The Future of AI – LLMs
Where Are GPTs Taking Large Language Models?
OpenAI recently announced GPTs – allowing radical customisation of ChatGPT for niche applications. This represents an profound development.
GPTs could let anyone create specialised variant models for areas like education, healthcare and finance. Immensely impactful capabilities.
However, huge ripple effects emerge on the AI landscape. Fragmentation risks, incentive gaps for knowledge sharing and control dynamics on issues like privacy.
This piece analyses 3 key dimensions GPTs influence for the future of large language models:
- Democratisation enables breakthrough applications but requires oversight on decentralised models
- Avoiding siloed progress as tailored models proliferate necessitates architecture for sharing
- Maintaining standards on transparency and accountability grows challenging
Early access to GPT’s – Frame’s How to use AI GPT
The Democratisation Dilemma
By empowering anyone to build specialised LLM variants, GPTs dramatically lower barriers to model development. This democratisation could lead to an exponential increase in models for narrow domains.
However, the quality and safety of these models will vary wildly. Without oversight, we could see issues like embedded biases, factual errors, harmful advice and misuse of private data. What governance mechanisms will OpenAI implement as an increasingly dispersed array of models emerge?
The Incentives Challenge
As more models emerge, there is a risk of fragmenting progress instead of knowledge sharing back to the commons. GPT creators may hoard training data, model checkpoints and insights as proprietary IP.
OpenAI will need compelling incentives to encourage contribution back to the central foundations that downstream variants build upon. Otherwise, we lose the open character that has made these models so versatile to date.
The Interoperability Quandary
If specialised models can’t interoperate, we lose the ability to combine capabilities. Medical models may not link workflows to insurance models. Scientific models can’t easily consume legal datasets.
Alignment mechanisms like the GPT Store could encourage common interfaces and architecture patterns. But OpenAI still has work ahead to make cross-boundary data flows and model pipelines frictionless for end users.
The Control Dilemma
As highly specialised and customised LLMs permeate business and society, OpenAI loses a measure of control. Models could operate under different norms on privacy, transparency and accessibility unless OpenAI sets firm policies.
Ambient accountability becomes harder with concealed data flows and interfaces through proprietary models. This underscores the urgency around algorithmic audits, enforceable ethics standards and monitoring systems as GPT adoption accelerates.
In summary, GPTs surface tension between customisation and consistency as AI proliferation gathers momentum. They underscore many open questions regarding development incentives, interface standards, API governance and responsible innovation principles across decentralised models.
How OpenAI balances democratised development with shared responsibility and coordination will prove critical in GPTs delivering societal good versus fragmentation.
Want to dive deeper on how alternative conversational AI models contrast?
Read our in-depth comparison of capabilities and limitations between ChatGPT and Claude. The analysis explores key dimensions like:
- Knowledge scope
- Language proficiency
- Use case potential
- Ethical dynamics
Get the full breakdown in our article ChatGPT 3.5 vs Claude: How Do the AI Assistants Compare?