#AI machine

[smashorpassobjects2]

[destiel-news-network]

(Source)

[victusinveritas]

[milkcryptid]

do people have no shame anymore?

[whaledocboi]

ai generated images make me increasingly sad and tired the more i see them in more and more casual contexts. i dont know how to explain, but it just fills the world with a bunch of nothing. no matter how visually stunning the pictures might be, there's nothing behind it for me. no dedication, no emotions, no feelings, no hard work or creativity, nothing i can truly think about, admire or enjoy. i dont think thats how art is supposed to be

[bogkeep]

there's something so deeply dystopian to me how tech companies don't understand that a forced convenience is not a convenience at all. i'm sure autocorrect is helpful for many, but a function that forcibly changes my actual written words and punctuation is taking away my language. photo filters can be nice but i need to choose using them myself or else i have lost the ability to take the picture i want. i don't want a machine to draw or write for me. taking away the option for me to do things manually feels like violence!!!! all this talk of endless opportunity, why are you RESTRICTING me

[sophiebaybey]

Not to preach to the choir but I wonder if people generally realize that AI models like ChatGPT aren't, like, sifting through documented information when you ask it particular questions. If you ask it a question, it's not sifting through relevant documentation to find your answer, it is using an intensely inefficient method of guesswork that has just gone through so many repeated cycles that it usually, sometimes, can say the right thing when prompted. It is effectively a program that simulates monkeys on a typewriter at a mass scale until it finds sets of words that the user says "yes, that's right" to enough times. I feel like if it was explained in this less flattering way to investors it wouldn't be nearly as funded as it is lmao. It is objectively an extremely impressive technology given what it has managed to accomplish with such a roundabout and brain-dead method of getting there, but it's also a roundabout, brain-dead method of getting there. It is inefficient, pure and simple.

[valtsv]

humanising a machinegirl by treating her like a person: based

"humanising" a machinegirl by turning her into a generically pretty waify anime girl: killing you with a hammer until you are dead

[kaiju-krew]

make art, wussy

[pdm-solutions]

Data-Driven Decision-Making: The Core of PdM Excellence

In the dynamic landscape of industrial operations, the shift towards predictive maintenance (PdM) is not just a technological leap; it's a strategic evolution fueled by the transformative force of data-driven decision-making. In this article, we explore the core of PdM solutions excellence—how harnessing the power of data propels industries towards unparalleled efficiency, cost savings, and operational resilience.

Let's read it out:

The Precision of Proactivity: From Reactive to Data-Driven Maintenance

Dive into the paradigm shift from reactive maintenance practices to the precision of proactive strategies. Explore how data-driven decision-making in PdM empowers industries to forecast equipment failures before they occur, eliminating the costly aftermath of unplanned downtime. Understand the proactive stance that data-driven insights provide, shaping a new era of maintenance precision.

Data as the Silent Sentinel: The Role of IoT and Sensors in PdM

Uncover the silent sentinels that drive data-driven decision-making in PdM—Internet of Things (IoT) devices and sensors. Explore how these interconnected technologies transform equipment into sources of actionable data. Illustrate real-world examples where IoT and sensors play a pivotal role in continuous monitoring, enabling predictive insights that revolutionize maintenance practices.

Beyond the Numbers: The Art and Science of Predictive Analytics

Data-driven decision-making transcends raw numbers—it's an art and a science. Delve into the realm of predictive analytics, where advanced algorithms and machine learning turn data into actionable intelligence. Showcase how these analytical tools not only predict potential issues but also optimize maintenance schedules, ensuring resources are utilized efficiently.

Minimizing Downtime, Maximizing Productivity: The Impact of Data Insights

Highlight the tangible impact of data-driven decisions on minimizing downtime and maximizing productivity. Illustrate scenarios where industries, armed with predictive insights, can schedule maintenance during planned downtime, avoiding disruptions to regular operations. Showcase how this strategic approach enhances overall productivity and contributes to the bottom line.

From Reactive to Proactive: A Success Story in Data-Driven PdM

Engage readers with a success story that exemplifies the journey from reactive to proactive maintenance through data-driven decision-making. Showcase a real-world example where an industry's adoption of PdM and data analytics resulted in significant improvements—be it reduced maintenance costs, increased equipment reliability, or a marked decrease in unplanned downtime.

Strategies for Implementation: Integrating Data-Driven PdM Effectively

Empower industries with practical strategies for implementing data-driven PdM effectively. Address common challenges such as data integration, cybersecurity concerns, and workforce training. Provide actionable insights on creating a seamless transition to a data-centric maintenance approach, ensuring that the integration of PdM becomes a catalyst for operational excellence.

Conclusion

By unraveling the intricacies of data-driven decision-making in the realm of predictive maintenance, this article aims to captivate and inform readers. It positions PdM not just as a technological upgrade but as a strategic imperative, where the mastery of data translates into operational excellence, cost savings, and a future-proof foundation for industrial success.

[bitchesgetriches]

31% of employees are actively ‘sabotaging’ AI efforts. Here’s why

"In a new study, almost a third of respondents said they are refusing to use their company’s AI tools and apps. A few factors could be at play."

[river-taxbird]

AI hasn't improved in 18 months. It's likely that this is it. There is currently no evidence the capabilities of ChatGPT will ever improve. It's time for AI companies to put up or shut up.

I'm just re-iterating this excellent post from Ed Zitron, but it's not left my head since I read it and I want to share it. I'm also taking some talking points from Ed's other posts. So basically:

We keep hearing AI is going to get better and better, but these promises seem to be coming from a mix of companies engaging in wild speculation and lying.

Chatgpt, the industry leading large language model, has not materially improved in 18 months. For something that claims to be getting exponentially better, it sure is the same shit.

Hallucinations appear to be an inherent aspect of the technology. Since it's based on statistics and ai doesn't know anything, it can never know what is true. How could I possibly trust it to get any real work done if I can't rely on it's output? If I have to fact check everything it says I might as well do the work myself.

For "real" ai that does know what is true to exist, it would require us to discover new concepts in psychology, math, and computing, which open ai is not working on, and seemingly no other ai companies are either.

Open ai has already seemingly slurped up all the data from the open web already. Chatgpt 5 would take 5x more training data than chatgpt 4 to train. Where is this data coming from, exactly?

Since improvement appears to have ground to a halt, what if this is it? What if Chatgpt 4 is as good as LLMs can ever be? What use is it?

As Jim Covello, a leading semiconductor analyst at Goldman Sachs said (on page 10, and that's big finance so you know they only care about money): if tech companies are spending a trillion dollars to build up the infrastructure to support ai, what trillion dollar problem is it meant to solve? AI companies have a unique talent for burning venture capital and it's unclear if Open AI will be able to survive more than a few years unless everyone suddenly adopts it all at once. (Hey, didn't crypto and the metaverse also require spontaneous mass adoption to make sense?)

There is no problem that current ai is a solution to. Consumer tech is basically solved, normal people don't need more tech than a laptop and a smartphone. Big tech have run out of innovations, and they are desperately looking for the next thing to sell. It happened with the metaverse and it's happening again.

In summary:

Ai hasn't materially improved since the launch of Chatgpt4, which wasn't that big of an upgrade to 3.

There is currently no technological roadmap for ai to become better than it is. (As Jim Covello said on the Goldman Sachs report, the evolution of smartphones was openly planned years ahead of time.) The current problems are inherent to the current technology and nobody has indicated there is any way to solve them in the pipeline. We have likely reached the limits of what LLMs can do, and they still can't do much.

Don't believe AI companies when they say things are going to improve from where they are now before they provide evidence. It's time for the AI shills to put up, or shut up.

[ahb-writes]

(from The Mitchells vs. the Machines, 2021)

[chechula]

Another random comic diary. I have a really tiresome job I should be doing(commission comic about washing machines. really. x_x) so I am drawing a lot of other things to avoid working on it :3

[maodun]

shout out to machine learning tech (and all the human-input adjustment contributors) that's brought about the present developmental stage of machine translation, making the current global village 地球村 moment on rednote小红书 accessible in a way that would not have been possible years ago.