Welcome to the second installment of my two-part series on SmartGPT. In the previous episode, we explored what SmartGPT is and its capabilities. Today, we'll explain why it is relevant.
As always, don’t worry if you find the rate of change overwhelming. Everyone is in the same position. Just this morning, I tuned into a pair of popular podcasts where guests were discussing the perceived limitations of Large Language Models (LLMs). I couldn't help but notice that some of the claims made about these limitations were patently incorrect, given what SmartGPT has demonstrated. Breakthroughs like this are happening at such a rapid pace that it's impossible for anyone to stay completely up-to-date. And that's perfectly fine - we all need to take a breath and embrace the pace of progress.
With that in mind, I'd like to share some key insights from SmartGPT that could help shape your LLM strategy:
First, prioritize systems over models. It's easy to get caught up in the battle of the "best" models - GPT-4 vs BERT, for instance - but the real power lies in the systems and what they can accomplish. Practical deployment of LLMs will involve systems that utilize a blend of models, tools, and software components. So, let's stop sweating over the "best" model.
Second, never underestimate the power of open-source in this evolution. The number of exploratory paths is simply too vast for any single organization to cover. When you consider the amalgamation of models in a system, different model architectures being published, model sizes, training data quality and quantity, and integration with other tools, the opportunities are endless. This is a space where open-source will undoubtedly reign supreme. Yes, there will be instances where the data, computational power, and capital of large corporations will be necessary for breakthroughs. But even then, companies like Meta (Facebook) are likely to fund open-source development, given the benefits they derive from such breakthroughs.
Lastly, it's crucial to acknowledge that solving real business problems with any solution will necessitate data science. There are too many potential optimization avenues and unique constraints within your organization. When you start considering factors like your available data, data quality, infrastructure, deployment patterns, integration points, data privacy policies, and user demands, it becomes clear that there's no one-size-fits-all solution to your biggest challenges. This is what we do at Prolego, so please reach out to Russ at russ@prolego.com if you think we can help you.
I hope this information proves useful. Have a fantastic day!
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