标签：#AR

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]

Send the pool guy packing. Let one of these robotic buddies maintain your water quality instead.

Hey everyone, Since there are many researchers here who regularly publish at top-tier ML conferences like ICML, NeurIPS, and ICLR, I wanted to ask about realistic paper timelines. In your lab or research setting, how long does it usually take to develop a paper from the initial idea to a complete submission, and then eventually to final acceptance? submitted by /u/Hope999991 [link] [留言]

I understand that this is a little unorthodox, but I'm desperately trying to download a copy of the ICDAR2013 Chinese Handwriting Recognition Competition Dataset. Unfortunately, the linked page in the Conference Archive: https://nlpr.ia.ac.cn/databases/handwriting/Download.html appears to be down, and has been down for the past few weeks consistently. I've checked every source I can find, like Kaggle, HuggingFace, remnant Google Drive and Baidu Netdisk links, even checking if someone's accidentally committed it to github, but no dice. I've tried every google dorking trick I know to no avail. Which brings me here. Please, if anyone has a copy of the Competition Dataset, I would be very grateful if you could share the ZIP with me. Thanks in advance! submitted by /u/Aathishs04 [link] [留言]

Chipmaker Groq is looking to raise $650 million in internal funding as it pivots from hardware to focus more on AI inference, the process of refining the way AI models respond to prompted requests, per Axios.

Chipmaker Groq is looking to raise $650 million in internal funding as it pivots from hardware to focus more on AI inference, the process of refining the way AI models respond to prompted requests, per Axios.

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

hi everyone. I'm trying to calibrate my expectations and would appreciate full honest perspectives from people involved/ with experience in hiring at places like Anthropic, OpenAI, Google DeepMind, Meta, etc (haven't started interviewing yet). I'm at a top ML university, but my advisor is not particularly well known in industry and doesn't have many industry connections. Looking around, I'm seeing peers with research records that seem comparable to mine (and in some cases arguably weaker) land interviews and jobs at top labs. My main question is: How much does advisor reputation and network actually matter? I understand it can help get an interview, but does it also help beyond that? For example: - do referrals from famous advisors meaningfully influence recruiter screens? - do they influence hiring committee discussions -- like they already know they want you ? - do they just help at borderline decisions? - or does their effect mostly disappear once the interview process starts? I'm trying to understand whether advisor connections mainly help open the door, or whether they continue to matter throughout the process -perhaps being the sole factor. To what extent do connections help candidates bypass normal evaluation? I'm not asking whether people completely skip interviews, but are there cases where strong recommendations from trusted researchers substantially change the process, the interview bar, or how mistakes are interpreted? Moreover, something else that confuses me: I frequently see people land roles that seem heavily focused on LLMs, agents, post-training, RLHF, etc., despite having little or no published work or prior experience in those areas during their PhDs. How does that happen? Are interview questions tailored to the candidate's background? If someone comes from probabilistic ML, computer vision, systems, optimization, theory, etc., are they evaluated differently? Or are they still expected to answer detailed LLM/agent questions even without prior exp

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.

Cognition makes Devin, the first and arguably most successful AI coding agent. But famed coder Wu says it isn't designed to supplant human programmers.

Until recently, a driver had maybe a six in ten chance of finishing a race.

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]

Made for those who hate having a TV in the home, Samsung’s Movingstyle monitor-on-wheels brings the entertainment when you need it and hides away in a closet when you don’t.

"This wasn't a success story. It started as survival." Intro Hey, I'm Supreeth C , a third-year engineering student, open source developer, and professional overthinker from Bengaluru. This is my first blog, and fair warning: it's long. Not "LinkedIn post with 5 bullet points" long. Actually long. This is the story of how I got selected for Google Summer of Code 2026 with CircuitVerse but more honestly, it's the story of how I almost didn't submit a proposal, almost quit twice, and spent a lot amount of time reading codebases on the Bengaluru Metro while missing my stop. Connect with me on GitHub and LinkedIn PS: I'm writing this at 4.05am, because sleep is a myth XD. Act I: The Prequel Second semester. Fresh-faced. Absolutely clueless. I joined Pointblank , the one genuinely breathable space in my Tier-3 college. I can say its the best student-run club overall and the main reason being : everyone around me was terrifyingly good . Codeforces experts and specialists, GSoC mentees, LFX mentees, Smart India Hackathon winners. People whose LinkedIn bios are of several lines. And me? I knew C++. That was it. That was my entire personality. Cue the imposter syndrome : that lovely feeling where you're convinced you snuck into a room you have no business being in, and everyone else is one conversation away from figuring it out. My solution? Chaos. I started learning everything simultaneously: web dev, Android, ML, DevOps, a bit of systems engineering. Jack of all trades, master of none, spiraling fast. I wasn't learning; I was collecting domains like Pokémon and actually using none of them. Then a senior said something that cut clean through the noise: "Find your own path." So I slowed down. Started from the basics of web dev. Attended many hackathons but always ended up in third or fourth and winning: zero . But something clicked anyway. Those hackathons introduced me to open source, and somewhere in that chaos, I gave myself a simple challenge: 4 pull requests for Hacktob

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]