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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

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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

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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

A descriptive summary of the physicochemical variables and Acinetobacter density of the waterbodies is presented in Table 1. The mean pH, EC, TDS, and SAL of the waterbodies was 7.760.02, 218.664.76 S/cm, 110.532.36mg/L, and 0.100.00 PSU, respectively. While the average TEMP, TSS, TBS, and DO of the rivers was 17.290.21C, 80.175.09mg/L, 87.515.41 NTU, and 8.820.04mg/L, respectively, the corresponding DO5, BOD, and AD was 4.820.11mg/L, 4.000.10mg/L, and 3.190.03 log CFU/100mL respectively.

The bivariate correlation between paired PVs varied significantly from very weak to perfect/very strong positive or negative correlation (Table 2). In the same manner, the correlation between various PVs and AD varies. For instance, negligible but positive very weak correlation exist between AD and pH (r=0.03, p=0.422), and SAL (r=0.06, p=0.184) as well as very weak inverse (negative) correlation between AD and TDS (r=0.05, p=0.243) and EC (r=0.04, p=0.339). A significantly positive but weak correlation occurs between AD and BOD (r=0.26, p=4.21E10), and TSS (r=0.26, p=1.09E09), and TBS (r=0.26, 1.71E-09) whereas, AD had a weak inverse correlation with DO5 (r=0.39, p=1.31E21). While there was a moderate positive correlation between TEMP and AD (r=0.43, p=3.19E26), a moderate but inverse correlation occurred between AD and DO (r=0.46, 1.26E29).

The predicted AD by the 18 ML regression models varied both in average value and coverage (range) as shown in Fig.1. The average predicted AD ranged from 0.0056 log units by M5P to 3.2112 log unit by SVR. The average AD prediction declined from SVR [3.2112 (1.46464.4399)], DTR [3.1842 (2.23124.3036)], ENR [3.1842 (2.12334.8208)], NNT [3.1836 (1.13994.2936)], BRT [3.1833 (1.68904.3103)], RF [3.1795 (1.35634.4514)], XGB [3.1792 (1.10404.5828)], MARS [3.1790 (1.19014.5000)], LR [3.1786 (2.18954.7951)], LRSS [3.1786 (2.16224.7911)], GBM [3.1738 (1.43284.3036)], Cubist [3.1736 (1.10124.5300)], ELM [3.1714 (2.22364.9017)], KNN [3.1657 (1.49884.5001)], ANET6 [0.6077 (0.04191.1504)], ANET33 [0.6077 (0.09500.8568)], ANET42 [0.6077 (0.06920.8568)], and M5P [0.0056 (0.60240.6916)]. However, in term of range coverage XGB [3.1792 (1.10404.5828)] and Cubist [3.1736 (1.10124.5300)] outshined other models because those models overestimated and underestimated AD at lower and higher values respectively when compared with raw data [3.1865 (14.5611)].

Comparison of ML model-predicted AD in the waterbodies. RAW raw/empirical AD value.

Figure2 represents the explanatory contributions of PVs to AD prediction by the models. The subplot A-R gives the absolute magnitude (representing parameter importance) by which a PV instance changes AD prediction by each model from its mean value presented in the vertical axis. In LR, an absolute change from the mean value of pH, BOD, TSS, DO, SAL, and TEMP corresponded to an absolute change of 0.143, 0.108, 0.069, 0.0045, 0.04, and 0.004 units in the LRs AD prediction response/value. Also, an absolute response flux of 0.135, 0.116, 0.069, 0.057, 0.043, and 0.0001 in AD prediction value was attributed to pH, BOD, TSS, DO. SAL, and TEMP changes, respectively, by LRSS. Similarly, absolute change in DO, BOD, TEMP, TSS, pH, and SAL would achieve 0.155, 0.061. 0.099, 0.144, and 0.297 AD prediction response changes by KNN. In addition, the most contributed or important PV whose change largely influenced AD prediction response was TEMP (decreases or decreases the responses up to 0.218) in RF. Summarily, AD prediction response changes were highest and most significantly influenced by BOD (0.209), pH (0.332), TSS (0.265), TEMP (0.6), TSS (0.233), SAL (0.198), BOD (0.127), BOD (0.11), DO (0.028), pH (0.114), pH (0.14), SAL(0.91), and pH (0.427) in XGB, BTR, NNT, DTR, SVR, M5P, ENR, ANET33, ANNET64, ANNET6, ELM, MARS, and Cubist, respectively.

PV-specific contribution to eighteen ML models forecasting capability of AD in MHWE receiving waterbodies. The average baseline value of PV in the ML is presented on the y-axis. The green/red bars represent the absolute value of each PV contribution in predicting AD.

Table 4 presents the eighteen regression algorithms performance predicting AD given the waterbodies PVs. In terms of MSE, RMSE, and R2, XGB (MSE=0.0059, RMSE=0.0770; R2=0.9912) and Cubist (MSE=0.0117, RMSE=0.1081, R2=0.9827) ranked first and second respectively, to outmatched other models in predicting AD. While MSE and RMSE metrics ranked ANET6 (MSE=0.0172, RMSE=0.1310), ANRT42 (MSE=0.0220, RMSE=0.1483), ANET33 (MSE=0.0253, RMSE=0.1590), M5P (MSE=0.0275, RMSE=0.1657), and RF (MSE=0.0282, RMSE=0.1679) in the 3, 4, 5, 6, and 7 position among the MLs in predicting AD, M5P (R2=0.9589 and RF (R2=0.9584) recorded better performance in term of R-squared metric and ANET6 (MAD=0.0856) and M5P (MAD=0.0863) in term of MAD metric among the 5 models. But Cubist (MAD=0.0437) XGB (MAD=0.0440) in term of MAD metric.

The feature importance of each PV over permutational resampling on the predictive capability of the ML models in predicting AD in the waterbodies is presented in Table 3 and Fig. S1. The identified important variables ranked differently from one model to another, with temperature ranking in the first position by 10/18 of the models. In the 10 algorithms/models, the temperature was responsible for the highest mean RMSE dropout loss, with temperature in RF, XGB, Cubist, BRT, and NNT accounting for 0.4222 (45.90%), 0.4588 (43.00%), 0.5294 (50.82%), 0.3044 (44.87%), and 0.2424 (68.77%) respectively, while 0.1143 (82.31%),0.1384 (83.30%), 0.1059 (57.00%), 0.4656 (50.58%), and 0.2682 (57.58%) RMSE dropout loss was attributed to temperature in ANET42, ANET10, ELM, M5P, and DTR respectively. Temperature also ranked second in 2/18 models, including ANET33 (0.0559, 45.86%) and GBM (0.0793, 21.84%). BOD was another important variable in forecasting AD in the waterbodies and ranked first in 3/18 and second in 8/18 models. While BOD ranked as the first important variable in AD prediction in MARS (0.9343, 182.96%), LR (0.0584, 27.42%), and GBM (0.0812, 22.35%), it ranked second in KNN (0.2660, 42.69%), XGB (0.4119, 38.60); BRT (0.2206, 32.51%), ELM (0.0430, 23.17%), SVR (0.1869, 35.77%), DTR (0.1636, 35.13%), ENR (0.0469, 21.84%) and LRSS (0.0669, 31.65%). SAL rank first in 2/18 (KNN: 0.2799; ANET33: 0.0633) and second in 3/18 (Cubist: 0.3795; ANET42: 0.0946; ANET10: 0.1359) of the models. DO ranked first in 2/18 (ENR [0.0562; 26.19%] and LRSS [0.0899; 42.51%]) and second in 3/18 (RF [0.3240, 35.23%], M5P [0.3704, 40.23%], LR [0.0584, 27.41%]) of the models.

Figure3 shows the residual diagnostics plots of the models comparing actual AD and forecasted AD values by the models. The observed results showed that actual AD and predicted AD value in the case of LR (A), LRSS (B), KNN (C), BRT 9F), GBM (G), NNT (H), DTR (I), SVR (J), ENR (L), ANET33 (M), ANER64 (N), ANET6 (O), ELM (P) and MARS (Q) skewed, and the smoothed trend did not overlap. However, actual AD and predicted AD values experienced more alignment and an approximately overlapped smoothed trend was seen in RF (D), XGB (E), M5P (K), and Cubist (R). Among the models, RF (D) and M5P (K) both overestimated and underestimated predicted AD at lower and higher values, respectively. Whereas XGB and Cubist both overestimated AD value at lower value with XGB closer to the smoothed trend that Cubist. Generally, a smoothed trend overlapping the gradient line is desirable as it shows that a model fits all values accurately/precisely.

Comparison between actual and predicted AD by the eighteen ML models.

The comparison of the partial-dependence profiles of PVs on AD prediction by the 18 modes using a unitary model by PVs presentation for clarity is shown in Figs. S2S7. The partial-dependence profiles existed in i. a form where an average increase in AD prediction accompanied a PV increase (upwards trend), (ii) inverse trend, where an increase in a PV resulted in a decline AD prediction, (iii) horizontal trend, where increase/decrease in a PV yielded no effects on AD prediction, and (iv) a mixed trend, where the shape switch between 2 or more of iiii. The models' response varied with a change in any of the PV, especially changes beyond the breakpoints that could decrease or increase AD prediction response.

The partial-dependence profile (PDP) of DO for models has a downtrend either from the start or after a breakpoint(s) of nature ii and iv, except for ELM which had an upward trend (i, Fig. S2). TEMP PDP had an upward trend (i and iv) and, in most cases filled with one or more breakpoints but had a horizontal trend in LRSS (Fig. S3). SAL had a PDP of a typical downward trend (ii and iv) across all the models (Fig. S4). While pH displayed a typical downtrend PDP in LR, LRSS, NNT, ENR, ANN6, a downtrend filled with different breakpoint(s) was seen in RF, M5P, and SVR; other models showed a typical upward trend (i and iv) filled with breakpoint(s) (Fig. S5). The PDP of TSS showed an upward trend that returned to a plateau (DTR, ANN33, M5P, GBM, RF, XFB, BRT), after a final breakpoint or a declining trend (ANNT6, SVR; Fig. S6). The BOD PDP generally had an upward trend filled with breakpoint(s) in most models (Fig. S7).

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Machine learning-guided determination of Acinetobacter density in ... - Nature.com

According to one of the executives at Facebook, reports related to Metaverses demise have been exaggerated more than they needed to be.

Meta hosted a press event in New York on 11 May announcing a new AI generative Sandbox tool for advertisers. Nicola Mendelsohn who is Metas Head of Global Business expressed that they are still very much interested in the Metaverse and reinstated that Mark Zuckerberg is very clear about that.

Responding to various reports by news media organizations showing how Meta is not interested in the Metaverse, Nicola explained that they are really interested in the Metaverse. He addressed the attendees saying that this whole Metaverse thing can take 5-10 years before they realize the vision of what theyre talking about.

Mendelsohns comments come as a defense against the growing speculation that Meta is focusing on artificial intelligence more than Metaverse in recent months during the period when the social media giant, Facebook Inc rebranded itself as Meta and couldnt stop talking about the Metaverse.

The recent surge in reports suggesting Meta is moving away from the Metaverse is because of AI tools dominating headlines. Speculations rose that Metas rebranding and announcement quickly faded as soon as artificial intelligence started making headlines and it made some analysts and critics think that Meta is moving towards the latest buzz trend and farther away from Metaverse.

The stance by Mendelson comes despite the fact that Metas Reality Labs lost $3.9 billion in the first quarter of 2023 which is $1 billion more than the first quarter of 2022.

Meta explained that to build the Metaverse and to make Quest virtual reality headsets, generative AI will play a huge part and will be used by brands and creators.

The newly launched AI Sandbox by the company will leverage generative AI to create text for ad copy aimed at different demographics, automatically crop photos and videos, and turn text prompts into background images for ads on Facebook and Instagram. Andrew Bosworth, CTO of Meta previewed the first incoming tools in March.

Nicola Mendelson explained that if you want to build a virtual world as a company its very difficult to do that but he said that with the help of machine learning and Generative AI, this can be done. John Hegeman, VP of Monetization at Meta said that the AI will help them to build the Metaverse more effectively. He further added, The Metaverse will be another great opportunity to create value for folks with AI.

Oncyber, which is a 3D world-building platform, launched an AI tool powered by OpenAIs ChatGpt that lets users customize their digital environments via text commands. Mendelson feels that the full vision of the company in relation to the metaverse could be challenged by Apples mixed reality headset, which is set to be announced soon.

Nancy J. Allen is a crypto enthusiast and believes that cryptocurrencies inspire people to be their own banks and step aside from traditional monetary exchange systems. She is also intrigued by blockchain technology and its functioning.

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AI and Machine Learning will help to Build Metaverse Claims Exec - The Coin Republic