Brent Lance

Applied Affective Computing https://bit.ly/3naKZQw provides State-of-the-art and emerging themes in affective computing, including a comprehensive review of the existing approaches to affective computing systems and social signal processing. Authors: Leimin Tian, Sharon Oviatt, Michal Muszynski, Brent Chamberlain, & Jennifer Healey. #AI #AffectiveComputing #MachineLearning #SocialRobots #BuiltEnvironments #Multimodal #DataCollection #ReinforcementLearning #Synthesizing #Natural and #Believable #EmotionalExpressions ACM, Association for Computing Machinery

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CodeAIR: The Next-Level Drone for Next-Level Learning! Now shipping! CodeAIR isn’t just a drone; it’s a complete learning experience that goes far beyond basic skills. https://lnkd.in/eQQnenjS What Makes CodeAIR’s Curriculum Different? It’s more than a set of tasks—it’s a deeper dive into understanding: 👏 Purposeful Learning: Each mission in CodeAIR’s curriculum is designed to teach why each step matters, from understanding sensor functions to grasping the science behind flight. Students learn more than just “how” to code; they learn “why” it works. 👏 Skill-Building with Depth: Our curriculum progresses thoughtfully, with each mission building on the last. Students gain technical skills, but also a deeper knowledge of the technology at play. 👏 Hands-On with Hardware: CodeAIR’s open design lets students interact with and understand the electronics, gaining insight into the inner workings of a real drone. 👏 Real-World Readiness: Our curriculum prepares students to think critically, solve problems, and apply industry-relevant concepts in coding, AI, and engineering. Equip your students with an educational journey that goes beyond the basics. Ready to inspire? https://lnkd.in/eKxcArYc #CodeAIR #CodeSpace #Python #Edtech #STEM #DRONES #computerscience #CTE #STEMEducation

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Why graduate programs should be creative in designing discussion board (DB) questions directly incorporating gpt4o as part of designing the DB questions, confronting the usage of GPT4o. Compare the performance of all these gen tools, and why this is important. It is great to see the competition is picking up much faster trying to beat GPT4o

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There were no survivors after FOSS Force's Christine Hall took a torch to the Open Source Initiative (OSI)'s Open Source AI Definition (OSAID) earlier today. - “Will the partnership with Eclipse help OSI understand that the Open Source AI Definition fails to meet just about any open-source smell test?” - Will Eclipse Foundation be able to put out the rapidly growing fire, or is their common board-level connection — a recurring theme in OSAID astroturfers — just fanning the flames with this MoU? - Did either organisations' membership sign-off on this, or was it yet another unilateral board-level decision? - And what will the Internal Revenue Service make of this incessant non-profit lobbying? So many questions, so few answers. Hopefully Mike Milinkovich and team know what they're signing up for, but my feeling is they've been misled on it like the rest of us, including the minority co-designers who also need the source (i.e., data) to assess and address ethical issues in AI including bias and fairness. Time will tell. #OpenSource #ArtificialIntelligence #AI #OSI #OSAID #Eclipse #Ethics #Bias #Fairness

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Avi Wigderson: The Pursuit of Complexity and Intellectual Curiosity Avi Wigderson, the 2023 ACM A.M. Turing Award recipient, has been recognized for his profound contributions to computational complexity theory, particularly in understanding the role of randomness and developing interactive zero-knowledge proofs. Wigderson's journey began in the 1970s when he chose computer science over mathematics at the behest of his parents, leading to a distinguished career at institutions like the Institute for Advanced Study in Princeton. His work has demonstrated that probabilistic algorithms can often be replaced with deterministic ones, and he has created randomness extractors to purify seemingly random data sources. His research is driven by a deep-seated intellectual curiosity, a motivation that he places above practical utility. His explorations span pseudorandomness, cryptography, learning theory, algorithms, and computational modeling. He is particularly intrigued by the enduring challenge of whether every problem solvable in polynomial time (P) has an efficient solution, a question central to computational theory for over 50 years. His contributions to zero-knowledge proofs have significant implications for cryptographic protocols, enhancing security and privacy. His work on expander graphs has broad applications, including error-correcting codes and fault-tolerant networks. He values collaboration and continuous learning, acknowledging the influence of his mentors and peers. His book, “Mathematics and Computation,” aims to make complex theories accessible to non-experts. He highlights the intellectual diversity and excitement of computational theory, encouraging others to embrace academic life despite its inherent challenges and frequent failures, which he views as learning opportunities Wigderson tells people that academia is a great life. “Nobody tells you what to do. You have to find what to do,” he said. Wigderson's perspective on failure is a testament to his resilience and positive outlook. He acknowledges That academia is a life of constant challenges and frequent failures, but he reframes these setbacks as valuable learning opportunities. “It’s not really [a] failure because you learn from these failures, and the process of trying is enjoyable. And when you do succeed, it’s a triumph.” #Wigderson #acmturingaward https://lnkd.in/g8Dn8MTE

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Quantum Machines very own Director of Architecture, Oded Wertheim shares some hard-earned wisdom into hybrid computing and quantum-classical integration in a recent InformationWeek article: "What Hybrid Quantum-Classic Computing Looks Like." Oded and our team have been pushing the boundaries of how quantum control systems act as the infrastructure for hybrid computing, enabling quantum computers to boost performance leveraging powerful classical resources, as well as the part of the stack where hybrid quantum-classical algorithms are made possible and performant. Here's a short excerpt: “The quantum processing unit doesn’t do much by itself. You need to interact with it, for example, to manipulate the state of a qubit or cause interaction between qubits,” says Oded Wertheim, director of architecture at quantum control system provider Quantum Machines. 🔗 You can read the full article here: https://hubs.la/Q02TfWB20 Talk to us if you're interested in exploring how to make the next leap in hybrid computing and hybrid control! #QuantumTechnology #QuantumMachines #QuantumComputing

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Researchers from University of Delaware describe a robust technique for visual-inertial #SLAM from monocular cameras. https://lnkd.in/gQJk4Bdq Paper title: A Consistent Parallel Estimation Framework for Visual-Inertial SLAM Paper authors: Zheng Huai and Guoquan (Paul) Huang #SimultaneousLocalizationAndMapping #RobotSensingSystems #VisualInertial

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🌟 Exciting Update! 🌟 After months of exploration into the AI space, I'm thrilled to introduce my first customized GPT, "Requirements Guru" available on open AI's GPT marketplace. What started as curiosity and a creative pursuit to minimize tedious tasks has turned into something transformative for my workflow. Navigating uncharted territory in AI wasn’t easy, but staying curious kept me motivated. This journey has truly been about innovation—creating tools that not only streamline work but also unlock greater productivity. Grateful for the growth, and excited to see where this takes me! If you have access to Chat GPT Plus you can check it out below and let me know your thoughts. https://lnkd.in/g-qnJGWz #AI #Innovation #RequirementsEngineering #Productivity #AlwaysLearning #SystemEngineering #Incose

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Chapters 12, 13, and 14 are now available for Outlier Detection in Python. These cover: - Collective outliers - Explainable outlier detection - Creating ensembles of outlier detectors Often in outlier detection, we're concerned not just with unusual events, but with unusual sequences of events. For example, a failed password attempt may not be unusual, but many within a minute for many different user accounts may be. With credit card purchases, a single large purchase may not be unusual for the cardholder, but many within an hour may be. With staff expenses, single claims for meals may be common, but many claims for meals within the same day for the same staff may be unusual. To detect these, we use tests for collective outliers, which are explained in chapter 12. It's also common in outlier detection for it to be necessary to know why items flagged as outliers were. For example, if a series of credit card purchases were flagged as anomalous, and therefore suspicious, for anyone investigating these, it's necessary to know what is anomalous to investigate them efficiently and accurately. We cover explainable AI techniques such as feature importances, proxy models, counterfactuals, and ALE plots and describe how to apply these to outlier detection. We also cover interpretable outlier detection techniques. When performing outlier detection, it's very common to use a ensemble of outlier detectors. This is probably much more common (and necessary) than with predictive models. Each detector uses a specific algorithm, which detects a certain set of outliers. To find a wide range of the outliers present in a dataset, it's usually necessary to use multiple detectors, but combining them has some nuances we cover and explain how to deal with. https://mng.bz/KZe4 #OutlierDetection #AnomalyDetection #python #DataScience #DataAnalytics #analyitcs #MachineLearning #FraudDetection #Finance  #GoodBook #anomalies #outliers

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Want to understand more about the status of quantum computing? My friends at Riverlane just published The Quantum Error Correction Report 2024. Quantum error correction (QEC) is necessary for meaningfull quantum computing. Riverlane is the technology leader in QEC. This report is a comprehencive review of the state-of-the-art and it gives context within the challenges of quantum computing.

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Why is a socio-technical governance perspective so important for reducing the risks from AI? Stephen Casper, a computer science PhD student at MIT, and a prominent researcher in the world of AI risk reduction, was recently on the Center for AI Policy podcast. At the end of the podcast he said: “I am an abjectly technical person, or at least that's my background. But I've become kind of increasingly curious about doing more work that's like socio-technically governance-relevant in recent years. Because I'm just kind of becoming increasingly pessimistic about technical solutions really making the difference or really saving us. I think lots of technical problems might be solvable through enough layers of Swiss cheese. And I think that solving them might not be neglected enough to warrant additional work from me in helping to solve them. And I think that the real challenge is likely to be societally focused. One thing that I think about here is that, you know, imagine we fully solved, like all of the technical alignment problems we might ever want to solve. Well, we've reduced the risks of accidents a lot, but we haven't necessarily reduced a ton of risks from negligence or misuse. And we might have even increased that risk by kind of making the pace of AI development more rapid. Do you know what I mean? So there's, in order to safeguard against ongoing perpetual into the future risks from AI, I think we want strong institutions. I think of AI as being maybe the same thing as like nuclear weapons. We've solved nuclear alignment. The nuclear technology makes energy when we want, it blows up when we want. But we're still in perpetual crisis and danger mode as a global community from it, for institutional reasons. And I think AI might be the same.“ All of this really resonates with me as someone who entered this domain from a behavior and systems change background. Safety is not a technical property. As Cas points out, nuclear technology is not socially safe even when it is technically safe. All AI risks emerge from human interaction with technology. None exist without human interaction. An AI won't develop itself (unless we enable that potential). I therefore see human behavior as the best lens and lever of focus in reducing risks from AI. We need to understand and influence who does what, and when. When you think about it carefully and thoroughly, this includes understanding biology, psychology and sociology (e.g., institutions and culture). But while we do this we also need a coherent, complete and shared understanding of the outcomes we want to avoid (i.e., the risks). This is one reason why I think it was worth investing time in the AI Risk Repository (see comments) alongside Cas. I highly recommend following Cas on Twitter (where he is most active) for more of his insights. He knows a lot more about the technical, and perhaps all, aspects of risks from AI than I do. Thanks also to Risto Uuk for bringing this to my attention

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CodeAIR: The Next-Level Drone for Next-Level Learning! Now shipping! CodeAIR isn’t just a drone; it’s a complete learning experience that goes far beyond basic skills. https://lnkd.in/eQuG6N6J What Makes CodeAIR’s Curriculum Different? It’s more than a set of tasks—it’s a deeper dive into understanding: 👏 Purposeful Learning: Each mission in CodeAIR’s curriculum is designed to teach why each step matters, from understanding sensor functions to grasping the science behind flight. Students learn more than just “how” to code; they learn “why” it works. 👏 Skill-Building with Depth: Our curriculum progresses thoughtfully, with each mission building on the last. Students gain technical skills, but also a deeper knowledge of the technology at play. 👏 Hands-On with Hardware: CodeAIR’s open design lets students interact with and understand the electronics, gaining insight into the inner workings of a real drone. 👏 Real-World Readiness: Our curriculum prepares students to think critically, solve problems, and apply industry-relevant concepts in coding, AI, and engineering. Equip your students with an educational journey that goes beyond the basics. Ready to inspire? https://lnkd.in/eBzXmcRR #CodeAIR #CodeSpace #Python #Edtech #STEM #DRONES #computerscience #CTE #STEMEducation

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Many of us may be familiar with the "Space Force" web series on Netflix or have seen the viral Instagram reel depicting a scientist frustrated with Microsoft's auto-updating system during a critical space mission. In line with the vision portrayed in Space Force of dealing with space obstacles, Dark, a Paris-based startup established three years ago, is striving to become the S.W.A.T. team of space. The company is actively developing a mobile platform aimed at addressing the challenges of space debris and conflict by launching, attaching to, and ultimately de-orbiting uncooperative objects in space. This mission is undeniably ambitious, as no company has yet achieved successful rendezvous and proximity operations—approaching and interacting with other objects in space. However, as Mr. Nelson Mandela famously said, "It always seems impossible until it's done". #Space #Rocket #Satellite #Aircraft #Mission #Technology #Innovations #Startup #Opportunities #Investment #NewMarket #Leadership

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Anyone interested in #aigovernance or #ailegislation should have eyes on this. There’s a more general principle: what guardrails should society have around society-changing technology that can roll out to an appreciable portion of the planets population in days or weeks, with legislation taking months or years to catch up. You can swap out ‘AI’ for ‘Social Media’ or ‘autonomous vehicles’ in any of this, for example.

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Great new article from Pei Dang and colleagues on using #llm Large Language Models for #Wayfinding, comparing their performance with humans, and evaluating the impact of spatial perception and spatial memory, #GISchat, https://lnkd.in/eh9idxJk Using agents to simulate human wayfinding processes helps to understand human decision-making and spatial cognition. The performance of large language models (LLMs) in agent-based wayfinding is still unclear. To address this, this study harnessed the thinking and reasoning capabilities of LLMs by designing an agent wayfinding framework based on cellular automata. This framework provides environmental descriptions in text form to the LLMs, enabling them to perceive the wayfinding environment and navigate within it. We simulated different spatial memory conditions for the LLM, including three types of spatial memories and one condition with no spatial memory, to observe its wayfinding behavior in a real-world setting. 

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