AI innovation
Thoughts on where the AI innovation will be happening
With the rollout of Chat GPT 3.5 and its successor, version 4, artificial intelligence has surged in popularity. Once the domain of a select few enthusiasts, today the entire development community eagerly embraces its potential.
Yet, ChatGPT no longer stands alone. First came Bard, then Meta introduced LLama, followed by Anthropic's Claude, and now Gemeni, Deep Mind’s latest model.
The interesting thing about Gemeni, is that it is only marginally better than Open AI’s model. It is too early to say what this means but it could well mean that we have reached saturation point when it comes to the capabilities.
Regardless, it's reasonably certain that a plethora of Language Model (LLM) options awaits in the future, with affordability likely on the rise. In my view, unless we see the advent of Artificial General Intelligence (AGI), improvements are likely to be marginal, signaling a slowdown in LLM innovation.
This leads to the deduction that the next wave of AI innovation is within applications. In the past, molding and integrating a model into software took six months of training; now, a similar process only demands half an hour.
Given this, existing software services with data, users, and established processes are poised to gain the most from AI integration.
In our case, every aspect of our software seems primed for enhancement through AI—this is the journey we have embarked on.
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