Silicon Valley’s billions of dollars on AI haven’t actually generated a return yet. Here’s why most companies should embrace ‘small AI’ instead

Here’s why most companies should embrace ‘small AI’ insteadBy Jason CorsoBy Jason Corso How can companies really win the AI race?Chip Somodevilla/Getty ImagesFor all of AI’s mise, most companies using it are not yet dering true value—to their customers or themselves

With investors keen to finally see some ROI on their AI investments, it’s time to stop generalizing and start thinking smaller

Instead of building epic models that aim to accomplish all feats, es looking to cash in on the AI gold rush should consider pivoting towards focused models that are designed for specific tasks

By attacking a singular blem with a fresh solution, innovators can create powerful, novel models that require fewer parameters, less data, and less compute power

Furthermore, it is possible to der this potential in various manners— through services atop commodity generalist models, retrieval-augmented systems, low-rank adaptation, fine-tuning, and more

What’s so bad big AI? Some enthusiasts may cringe at the word “small,” but when it comes to AI, small does not mean insignificant, and bigger is not necessarily better

Models OpenAI’s GPT-4, Google’s Gemini, Mistral AI’s Mistral, Meta’s Llama 3, or Anthropic’s Claude cost a fortune to build, and when we look at how they perform, it’s not why most es would want to get into that game to begin with

Even as big players monopolize the field, their sexy, headline-making generalized foundational models seem to perform well enough on certain benchmarks, but whether this performance generalizes to actual value in terms of increased ductivity or similar remains un

In contrast, focused AI that answers specific use cases or pain points is cheaper, faster, and easier to build

That’s because successful AI models rely on high-quality, well-managed, and ethically sourced data, along with an understanding of how all that data impacts model performance

With this challenge integral to why over 80 percent of AI jects fail, training a more focused model requires fewer parameters and much less data and compute power

This is not an argument for green AI but for bringing some realism back into the AI hype cycle

Even if the model itself is a large prietary one, the tighter the focus, the smaller and more manageable the number of possible outputs to consider becomes

With less token length, models optimized for a specific task can run faster and be highly robust and more performant, all while using less data

Bonsai employs patented AI models, powerful data, and computer-vision software to power autonomy systems for plucking and picking in harsh environments

While Bonsai’s algorithms rely on massive datasets that are being continuously d, with its narrow focus, this physical AI trailblazer was tapped as Ag Breakthrough’s Precision Agriculture Solution of the Year

Even Big players are working to focus their AI offerings with smaller, more powerful models

Microsoft currently uses OpenAI’s GPT-based nology to power Copilot, a suite of smaller AI tools built into its ducts

These models are more focused on software, coding, and common patterns, allowing them to be more easily fine-tuned than the general ChatGPT and better at generating personalized content, summarizing files, recognizing patterns, and automating activities via mpts

With OpenAI jecting big returns when it releases PhD-level ChatGPT agents, the ideal is that one day, we will all have our own agents—or AI assistants—that use our personal data to act on our behalf without mpts

It’s an ambitious future, notwithstanding the privacy and security concerns

While the jump from where we are now to where we could be going seems to be a huge one, building it piece by piece is a , lower-risk apach than assuming a massive monolith is the answer

AI innovators who in on specificity can build a growing, nimble team of expert models that increasingly augment our work instead of one costly, mediocre assistant who is fat with parameters, eats massive data sets, and still doesn’t get it right

How small AI will keep the bubble from bursting By creating lighter computing infrastructures that focus on the right data, es can fully maximize AI’s potential for breakthrough results even as they cut down the immense financial and environmental costs of the nology

Amid all the hype around AI and the behemoth Big models fighting for headlines, the long arc of innovation has always relied on incremental, practical gress

With data at the heart of the models that are indeed changing our world, small, focused AI mises faster, more sustainable, and cost-effective solutions—and in turn, offers both investors and users some much-needed ROI from AI

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