Baita Services

We have a brand new feature for Baita: Real-time learning for our intermediate language model (ILM). We have a system called a resolver that catches when the ILM is not confident about a match, and determines how to handle it with a statistical model. The new feature works out what training data would have made the ILM more accurate given the resolver's output, and adds that back in to the original training dataset (only for trusted users). And it can do this all on the fly! Right now this feature is limited to mismatches caught by the resolver (an automated system), but we are working to engage real time learning directly from user feedback in the conversation itself. Once done, this will be a huge step forward in AI development. See a demo here: https://lnkd.in/gZkT99YK #artificialintelligence #ai #languagemodel

ChatGPT had quite a hiccup yesterday* with spouting unexpected and irregular outputs. This is a big problem with black box models, particularly LLMs: without explainable AI there is no good way to predict or detect these events. Effectively they are SLA failure events that cannot be easily monitored with automated tooling. And unfortunately as a lion's share of AI company's are built upon OpenAI's models, these errors propagate downstream and impact end users. Something I put a lot of effort into Baita are developer dashboards, logging, and inspection tooling. Every model, base model, and submodel comes with state tracking tools and all state data is open for developers to inspect. Plus runtime debugging tools, it's one of the benefits of running AI models on a virtual machine! #artificialintelligence #gpt #explainableai *Article: https://lnkd.in/gW8x9rQB

Here is a follow up to my last AGI theory video. This one goes into more detail about how we design algorithms that recursively break down problems without prior training or coding by a developer. Fair warning, it does go quite a bit into math and AI abstractions. #artificialintelligence #artificialgeneralintelligence #agi https://lnkd.in/gxJbiu9M

Hah, talk about crazy timing. Last week I demoed Baita's ability to easily store and recall long term information across multiple integrated apps (see https://lnkd.in/gZdyY65f), and guess what OpenAI just published today today: their experiments with long term memory (https://lnkd.in/gWZ2cnpd). Question is, will they be able to keep up with our tech? If you have long-term data needs with AI, we are ready to help you today. #artificialintelligence #ai #gpt

Our Baita AI can now be trained to interface with multiple applications and submodels at the same time, each tuned to solve a specific problem. We also avoid the accuracy limitations of context windows as seen in traditional LLM-backed AI. In the following demo, the submodels are previously trained on checkers, tic tac toe, and a separate video generation app. All running locally snappily on laptop hardware (although we can use gpt models if needed). If you want to know how to cut your AI costs by an order of magnitude while improving reliability, ping me. For the latest demo: https://lnkd.in/gZdyY65f