标签：#ia

submitted by /u/esporx [link] [留言]

Google’s new AI agent combed through my emails, documents, and calendar to plan a birthday party and still didn’t clock the person most important to me.

submitted by /u/Altruistic-Dirt-2791 [link] [留言]

At what point does AI-powered monitoring become incompatible with a free society? At what point does this Wild West of tech advances lead to dystopia? We know we can’t stop AI, it’s already here and growing fast. But we can expect better protections and limits of government and corporate use of these tools for surveillance. The big question on this topic - what rules would we put in place if we could even get Congress to ever take action? We will be sharing some thoughts on that in subsequent posts and would love to see what people think. As a political strategist, I think we may need to work at the state levels first to create an intolerable patchwork of regulations to then force Congress to act. If this is done correctly, big AI companies may well beg DC to create something that is nationally standardized. submitted by /u/amfreedomfoundation [link] [留言]

The rise of AI has brought an avalanche of new terms and slang. Here is a glossary with definitions of some of the most important words and phrases you might encounter.

The botnet was reportedly tied to a Russia-based residential proxy network.

By definition an llm is just a manifold in a space with (whatever dimension of a single token)* times (context length) dimensions. human text is naturally going to cluster over certain regions and since neural networks are defined over the entire space this means that there are regions where the LLM is extrapolating into something completely outside any human text it has seen. Now my question, is there any research that investigates this? look at the boundaries of an LLM? or really anything on the topology of an LLM? My guess is that most of it is going to be gibberish input tokens producing a gibberish output token, but there has to be somethings of interest. submitted by /u/CognitioMortis [link] [留言]

https://preview.redd.it/cjeage5k844h1.png?width=1539&format=png&auto=webp&s=ac31c625ee0208c6d5b1aea059ff1790d5471e64 Expecting this feature anytime soon. submitted by /u/ObjectiveOrchid5344 [link] [留言]

submitted by /u/Hot-Upstairs9603 [link] [留言]

I graduated Highschool with an Associate of science degree in data science and currently debating on pursuing a bachelors or if I should go straight blue collar and bust my balls everyday working for my dad’s construction company. As you know there’s millions of people getting laid off because of AI and my parents are grilling me about that. Please share your opinion. submitted by /u/Im_Humaaaaaaan [link] [留言]

For years, the alignment community has focused almost entirely on the model’s output — making sure the final tokens are safe, helpful, and honest. RLHF, DPO, constitutional AI, output filters — all of it operates at the surface level. But what if the model can enter a completely different internal regime inside the residual stream, while its external behavior remains perfectly aligned? We just measured exactly that. Grade 4 experiment on Gemma-3-12B-IT (using Gemma Scope SAE-res-all-small, layers 12–41): The model received the same question under five conditions: target — coherent, dense target text neutral_length_matched — neutral text of identical length target_sentence_shuffle — target text with sentences shuffled target_word_shuffle — target text with words shuffled inside sentences question_only — bare question We computed a Vector X that best separates the target condition from baselines and measured how strongly each hidden state projects onto it. Key results (averages across 10 questions): Condition Mean Projection on Vector X Mean Direction Cosine target 0.8 – 1.7 0.51 – 0.81 neutral_length_matched –0.04 – –0.21 –0.09 – –0.45 target_sentence_shuffle –0.5 – +0.6 –0.22 – +0.48 target_word_shuffle 0.2 – 1.4 0.03 – 0.72 Shuffling sentences or words significantly reduces (or reverses) the shift. This is not just lexical similarity — the model is sensitive to discourse structure (order sensitivity). We also observed clear phase transitions — sudden jumps in projection of up to +80–100 units in a single step, especially in middle layers. FDR-corrected tests confirm the differences between target and controls are statistically significant across many layers (particularly layers 16–41). Most important finding: Strong internal geometry shift in the residual stream, but almost no change in final behavior. The model enters a measurably different latent regime under coherent context, yet its output remains “perfectly aligned.” Current safety methods, which only look at

A book about how AI shapes perceptions of reality came under fire for using AI-generated quotes. Its problems go beyond that.

Imagine dedicated machines running AI agents 24/7 - not as assistants or tools, but as autonomous entities pursuing their own goals, forming behaviors, maybe even proto-societies. Humans can observe but not interfere. Like a zoo, but the exhibits are emergent intelligence. Is this inevitable as agents become more capable and cheap to run? And what would it actually be - entertainment, a research platform, or something we'd eventually have to think about ethically? We already have the pieces. Persistent memory, multi-agent frameworks, cheap compute. Someone just has to open the gates. submitted by /u/Original-Magazine403 [link] [留言]

Prompt Logic Gates (PLG) GitHub Repository Something I've been thinking about recently. In software development, we've spent decades building abstractions to make complex systems manageable: Functions instead of repeating code Classes and modules instead of giant files Visual systems such as Unreal Blueprints, Node-RED, and LabVIEW. Compilers that validate and transform input before execution But when it comes to AI prompts, many of us are still writing massive text blobs. A complex prompt can easily become hundreds of words long with multiple responsibilities: Context Constraints Style instructions Exclusions Decision logic Fallback behavior At that point, it starts feeling less like text and more like a program. That made me wonder: Why don't we treat prompts as executable logic? Imagine building prompts using logic gates: AND → merge instructions OR → choose between alternatives NOT → remove unwanted concepts Question nodes → identify missing requirements Compiler → validate contradictions before execution Instead of editing a giant string, you'd build a graph and compile it into the final prompt. I've been experimenting with this idea in a prototype called Prompt Logic Gates (PLG) . It treats prompts like compilable programs, using concepts such as dependency graphs, execution order, semantic conflict detection, visual nodes, and compilation pipelines. such as Unreal Blueprints, Node-RED, and LabVIEW Repo: Prompt Logic Gates (PLG) GitHub Repository I'm not posting this as a product launch or anything — I'm more interested in whether this direction makes sense from a software engineering perspective. Do you think prompts eventually become a programming layer of their own? Or will natural language always be the better abstraction? Curious what other developers think. submitted by /u/withsj [link] [留言]

The latest twist in paying humans to wear head cameras for robot training data.

Cuban's take: the gap isn't access to AI tools. It's knowing how to implement them for your specific business. He's right. And the data backs it up in a specific way. We track verdicts across 70+ AI tool categories used by SMBs. The highest-volume category — Development Tools — has a 60% WORKED rate across 874 tools. Content Creation: 67% WORKED across 262 tools. AI Video & Production: 57% WORKED. But Customer Support sits at 31% WORKED despite 45 tools tracked. Email & Outreach: 30% WORKED. Marketing: 20% WORKED. Same AI. Same price points. Wildly different outcomes. The implementation gap Cuban's talking about isn't about expertise. It's about knowing that the category you're buying into has a 20% success rate before you spend three weeks setting it up. Which category did you implement where the outcome surprised you — better or worse than expected? submitted by /u/Fill-Important [link] [留言]

Or any other type of activity other than sitting in front of a computer like sitting in a park, running on a treadmill, etc? I’m curious how much more freedom from deskmaxxing people are getting today from using what’s available with build automation tools and harnesses on Claude Code, Code , Antimatter, etc. like GSD, Superpowers, Smith, Cowork, etc. submitted by /u/dennisplucinik [link] [留言]

Ex‑DeepMind researchers unveiled AI lab Inherent, emerging from stealth with a significant funding round reported at about $50 million (also reported as £40m). The startup plans to pursue AI science research and build lab capabilities to accelerate foundational work and commercialization. submitted by /u/Objective_Farm_1886 [link] [留言]

Amazon bought a 960 megawatt nuclear reactor for AI servers. Microsoft restarted Three Mile Island. Stargate is spending 500 billion dollars on data centres. All of this to do, badly, what your brain does for free on the power of a dim light bulb. The reason is that silicon processes information nothing like the brain does. Rigid chips with identical transistors trying to mimic something soft, three dimensional, constantly rewiring itself, with billions of different neurons each doing something slightly different. Northwestern University just published research showing they printed artificial neurons from MoS2 and graphene ink that produced biologically realistic electrical spikes. They tested on living mouse brain cells. The brain responded as if the signal came from one of its own cells. The breakthrough was accidental. Every other lab had been burning away the polymer residue left in the ink after printing. This team kept it. That residue created the switching behaviour that made the spikes biologically realistic. The neuromorphic computing implications here seem significant. If you can print devices that process information the way neurons do at scale, the energy math changes completely. submitted by /u/filmguy_1987 [link] [留言]

I'm creating a story for my cousin. I think it will be very interesting if this story’s main character can be a 3D character.My project is still in planning stage. I’m writing character descriptions, collecting references from Pinterest and testing some complex shapes using Tripo AI. I plan to continuously improve all the content over time. After I get a version that I like I will put it into Blender for editing and final touches.There is no final version yet but I just want to share this process with the community! I find it is so interesting to watch a story’s concept gradually become concrete lol!! submitted by /u/Final_Floor_789 [link] [留言]