The AI backlash couldn't have come at a better time
Developers are frustrated with AI hype, seeking practical applications. Tools like RamaLama simplify deployment, while trends favor smaller, relevant models. Organizations aim to integrate AI into routine operations effectively.
Read original article
Developers are expressing frustration with the hype surrounding artificial intelligence (AI), feeling overwhelmed by its portrayal as a cure-all without practical applications. This backlash may serve as a catalyst for organizations to adopt AI more effectively. Attendees at a recent conference demonstrated their weariness towards AI discussions, indicating a desire for pragmatic solutions that integrate AI seamlessly into their workflows. The focus is shifting from grandiose claims about AI's transformative potential to its practical utility in everyday tasks. Tools like the open-source RamaLama project aim to simplify AI integration by using OCI containers, allowing users to easily discover and deploy AI models without complex configurations. Additionally, there is a growing trend towards smaller, more relevant AI models that can be fine-tuned for specific business needs, promoting transparency and trust. The use of Linux containers is highlighted as a means to facilitate safe experimentation and deployment of AI applications. As organizations seek to democratize AI, the emphasis is on making it a routine part of operations rather than a buzzword. This shift mirrors past technological transitions, suggesting that AI will eventually become a standard component of business infrastructure.
- Developers are frustrated with the hype around AI and seek practical applications.
- The backlash against AI may lead to more effective integration in organizations.
- Tools like RamaLama simplify AI deployment through OCI containers.
- There is a trend towards smaller, business-relevant AI models.
- Linux containers facilitate safe experimentation and deployment of AI applications.
Related
AI companies are pivoting from creating gods to building products
AI companies are shifting from model development to practical product creation, addressing market misunderstandings and facing challenges in cost, reliability, privacy, safety, and user interface design, with meaningful integration expected to take a decade.
I don't want your AI
Skepticism about AI in product development is rising, as many products labeled as AI fail to enhance user experience. Successful development should prioritize user needs and effective engineering over AI hype.
We can all be AI engineers – and we can do it with open source models
The article highlights the decreasing barriers to AI engineering, emphasizing essential components for AI applications, the importance of open-source models for data privacy, and the accessibility of development tools for all.
AI is the future of development, but not as I imagined
Giacomo Alonzi from Moze discusses how AI has transformed development roles, emphasizing the rise of the "Product Engineer" and encouraging developers to embrace AI for enhanced efficiency and career opportunities.
AI's Slowdown Is Everyone Else's Opportunity
A slowdown in AI development allows businesses to reassess strategies and focus on return on investment, as leading developers report diminishing returns in model performance and challenges to continuous improvement.
5 commentsSecond, so far in my (very limited) experience, it sucks. I tried an assisted search tool the other day to search for "Do any EV charging stations offer 220v AC as an output?". The results had nothing to do with that input as a structured sentence. It was still just a bunch of links to EV charging articles, which were mostly shit in and of themselves. So the results list sucked, and the individual articles linked to, also sucked (which admittedly is not the fault of the sucky search algorithm).
This seemed like a prime example of where an LLM could shine: properly interpreting the grammar of my inquiry question, to find results specifically related to that question. But it just didn't work.
I don't think this will be an impediment at all to ownership eliminating labor jobs to be replaced by bots. Mostly because most modern ownership policy has very little concern of whether their service sucks or not.
What is a hype are LLMs. And ChatGPT I'm particular. When the masses realised they could talk to an AI and it responded like a human they ascribed all sorts of human traits to it. Which is wrong, it's just a statistical analysis.
AI can get more intelligent but LLMs aren't the way. Even OpenAI states this in their roadmap to general AI. But the market craze has led them to push LLMs more.
The grift has been at unbearable levels for months now and it actually drove me to delete my X account recently.
Perhaps THAT is why we roll our eyes, not our impatience at how we can obtain the alleged benefits of the application of such monstrous poo pipelines.
My opinion, of course, but i will never make peace with what I regard as a bad idea, fundamentally speaking.
Related
AI companies are pivoting from creating gods to building products
AI companies are shifting from model development to practical product creation, addressing market misunderstandings and facing challenges in cost, reliability, privacy, safety, and user interface design, with meaningful integration expected to take a decade.
I don't want your AI
Skepticism about AI in product development is rising, as many products labeled as AI fail to enhance user experience. Successful development should prioritize user needs and effective engineering over AI hype.
We can all be AI engineers – and we can do it with open source models
The article highlights the decreasing barriers to AI engineering, emphasizing essential components for AI applications, the importance of open-source models for data privacy, and the accessibility of development tools for all.
AI is the future of development, but not as I imagined
Giacomo Alonzi from Moze discusses how AI has transformed development roles, emphasizing the rise of the "Product Engineer" and encouraging developers to embrace AI for enhanced efficiency and career opportunities.
AI's Slowdown Is Everyone Else's Opportunity
A slowdown in AI development allows businesses to reassess strategies and focus on return on investment, as leading developers report diminishing returns in model performance and challenges to continuous improvement.