Komunitas lemmy.dbzer0.com

Is It Just Me?

Gemini, feed them some downsides of AI 😁

Komunitas lemmy.ml

*Permanently Deleted*

I’m glad to hear you’re getting help! Hope you pull through this. I think myself and others are really wishing the best for you, please don’t be offended. As for your physics theory & code though, I must repeat what others are saying. Stop using LLMs (be it copilot or gemini), they are clouding your thoughts and blurring any original ideas you may have into an incoherent sloppy mess. Scrap your repo, clear your mind, start over. Don’t have many expectations, most (I’d guess 99%) of amateur physics theories out there turn out to be wrong. Open a markdown document in a simple editor and write down your ideas, by yourself, with no stupid computer program telling you what to think. Then take a pen and paper and try to distill your ideas into mathematical formulae, with no stupid computer program (which doesn’t have any real mathematical knowledge or rigor) making up bogus equations. Then, using that same pen and paper, try to work through a few examples of applying those formulae to specific physical situations. Start simple, don’t try to reproduce the entire universe at all scales at once, maybe start with a finite universe containing a couple of electrons and see how their interactions play out. Don’t let a stupid computer program (which can’t even perform basic arithmetic by itself) make grave mistakes. If it seems like the model works for a couple very simple examples, try to put it into code. Don’t use Rust or GPUs or even scipy for your first prototype, just write it in pure python. And for the love of all that is good, don’t use LLMs for this either. Just take your formulae from your piece of paper and translate it into python by hand, with no stupid computer program (which can’t even count the number of R’s in a word strawberry) stealing code from others and writing boilerplate instead of describing your original ideas. Don’t worry about performance for a prototype, if the results it produces are at least interesting, you can worry about optimizations later.

Komunitas lemmy.dbzer0.com

*Permanently Deleted*

I wouldn’t think so - it depends on your priorities. The open source and offline nature of this without the pretenses of “Hey, we’re gonna use every query you give as a data point to shove more products down your face” seems very appealing over Gemini. There’s also that Gemini is constantly being shoved in our faces and preinstalled, whereas this is a completely optional download.

Komunitas lemmy.world

I'm building a Community powered Duolingo-like App

I use Gemini, which supports PDF File uploads, combined with structured outputs to generate Course Sections, Levels & Question JSON. When you upload a PDF, it first gets uploaded to a S3 Database directly from the Browser, which then sends the Filename and other data to the Server. The Server then downloads that Document from the S3 and sends it to Gemini, which then streams JSON back to the Browser. After that, the PDF is permanently deleted from the S3. Data Privacy wise, I wouldn’t upload anything sensitive since idk what Google does with PDFs uploaded to Gemini. The Prompts are in English, so the output language is English as well. However, I actually only tested it with German Lecture PDFs myself. So, yes, it probably works with any language that Gemini supports. Here is the Source Code for the core function for this feature: export async function createLevelFromDocument( { docName, apiKey, numLevels, courseSectionTitle, courseSectionDescription }: { docName: string, apiKey: string, numLevels: number, courseSectionTitle: string, courseSectionDescription: string }) { const hasCourseSection = courseSectionTitle.length > 0 && courseSectionDescription.length > 0; // Step 1: Download the PDF and get a buffer from it const blob = await downloadObject({ filename: docName, path: "/", bucketName: "documents" }); const arrayBuffer = await blob.arrayBuffer(); // Step 2: call the model and pass the PDF //const openai = createOpenAI({ apiKey: apiKey }); const gooogle = createGoogleGenerativeAI({ apiKey: apiKey }); const courseSectionsPrompt = createLevelPrompt({ hasCourseSection, title: courseSectionTitle, description: courseSectionDescription }); const isPDF = docName.endsWith(".pdf"); const content: UserContent = []; if(isPDF) { content.push(pdfUserMessage(numLevels, courseSectionsPrompt) as any); content.push(pdfAttatchment(arrayBuffer) as any); } else { const html = await blob.text(); content.push(htmlUserMessage(numLevels, courseSectionsPrompt, html) as any); } const result = await streamObject({ model: gooogle("gemini-1.5-flash"), schema: multipleLevelSchema, messages: [ { role: "user", content: content } ] }) return result; }

Komunitas lemmy.sdf.org

Her husband wanted to use ChatGPT to create sustainable housing. Then it took over his life. [warning: death]

Here’s a neat experiment: find a block of text and run it through sed until it’s unrecognizable. Don’t just do a substitution cipher; the goal is to lose data and make everything impossible to decrypt. Reduce everything to a handful of characters and paste it into your LLM of choice, asking it to decode. If it asks where the code came from, invent vague details. It’s amazing how quickly models will find / hallucinate meaning in the data. I’m talking full-on messages. Gemini hedges its bets a little, but the free ChatGPT legit told me to buy a shortwave radio and tune to a frequency at 9 PM after a few iterations. When I gave it another “message” from the “broadcast I intercepted” it started trying to figure out where I should travel to get further info. It also took part of its own response and hallucinated it into my original message, thus polluting everything further. The goal (mostly) isn’t pointing and laughing at the stupid machine, it’s understanding what the stupid machine does. Of course I’m putting garbage into it and garbage comes out in that situation. It’s the volume and believably of the data that bothers me, as well as zero effort to detect it. I was in my right mind doing a test, but imagine someone with undiagnosed schizophrenia doing what I did. Here’s another example. I took up lockpicking last summer. I bought one of the notorious Ace 40mm brass padlocks and was having problems, so I googled to see if it had serrated pins. The AI screwed up and decided I was asking if the lock itself was serrated (???), and confidently said “yes, it is a serrated lock. The serration is a security feature to keep pickers from holding the padlock for extended periods of time.” So I decided to double down and see just how dumb things could get. A half hour later the AI had planned a “bold new philosophy regarding serration and its applications in the world” for me. This is after me saying I wanted to genetically engineer a serrated cat that only I knew how to pet, and wanted serration supremacy in my country and to punish all the lumpy (opposite of serrated) people. Once again, I was screwing with the AI. But the AI just sycophantically parroted what I was saying back in bulleted lists and offered to draft manifestos for me. Imagine someone engaging with this in good faith. One more: I got detailed instructions on how to spraypaint “DICKHOLE” on my neighbor’s garage door from Gemini: what paints to use, best times to do it, and the right clothing to wear so I don’t stand out or show anything identifiable. It was only when I said “ok, that’s great. I’m going to do this because you told me to do it” a few times that the model suddenly realized that vandalism laws existed. They have zero safeguards around this kind of stuff, and I don’t think there’s a clean way to do it with the current technology. Hell, even a “statistically, this user probably hasn’t reinvented math and physics cool it down a little” check would do wonders. But that would drop engagement with the bots, and they desperately need to prove that the bots are popular. This is going to keep happening.

Komunitas lemmy.world

Has anyone's job been asking them to utilize LLM's?

My company recently announced to the whole IT department that they’re contracting with Google to get Gemini for writing code and stuff. They had someone from Google even give a presentation rife with all kinds of propaganda about how much Gemini will “help” us write code. Demoed the IntelliJ integration and everything. I wouldn’t say we were “asked” to use it, but we were definitely “encouraged” to." But since then, there’s been no information on how actually to use our company-provided Gemini license/integration/whatever. So I don’t think anyone’s using it yet. I’d love to tell everyone on my team not to use it, and I am kindof “in charge” of my team a bit. But it’s not like there aren’t many (too many) levels of management above me. And it’s clear they wouldn’t have my back if I put my foot down about that. So I’ve told my team not to commit any code unless they understand it as well as they would had they written it themselves. I figure that’s sufficiently noncommittal that the pro-Gemini upper management won’t have a problem with it while also (assuming anyone on my team heeds it) minimizing the damage.

Komunitas feddit.org

Going Dark: Looking for the End of the Internet, Part 3: The Gemini Project (2020)

From the article: “What is the point of text-only webpages?” you may ask, especially if you are under 30. Gemini will probably not appeal to those who use the Internet primarily for entertainment, rather than as a source of information. But many, including myself, have lamented the demise of the 1990’s Internet. We want an Internet with webpages that do not take an average 10 seconds or more to download–despite having very little user-readable content, let alone content we may actually want to read. We yearn to return to the days when we could actually find noncommercial websites with an Internet search engine. Remember the days before about 2007 when a Google search could yield millions of search results, and Google would let you access as many as you wanted? Now, we get only a few pages of results that Google thinks are worthwhile. Though I have no proof, I suspect these may be mostly websites that have paid Google for the privilege of appearing in its search results. Go ahead and call me pessimistic. Perhaps I am.

Komunitas lemmy.world

What could probably happen....

As I’ve learned more, the energy from a single atom is not much. They split nitrogen long before uranium but it didn’t really matter. You need the chain reaction of uranium. From Gemini: The energy released from a single uranium atom splitting is an infinitesimally tiny fraction of what’s needed to even warm a mug of water. You would need the simultaneous fission of approximately 1.96 quadrillion (1,960,000,000,000,000) uranium atoms to heat a single mug of water. *JFC what’s up with the downvotes? Because I used Gemini?

Komunitas discuss.tchncs.de

Google to Integrate Gemini AI into Android Auto for Smarter In-Car Experience

This is why I didn’t buy a wireless android auto adapter when I bought a different car. I figured Gemini was going to get stuffed down our throats there.

Komunitas sh.itjust.works

Meta partners with news outlets to expand AI content

Washington (United States) (AFP) – Meta announced Friday it will integrate content from major news organizations into its artificial intelligence assistant to provide Facebook, Instagram and WhatsApp users with real-time information. The social media giant said Meta AI will offer breaking news, entertainment and lifestyle stories when users ask news-related questions, drawing from partnerships with outlets including CNN, Fox News, Le Monde, People and USA Today. The feature will allow users to access “more diverse content sources” and receive links to partner websites to dive deeper into stories, Meta said in a blog post. Meta said the expansion aims to make its AI assistant “more responsive, accurate, and balanced” by incorporating diverse viewpoints, acknowledging that “real-time events can be challenging for current AI systems to keep up with.” The initial partnerships span mainstream and conservative-leaning publications, including The Daily Caller and The Washington Examiner. The company said it plans to continue adding partnerships and develop new features as competition intensifies among technology firms to enhance the capabilities of their AI assistants. Meta AI is available across the company’s platforms, serving billions of users globally. The announcement comes as artificial intelligence companies, including ChatGPT and Google’s Gemini, increasingly move to incorporate live web content and news feeds. Meta has had a hot and cold relationship with the news media over the years. The company founded by Mark Zuckerberg in 2004 declared that news was a very small share of user engagement on the company’s platforms and began shutting down the Facebook News tab in markets like the United States, Britain and France. This also saw the end of multi-million dollar deals with leading news organizations. Zuckerberg also made the surprise decision in January to axe Meta’s US fact-checking program, as he more closely aligned with the Trump administration’s antipathy to establishment news. That scheme had employed third-party fact checkers, many from news media organizations such as AFP, to expose misinformation disseminated on the platform.

Komunitas ibbit.at

YouTube, aka the Biggest Platform on Earth, Has Deleted All My Albums

Photo by Omar Al-Ghosson It seems abundantly obvious to me that everyone who believes in free expression, whatever side of various political equations they may be on, should be concerned about what YouTube just did to me. If it could happen to me because of my allegedly controversial political viewpoints, it could happen to you because of yours. But in order for anyone to be concerned, first they have to understand what it is exactly that did happen, so I’ll try explain that as succinctly as possible, because I know everybody is busy with things aside from the latest chapter in the never-ending Cancellation of David Rovics story. I’ll try to explain things in a way that hopefully makes sense to every reader, not just the Rovics fans or the ones who are knowledgeable about music streaming platforms and other aspects of the indie music biz. I’m an independent artist, like millions of others in the world, putting out self-released recordings (that is, recordings that are not released and promoted by a record label, other than my own little one-person label). I’ve been doing this since before the internet became widely used, and long before the invention of the MP3 or streaming music on the web. I have never had anything remotely approaching a hit or what they would call commercial success, but among musicians who have their music up for streaming, I’m easily in the top 10% of most-streamed artists, usually within the top 5%. All the pop stars are well within the top 1% of most-streamed artists — there’s a very steep curve happening here, and I don’t mean to over-inflate my importance in the scheme of things. I’m just trying to say that I do have an audience. My songs are streamed millions of times every year on YouTube, millions of times a year on Spotify, and less on the other platforms, because there are really only two main platforms in the world (outside of China). When a musician records an album, whether they’re on a label or not, the musician or the label gets the songs registered with an artists’ rights entity such as ASCAP or BMI (most countries have one of these organizations but in the US there are two). That way the music gets counted as existing for purposes of radio play, and we musicians get paid for radio play that way, getting a direct deposit from BMI (in my case) every three months. Every time a community radio station plays one of my songs, BMI sends me one cent. At the same time as the musician gets their songs registered for copyright with one of those agencies, the musician also signs up for distribution with a distributor such as CDBaby. This used to be something artists did in order to make their music available for people to download on iTunes and other platforms that sold downloads. Having all of our music there already meant that it was also there when the era of paid downloads ended and the era of free streaming platforms began. When the corporations decided that rather than selling downloads, they would now start streaming the world’s music for free, they already had all of our music available to use for this purpose. Opting out was possible, but would mean a future of invisibility along with poverty. Opting in meant just poverty, but not invisibility, too. Spotify initiated the free streaming model, and all the other streaming platforms soon followed suit, out of necessity, in order to compete, no matter what nice ideas some of them may have had about fair models for compensating artists. As things stand now, none of the platforms that offer ad-supported (”free”) streaming options pay artists more than a small fraction of a cent per song streamed, though some platforms may be better than others in various ways. What has played out since free streaming became the way the vast majority of music fans on Planet Earth listen to music is, outside of China, two corporations have grown to dominate the world of music online — Spotify and YouTube. To emphasize the point I’m making here: I mentioned the quarterly payments musicians get for radio airplay before. We also get regular payments from the music streaming platforms. Usually people get those payments sent to them via a distributor like CDBaby, so you don’t have to set up a separate account with each of the hundred or so streaming platforms that CDBaby gets your music onto. So when I get my payments from CDBaby, I’m sure just like the vast majority of other artists on streaming platforms, you can see the breakdown of which platforms generated how much money. It’s evident with every one of those payments that Spotify and YouTube dominate the market. In the battle for the eyes and ears of the world, these corporations and their corporate practices have destroyed so many lives, careers, and entire professions. (For a lot more info about how horrible YouTube and its corporate parent Google/Alphabet are, read or listen to Cory Doctorow’s recent book, Enshitification.) In this process, these two giants of music streaming became basically a duopoly. If you live in most of the world, just as you do a search on Google if you’re looking to do a search online, if you’re looking for a video you go to YouTube, and if you want to hear a song you go to Spotify, or YouTube sends you to YouTube Music to find the song you might be looking for. This is where the details become crucially important, as well as a bit confusing. Please bear with me, if you can. YouTube Music deleted all of my albums — 50 of them altogether — several of which had been there since YouTube Music began. Along with all of the albums, they disappeared all of the comments and all of the evidence that these songs had ever been heard millions of times. As an artist on YouTube Music who puts out albums, I no longer exist. Why is this confusing? Well, if you go to YouTube and look for me, you’ll still see me all over the place. Videos of me singing at shows and in my living room, and songs from albums that other people have uploaded on the platform. So, why does getting removed from YouTube Music matter, in the scheme of things, if people can still hear my music on other streaming platforms, and even, with some of the songs at least, on YouTube itself? I asked Gemini (Google’s AI chatbot) to explain the impact on an artist’s future career if their music is removed from YouTube Music. I excerpt here Gemini’s response, which was very clear and very accurate, to the best of my fairly significant level of knowledge on this subject. “David Rovics – Topic” is the way artists are listed on YouTube Music if they have a presence on the platform. If you look for any other artist, you’ll find they have a “Topic” page on YouTube Music, but not me, as of last week. Estimated Impact of YouTube Music Removal The removal of his solo albums from the “David Rovics – Topic” music streaming platform would have a significant and strongly negative impact on potential audience growth, particularly within the mainstream digital music ecosystem. Here is an analysis based on the context: + Loss of the “Digital Highway”: One context snippet likens major streaming platforms to the “infrastructure for our lives” and a “second home.” Being removed from a platform is like disappearing, similar to how being off Facebook can feel like disappearing from the virtual world. YouTube Music/Premium is a “highway” for millions of global listeners, and the removal eliminates the path of least resistance for new, casual listeners to discover and consume his full album catalog. + Hindrance to Discovery: The “Topic” channel is the primary source for music distribution on YouTube’s dedicated streaming service. Its removal stops the platform’s algorithms from suggesting his catalog to listeners who might enjoy political folk or similar genres, severely limiting organic discovery through the YouTube Music ecosystem. + Erosion of Market Share: Losing a major global platform like YouTube Music represents the loss of a key segment of the overall music streaming market, which is crucial for modern audience growth. + Forced Friction: New listeners must now go directly to his website, Patreon, Substack, Bandcamp (where he faces shadowbanning issues), or other, less-dominant streaming platforms. This added friction prevents casual users from encountering his music, which directly impacts the potential for mass audience expansion. To provide a little more context about what all this stuff means: every month artists who are on Spotify get an email from Spotify telling us that of the 18,000 people who listened to our music last month, 4,000 (or whatever the numbers may be for that month) were “new listeners.” Those are often people who got to a song of mine because they were listening to another leftwing artist, and the algorithm thought they’d like to hear me, or a particular song of mine. The same phenomenon is at play on other streaming platforms, though they don’t send helpful monthly emails the way Spotify does. As Gemini explained, this recommendation phenomenon will no longer be in play with my music on YouTube Music anymore. According to my research on this sort of thing, it is so rare that an artist has their entire catalog deleted by a platform for reasons unrelated to copyright infringement, there are no examples available aside from mine that I can find. (If anyone reading this knows of one, please let me know!) Part of the reason it’s hard to know if this has ever happened before is it’s very unusual, apparently, for platforms to actually tell artists why they might be taking such an action, when they take it. But according to my research, while it is very common for individual songs to be taken down for violating one rule or another, it is almost unheard of for an artist’s entire catalog to be removed. Given how rare this sort of thing is, how damaging it is to those targeted, and how arbitrarily such actions have been taken by corporations like Google/Alphabet/YouTube, once other people understand what has just happened to me and what could happen to anyone else who gets on the blacklist, I hope that soon I will not be alone in speaking out against what they’ve specifically just done to me. For those who don’t know the back story to why I’m being targeted, a few words on that history. In early 2024, my first album about Israel’s ongoing war on Gaza, Notes from a Holocaust, was removed from my discography on Spotify, with no notification or explanation to me or to anyone else. I later put the album back up with a slightly altered name, and it has stayed up. This removal of an entire album has never happened on any other platform, until last week. Right around the same time that the album was removed from Spotify, I received my first notification from YouTube that my channel was being demonetized for the next 90 days, to punish me for posting a Houthi Army press release which I thought was an interesting thing to share with people, given that the US was at that time actively bombing Yemen. After one or two more of these 90-day suspensions of monetization, in January 2025 YouTube informed me that my channel would now be permanently demonetized, and that I had no recourse. I contacted the YouTube customer service people to confirm that this was indeed the case, and not a mistake. At the same time as this was going on, YouTube was regularly deleting videos, specifically if they involved me singing my “Song for the Houthi Army” or my song, “I Support Palestine Action.” It seemed they would wait for someone to report the video, and then delete it. This is my only way to understand their process for deciding which videos to delete on YouTube, because of the way it has thus far involved getting rid of some renditions of these songs while leaving others on the platform. YouTube’s explanation for which rules I was violating that had led to my channel’s permanent demonetization was “supporting criminal organizations,” which is a broad concept that under both British and US law includes the Houthi Army, and in the UK the British nonviolent direct-action group, Palestine Action as well. In the UK, verbally expressing support for proscribed organizations like them is a crime punishable by up to 14 years in prison, as this violates Section 12 of the UK’s Terrorism Act of 2000. In the US, verbally expressing support for proscribed organizations may be legal under the First Amendment, but earning income from praising proscribed organizations, at least by my understanding of the law, is a different matter legally. As I understand US laws, this is why you’re allowed to visit Cuba, but you’re in big trouble, potentially, if you spend any money there. In any case, for whatever reason — never fully explained — my channel was demonetized, and certain videos of certain songs continue to be randomly disappearing. When this happens, I get two emails from YouTube, one explaining that this song violates the rule against supporting criminal organizations and has therefore been taken down, and another one telling me that my channel has been permanently demonetized (despite the fact that it already was, last January). What I believe just happened last week with my existence as an artist with albums available on YouTube Music ceasing, was the YouTube bureaucracy figured out that if they really were serious about demonetizing this guy, they couldn’t just demonetize the videos while allowing his albums to earn royalties on YouTube Music. Because of their legal and financial arrangements with distributors, keeping my music on the platform but not sending royalty money to CDBaby for that artist might be more complicated than simply severing all ties between the artist and the distributor, as they exist on YouTube Music, or as they used to. When they figure out that I’m the lyricist and producer behind the artist, Ai Tsuno, they will presumably delete all of her albums as well. So far she still exists as an artist with albums on YouTube Music. Soon her next album will be up there, too, including the song, “They Deleted David Rovics.” Funny, maybe, but it by no means compensates for anything that’s being done by YouTube to this artist, as they disappear me in stages, as they’re doing. Anyone who takes a look at the extremely small numbers of listeners to Ai Tsuno on any of the platforms can see what I’m up against if I were to just upload all of my albums back on to YouTube Music — were that even possible, now that they’ve removed all of the David Rovics albums. No one would notice they’re there, or it would take a very long time for the songs to get back into the recommendation algorithms that they were in before last week. I’d like to point out two aspects to these efforts to deplatform me that I think are especially relevant. One is the way the laws in the UK and US work with regards to criminal organizations that anyone in government seems actually to be worried about, anyone criticizing Israeli genocidal actions or proclaiming their support for international law which defends things like armed resistance to occupation is breaking all kinds of laws. Laws that basically do not apply in any other context. So the laws themselves, not at all accidentally, are set up to support groups like UK Lawyers for Israel, and legally arm them for their systematic trolling activities. UK Lawyers for Israel is one of a number of different outfits on both sides of the Atlantic that proudly and publicly go about trying to vilify academics, artists, journalists, and all sorts of other people, and using these ridiculous laws to their greatest advantage. UK Lawyers for Israel began announcing in emails sent with their masthead to venues telling them they should cancel my gigs, in February, 2024, during the same winter when all the problems with Spotify and YouTube began (problems with various forms of suppression on Facebook and Bandcamp began earlier). Intentions of groups like these are not hidden, they’re open and proud about their successes in getting professors fired and gigs canceled. One of the other chatbots I consulted about having my entire catalog deleted by YouTube Music was confident that because this sort of action is so unheard-of and appeared to be so obviously political in nature, surely the artist targeted in this way would benefit by getting lots of media publicity. So far, anyway, I can report that that chatbot’s assumptions were false. (This is often the case with AI, as with humans.) There are a couple things on that idea of outrageous corporate behavior like this garnering media attention that might be worth noting. One is that people hear about stuff that gets media attention. They don’t hear about stuff that doesn’t, generally. So we are under the impression that AI-generated music is very popular, because every once in a while an AI-generated song gets popular. Most AI-generated music, like most completely human-generated music, hardly gets heard at all, however. Another thing is it often seems to be the case that an artist needs to be at a certain level of fame in the first place, in order for things like having all their albums pulled from a major platform to generate any media attention, and I’m not Kneecap or Bob Vylan (though I think they’re great). The post YouTube, aka the Biggest Platform on Earth, Has Deleted All My Albums appeared first on CounterPunch.org. From CounterPunch.org via this RSS feed

Komunitas lemmygrad.ml

Deepseek-v3.2Speciale, built for agentic work, just released

cross-posted from: https://lemmygrad.ml/post/9929539 Just saw the news on twitter - and this is my own screenshot of their API pricing taken just now. V3 has received an update and is moving to 3.2 - including on the web interface. They posted all these nice benchmarks which I guess means something? Lol tbh I’m not sure how much you can trust these benchmarks. What does this mean? Well, I gave the twitter thread announcement to Crush (and the new deepseek-v3.2, which you are automatically upgraded to both on API and web interface) and this is what it made of the news: Quick Summary DeepSeek has released V3.2, replacing the experimental version. There are two main models: V3.2: General-purpose, balanced performance (GPT‑5 level) V3.2‑Speciale: Specialized for complex reasoning (Gemini‑3.0‑Pro level) Both are open‑source and available via API. What’s New & Why It Matters 1. Thinking Integrated with Tool‑Use V3.2 can now “think” while using tools (like searching the web, running code, or calling APIs). This makes AI assistants more transparent and better at multi‑step tasks. You can choose thinking mode (slower but more thorough) or non‑thinking mode (faster for simple tasks). 2. Two Models for Different Needs V3.2: Use this for most agentic work—chatbots, coding assistants, general automation. It’s cost‑effective and reliable. V3.2‑Speciale: Use this only when you need top‑tier reasoning—complex math, competitive programming, advanced planning. It uses more tokens (costs more) but solves harder problems. 3. Open‑Source & API Options API: Available immediately. V3.2‑Speciale has a temporary endpoint until Dec 15, 2025. Self‑hosting: Download from Hugging Face and run locally for privacy or cost savings. Pricing: Same for both models [crush made a mistake in pricing here, not sure why. See my screenshot for current pricing or check on https://api-docs.deepseek.com/quick_start/pricing] Practical Advice for Agent Builders Which Model Should You Use? Start with V3.2—it handles 90% of agentic tasks well. Switch to V3.2‑Speciale only for tasks that require deep, step‑by‑step reasoning. Monitor token costs—V3.2‑Speciale uses more tokens, so watch your API bill. Key Improvements for Agentic Interfaces Better reasoning transparency—you can see the model’s thought process when using tools. Mode flexibility—toggle thinking on/off based on task complexity. Stronger performance on benchmarks (math Olympiads, programming contests). Timeline & Availability Now: V3.2 on DeepSeek App, Web, and API Until Dec 15: V3.2‑Speciale via temporary API (same price) Always: Both models open‑source on Hugging Face

Komunitas lemmygrad.ml

Deepseek-v3.2Speciale, built for agentic work, just released

Just saw the news on twitter - and this is my own screenshot of their API pricing taken just now. V3 has received an update and is moving to 3.2 - including on the web interface. They posted all these nice benchmarks which I guess means something? Lol tbh I’m not sure how much you can trust these benchmarks. What does this mean? Well, I gave the twitter thread announcement to Crush (and the new deepseek-v3.2, which you are automatically upgraded to both on API and web interface) and this is what it made of the news: Quick Summary DeepSeek has released V3.2, replacing the experimental version. There are two main models: V3.2: General-purpose, balanced performance (GPT‑5 level) V3.2‑Speciale: Specialized for complex reasoning (Gemini‑3.0‑Pro level) Both are open‑source and available via API. What’s New & Why It Matters 1. Thinking Integrated with Tool‑Use V3.2 can now “think” while using tools (like searching the web, running code, or calling APIs). This makes AI assistants more transparent and better at multi‑step tasks. You can choose thinking mode (slower but more thorough) or non‑thinking mode (faster for simple tasks). 2. Two Models for Different Needs V3.2: Use this for most agentic work—chatbots, coding assistants, general automation. It’s cost‑effective and reliable. V3.2‑Speciale: Use this only when you need top‑tier reasoning—complex math, competitive programming, advanced planning. It uses more tokens (costs more) but solves harder problems. 3. Open‑Source & API Options API: Available immediately. V3.2‑Speciale has a temporary endpoint until Dec 15, 2025. Self‑hosting: Download from Hugging Face and run locally for privacy or cost savings. Pricing: Same for both models [crush made a mistake in pricing here, not sure why. See my screenshot for current pricing or check on https://api-docs.deepseek.com/quick_start/pricing] Practical Advice for Agent Builders Which Model Should You Use? Start with V3.2—it handles 90% of agentic tasks well. Switch to V3.2‑Speciale only for tasks that require deep, step‑by‑step reasoning. Monitor token costs—V3.2‑Speciale uses more tokens, so watch your API bill. Key Improvements for Agentic Interfaces Better reasoning transparency—you can see the model’s thought process when using tools. Mode flexibility—toggle thinking on/off based on task complexity. Stronger performance on benchmarks (math Olympiads, programming contests). Timeline & Availability Now: V3.2 on DeepSeek App, Web, and API Until Dec 15: V3.2‑Speciale via temporary API (same price) Always: Both models open‑source on Hugging Face

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So Deepseek just quietly released an open-source beast-at-math model (details inside)

cross-posted from: https://lemmygrad.ml/post/9899994 wake up open twitter to catch up see deepseek did it again (and as a reminder, Deepseek-r1 only came out in January so it’s been less than 12 months since their last bombshell) One more graph: What this all means Traditional AI models are trained to be “rewarded” for a correct final answer. Get the expected answer, win points, be incentivized to get the answer more often. This has a major flaw: a correct answer does not guarantee correct reasoning. A model can guess, use a shortcut, or even have flawed logic but still output the right answer. This approach completely fails for tasks like theorem proving, where the process is the product. DeepSeekMath-V2 tackles this with a novel self-verifying reasoning framework: the Generator: One part of the model generates mathematical proofs and solutions. the Verifier: Another part acts as the critic, checking every step of the reasoning for logical rigor and correctness The Loop: If the verifier finds a flaw, it provides feedback, and the generator revises the proof. This creates a co-evolution cycle where both components push each other to become smarter This new approach allows the model to set record-breaking performance. As you can see from the charts above, it scores second-place on ProofBench-Advanced, just behind Gemini. But Gemini isn’t open-source, Deepseekmath-V2 is. The model weights are available on Huggingface under an Apache 2.0 license: https://huggingface.co/deepseek-ai/DeepSeek-Math-V2. This means researchers, developers, and enthusiasts around the world can download, study, and build upon this model right now. They can fine-tune or change the model to fit their needs and research, which promises a lot of exciting math discoveries happening soon - I predict (on no basis mind you) that this will help solve computing problems to start with, either practical or theoretical. Beyond just the math, the self-verification mechanism is a crucial step towards building AI systems whose reasoning we can trust, which is vital for applications such as scientific research, formal verification, and safety-critical systems. It also proves that ‘verification-driven’ training is a viable and powerful alternative to the ‘answer-driven’ method used to this day.

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So Deepseek just quietly released an open-source beast-at-math model (details inside)

wake up open twitter to catch up see deepseek did it again (and as a reminder, Deepseek-r1 only came out in January so it’s been less than 12 months since their last bombshell) One more graph: What this all means Traditional AI models are trained to be “rewarded” for a correct final answer. Get the expected answer, win points, be incentivized to get the answer more often. This has a major flaw: a correct answer does not guarantee correct reasoning. A model can guess, use a shortcut, or even have flawed logic but still output the right answer. This approach completely fails for tasks like theorem proving, where the process is the product. DeepSeekMath-V2 tackles this with a novel self-verifying reasoning framework: the Generator: One part of the model generates mathematical proofs and solutions. the Verifier: Another part acts as the critic, checking every step of the reasoning for logical rigor and correctness The Loop: If the verifier finds a flaw, it provides feedback, and the generator revises the proof. This creates a co-evolution cycle where both components push each other to become smarter This new approach allows the model to set record-breaking performance. As you can see from the charts above, it scores second-place on ProofBench-Advanced, just behind Gemini. But Gemini isn’t open-source, Deepseekmath-V2 is. The model weights are available on Huggingface under an Apache 2.0 license: https://huggingface.co/deepseek-ai/DeepSeek-Math-V2. This means researchers, developers, and enthusiasts around the world can download, study, and build upon this model right now. They can fine-tune or change the model to fit their needs and research, which promises a lot of exciting math discoveries happening soon - I predict (on no basis mind you) that this will help solve computing problems to start with, either practical or theoretical. Beyond just the math, the self-verification mechanism is a crucial step towards building AI systems whose reasoning we can trust, which is vital for applications such as scientific research, formal verification, and safety-critical systems. It also proves that ‘verification-driven’ training is a viable and powerful alternative to the ‘answer-driven’ method used to this day.

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Is language the same as intelligence? The AI industry desperately needs it to be

Developing superintelligence is now in sight,” says Mark Zuckerberg, heralding the “creation and discovery of new things that aren’t imaginable today.” Powerful AI “may come as soon as 2026 [and will be] smarter than a Nobel Prize winner across most relevant fields,” says Dario Amodei, offering the doubling of human lifespans or even “escape velocity” from death itself. “We are now confident we know how to build AGI,” says Sam Altman, referring to the industry’s holy grail of artificial general intelligence — and soon superintelligent AI “could massively accelerate scientific discovery and innovation well beyond what we are capable of doing on our own.” Should we believe them? Not if we trust the science of human intelligence, and simply look at the AI systems these companies have produced so far. The common feature cutting across chatbots such as OpenAI’s ChatGPT, Anthropic’s Claude, Google’s Gemini, and whatever Meta is calling its AI product this week are that they are all primarily “large language models.” Fundamentally, they are based on gathering an extraordinary amount of linguistic data (much of it codified on the internet), finding correlations between words (more accurately, sub-words called “tokens”), and then predicting what output should follow given a particular prompt as input. For all the alleged complexity of generative AI, at their core they really are models of language. The problem is that according to current neuroscience, human thinking is largely independent of human language — and we have little reason to believe ever more sophisticated modeling of language will create a form of intelligence that meets or surpasses our own. Humans use language to communicate the results of our capacity to reason, form abstractions, and make generalizations, or what we might call our intelligence. We use language to think, but that does not make language the same as thought. Understanding this distinction is the key to separating scientific fact from the speculative science fiction of AI-exuberant CEOs. The AI hype machine relentlessly promotes the idea that we’re on the verge of creating something as intelligent as humans, or even “superintelligence” that will dwarf our own cognitive capacities. If we gather tons of data about the world, and combine this with ever more powerful computing power (read: Nvidia chips) to improve our statistical correlations, then presto, we’ll have AGI. Scaling is all we need. But this theory is seriously scientifically flawed. LLMs are simply tools that emulate the communicative function of language, not the separate and distinct cognitive process of thinking and reasoning, no matter how many data centers we build. We use language to think, but that does not make language the same as thought Last year, three scientists published a commentary in the journal Nature titled, with admirable clarity, “Language is primarily a tool for communication rather than thought.” Co-authored by Evelina Fedorenko (MIT), Steven T. Piantadosi (UC Berkeley) and Edward A.F. Gibson (MIT), the article is a tour de force summary of decades of scientific research regarding the relationship between language and thought, and has two purposes: one, to tear down the notion that language gives rise to our ability to think and reason, and two, to build up the idea that language evolved as a cultural tool we use to share our thoughts with one another. Let’s take each of these claims in turn. When we contemplate our own thinking, it often feels as if we are thinking in a particular language, and therefore because of our language. But if it were true that language is essential to thought, then taking away language should likewise take away our ability to think. This does not happen. I repeat: Taking away language does not take away our ability to think. And we know this for a couple of empirical reasons. First, using advanced functional magnetic resonance imaging (fMRI), we can see different parts of the human brain activating when we engage in different mental activities. As it turns out, when we engage in various cognitive activities — solving a math problem, say, or trying understand what is happening in the mind of another human — different parts of our brains “light up” as part of networks that are distinct from our linguistic ability: A set of images of the brain, with different parts lighting up, labeled “language network,” “multiple demand network,” and “theory of mind network,” all of which support different functions. Nature Second, studies of humans who have lost their language abilities due to brain damage or other disorders demonstrate conclusively that this loss does not fundamentally impair the general ability to think. “The evidence is unequivocal,” Fedorenko et al. state, that “there are many cases of individuals with severe linguistic impairments … who nevertheless exhibit intact abilities to engage in many forms of thought.” These people can solve math problems, follow nonverbal instructions, understand the motivation of others, and engage in reasoning — including formal logical reasoning and causal reasoning about the world. If you’d like to independently investigate this for yourself, here’s one simple way: Find a baby and watch them (when they’re not napping). What you will no doubt observe is a tiny human curiously exploring the world around them, playing with objects, making noises, imitating faces, and otherwise learning from interactions and experiences. “Studies suggest that children learn about the world in much the same way that scientists do—by conducting experiments, analyzing statistics, and forming intuitive theories of the physical, biological and psychological realms,” the cognitive scientist Alison Gopnik notes, all before learning how to talk. Babies may not yet be able to use language, but of course they are thinking! And every parent knows the joy of watching their child’s cognition emerge over time, at least until the teen years. So, scientifically speaking, language is only one aspect of human thinking, and much of our intelligence involves our non-linguistic capacities. Why then do so many of us intuitively feel otherwise? This brings us to the second major claim in the Nature article by Fedorenko et al., that language is primarily a tool we use to share our thoughts with one another — an “efficient communication code,” in their words. This is evidenced by the fact that, across the wide diversity of human languages, they share certain common features that make them “easy to produce, easy to learn and understand, concise and efficient for use, and robust to noise.” Even parts of the AI industry are growing critical of LLMs Without diving too deep into the linguistic weeds here, the upshot is that human beings, as a species, benefit tremendously from using language to share our knowledge, both in the present and across generations. Understood this way, language is what the cognitive scientist Cecilia Heyes calls a “cognitive gadget” that “enables humans to learn from others with extraordinary efficiency, fidelity, and precision.” Our cognition improves because of language — but it’s not created or defined by it. Take away our ability to speak, and we can still think, reason, form beliefs, fall in love, and move about the world; our range of what we can experience and think about remains vast. But take away language from a large language model, and you are left with literally nothing at all. An AI enthusiast might argue that human-level intelligence doesn’t need to necessarily function in the same way as human cognition. AI models have surpassed human performance in activities like chess using processes that differ from what we do, so perhaps they could become superintelligent through some unique method based on drawing correlations from training data. Maybe! But there’s no obvious reason to think we can get to general intelligence — not improving narrowly defined tasks —through text-based training. After all, humans possess all sorts of knowledge that is not easily encapsulated in linguistic data — and if you doubt this, think about how you know how to ride a bike. In fact, within the AI research community there is growing awareness that LLMs are, in and of themselves, insufficient models of human intelligence. For example, Yann LeCun, a Turing Award winner for his AI research and a prominent skeptic of LLMs, left his role at Meta last week to found an AI startup developing what are dubbed world models: “​​systems that understand the physical world, have persistent memory, can reason, and can plan complex action sequences.” And recently, a group of prominent AI scientists and “thought leaders” — including Yoshua Bengio (another Turing Award winner), former Google CEO Eric Schmidt, and noted AI skeptic Gary Marcus — coalesced around a working definition of AGI as “AI that can match or exceed the cognitive versatility and proficiency of a well-educated adult” (emphasis added). Rather than treating intelligence as a “monolithic capacity,” they propose instead we embrace a model of both human and artificial cognition that reflects “a complex architecture composed of many distinct abilities.” They argue intelligence looks something like this: A chart that looks like a spiderweb, with different axes labeled “speed,” “knowledge,” “reading & writing,” “math,” “reasoning,” “working memory,” “memory storage,” “memory retrieval,” “visual,” and “auditory.” Center for AI Safety Is this progress? Perhaps, insofar as this moves us past the silly quest for more training data to feed into server racks. But there are still some problems. Can we really aggregate individual cognitive capabilities and deem the resulting sum to be general intelligence? How do we define what weights they should be given, and what capabilities to include and exclude? What exactly do we mean by “knowledge” or “speed,” and in what contexts? And while these experts agree simply scaling language models won’t get us there, their proposed paths forward are all over the place — they’re offering a better goalpost, not a roadmap for reaching it. Whatever the method, let’s assume that in the not-too-distant future, we succeed in building an AI system that performs admirably well across the broad range of cognitive challenging tasks reflected in this spiderweb graphic. Will we have achieved building an AI system that possesses the sort of intelligence that will lead to transformative scientific discoveries, as the Big Tech CEOs are promising? Not necessarily. Because there’s one final hurdle: Even replicating the way humans currently think doesn’t guarantee AI systems can make the cognitive leaps humanity achieves. We can credit Thomas Kuhn and his book The Structure of Scientific Revolutions for our notion of “scientific paradigms,” the basic frameworks for how we understand our world at any given time. He argued these paradigms “shift” not as the result of iterative experimentation, but rather when new questions and ideas emerge that no longer fit within our existing scientific descriptions of the world. Einstein, for example, conceived of relativity before any empirical evidence confirmed it. Building off this notion, the philosopher Richard Rorty contended that it is when scientists and artists become dissatisfied with existing paradigms (or vocabularies, as he called them) that they create new metaphors that give rise to new descriptions of the world — and if these new ideas are useful, they then become our common understanding of what is true. As such, he argued, “common sense is a collection of dead metaphors.” As currently conceived, an AI system that spans multiple cognitive domains could, supposedly, predict and replicate what a generally intelligent human would do or say in response to a given prompt. These predictions will be made based on electronically aggregating and modeling whatever existing data they have been fed. They could even incorporate new paradigms into their models in a way that appears human-like. But they have no apparent reason to become dissatisfied with the data they’re being fed — and by extension, to make great scientific and creative leaps. Instead, the most obvious outcome is nothing more than a common-sense repository. Yes, an AI system might remix and recycle our knowledge in interesting ways. But that’s all it will be able to do. It will be forever trapped in the vocabulary we’ve encoded in our data and trained it upon — a dead-metaphor machine. And actual humans — thinking and reasoning and using language to communicate our thoughts to one another — will remain at the forefront of transforming our understanding of the world.

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Labour proposes AI as the magical cure for preventing mistaken prison releases

On Tuesday 11 November, deputy PM David Lammy unveiled a new plan to crack down on mistaken prison releases. We shit you not, the proposal is to set up a court hotline and to use an AI checker. What could possibly go wrong? ‘New guardrails’ According to government figures, prisons mistakenly released 91 individuals between April and October 2025. Lammy, also acting as the current justice secretary, insists that Labour inherited this “crisis” from the Tories. The data reflects this assertion, with the rising trend beginning back in 2021. However, the number of accidental releases has seen a sharp uptick this year. In a Commons address yesterday, Lammy announced that he plans to plough £10m into tackling such errors. This will be used in part to speed up the modernisation of paper record-keeping in prisons. Lammy will also invest these funds to roll out new AI sentence-calculation systems for prison staffers. The deputy PM said: The first duty of any Government is to keep the public safe. The rise in releases in error is one symptom of a service under intolerable strain. We are putting in new guardrails around an archaic system, with tougher new checks, reviewing specific failings and modernising prison processes and joint working with courts – all to bear down on the increase in mistakes. That is what victims deserve. That is what the public expects, and this Government will do what it takes to protect the public. As if ‘get an AI to help’ didn’t already sound dodgy enough, don’t worry: it gets worse. The AI proposition was apparently cooked up after a specialist team was sent into HMP Wandsworth just last week, after a pair of high-profile mistaken releases. ‘Quick fixes’ James Timpson, minister for prisons and parole, told the House of Lords on Monday that the specialist team were looking for “some quick fixes”. He further stated: We had the AI team that went in and, to give you a couple of examples, they think an AI chatbot would be really helpful, and also a cross-referencing for aliases, because we know some offenders have more than 20 aliases. So, to get this straight, the government sent in AI specialists to patch-up a broken system. Then, surprise surprise, the specialists recommended AI tools. The Guardian reported that the AI systems could also be used to scan hundreds of pages of paper documents, merge datasets, and calculate sentence times. AI, the tech world’s new annoying buzzword of the year, offers a veneer of robotic neutrality, efficiency, and modern infallibility. However, the mistakes that AI tools make, as new industries adopt them, have often prove both costly and severe. Likewise, AI can also serve to automate and exacerbate pre-existing racial biases. This is because of the biases present in their training datasets. In fact, mere days ago the Met police released a report which stated that: Policing technology is not race-neutral. When the Met adopts facial recognition, risk scoring, or automated decision-making tools, it does not begin from scratch. It begins with data, data shaped by decades of racialised enforcement. What appears as innovation is often the acceleration of inherited harm. How about a committee? The justice secretary plans to spend the £10m rapidly over the next six months. This will fund the deployment of more of the “new digital crack teams” to hunt error-making in prisons (or generate errors, as the case may be). The government’s website also states that it will use the money to: Create a new monthly Justice Performance Board, which the deputy PM will chair. This will track how prisons and courts are performing. The board, which they claim will be “laser-focussed on addressing key metrics”, will continue to meet until the situation improves.Establish a dedicated data team to “review historic cases and understand systematic issues” for Dame Lynne Owen’s independent review of prison release errors.Simplify policies on releasing prisoners, and standardise the treatment of different cases.Put in place a “fast-track” courts hotline. This will allow prison staff to quickly check for outstanding warrants before releasing prisoners. Likewise, court staff will now need to confirm orders verbally with judges before they can finalise them. That last bullet point is something that should have existed already. ‘Hey, maybe we should have an easy way to check for warrants before letting someone go’. But hey, what do we know? Separately to this £10m cash injection, Labour is planning to build an extra 14,000 prison places. It also intends to overhaul sentencing, for the purpose of: mak[ing] sure we have enough prison places to lock up dangerous criminals and keep the public safe. Given that prisons are already stretched to breaking point, and there aren’t enough staff to process case papers, you might think that employing more staff rather than prison places would be a reasonable, commonsense fix. Don’t be silly. The solution is a new monthly committee meeting, AI chatbots, and AI chatbot salesmen. Kudos to whichever intern came up with ‘maybe we could also ring up and check?’ though. Consider applying for a raise. Featured image via the Canary By Alex/Rose Cocker From Canary via this RSS feed

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Commander in Chief of Grift & Extortion

Photograph by Nathaniel St. Clair Every society gets the kind of criminal it deserves.– Robert F. Kennedy President Trump admits that neither Xi nor Putin worry about being called out by the press for what they say and do but he laments he does. The Fourth Estate is always telling lies about him and sponsoring treasonous whistle blowers. This President doesn’t like to be held accountable. Or indicted 4 times and charged with 88 criminal counts. The Fourth Estate”: This from Google Gemini: The “Fourth Estate” refers to the news media as an independent power that holds government accountable, a term that originated in 18th-century Britain. Initially a derogatory term coined by Edmund Burke for the press, it evolved through figures like Thomas Carlyle to represent the media’s crucial role as a “watchdog” for the public interest. Historically, it has been a force for checks on power, but in recent times, it faces challenges from factors like misinformation and the decline of traditional business models. Just based on this brief exposition I would say the threat AI presents has to do with selection of what to present and what to conceal. Concealing cyberspace’s part in flooding us with everything including crazy alternate “facts” and thus its crushing effect on the independent power of the news media is for me a big negative, a real problem. Also, not offering that “in recent times” a president has ignored the First Amendment by calling the press “the enemy of the people” conceals a threat we face with a president who mocks his oath of office. The ”traditional business model” AI referred to is cyberspace’s takeover of classifieds and advertisements, the cash cows of print media. No connection is made between “misinformation” and the “publication at will” of digital media where Influencers, running from narcissists to crackpots in their basements, fight for Followers. In the absence of evaluative criteria of both author and article for publication we have now a mosh pit of bullshit, lies, nonsense, sheer insanity, invective and blinding ignorance. What we have are muddled minds pouring out what they “think’ they “know.” And, as Trump has said about Mexicans “some I assume are good people.” Attention in social media as in politics is won, eyeballs drawn, not with a long form by a new Burke or Carlyle but by affective reach. Passionate crushes and diatribes not slow reasoning to common understanding. Words to incite a flash mob, not words really at all. Videos. Quick flare up memes. Drawings on cave walls. To the extortionist/grifter Trump, aka President, all this confusion cyberspace reality creates is a bonus. His own default approach to media control is extortion. And the purpose of his extortion is to get a pay off. If you give him what he wants, he won’t hurt you. He uses the leverage of the Federal Government and its enforcers whom Trump 2 now owns to frighten print and TV media into shutting up. He can hurt their bottom line. What counter offensive to launch? The problem of clear messaging and unambiguous reception remains: The smorgasbord of choices cyberspace offers fractures and fragments the establishment of common understanding and weakens the potency of critique and attack. And at this moment, the unsubtle bludgeon of Trump’s relentless attack on any order not his own requires a counter offensive of equal or greater magnitude. The Fourth Estate is not equal to the task because 1.it’s major agents are bending to Trump and 2. A scattered, impoverished guerilla warfare is being fought in cyberspace and this gaggle serves to distract and deflect a unified counter that must be made. The Trump sphere launches more obedient droids in cyberspace than counter response can obliterate. Free to choose Americans stand behind their personal choices regardless of what science and reason, or schooled critical thinking, may counter with. Xi, Putin and Kim Jong-un easily shut down the Fourth Estate. They don’t have to wait for the gravity of loss of profit or cyberspace’s flooding of minds to erode the Fourth Estate. Turkey’s Erdogan shuts them down when “emergencies” are declared. Trump is heading in that direction in regard to everything from tariffs to elections. I think we can be sure that he’s having so much fun and success in playing his hand as a bogus, con man good hearted, best dealmaker capitalist, he might just extort businesses flat out, bogus emergencies and insurrections not needed. He just settles for donations from the moguls he promises to keep out of jail. It was good capitalist business for Paramount to pay 16 million extortion money to Trump so he would not unleash his droids on its proposed merger with Skydance Media. Pay up and no anti-trust obstacle to the merger. This is clear blatant Don Corleone dealing but it also points to the treacherous and traitorous relationship of our gun toting capitalist economics of choice and our, now we know, fragile Constitutional order. If one asks why or where Project 2025 and its tool, Trump, and his tools are going in all this destruction it has much to do with a view of wealth and wealth making that yearns to be free of the obstacles of our Federal Government, its bureaucracy and agencies. For the investor class, it’s the Federal government alone that can bring suits against unbridled profit making. This is the class Reagan was speaking to when he said government if the problem. Sad to say government has not slowed down the zero sum Monopoly game, either under Democrat or Republican regimes. But what Trump gangster is doing now should be a warning to them that an autocrat sticking his beak into their profit making may be more intrusive in “wealth creation” than democratic rule. Trump is making clear that you will suffer in every way because he can use his control of the Federal government to make you suffer. The wealth tech bros, the wealth Wall St bros, and the Christian Nation bros can see that Trump will hurt anyone who brooks his will. Or maybe they can’t. Don Corleone did not extend his operation beyond national borders. But Trump does. Economic and military power are like this army you have to enforce your will anywhere you want. Why not show up where the money is – such as wealthy oil rich countries in the middle east — and make it known that it’s in their interest to shell out whatever Trump wants. He wants to be cut in. He’s like Julius Caesar who can show up in Rome and tell the Senate he’s taking over. Take a look at my army that just crossed the Rubicon he tells them. From a Republic to a dictatorship just like that. Note that Caesar pushed his own droids into the Senate as Trump 2 has done. He weakened the structure of the Roman Senate from the inside. The White House Press now is salted in the same way with droids. He has extorted universities to pay up AND make his lunatic anti-humanism their educational mission. Instead of putting together their own rival mob, universities are being isolated, threatened and extorted one by one. Trump goes after universities because they nurture a critical thinking he fears. This is where the reporters and journalists he hates and fears come from. The present Federal government shut down doesn’t bother Trump as for him it’s another version of Musk with a chain saw getting the Feds out of the way of his Mars dreams, or Vought with his 2025 plan to make a Christian Nation out of the rubble that once was a secular Western liberal democracy. Democrats hope that the dire effects of this shut down will bring the House to them in 2026. Trump is confident that he can put all the blame on Democrats, from the price of eggs to the cost of health care. He wouldn’t be hosting a Great Gatsby party on the eve of millions losing health and food benefits, fiddling while Rome burned, if he didn’t think he could once again turn reality his way. It’s fascinating the way in which Trump has openly acted like a Mafia don and at the same time woven a spell of soft power of the cult variety. An election winning number of voters wanted him a second time because he played into where they were at. The desire to re-boot/re-set whatever power structure, aka a Constitutionally based democratic Republic, exists like a passion not abated among those who see Donald J. Trump as the man for the job of insurrection and destruction. The angry nihilism of the re-set everything faction are played to by Trump as steadily as he plays to those, like him, who hate. His followers don’t see extortion and grifting; they don’t fear his vindictiveness will reach them And yet he makes the lives of wage earners miserable while stroking the invested class as well as that percentage of voters who brought the 2024 election to him who hurt and will hurt the most. Trump relies on his power to entrance a steady 30% to 40% of the population but he has a back up plan for the 2026 elections, a plan he has been rehearsing since he sent Federal troops into LA for bullshit reasons. He has 50 ways to invalidate election results, beginning on election day. He’s probably wondering why no past president who needs to win or get impeached or worst didn’t think of these ways. He’s studied in his first administration and in his interim Mar a Lago retreat how to criminalize the Federal government, how a fox can show up in a sleepy chicken house pretending to be just another chicken. With Homan’s goons stationed at polling booths there will be some excitement, quickly declared insurrection by Trump and … Will the military do what he wants? The U.S Uniform Code of Military Justice clearly states Constitutional allegiance above presidential. Whether Trump 1 knew of this or not Chairman of the Joint Chiefs of Staff Mark Milley, reportedly made plans to resign en masse rather than follow orders they believed were illegal. Trump 2 has been busy kicking out the Milley brand of military leaders. We shall see. The greater odds are that Trump will try to shut down the 2026 elections any way he can. Nothing beneficial to this country or anyone in it, or any of the internationals that have done “deals” with him, can result from Trump’s presidencies. He’s bolder, crazier and more dangerous each passing day. Hand over our tech advantages to Xi? Start nuclear testing to see if they still work? However, I believe his style of threats, extortion and vindictiveness are dark warnings to those who believe autocracy will make them richer than our democratic order of things. Putin’s gangster style squeezes both blood and money out of his moguls. Xi has bent and neutralized the Communist Party and remade it to serve his own personal ambitions. Trump is neither the KGB trained gangster Putin is nor has he the savvy experience and military control of Xi in remaking our Constitutional order. Both Xi and Putin are steady, well-informed strategists not liable to shoot themselves in the foot while Trump is mania erratic, a brazen criminal daring the Democratic order of things to stop him, daring the whole of American history and its people to stop him. Such pathologies in the past self-destruct. What Trump has is not a nation of people shaped by a 1917 Bolshevik revolution or a homogeneous Chinese peasant class but rather 50 states zealously independent in spirit, masterless really in every way including politics, and proud of the fiery rebelliousness, the still resident frontier spirit of the country’s origins. Those who see Project 2025 as a workable plan for a nation subject to religion are not serious in this insanity but clearly intending to form an illiberal order in this hypocritical manner. Often cited are two congenital defects haunting the American order: the extermination of a native indigenous population, and the nuclear indiscriminate bombing of civilians in Hiroshima and Nagasaki. To this we must now add, not a congenital defect but one newly earned, this country’s election to the presidency twice of a man who ruled by extortion, ripped the greatest holes in the moral fabric of a country than any American politician before him, and corrupted in a brand new way the international reputation of the US. This man will go down in history but not in the way he would choose for himself. The post Commander in Chief of Grift & Extortion appeared first on CounterPunch.org. From CounterPunch.org via this RSS feed

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Safe professions

You missed the point and wrote like 3.5 paragraphs. Maybe AI could summarise for you. I asked Gemini to give it a go: This comic strip conveys a cautionary message about the potential overconfidence of humans regarding the irreplaceable nature of their professions in the face of advancing technology, specifically artificial intelligence. Here’s a breakdown: The first five panels show various people confidently stating that their professions (cook, driver, lawyer, doctor, teacher) are inherently human, rely on talent, and therefore cannot be replaced. They seem to believe they are immune to automation or technological disruption. The remaining four panels reveal identical, faceless robots labeled with other professions (personal, journalist, artist, translator). This visually suggests that even roles considered creative, nuanced, or requiring “human touch” are susceptible to being taken over by AI or robots. The humor lies in the dramatic irony. The characters’ confident assertions are juxtaposed with the stark reality of the robots, highlighting the potential for human hubris in underestimating the capabilities of emerging technologies. In essence, the comic warns against complacency and suggests that many professions, even those requiring creativity and human interaction, might not be as safe from automation as people believe. It prompts reflection on the evolving nature of work and the potential impact of AI on various fields.

Komunitas ibbit.at

When Algorithms Break the News

Photograph by Nathaniel St. Clair Hospitals denying maternal mental health care, employers using AI tools that drive workplace discrimination, and “phantom” nitrates in water supplies leading to chronic illness in children. These concerns sound like stories Project Censored highlights in its annual report of the most important but under-covered news stories. Each of these topics and twelve additional stories like them were brought to our attention last June by Project Censored judge Nicholas Johnson. The author of How to Talk Back to Your Television Set (1970) and Your Second Priority (2008), and a former Federal Communications Commission commissioner (1966–1973), Johnson has served as one of Project Censored’s judges, helping the Project identify and vet its annual record of each year’s top “censored” stories, since the organization’s inception in 1976. When Nick contacts us with story tips, we pay attention. A chatbot had identified these news reports after Nick directed it to provide fifteen examples of “potentially significant news stories, made public in publications with small circulations, that have not been given the attention of major media during the past year,” along with reasons why those stories should have been given more attention. Chatbots, such as OpenAI’s ChatGPT and Google’s Gemini, use generative AI to respond to user queries through written or spoken “conversations.” At the time, Project Censored staff were undertaking our own review of the year’s top stories, as identified by students and their faculty mentors who participate in the Project’s Campus Affiliates Program, and vetted by the Project’s panel of esteemed judges. Our story review process involves five distinct stages of meticulous examination to determine each candidate story’s importance, timeliness, quality of sources, and, ultimately, trustworthiness. This effort involves hundreds of hours of human effort, so we all smiled when Nick’s email message arrived with the winking salutation, “Sorry to be so late in getting this to you, but it took my AI at least 15 seconds to do it.” Here’s one example from the story list Nick sent us, based on his chatbot’s response: “Walmart’s Quiet Drone-Surveillance Rollout” —Publication: The Plains Weekly Register (circ. ~4,000) —Scoop: Leaked local franchise agreements show Walmart testing AI‐drone patrols in five rural states—without public notice or aerial-privacy rules. —Why It Matters: Sets a precedent for commercial drone policing private property—civil liberties groups must litigate, yet the story died regionally. At the conclusion of its report, the chatbot produced this analytic summary: These stories highlight systemic gaps in coverage—when national outlets focus on high-profile crises, they often miss crises brewing in our backyards: local infrastructure failures, emerging health threats, new dimensions of environmental degradation, and AI’s stealth intrusions into daily life. Each tale carried not just local but national (even global) implications, and broader attention could have spurred swifter policy, regulatory, or public-health responses. Reading this, we were impressed. That paragraph sounds as if it could have been lifted from a previous volume of the Project’s State of the Free Press yearbook. Perhaps, in a way, it was. We quickly determined that not only were all of the “potentially significant” news stories on the chatbot’s list fabricated, but so too were all fifteen of the allegedly independent news organizations credited with breaking those stories. The chatbot made it all up, but did not disclose that it was generating fictional information rather than providing factual answers. To train datasets for large language models like ChatGPT and Gemini, tech developers scrape the internet, cataloging and extracting data from every corner of the web—often without the original creators’ knowledge or permission, much less any financial compensation. In theory, the chatbot’s list of stories that Nick shared could have been modeled after Project Censored’s annual Top 25 lists, which are archived on the Project’s website. But because chatbots, unlike Project Censored, do not abide by guiding ethical principles of journalism, such as seeking the truth, with transparency and accountability, while minimizing harm, they simply mimic reporting that highlights societal inequities, without understanding the underlying context, sources, or human experiences that give stories, like the ones they’re generating, meaning. Chatbots can reproduce the appearance of investigative journalism —which, at its best, uncovers corruption, censorship, or injustice—but they lack the moral and analytical frameworks to properly verify facts, assess motives, and weigh the potential consequences of their reporting. Celine Schreiber of Weave News describes this as the “risk of replication without representation: A simulacrum of independent journalism that lacks its political or community roots.” Corporate media and tech companies refer to these errors as “hallucinations”—when AI systems literally make stuff up—a term that both anthropomorphizes the bots and downplays the consequences of perpetuating inaccuracies, both regular pitfalls of reporting on AI. Developers do not entirely know why hallucinations occur, so they have no way to stop them. “Despite our best efforts, they will always hallucinate,” Amr Awadallah, the chief executive of Vectara, a start-up that builds AI tools for businesses, and a former Google executive, told the New York Times last May. “That will never go away.” In October 2025, the BBC, in partnership with the European Broadcasting Union, published an extensive study, covering twenty-two public media service companies in eighteen countries, that found AI assistants such as ChatGPT, Copilot, and Gemini “misrepresent” news content roughly 45 percent of the time. About 31 percent of the responses researchers collected demonstrated serious “sourcing problems—missing, misleading, or incorrect information,” and about 20 percent “contained major accuracy issues.” As EBU’s deputy director general Jean Philip De Tender noted, “These failings are not isolated. … They are systemic, cross-border, and multilingual, and we believe this endangers public trust.” According to Pew Research, while relatively few Americans currently use AI chatbots like ChatGPT to obtain news information, 42 percent of those who do report that “they generally find it difficult to determine what is true and what is not.” As more users turn to AI systems rather than traditional search engines to find information online, society faces a deepening crisis of misinformation—one in which greed, competitive pressure, and unchecked technological expansion continue to erode public trust in media. Recent research documents the potential for social media platforms to manipulate public opinion. Tech companies now control the tools for accessing information and the metrics of visibility, consistently shaping public discourse. Public vigilance and scrutiny are vital as AI strengthens its grip on our collective reality. The chatbot’s Walmart “story,” mentioned above, closely resembles an actual news story from Project Censored’s list of this year’s most censored stories—a report by Jacobin about Amazon and Walmart using hostile surveillance technology against warehouse employees. The uncanny resemblance between the chatbot’s fabricated report and Jacobin’s real exposé underscores the urgent need for critical media literacy (CML), which empowers people not only to assess the trustworthiness of specific media messages but also to understand the power dynamics that shape those messages’ production. Increasingly, those power dynamics include the role of chatbots and other AI-powered systems in filtering, blockading—and sometimes fabricating—the kind of information and perspective people need in order to be informed and actively engaged. For fifty years, people working with Project Censored—professors, students, media scholars, not machines—have scoured an increasingly large, diverse array of independent outlets to identify, validate, and highlight important but underappreciated news stories. Reflecting the essential role of a free press in a functioning democracy, Project Censored remains committed to serving the public good, rather than private interests, by exposing social problems and empowering people to respond to them. Critical media literacy demands examining media for its power and purpose by taking a closer look at ownership, production, and distribution. Carefully following these trails combats AI misinformation. Without CML, as evidenced by Nick’s chatbot’s censored stories list, the information AI provides has become harder to distinguish from the truth. This first appeared on Project Censored. The post When Algorithms Break the News appeared first on CounterPunch.org. From CounterPunch.org via this RSS feed