#88 LLM to the Rescue: Why a Team of Generative Agents is Your Next Big Move
Our recent exploration took us through the fascinating realm of ChatGPT's "code interpreter" feature. Picture a dynamic tool, a shapeshifter, capable of donning various expert hats—sometimes a graphic artist, then a developer, and later a data analyst. This chameleon-like versatility is all thanks to generative agents, eliminating human intervention.
The Rise of T-skilled Large Language Models
A revolution is underway in the realm of Large Language Models (LLMs). We are witnessing the emergence of "T-skilled" LLMs, capable of replicating human expertise in specific domains while demonstrating proficiency in diverse areas. These generative agents epitomize the harmonious blend of knowledge, skill, and versatility.
Envision commanding an accomplished, multidimensional team of experts driven by LLM and cultivated with T-shaped skills. These dedicated digital soldiers stand ever-vigilant, operating tirelessly day and night, forming an unrivaled dream team of generative agents.
The Augmentation of Generative Agents
In the earlier days, my digital task force was characterized by two distinct units: Team GPT, featuring two tireless instances operating around the clock, and Team Bard, a rapid response unit reputed for its speed and accuracy in fetching facts. Both teams served as trusted allies, executing their duties with remarkable precision and finesse, thereby laying a foundation of efficiency and reliability that was nothing short of awe-inspiring.
In a significant stride forward, a promising new player, Anthropic's Claude v2, was recently incorporated into my lineup. Despite being in its nascent stages, Claude2 has demonstrated its unique capabilities in an impressive manner. Its contributions not only reinforced the strength of the existing teams but also added a distinct layer of versatility to the collective output.
Claude2’s potential has already begun to manifest, successfully catching the spotlight with its distinctive strengths and creative problem-solving approaches. This new addition has undeniably amplified our collective capabilities, promising a future filled with limitless possibilities as we continue to harness the power of these generative agents.
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The Onboarding of New Generative Agents
Onboarding a new generative agent involves a test of abilities through carefully devised questions. These inquiries, backed by data, are usually overlooked due to human bias. The initial response from all agents is incorrect to the first seemingly easy question. However, when pressed, they provide the correct answer, reinforcing the need for persistent inquiry.
A complex astrophysics question overwhelmed ChatGPT in a second test, even with its Wolfram Alpha plugin. On the contrary, Claude solved it in seconds, exhibiting cognitive abilities parallel to humans. Such trials help me assemble the best ensemble, emphasizing each generative agent's strengths.
The Paradox of New Technology
Emerging LLMs like Claude present a paradox. On the one hand, they simplify complex tasks like calculating intricate star movements. On the other hand, they may struggle with simpler tasks like fetching specific company information.
For instance, when questioned about Roost.ai, Claude's initial responses were vague. It was only after persistently prodding that it provided an accurate response. This illustrates that unguided generative agents can take us on an unforeseen journey.
Wrap-up
As we journey deeper into the domain of Large Language Models, a plethora of opportunities awaits. The emergence of robust LLM-driven chatbots, including ChatGPT, Bard, and Claude2, has fundamentally altered how we approach problem-solving, data analysis, and creativity. If you're not utilizing these generative agents, you're simply missing out on a vast world of untapped potential.
However, it's critical to handle these digital forces with care and vigilance. Without proper guidance, these generative agents might take you on a confusing and aimless journey, much like human interactions can go astray without clear direction. But with careful navigation, this journey with generative agents can lead to an abundance of remarkable breakthroughs.
6 months ago it looked like AI / LLMs were going to bring a much needed revival to the venture startup ecosystem after a few tough years. With companies like Jasper starting to slow down, it’s looking like this may not be the case. Right now there are 2 clear winners, a handful of losers, and a small group of moonshots that seem promising. Let’s start with the losers. Companies like Jasper and the VCs that back them are the biggest losers right now. Jasper raised >$100M at a 10-figure valuation for what is essentially a generic, thin wrapper around OpenAI. Their UX and brand are good, but not great, and competition from companies building differentiated products specifically for high-value niches are making it very hard to grow with such a generic product. I’m not sure how this pans out but VC’s will likely lose their money... more information here : https://www.dhirubhai.net/pulse/6-months-ago-looked-like-ai-llms-atlas-product-management/?trackingId=jvB60muqSxSvDeG%2B%2BBa3Fg%3D%3D. If you enjoyed this post, don't forget to follow. I share one long-form post per week covering AI, startups, open-source, and more. That's all folks! Thanks for reading
CEO/Founder | Field CTO | Expert Attention Trainer
1 年For our general education in university, the model always followed T-shape skills. All engineering systems have trade offs so having ensemble models is usually the winning strategy