AI Dynamics

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  • Human-Computer Collaboration Beyond Individual Capabilities

    By

    @whats_ai

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    "Beyond what humans or computers can do alone" is exactly the right framing. Not replacement, collaboration.

    → View original post on X — @whats_ai

  • Memory Importance in Viral Blog Discourse

    By

    @whats_ai

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    This blog made the rounds for a reason. Memory isn't optional!

    → View original post on X — @whats_ai

  • High-Signal Trajectories and DPO for Agent Optimization

    By

    @akshay_pachaar

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    Good solution. Btw, Once you’ve identified the high-signal trajectories, you can also pair them with counterfactual continuations (what the agent should have done at the point of failure) to construct preference pairs for DPO. So the signals don't just act as a debugging tool

    → View original post on X — @akshay_pachaar

  • Muse Spark outperforms expectations after year without Meta models

    By

    @emollick

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    I think Muse Spark came in far better than most were expecting as the first new model attempt from Meta, especially given the fact that it has been a year since Llama 4 with no models at all (and that Llama 4 was generally considered a dead end).

    → View original post on X — @emollick

  • AI: Solution to the Energy Crisis, Not the Problem

    By

    @scobleizer

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    People haven't figured out yet that AI makes EVERYTHING more efficient. The man who won the Nobel Prize just told the world that AI is not the energy crisis, it is the cure for it. Everyone has been screaming about how much electricity AI consumes, the data centers, the training runs, the billions of queries every single day. Hassabis just said AI will extract 30 to 40 percent more efficiency out of national power grids, grids that, right now, operate at only 30 percent of their total capacity according to Stanford researchers. That means the grids we already built are massively underused, and AI is the key to unlocking what is already there. But that is the smallest part of what he is saying. He is saying AI will crack nuclear fusion, the energy source that has been 30 years away for the last 60 years and DeepMind is already working with Commonwealth Fusion in the US to help AI contain plasma inside fusion reactors. His personal mission is to use AI to discover a room-temperature superconductor, a material that would allow electricity to travel with zero loss, something physics has never been able to deliver. The implications of that alone would reshape the entire global economy overnight. He is also saying AI will design next-generation batteries and build the best climate modeling systems humanity has ever had, using them to figure out exactly where the planet is breaking down. Think about what this means, the thing everyone is blaming for the energy crisis is the same thing being bet on to end it forever. [Translated from EN to English]

    → View original post on X — @scobleizer, 2026-04-12 06:03 UTC

  • Atlas Robot Demonstrates Advanced Heavy Lifting Capabilities
    Atlas Robot Demonstrates Advanced Heavy Lifting Capabilities

    By

    @ronald_vanloon

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    From Walking to Heavy Lifting: Atlas Shows Off Next-Level Skills by @lukas_m_ziegler #Robotics #ArtificialIntelligence #Innovation #MI #ML #Tech

    → View original post on X — @ronald_vanloon, 2026-04-12 05:27 UTC

  • Machine Learning Transforms IoT Computing with New Perspectives
    Machine Learning Transforms IoT Computing with New Perspectives

    By

    @gp_pulipaka

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    #MachineLearning Puts New Lens on #IoT Computing! by @gp_pulipaka! #BigData #Analytics #DataScience #AI #IIoT #PyTorch #Python #RStats #TensorFlow #Java #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode geni.us/New-Lens-IoT

    → View original post on X — @gp_pulipaka, 2026-04-12 04:57 UTC

  • GPT, Generative AI, and LLMs Conference by Aver Conferences
    GPT, Generative AI, and LLMs Conference by Aver Conferences

    By

    @gp_pulipaka

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    GPT, Generative AI, and LLMs! @AverConferences #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysOfCode geni.us/Aver-Confer

    → View original post on X — @gp_pulipaka, 2026-04-12 04:57 UTC

  • GPT, Generative AI and LLM Conference at Star Conferences
    GPT, Generative AI and LLM Conference at Star Conferences

    By

    @gp_pulipaka

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    GPT, Generative AI and LLM! @starconfs #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode cstar.global

    → View original post on X — @gp_pulipaka, 2026-04-12 04:57 UTC

  • Machine Learning for Obesity Assessment Using 3D Body Scanning
    Machine Learning for Obesity Assessment Using 3D Body Scanning

    By

    @gp_pulipaka

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    Grade Body Measurement with Machine Learning Grading a patient’s obesity level is a critical component of effective healthcare. Obesity is a significant risk factor for a range of serious diseases, including chronic conditions, type-2 diabetes, heart disease, and certain cancers. Understanding a person's obesity status can serve as a powerful catalyst for individuals to take control of their weight. Additionally, intentional weight management not only mitigates health risks but also offers the compelling benefit of reducing disease susceptibility. Despite its widespread use, the Body Mass Index (BMI) – the standard metric defined by the World Health Organization (WHO) – fails to capture the complexities of obesity, as it overlooks essential body-type variations. Furthermore, nutritional needs differ markedly across regions and body types, underscoring the necessity for a more nuanced approach to obesity assessment. Traditional anthropometric measurements, while effective, are often impractical due to the requirement for trained professionals to perform them accurately. In response to this challenge, innovative research utilizing 3D scanning technology is gaining momentum as a less-invasive and more accessible alternative. Unlike Computed Tomography (CT) or Dual-energy X-ray absorptiometry (DXA)—considered the gold standard for measuring body fat percentage (bf%)—3D scanners eliminate the risks associated with radiation exposure during frequent assessments. Moreover, evaluating health risks demands a multifaceted approach rather than relying solely on a singular measure. In this study, we collected paired data from 3D body scans and DXA for a Korean population, providing a more comprehensive understanding of obesity and paving the way for improved health management strategies. This pioneering research has the potential to transform how we assess and respond to obesity, ultimately leading to healthier outcomes for individuals and communities alike. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode References Jeon, S., Kim, M., Yoon, J., & et al. (2023). Machine learning-based obesity classification considering 3D body scanner measurements. Scientific Reports, 13, 3299. Retrieved February 27, 2025, from doi.org/10.1038/s41598-023-3…

    → View original post on X — @gp_pulipaka, 2026-04-12 04:57 UTC