolve around qi: Acupuncture banishes unhealthy qi and circulates healthy qi from the outside; herbal medicines do so from the inside.

On my parents charts, the meridians and acupuncture points are depicted like a subway map and seem to float slightly upward, tethered only loosely to the recognizable shapes of intestines and joints underneath. This lack of visual correspondence is reflected in the science; little evidence has been found for the physical existence of meridians, or of qi. Studies have investigated whether meridians are special conduits for electrical signalsbut these experiments werebadly designedor whether they arerelated to fascia, the thin stretchy tissue that surrounds almost all internal body parts. All of this work is recent, and results have been inconclusive.

In contrast, the effectiveness of acupuncture, particularly for ailments likeneck disorders andlow back pain, is well-supported in modern scientific journals. Insurance companies are convinced; most of my mothers patients come to her for acupuncture because its covered by New Zealands national insurance plan.

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The Surprising Synergy Between Acupuncture and AI - WIRED

The Yin and Yang of A.I. and Machine Learning: A Force of Good … – Becoming Human: Artificial Intelligence Magazine

Photo by Andrea De Santis on Unsplash

As Artificial Intelligence (AI) and Machine Learning (ML) technologies have become more sophisticated, theyve permeated almost every aspect of our lives. These advancements hold incredible potential to transform society for the better, but they also come with a dark side. So much hype for AI has kicked off this year, spurred by the introduction of Open AIs ChatGPT. However, AI and ML have been around for a while, really kicking into full gear in the 2010s. We are just seeing the outcome for these developments now.

In fact, the 2020s will be defined by advancements of AI and ML. We are just scratching the surface with the potential for these advanced technologies. At its core though, stands the human intention and intervention. AI and ML can serve both as a force of good and a force of evil. However, they, undoubtedly have the potential to revolutionize industries while also posing some serious threats if misused.

The rise of AI and ML presents a double-edged sword. On one hand, these technologies have the potential to revolutionize industries, improve lives, and protect the environment. On the other hand, they can also lead to job displacement, loss of privacy, and perpetuation of biases.

It is up to us as a society to ensure that we harness the power of AI and ML for good while mitigating their potential for harm. By implementing thoughtful regulation, fostering ethical AI practices, and prioritizing transparency, we can harness the benefits of these technologies while minimizing the risks.

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The Yin and Yang of A.I. and Machine Learning: A Force of Good ... - Becoming Human: Artificial Intelligence Magazine

Space Systems Command to Host Reverse Industry Event Focused … – Space Operations Command

EL SEGUNDO, Calif. -- EL SEGUNDO, CA Space Systems Command (SSC) will host a Reverse Industry Day focused on Artificial Intelligence/Machine Learning (AI/ML) for Space May 17-18, 2023 at the Microsoft Silicon Valley Campus in Mountain View, Ca.SSCs AI/ML Reverse Industry Day, one in a series of space capability and mission area-themed events, will focus on educating government and space industry professionals on how AI/ML can improve effectiveness across all U.S. Space Force mission areas. Focus areas will include communicating where AI/ML will help solve space mission area objectives, supported through Space Force investments and future budgets; matching industry partners with government customers to show AI/MLs art of the possible; and enabling opportunities for collaboration between government, industry, investment banking and venture capital.We look at artificial intelligence and machine learning as a game-changing breakthrough technology that will really help ensure our ability to protect and defend the United States, especially in space, said Col. Joseph J. Roth, director of SSCs Innovation & Prototyping Delta.What this Reverse Industry Day is going to help us do is find out how we can better leverage these technologies for the Space Force, Roth said. Some key areas where we already use AI and machine learning are cyber security and space domain awareness. But thats just scratching the surface of all the capabilities that this technology can provide to us.For example, in the space domain awareness arena, if you have a high-value space asset such as a satellite, operators and warfighters, including Space Force Guardians, need to be able to understand and interpret a lot of information and data quickly, Roth said. Is that dot a defunct satellite? A piece of space debris? An adversary moving a little too close to your valuable satellite?Artificial Intelligence and Machine Learning can help better tip and cue to potential threats to satellites on orbit by cutting through the noise faster than humans can, so youll need fewer operators to fly these systems and youll have better protections to think through on how to protect our systems if were ever attacked, Roth said.AI and Machine Learning wont replace humans, but it has the potential to make them more effective and efficient, said Brian Gamble, an industry engagement leader within Front Door, SSCs initiative to drive communication across the space enterprise and help industry and investors navigate the government acquisition labyrinth.Its really the government and the U.S. Space Force trying to take advantage of all the great innovation thats occurring in our industrial base, Roth said.The two-day event will feature a variety of keynote speakers and presentations; panel discussions with SSC, space industry leaders, and investors in space and AI/ML technologies; tours of the Microsoft facility; and one-on-one meetings between government, space industry leaders. For the first time ever, financial institutions and venture capitalists have been invited to attend, providing that critical third component how to secure funding. More than 100 companies and nearly 300 professional have already registered to attend.One of the first objectives is just trying to get a sense of what the realm of the possible is when it comes to AI and machine learning, within our different mission areas, Gamble said.Unlike a traditional SSC Industry Day, where government officials with a specific need meet with industry representatives to do market research, Reverse Industry Days are focused more on hearing from industry what is possible, Roth said. The events also provide a good opportunity for companies who havent previously worked with government to meet SSC officials and get all their questions answered. Over the last year, SSC has hosted more than 10 of these events.For more information about SSCs AI/ML Reverse Industry Days and other events, visit SSCs Front Door webpage.

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Space Systems Command to Host Reverse Industry Event Focused ... - Space Operations Command

AI, machine learning will be critical to USSOCOMs future, official says – Military Embedded Systems

News

May 10, 2023

Technology Editor

Military Embedded Systems

SOF WEEK 2023 TAMPA, Florida. Artificial intelligence (AI) and machine learning (ML) technologies will be crucial in equipping the U.S. Special Operations Command (USSOCOM) with a competitive edge in future years, according to Assistant Secretary of Defense for Special Operations and Low-Intensity Conflict (SO/LIC) Christopher P. Maier in his keynote address on May 10 at the 2023 SOF Week annual conference.

To address future capability development, Maier pointed to resourcing priorities jointly issued by SO/LIC and SOCOM, which form the basis of the agencys five-year program objective memorandum (POM) and the presidents budget for fiscal 2024. Among these priorities is integrating data-driven technologies that leverage AI and machine learning, he said.

Maier acknowledged the challenge of operating in complex and unclear environments, stating that operators will often "lack perfect information, but still need to take decisive action." In these situations, AI and machine learning technologies can provide valuable support, making it essential for industry partners to collaborate on the development and implementation of these solutions.

"The essential relationship between SO/LIC and SOCOM [...] is defined by multi-layer collaboration and near continuous engagement from top leadership to the most junior workers levels," Maier said, who also underscored the importance of automation in maintaining enduring advantages when confronting future challenges.

When asked about the Department of Defense's investment in AI and robotics, Maier affirmed that it was vital to invest in these technologies to enhance warfighter capabilities.

He said it all starts with appropriating a budget and executing an acquisition program -- things that are not always glamorous but are essential for us to continue to be competitive," Maier said. The technology being developed by industry partners allows SOF operators to "win each and every day, he added.

USSOCOM has been pursuing several projects in the realm of AI/ML to bolster the capabilities of Special Operations Forces. One of the top projects is the development of the Hyper-Enabled Operator (HEO) concept, which focuses on integrating advanced AI algorithms, data analytics, and communication technologies to provide SOF operators with real-time access to mission information. The HEO concept aims to reduce cognitive burden on operators by providing them with actionable intelligence and situational awareness, allowing them to make more informed decisions in complex operational environments.

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AI, machine learning will be critical to USSOCOMs future, official says - Military Embedded Systems

Multidimensional Mass Spectrometry and Machine Learning: A … – Technology Networks

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We developed and demonstrated a new metabolomics workflow for studying engineered microbes in synthetic biology applications. Our workflow combines state-of-the-art analytical instrumentation that generates information-rich data with a novel machine learning (ML)-based algorithm tailored to process it.

In our roles as Pacific Northwest National Laboratory (PNNL) scientists, we led this multi-institutional study, which was published in Nature Communications.

Metabolites are small molecules produced by large networks of cellular processes and biochemical reactions in living systems. The sheer diversity of metabolite classes and structures constitutes a significant analytical challenge in terms of detection and annotation in complex samples.

Analytical instrumentation able to analyze hundreds of samples in ever faster and more accurate ways is critical in various metabolomics applications, including the development of microorganisms that can produce desirable fuels and chemicals in a sustainable way.

Multidimensional measurements using liquid chromatography (LC), ion mobility and data-independent acquisition mass spectrometry (MS) improve metabolite detection by linking the separations in a single analytical platform. The potential for metabolomics has been previously demonstrated, but this kind of multidimensional information-rich data is complex and cannot be processed with traditional tools. Therefore, algorithms and software tools capable of processing it to extract accurate metabolite information are needed.

We optimized a combination of sophisticated instruments for fast analyses and generated multidimensional data, rich in information that can be used to tease apart complex metabolomes.

For the computational method, Dr. Bilbao created a new algorithm, called PeakDecoder, to enable interpretation of the multidimensional data and ultimately identify individual molecules in complex mixtures. Our algorithm learns to distinguish true co-elution and co-mobility directly from the raw data of the studied samples and calculates error rates for metabolite identification. To train the ML model, it proposes a novel method to generate training examples, similar to the target-decoy strategy commonly used in proteomics. Once the model is trained, it can be used to score metabolites of interest from a library with an associated false discovery rate. And contrary to existing methods, it can also be used with libraries of small size.

The key outcomes of the paper were:

The method takes a third of the sample analysis time of previous conventional approaches by using optimized LC conditions. PeakDecoder enables accurate profiling in multidimensional MS measurements for large scale studies.

We used the workflow to study metabolites of various strains of microorganisms engineered by the Agile BioFoundry to make various bioproducts, such as polymers and diesel fuel precursors. We were able to interpret 2,683 metabolite features across 116 microbial samples.

However, it should be noted that the current algorithm is not fully automated due to software dependencies and requires a metabolite library acquired with compatible analytical conditions for inference.

We are working on the next version of the algorithm leveraging advanced artificial intelligence (AI) methods used in other fields, such as computer vision. A user-friendly and fully automated version of PeakDecoder will support other types of molecular profiling workflows, including proteomics and lipidomics. Performance will be evaluated with more types of experimental data and AI-predicted multidimensional molecular libraries. The new version is expected to provide significant advances for multiomics research.

Reference:Bilbao A, Munoz N, Kim J, et al. PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements. Nat Commun. 2023;14(1):2461. doi:10.1038/s41467-023-37031-9

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Multidimensional Mass Spectrometry and Machine Learning: A ... - Technology Networks