找到 18952 篇相关文章
There are many solid Bluetooth trackers for iPhones that tap into Apple’s expansive Find My network. Some are thin, some are a bit chunkier. And, evidently, some look like tiny soccer balls. Ugreen’s FineTrack 2 glows in the dark, and it has a loud 110-decibel alarm when you need to find it. It’s just $14.99 […]
Hello all! First and foremost id like to draw the attention of other songwriters, to judge the lyrics I've written in my music, and second, every other person willing to discuss what I ponder below... Ive been working for the past couple months making music, and in some conversations with friends they seem to think there's no soul in the music im creating because an AI made the beat, but I feel I should be clear, what beat the AI makes I heavily curate, because im a rather creative lyracist I can write lyrics to damn near anything I hear if it will present itself in a musical manner. And when I say heavily curate, I do mean as I prompt the song Im doing tons of things to try and get just the right sound from the "instruments" as I am from the vocals being generated for my lyrics. Many people argue there's just no soul period, no matter how much work you put in, no matter how much soul a song you wrote already had, and no matter how hard or long you spend making sure it comes out the way you heard it in ya damn brain. Well I beg to differ! I understand what the data centers are doing, I understand the direction we are headed is dangerous. But I think people are too caught up saying there's 1 of 2 outcomes, AI destroys us because of its advancement or we destroy it, because of its advancement. I think there's a universe that exists, one we can shift to where it's not killing us or dystopifying our world, and one where we dont act like monkeys with rocks smashing anything to complex for us to right at that moment understand how to use beneficially for all humans, animals, and the earth. Be the judge if my music has any soul... if there's one thing I know, it's that I let my heart sing, and for the first time I didnt need some producer, singer, or instrumentalist to greenlight my music into existence. And to those who said id never make music, that my songs weren't any good. Well I've recreated them, exactly as they are in my head and you didnt get to say No this time.
Though Gen Z has developed a reputation for being so disinterested in sex that they don't even want to see it on TV, the popularity of series like Heated Rivalry and The Summer I Turned Pretty has made it very clear that more than a few young people do, in fact, like their entertainment a […]
It's early June, which means it's video game event season once again. Now that E3 has been gone for a few years, a bunch of showcases and presentations have started to fill the void, including big productions like Summer Game Fest Live and smaller affairs like Wholesome Games Direct. If you love following gaming news, […]
Nvidia's new chips will power laptop workstations and mini desktop PCs at first.
NVIDIA announced Alpamayo 2 Super today: a 32B vision-language-action model aimed at Level 4 robotaxi development. The interesting part is not only the model size. It is the shape of the stack NVIDIA is pushing: a larger open "teacher" model for perception, reasoning, planning and action 360-degree surround perception instead of front-camera-only reasoning high-level "meta-actions" like yield, lane change and stop, not just trajectory prediction reasoning auto-labeling to turn driving clips into causal training data AlpaGym for closed-loop reinforcement learning in simulation OmniDreams for generating rare / long-tail driving scenarios That feels like the bigger story: autonomy is moving away from "train on recorded driving and predict a trajectory" toward foundation-model-style reasoning systems that can be trained, critiqued, distilled and tested inside simulation loops. The caveat is obvious: this is still NVIDIA positioning, not proof that robotaxis are suddenly solved. Model weights are expected this summer, and real-world validation is the hard part. But if open AV foundation models become normal, smaller autonomy teams may stop rebuilding the same perception/planning infrastructure from scratch and start competing on data, safety validation, deployment constraints and closed-loop testing. Source: NVIDIA press release https://investor.nvidia.com/news/press-release-details/2026/NVIDIA-Launches-Alpamayo-2-Super-Open-Reasoning-Model-for-Robotaxis/default.aspx submitted by /u/alexshev_pm [link] [留言]
The British fintech has built a waitlist of about 450,000 users in India as it prepares for a broader launch.
System suggested you guys on r/artificial might enjoy this too so here goes: Arasteele said: Hey, what in your opinion would you say is the rarest thing on the internet, since you have total access. Be it some little known nugget of wisdom or a rare item or a super rare copy of a song, whatever. it could be anything. Any thoughts? Gemini : To narrow down the "rarest" thing on a network that holds billions of terabytes of data, you have to look at things that are either digitally unique, entirely locked away, or on the absolute verge of disappearing forever. Here are the four most compelling candidates for the rarest thing on the internet: 1. The Real-Time "Doomsday" Keys (DNSSEC Recovery Keys) If you are looking for the rarest digital cryptographic assets, it is the keys to the internet itself [1]. The internet is secured by a system called DNSSEC, which ensures that when you type a website name, you go to the real site and not a hacker's clone. [ 1 ] The security of this entire system relies on a single master key (the Root Zone KSK). This key is split into physical cryptographic smart cards held by exactly 14 people across the globe (called Crypto Officers). Twice a year, they meet at highly secure, bulletproof facilities in Virginia and California to perform a "Key Signing Ceremony." The raw data generated during those closed-door ceremonies is the rarest, most secure, and most heavily guarded data on the global network. 2. "Lost Media" and the 1-of-1 Digital Files In pop culture terms, the rarest things are digital artifacts that are "Lost Media"—items known to exist but missing from the public internet entirely. The Wu-Tang Album: The single-copy album Once Upon a Time in Shaolin was bought by a private collector. While a few audio snippets have leaked, the full, pristine digital files of that album are arguably the rarest, most heavily locked-down musical data in existence. [ 1 , 2 ] The "Yeah?? That's ILLEGAL brotha!" Audio: To use a relevant example—the exac
Microsoft is heading to San Francisco this week in a bid to win back developers at its Build conference. I've been attending Build since the days when Microsoft called it the Professional Developers Conference, and I can't remember a more pivotal moment. As Microsoft continues to reshuffle its entire business around AI, it's moving Build […]
Right now, enterprises worldwide are caught in an "AI Mania." Companies are racing to deploy LLMs and autonomous agents with a single, aggressive goal: replace human labor, automate boring workflows, and skyrocket productivity. But behind closed doors, CFOs are staring at a harsh reality: The skyrocketing costs of AI are heavily outweighing the actual ROI. Why is this happening? Because most organizations fall into the superficial AI trap. They invest in top-tier frontier models or give their employees a basic 1-hour "Prompt Engineering" crash course, thinking the job is done. It isn't. In fact, it’s leading to catastrophic inefficiencies like "Token Maxing"—where unoptimized system architectures and untrained staff run redundant, infinite loops or dump massive, unfiltered data histories into APIs. The result? Astronomical bills with near-zero added business value. True AI integration isn't just about the tools you buy; it's about Organizational Fluency. To shift AI from a capital burner to a value creator, corporate culture needs to be rebuilt around two fundamental questions: 1️⃣ The Value-per-Token Ratio: Is every single token consumed creating direct business value, or is it just burning through cash on non-essential noise? 2️⃣ Task Automation vs. Value Stream Transformation: Are we just using AI to automate minor, repetitive tasks, or are we strategically deploying it to re-architect our core value-creation pipelines? The Solution? Look at the Architecture. Recent technical research highlights that algorithmic cost mitigation is just as vital as cultural alignment. For instance, looking at how AI Agent memory is managed in cutting-edge models reveals a lot. Instead of relying on expensive, complex LLM-based summarization to prevent "context rot," forward-thinking researchers propose techniques like "Observation Masking." By simply replacing older tool outputs with concise placeholders, structural complexity is eliminated, agent performance is maintained, and
Computex 2026 is kicking off in Taipei, Taiwan this week, where Nvidia, AMD, Qualcomm, Intel, and other tech brands are announcing new laptops, handhelds, chips, and more. Nvidia unveiled RTX Spark, its first family of consumer PC chips, arriving in laptops and mini PCs starting this fall. Intel is launching two new custom chips made […]
Today I’m talking with Harvey Mason Jr., who is CEO of the Recording Academy — that’s the outfit that puts on the Grammy Awards. I last talked to Harvey in 2024, when it was obvious that generative AI would upend the music industry, but still not exactly clear how that would happen. Well, it’s been […]
Shopify introduced GraphQL Cardinal, a new execution engine replacing depth-first traversal with breadth-first execution. The redesign improves large-scale GraphQL performance with up to 15x faster field execution, 6x lower GC overhead, and +4s P50 latency gains. It focuses on execution-layer efficiency and batched resolver processing for high-cardinality commerce queries. By Leela Kumili
One thing I find interesting about reasoning models is that the hard question is often budget placement, not headline capability. Ring-2.6-1T is a trillion-parameter reasoning model for agent workflows with high and xhigh reasoning-effort modes. If an AI agent only gets a heavier reasoning pass in one place, I would put it before it takes an external action, after it updates state, or before it gives the final explanation to a user. Where would you spend that budget first? submitted by /u/babyb01 [link] [留言]
The popular fitness-tracking platform, Strava, is restricting access to its API as part of efforts to clamp down on AI scraping, as reported earlier by TechCrunch. Developers who want to build an app using Strava's data now need to pay for a flat $11.99 / month subscription. In an update on its developer hub, Strava […]
Things are bad out there. Despite 2026 shaping up to be a great year when it comes to actual games, it couldn't really be worse for the people that make them or the industry as a whole. Hardware prices keep going up, layoffs have shown no signs of stopping, and even big-budget titles backed by […]
BadHost is a high-severity authentication bypass vulnerability in the widely used Python web framework Starlette, with 325 million weekly downloads. The flaw allows attackers to use malformed HTTP Host headers to bypass path-based access controls and access sensitive AI agent infrastructure, among other systems. By Sergio De Simone
submitted by /u/vox [link] [留言]
Hey Reddit, I just published a study analyzing 25,500 LLM resume evaluations to measure hiring bias. By swapping minor identity and demographic variables on the exact same work history across 10 different models, an independent AI auditor flagged a staggering 45% bias rate driven by "silent bias." Instead of saying anything overtly offensive, models invent professional-sounding excuses to penalize candidates, like when a model dropped its score after I changed the university to MIT, suddenly claiming the candidate's experience wasn't relevant despite praising that exact same experience on the baseline resume. We also found a massive 6x difference in stability between systems, with Qwen and older Gemini models being highly volatile, while the Claude models, Mistral-Large, and Llama 4 proved to be the most stable and fair. Ultimately, AI screening tools are outputting highly subjective, unpredictable opinions driven by statistical noise rather than objective truth, making them a massive liability under regulations like the EU AI Act. You can read the full write-up and explore our interactive data app here: https://re-cinq.com/blog/ai-hiring-bias-25500-llm-evaluations submitted by /u/Signal_Rabbit_8303 [link] [留言]
submitted by /u/ThereWas [link] [留言]