But it’s not just about fancy AI chatbots and text-to-image generators.
There are some highly speculation but incredibly impressive AI tools on the horizon.
Search engines currently use keyword-centric algorithms to provide relevant information to users.
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The concept of semantic search has been around for quite some time.
However, companies have difficulty implementing such functionality due to how slow and resource-intensive semantic search can be.
The solution is to map out vector embeddings and store them in a largevector database.
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There is currently no publicly available LLM that can match the raw performance of GPT-4.
You may remember the concepts ofAI agents from Auto-GPT, the AI tool that automates its actions.
The idea is for the agent to attain full autonomy through constant self-assessment and self-correction.
The problem is that the current models used in AI agents have little semantic understanding.
Multi-agent frameworks are set up to emulate how a startup company would work.
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