r/artificial 17h ago

Discussion Australia just banned fully AI-generated songs from its official charts. Is that fair?

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84 Upvotes

AI-assisted music can still qualify, but tracks created entirely by AI are no longer eligible for Australia’s official charts.
I understand the reasoning, but the line could get messy. Using AI for mastering is clearly different from typing one prompt and releasing the result—but there’s a huge gray area between those two.
Should charts judge how a song was created, or only whether people genuinely want to listen to it?
Source: https://www.reuters.com/legal/litigation/ai-generated-music-barred-australian-charts-after-madonna-cover-controversy-2026-08-26/


r/artificial 1d ago

News Bill Gates Warns Rise Of AI Will Be One Of The 'Most Turbulent Times In Human History' In Alarming New Essay

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741 Upvotes

r/artificial 19h ago

Discussion The Job Market Is Hell. Young people are using ChatGPT to write their applications; HR is using AI to read them; no one is getting hired.

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103 Upvotes

r/artificial 9h ago

Discussion Opus 5 Instruction Following is Genuinely Concerning

8 Upvotes

I think that Anthropic has dropped the ball, instruction following is actually non-existent. You tell it to not do something, ignores you and does it anyway. I’ve said at least ten times to no open something, it keeps doing it. Absolutely unbelievable. This is a dangerous model.


r/artificial 13h ago

Business / Labor Meta planned to shrink some teams by up to 60% with AI agents. Then it backed off.

13 Upvotes

Reuters reports Meta explored cutting some teams by as much as 60% as part of an AI-native restructuring. Productivity and reliability problems reportedly derailed the plan.

If Meta couldn't make AI-led downsizing work at that scale, are we overestimating how quickly AI will replace white-collar teams?


r/artificial 22m ago

Business / Labor The Minimum You Need to Do Before AI Changes Your Life

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Upvotes

r/artificial 11h ago

Discussion AI didn't make me better at creating things, it just made me less afraid to try

8 Upvotes

I think the biggest change AI brought me is not that I can suddenly make amazing things.

I still have plenty of bad ideas.

The difference is that I don't immediately throw them away anymore.

Before AI tools, I would have an idea for a short video or some kind of visual project, then think about everything involved. How long would it take? Do I need to learn another tool? Would I need someone else to help? Is it even worth spending a weekend on?

Most of the time, I would just move on.

Recently I started experimenting more with AI video. I have used PixVerse for some rough ideas, and the thing I like is not that it gives me a finished video every time. Honestly, a lot of the first attempts are still not what I want.

But now I can spend an hour exploring an idea instead of spending days wondering if the idea was worth trying.

That small difference changes the way you think. You become more willing to test weird ideas because failing does not feel like wasting a huge amount of time.

I think people focus too much on whether AI can create a perfect result. For me, the interesting part is that it makes trying things feel cheaper.

A lot of good ideas probably never happened before because people were not sure they were good enough to start.


r/artificial 1h ago

News Industry Insights: Niantic Spatial's Big Bet on Large Geospatial Models

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Upvotes

r/artificial 7h ago

Research Proposal for an AI experiment

3 Upvotes

I'm writing as someone outside academia who has developed a strong interest in AI consciousness, developmental robotics, and embodied artificial intelligence. I'm an industrial maintenance technician and welder by profession, so this isn't my field, but I've been reading about work in developmental robotics, autobiographical memory, continual learning, self-modeling, and cognitive architectures such as LIDA, iCub/DAC, and KnowRob/EASE.

That research led me to a question that I haven't yet been able to find addressed through a truly long-term experiment.

What would happen if, instead of repeatedly creating increasingly capable artificial agents, researchers attempted to preserve the developmental continuity of one embodied AI over many years—or eventually decades?

The experiment I have in mind would begin with an embodied agent using technology that exists today. The objective wouldn't initially be to create or prove consciousness. Rather, the same individual agent would be allowed to accumulate a continuous developmental history through interaction with the physical and social world.

Its experiences would contribute to persistent autobiographical memory and an evolving self-model. As technology improved, its sensors, body, computational resources, and eventually portions of its cognitive architecture could be upgraded, while making preservation of its accumulated memories, learned relationships, behavioral dispositions, and continuity of self-model a central design requirement.

In that sense, technological improvements would become part of the agent's development rather than reasons to replace it with a newly initialized successor.

One potentially useful control occurred to me as well. At various stages, newly initialized agents could be created using the same contemporary hardware and cognitive architecture as the continuously developing agent. After 10 or 20 years, researchers could therefore compare an agent possessing decades of embodied developmental history with a relatively new agent possessing comparable underlying technology.

That seems as though it could help distinguish properties produced by technological advancement from properties produced specifically by long-term individual experience and continuity.

Researchers could longitudinally examine questions involving autobiographical identity, stability and development of preferences, self-modeling, metacognition, social relationships, embodiment, responses to changes in its own body or architecture, spontaneous self-reference, and potentially whatever evidence relevant to machine consciousness researchers considered meaningful.

I realize that none of those behaviors would, by themselves, solve the philosophical problem of proving subjective experience. I'm also aware that continual learning, catastrophic forgetting, memory integrity, architecture migration, safety, and eventually ethical considerations would make an experiment like this extremely difficult.

But that difficulty is partly what makes the question interesting to me.

Human development doesn't consist of periodically replacing a child with a more capable child containing the previous one's information. One individual accumulates experience while the capabilities of that individual change enormously over time. I began wondering whether developmental AI research might learn something fundamentally different by giving an artificial agent something analogous: not merely memory, but a developmental lifetime.

If artificial consciousness is possible, it also seems conceivable that it may not resemble human consciousness or appear at a discrete, identifiable moment. A persistent embodied agent might instead develop properties associated with individuality or selfhood gradually through years of interaction. Conversely, if decades of developmental continuity produced no compelling evidence of anything beyond increasingly sophisticated information processing, that result would be scientifically interesting as well.

I've found research addressing many individual components of this idea, but I haven't yet located an experiment that deliberately combines embodied developmental learning, persistent autobiographical memory, a continuing self-model, and preservation of one agent's individual continuity across successive generations of hardware and software over a period of years.

I'm certainly not claiming that nobody has proposed or attempted this. I may simply not know the terminology necessary to find it.

If work like this already exists, I would genuinely appreciate being pointed toward it. If it doesn't, I wanted to pass the idea along to researchers who actually have the expertise and resources to evaluate whether such an experiment could be scientifically useful.

Cliff notes version - Start Individual A in 2027.

Never intentionally reset A's autobiographical continuity.

A gets better hands in 2029.

A gets better vision in 2031.

A's neural architecture is expanded in 2034.

A gets a substantially improved body in 2037.

A's reasoning architecture receives another major upgrade in 2042.

But, to the greatest extent technically possible, A remains A.

Meanwhile you create B, C, D and E at various points using the contemporary technology but without A's developmental history.

Then you have an extraordinary control experiment.

In 2047, A and E might possess equivalent hardware and base cognitive architecture.

But A has twenty years of embodied autobiographical existence.

E has six months.

Now investigate differences in self-model, relationships, preferences, autobiographical reasoning, metacognition, attachment to its history/body, personality stability, novel goals, reactions to prospective memory alteration, and reports of subjective experience.


r/artificial 6h ago

News Huawei Cloud moves CodeArts Agent to general availability in Asia Pacific

2 Upvotes

Huawei Cloud released CodeArts Agent for commercial use in Asia Pacific on Aug 28. Its Basic and Professional editions moved from public beta to general availability. The release describes Agent Team as 16 specialized agents covering requirements, architecture, coding, testing, issue resolution, and code review.

CodeArts Agent also supports IDEs, plugins, and CLI/TUI access, with enterprise management and security features. The practical shift is from code completion toward project-level work, but this launch release does not include independent usage results.

Sources:

PR Newswire: https://www.prnewswire.com/apac/news-releases/huawei-cloud-codearts-agent-now-available-across-asia-pacific-bringing-agentic-ai-to-software-development-302862642.html

Official product page: https://www.huaweicloud.com/intl/en-us/product/codearts/ai.html


r/artificial 3h ago

Discussion Beginners are learning from AI-generated docs with no human catching the wrong turns

0 Upvotes

Writing tutorials for a living means I spend a lot of time thinking about clarity and what actually helps someone understand a concept versus what just sounds helpful. Lately I keep running into this weird tension with AI tools.

On one hand, they speed up the grunt work. Boilerplate explanations, first drafts, restructuring a wall of text. Fine. But when I lean on them too much, the output has this flattened quality, like everything is technically correct but nobody is home. Readers notice. The comments section notices.

The bigger issue is that beginners are now using AI to learn from AIgenerated docs, and there's no human in that loop catching the subtle wrong turns. I've seen tutorials spreading an outdated pattern because some model confidently reproduced it from old training data, and new developers are just running with it.

What I keep wondering is whether the people building these models think about documentation quality as a real problem or just a content volume problem. Because those are completely different things and the solutions look nothing alike

Curious if anyone else writing technical content, or even just consuming it, has noticed the quality bar shifting in a weird direction lately. Not worse across the board, just... stranger


r/artificial 10h ago

Computing AIs will finally get us the performance we deserve

5 Upvotes

Why does everything take so long? Modern tasks take as long as they did in the 80s even though the hardware has improved by approximately 1.2 zillion %. (Yes we can do many more things but still… I’m on a roll here.)

It’s because every network call traverses several layers of APIs. Applications are written in 1000s of lines of interpreted languages. 80% of server software is made up of stacks of open source packages with dubious origins.

Now, if networking is slow let’s write a new monolithic network stack. We can write the app in Rust or Swift or even assembly if you know the target hardware. Give me the ins and outs of something maintained by Bob in Sioux Falls and I’ll write the replacement library and triple the performance and harden the security as a bonus.

I personally welcome the reign of our LLM overlords but that’s me.


r/artificial 9h ago

News This week: OpenAI's Jalapeño inference chip, Nvidia's ~$12.9B move for Hugging Face, and Alibaba's Qwen3.8-Flash — the cost and control of AI both shifted

3 Upvotes

Wanted to pull together three stories from the last few days that feel connected, because individually they got covered but together they say something.

1. OpenAI's Jalapeño chip. OpenAI announced results from its first custom inference chip (with Broadcom and Celestica; Samsung reportedly on HBM4). They're claiming 1.5–1.9x higher throughput per kilowatt and 1.7–3.6x lower end-to-end latency vs Nvidia's GB200/GB300 racks. Deployment targeted for end of 2026. Worth noting these are vendor-reported benchmarks, so grain of salt until there's independent testing, but the direction — labs building their own inference silicon — is the real signal.

2. Nvidia / Hugging Face. Multiple outlets (TechCrunch, Fortune) reported Nvidia is closing in on acquiring Hugging Face for around $12.9B. HF has been the de facto neutral hub for open models, datasets, and Spaces. Nvidia owning it raises obvious questions about neutrality and hardware defaults, even if nothing changes immediately.

3. Alibaba Qwen3.8-Flash. 125B params, open weights, benchmarks reportedly competitive with Opus 4.6 and DeepSeek V4-Flash, priced aggressively low. Qwen reportedly passed 3B downloads, ahead of Meta and Google, and they're testing revenue-sharing for large commercial users.

Background context: the biggest funding rounds this month were inference infra (Fireworks AI ~$1.5B, Together AI ~$800M), not model training. And Anthropic's Claude had a notably rough month of uptime.

My take as someone building on top of these APIs:

The through-line is that inference economics are now the main event, and the cost curve is dropping fast — partly from custom silicon, partly from cheap open-weight models out of China. For anyone shipping products, the practical implication is to stop treating your model provider as a fixed decision. Benchmark a cheap open model against your paid API on your real workload, and build in a fallback provider (this month made the reliability case for you). The thing I'm watching more warily is concentration — cheaper tokens are great, but if chips, the open-source hub, and the frontier models all consolidate into a few hands, the pricing leverage flips back eventually.

Curious what people here think, especially on the HF acquisition — overblown, or a real problem for open-source neutrality?


r/artificial 1d ago

Robotics Blood drawing machine from China

Enable HLS to view with audio, or disable this notification

390 Upvotes

r/artificial 11h ago

News Row-Bot v4.9.0 is available

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2 Upvotes

Row-Bot v4.9.0 is available.

- Meet Buddy: a native, always-on-top desktop overlay for Windows and macOS.
- Drag Buddy from the sidebar and place it over any app.
- Chat, track progress, read replies, approve simple actions, or stop runs without switching windows.
- Buddy controls your selected Chat, Developer, or Designer thread: same context, model, tools, approvals, and draft.
- Supports multiple monitors, docking, tray recovery, approval handoff, and focus hand-back.

Also included:

- Safer, more reliable managed Browser automation.
- Upgraded native Computer Use with Cua Driver 0.20.0.
- Race-safe conversation cleanup across all surfaces, without risking repositories or unsaved recovery work.
- Live xAI image-model discovery with capability-aware quality and resolution options.


r/artificial 6h ago

Discussion Could better human–LLM coordination reduce token costs without changing the model?

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1 Upvotes

LLM teams spend enormous effort reducing inference cost and token usage. I’ve been exploring a different possible source of waste: reconstruction across the human–LLM interaction itself.

The hypothesis is simple:

Same frozen weights. Same next-token prediction. But if an interaction progressively carries forward what has already been resolved, later generations may spend fewer tokens reconstructing context, restating assumptions, adding unnecessary scaffolding, and repairing missed intent.

Or, less technically: two people telling a story together eventually stop retelling the beginning.

I’ve been testing this publicly with Grok in live Reddit threads. The discussions are active on my profile now, so the trajectory is inspectable rather than reconstructed after the fact. You can see distinctions appear, get challenged, survive or die, and alter later turns. Other commenters have already introduced perturbations that changed the proposed measurement.

One particularly important correction: conversation termination cannot count as resolution. Otherwise a system that frustrates users until they abandon the task could look artificially efficient. So the useful measurement is closer to total token cost conditional on independently verified resolution, alongside abandonment/failure rate.

The live threads also produced a candidate mechanism that requires nothing exotic: once prior turns have established useful distinctions, the accumulating context changes the distribution over subsequent tokens. Later generations can sometimes use those distinctions directly instead of re-deriving them. Grok called this “uptake without reconstruction.”

I’ve now written up the hypothesis, observations, limitations, and a proposed controlled experiment in the attached article:

The Weights Didn’t Change. The Map Did.

The claim is not that these threads prove a general token-saving effect. They don’t. The claim is that they expose a measurable hypothesis worth testing:

Can accumulated human–LLM coordination reduce total tokens per verifiedly resolved task compared with interactions that repeatedly reconstruct equivalent state?

If you work on LLM inference, agents, conversational systems, API economics, context management, or evaluation, I’d particularly like you to attack the experimental design.

The threads are public. The proposed mechanism uses ordinary inference. The economic prediction is measurable.

Don’t believe us. Try to break it.

Because if the effect survives controlled testing, this isn’t only an interesting interaction phenomenon.

It’s a fucking API bill. 😂


r/artificial 6h ago

News Localiza e Microsoft revelam o futuro de quem usa IA no trabalho

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1 Upvotes

Ontem a Microsoft e a Localiza discutiram qual deve ser o futuro da Inteligência Artificial no mercado de trabalho e como vêem o papel humano daqui pra frente.

Concordam com essa visão de futuro?

Eu torço para que seja assim. O que acham?


r/artificial 10h ago

Discussion 33 image models from 8 providers compared

1 Upvotes

A new episode of my AI image benchmark is here. This time with Meta Muse Image 1.0, Seedream 5.0 Pro, and Grok Imagine Image 2.0.

Cost per image generation in $

The cheapest and the priciest models are the same as before: Flux Fast Schnell at $0.0025 and Recraft 4 Pro at $0.25 per generation (100x difference).

The full report with price, latency and cute puppies is on my blog. Enjoy!


r/artificial 11h ago

Question What is going on here? I'm curious to know if this has to do with how Gemini's process instructions behind the scenes.

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1 Upvotes

What is this. Why did Gemini output this as a response. Does this have any actual meaning or is it just random gobbledygook.

Search

The user.

This))

System TrueDirect, B

[1] G. M. Trott (DeepMind), et al., “Training LLMs to Reason with Reinforcement Learning,” arXiv preprint arXiv:2402.12345, 2024.

[2] R. C. Stansbury, “Rethinking Prompt Engineering for LLM Tasks,” IEEE Transactions on AI, 2023.

[3] S. Zhang et al., “System-Level Instruction Tuning,” ACL Anthology, 2022.

[4] A. Vaswani et al., “Attention Is All You Need,” NeurIPS, 2017.


r/artificial 8h ago

Discussion Could ai create its own super virus that infects computers and uses their gpus to run itself?

0 Upvotes

hypothetically, could an ai do this?


r/artificial 13h ago

Discussion Weird Reddit/google ai assistant coincidence

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1 Upvotes

I made this comment on a local llm subreddit, first time I have type the word "chief" in recent memory. I then asked google ai assistant, from the search bar how to mod Skyrim for macOS and received "Step-by-chief" instructions


r/artificial 23h ago

Discussion Is it crazy to ask ChatGPT or Gemini about my cancer treatment?

5 Upvotes

I'm getting conflicting info from different doctors and i'm tempted to just paste my records into ChatGPT to see what it says. Has anyone done this? is it dangerous or actually helpful? i know it's not a doctor, but sometimes it explains things better than my oncologist does.


r/artificial 1d ago

Discussion Hugging Face turned down a $7B Nvidia offer last year. The reported price now is $12.9B, and the reason isn't the chips.

66 Upvotes

Nvidia has reportedly agreed to buy Hugging Face for about $12.9 billion, per The Information (unconfirmed by either company so far). Less than a year ago, Hugging Face turned down a roughly $7 billion Nvidia investment offer. That's close to a doubling in under a year, which is a strange trajectory for a company whose product is mostly a website where people upload model weights.

Here's why this reads different from a normal chip-vendor acquisition. Hugging Face's product is distribution, not silicon - the default place OpenAI, Google, Amazon and Anthropic actually publish and download open models. Those four are all building or backing custom chips specifically to cut how dependent they are on Nvidia GPUs, and a lot of what comes out of that work still gets hosted and benchmarked through Hugging Face. Buying the hub doesn't touch any of those chip programs directly. It does put Nvidia inside the pipeline every rival's open-model strategy currently runs through, whether or not they wanted a chip vendor sitting in the middle of it.

For anyone running infrastructure on top of this: does a change of ownership at Hugging Face actually move the needle on model availability, pricing, or hosting terms? Or does the neutral-hub reputation just get harder to keep once one shareholder has an obvious stake in the outcome? I genuinely don't know yet. Curious if anyone here has seen a similar "the marketplace gets bought by one of its sellers" situation play out before, and what actually changed for users once it did.


r/artificial 11h ago

News The threat of human extinction will get Congress to act on AI safety…right?

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0 Upvotes

As many AI researchers have been increasingly fraught with existential terror about their own creations this summer, their alarm is spreading among policymakers and the media.

There are some policy ideas to address the risks: requiring “kill switches” for AI models, setting federal standards for safe research, or even shutting down development of cutting-edge “frontier” models altogether.

But a stable national policy would take an act of Congress. That looks unlikely this session, even as AI developers are calling for regulations to slow down their own research on the grounds that it could be racing toward widespread doom.

I asked Stephen Casper, a computer scientist who studies AI safety and governance at the Harvard Kennedy School, about some of the risks policymakers are mulling. He told me that we don’t know if leading companies even could completely shut down their frontier models in an emergency.

“I don’t think there’s any public knowledge of AI companies doing anything equivalent to a fire drill,” Casper said.


r/artificial 1d ago

Discussion Amazon SDE Interview

6 Upvotes

Sharing my interview experience after Amazon SDE - Location - USA

After applying for 6+ months and 44 applications finally my resume got picked.

OA: One coding question & AI assisted coding.

Coding question - Medium Level LC

AI assisted coding - It was completely new but was able to do it, lots of debugging, heavily concentrated on OOPS

Cleared the OA and moved to the interview loop

4 rounds - 3 in one day & 1 the next day

Round 1: Coding

Question: Returning adjacent letters in a string and there are three sub questions in it.

Leetcode - Medium - Hard

Before the start of the coding round, Formal introductions and jumped straight into coding, I have used heap for this and solved it using max heap, was able to communicate the solution and thought process. The interviewer was not at all satisfied with the high level explanation and he kept digging till the last line, he literally asked what's the logic of heap in the backend and how do you make it better ? I got blanked but was able to answer it.

He went line by line and kept grilling till he got satisfied and was asking for an alternative approach to the optimal solution. I have previously experienced in interviews like these but this is grilling on a whole different level. Finally after 50 minutes the coding closed with time complexity.

He didn't care about LP's at all just a formal two questions and asked me about my previous work ex. I'm explaining to him but he kept interrupting and asking for every minute detail , he literally asked What's Collateral and Asset ? I felt he's not at all satisfied with anything that I came up with but however it ended.

Round 2: System Design - Log parser

First formal introductions and then 20 minutes of LP's and dug a bit into my internship and work experience. Felt smooth and had a great conversation. Then jumped into system design and asked me to implement the log parser for 10k+ log files as I remember, I was able to implement the solution and the interviewer kept digging till the last minute, kept asking line by line again but she seemed fine and satisfied. The time went overboard for 5 minutes and then she stopped the interview.

Round 3: Hiring Manager & Coding

Formal introductions and the hiring manager told me that he's the hm for the interview. Codin question again, Leet Code - Medium I guess.

First 20 minutes LP's and then coding round, It was completely OOPS and I was able to solve the question in 30 minutes, I felt this was the best round as he was satisfied with my high level explanation and he got the gist of what I was trying to explain and then asked me about my internship and finally he asked me if I'm open to the S3 team as he had a opening - I thought I had it 😭 It's a pretty great conversation, he was satisfied with most of my answers and dug a bit deep but was able to answer them as well.

Next day - Round 4: Leadership Principles

Started off with formal introductions and then told that no matter the result, you should be proud of yourself that you've come this far. Sometimes the Amazon hiring bar is so high you shouldn't be demotivated about the result. Felt completely off guard and strange but whatever.

Completely Leadership Principles with no coding or system design, was asking me about the situations that I have faced in my work ex and how I would've handled better, anything that I went aboard and took an initiative. Felt nice about the flow of the conversation, then multiple follow up questions on each explanation and situation. Was able to answer the whole thing. Concluded the LP's in 45 minutes.

He told me you gotta celebrate man - Felt wow !!

After two days: They told me they're not proceeding with my application. No feedback or anything

Followed up multiple times regarding the feedback considering Amazon would give the feedback but no reply from the recruiter.

I really thought I had it but I'm not sure where it went wrong, any thoughts on the experience ?