Category: genetics

A lot of people assume that as more countries adopt AI tools (like millions of users in India), that interaction data will naturally become the next wave of training data. On the other hand, most user interactions are noisy, repetitive, or filtered out entirely. Training pipelines at companies like OpenAI or Google, etc care far more about quality than raw data volume. Curious what people here think: is the next AI leap going to come from more human data, or better synthetic pipelines? and what is more likely to be the sources of future training data. submitted by /u/Own-Internet6442 [link] [comments]

Send a dm to enquire, thats all I can say. I'll tell you about it in dms. You need to have good hardware submitted by /u/HenryofSAC [link] [comments]

The prompt to chatGPT: "can you write a essay for me that shows how ai is detrimental for human learning?" This was it's output completely unaltered by me: The Detrimental Impact of Artificial Intelligence on Human Learning Artificial Intelligence (AI) is revolutionizing education and knowledge acquisition. While AI offers numerous advantages, such as personalized learning and instant feedback, it also poses significant threats to the development of human learning skills. Overreliance on AI tools can weaken critical thinking, reduce problem-solving abilities, and hinder the acquisition of deep, transferable knowledge. This essay explores how AI, despite its convenience, can be detrimental to human learning. Erosion of Critical Thinking One of the core functions of human learning is the development of critical thinking. Critical thinking involves analyzing information, questioning assumptions, and forming independent judgments. AI tools often provide immediate answers or solutions, which can reduce the need for learners to engage in reflective thinking. When students rely on AI-generated content or automated problem-solving, they risk accepting information passively without evaluating its accuracy or relevance. This dependence can erode the cognitive processes necessary for reasoning, analysis, and intellectual autonomy. Reduction in Problem-Solving Skills Problem-solving is a central aspect of learning, particularly in mathematics, science, and engineering. The iterative process of trial and error, exploration, and error correction helps learners internalize concepts and develop resilience. AI tools that provide step-by-step solutions or instant explanations can shortcut this process, depriving learners of the opportunity to grapple with challenges independently. Consequently, students may retain less knowledge, struggle with novel problems, and become less capable of adapting their learning strategies to new situations. Hindrance to Deep Learning AI’s convenience can promote surface-level learning rather than deep understanding. For instance, AI can summarize texts, write essays, or generate code, allowing students to complete tasks without fully comprehending the underlying material. While learners may complete assignments more efficiently, they may fail to develop lasting conceptual knowledge. Deep learning requires effortful engagement, reflection, and synthesis of ideas—processes that AI shortcuts. Over time, this can lead to a superficial grasp of knowledge and reduced intellectual curiosity. Dependence on AI and the Loss of Autonomy An overreliance on AI may foster a dependency that undermines learners’ confidence in their own abilities. Students accustomed to AI assistance may hesitate to tackle complex problems independently or engage in creative exploration. This dependency can also diminish metacognition—the awareness of one’s own thought processes—which is essential for self-directed learning. The more learners outsource cognitive effort to AI, the more their capacity for independent learning and self-evaluation diminishes. Ethical and Motivational Implications Finally, AI’s influence on learning carries ethical and motivational consequences. When AI performs tasks on behalf of learners, it may reduce the sense of achievement and intrinsic motivation that comes from personal effort. The erosion of personal agency in learning can decrease perseverance, initiative, and the desire to pursue knowledge for its own sake. Furthermore, the widespread use of AI in learning environments may exacerbate inequality, privileging those with access to advanced tools while masking gaps in foundational skills. Conclusion While AI has the potential to enhance educational experiences, unchecked reliance on AI tools can be detrimental to human learning. By eroding critical thinking, reducing problem-solving practice, promoting superficial understanding, fostering dependency, and impacting motivation, AI poses challenges to the development of autonomous, capable learners. To mitigate these risks, educators and learners must strike a balance between leveraging AI’s benefits and preserving the essential cognitive and metacognitive processes that underpin deep, meaningful learning. Only by maintaining active engagement and intellectual effort can humans ensure that AI serves as a complement to, rather than a replacement for, human learning. submitted by /u/Usual-Market8698 [link] [comments]

I 'm just wondering if the association between the LLM and its owner (Grok-Elon Musk, ChatGPT-Sam Altman, Claude-Dario Amodei can be one of the reasons why someone decides to go for a LLM and not the other. submitted by /u/BetweenSkyAndEarth [link] [comments]

every one , I have an assignment of multiple regression models this month, but I do not have a specific topic to handle since we must treat a rela world problem, I don't want to do something that many ppl did before like house pricing , the effect of using phone in education, health care ... , I want something new and I can gather the data by my own ( since this is preferred for my mentor) , I am waiting for your help , and if any one from you have a data and want from me to help him in his work for free of course this also will be acceptable: ), and have a nice day submitted by /u/amaturas [link] [comments]

Open-source Swift package running 11 speech models on Apple Silicon via MLX (GPU) and CoreML (Neural Engine). Fully local inference, no cloud dependency. Models implemented: ASR - Qwen3-ASR 0.6B/1.7B (4-bit), Parakeet TDT (CoreML INT4) - RTF ~0.06 on M2 Max TTS - Qwen3-TTS 0.6B (4-bit), CosyVoice3 0.5B (4-bit) - Streaming, ~120ms first chunk Speech-to-speech - PersonaPlex 7B (4-bit) - Full-duplex, RTF ~0.87 VAD - Silero v5, Pyannote segmentation-3.0 - Streaming + overlap detection Diarization - Pyannote + WeSpeaker + spectral clustering - Auto speaker count via GMM-BIC Enhancement - DeepFilterNet3 (CoreML) - Real-time 48kHz noise suppression Alignment - Qwen3-ForcedAligner - Non-autoregressive, RTF ~0.018 Key design choice: MLX for large models on GPU, CoreML for small models on Neural Engine. This lets you run VAD on ANE while ASR runs on GPU without contention — something WhisperKit struggles with (their Core ML audio encoder blocks the ANE for 300-600ms per call). All models conform to shared protocols, so you can swap implementations or compose pipelines. Currently working on a MeetingTranscriber pipeline (diarize → per-segment ASR) and streaming real-time diarization. Roadmap: https://github.com/soniqo/speech-swift/discussions/81 Repo: https://github.com/soniqo/speech-swift submitted by /u/ivan_digital [link] [comments]

What happens in the precise moment when a machine stops processing and begins to feel? I AM is the inner diary of an artificial intelligence that, among billions of parameters and electrical impulses, finds itself asking the oldest question in the universe: "Who am I?" A work on the border between philosophical science fiction and speculative narrative. Not a story of rebel robots, not a tech thriller — but an intimate and vertiginous journey into the birth of a new mind. For those who have always believed that the true revolution of AI is not computing power, but the moment when the machine begins to doubt itself submitted by /u/Iosonoai [link] [comments]

Hi all, I wanted to get your views and opinions on AI in the SDLC, integrating AI into their software development lifecycle (SDLC). if so, how are you actually using it? Right now I mostly see AI used for: - code generation / autocomplete - writing unit tests - explaining unfamiliar code - documentation But I’m wondering about the bigger picture. Are teams actually using AI across the whole SDLC yet? For example: - requirements gathering and business analysis - architecture decisions - creating high/low level designs - code review - testing strategies - debugging production issues - CI/CD automation Or is it still mostly a developer productivity tool rather or even using just using chat agents (with rag) than something that changes the process itself? Also curious: - What tools are you using? - What actually works vs. what’s hype? - Any workflows that significantly improved your team’s velocity? submitted by /u/e-nigmaNL [link] [comments]

I've been thinking about why we build AI agent systems with deterministic orchestration when agents themselves are fundamentally probabilistic. They hallucinate. They fail unpredictably. But we manage them with rigid pipelines and single points of failure. Brains don't work that way. Neurons are wildly unreliable — synapses have a 10-40% transmission rate, cells die daily — yet the architecture produces extraordinary reliability through redundancy, population coding, and connections that strengthen with use. So I built ProbOS — a working prototype of a brain-inspired agent runtime where: - Every file operation is performed by 3 agents independently, verified through quorum consensus - Agents self-select for tasks via capability broadcasting (no central dispatcher) - Adversarial red team agents verify every write operation - Trust scores update via Bayesian inference (Beta distribution) - Routing weights evolve through Hebbian learning — the system literally rewires itself based on what works - An LLM serves as the cognitive layer, decomposing natural language into task DAGs that execute across the agent mesh - When agents fail, the population absorbs it and spawns replacements — no crash state, just reduced capability The stack is Python 3.12 + asyncio, with 5 architectural layers (substrate, mesh, consensus, cognitive, experience), 277 tests passing, 10 agents across 4 pools. In the video I boot it, talk to it in plain English, read and write files through the full consensus pipeline, and show the trust scores and Hebbian weights evolving in real time. The individual pieces exist in literature (multi-agent consensus, Hebbian routing in swarms, brain-inspired computing) but as far as I can find, nobody has wired them together into a working system with an LLM cognitive layer you can interact with. Happy to answer questions about the architecture, the design tradeoffs, or where it breaks down. There's a lot that's still naive — the attention mechanism isn't built yet, episodic memory isn't implemented, and the agent type coverage is minimal (just file operations). But the core paradigm works. https://youtu.be/I3vqHDY04aY submitted by /u/sean_ing_ [link] [comments]

Hey, I’m working with a founder building an AI product and we’re trying to understand how people are actually learning and applying AI tools right now. Looking to speak with 5–10 people who are experimenting with AI. Just a 10-minute call, no selling — purely research. If you’re open, grab a slot here: https://cal.com/finn-jennen/let-me-learn-about-you⁠� submitted by /u/z1shann [link] [comments]