The panel dives into the hottest advancements in AI and machine learning, from large language models to small language models tailored for enterprises. Conversations highlight the critical need for privacy and security in AI solutions, addressing generative AI's implications. Experts discuss managing large models effectively and the exciting blend of AI with blockchain. Predictions for the next year reveal a focus on practical applications and innovation, while visions for recovery from the AI winter inspire hope for a vibrant tech landscape ahead.
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insights INSIGHT
Scaling and Context in LLMs
The scale of LLMs keeps increasing, with models like GPT-4 being multimodal and larger than predecessors.
Longer context windows improve use cases but don't fully replace targeted retrieval approaches like RAG.
volunteer_activism ADVICE
Use Small Models with RAG
Use smaller open-source language models with RAG to handle proprietary data securely and cost-effectively.
Deploy these models on private clouds or edge devices for better privacy control.
question_answer ANECDOTE
Multimodal Models Help with OCR
Internal use of GPT-4's multimodal feature was less effective for image generation.
OCR capabilities in multimodal models provided significant value for developers handling screenshots and stack traces.
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In this episode of the podcast, members of the InfoQ editorial staff and friends of InfoQ will be discussing the current trends in the domain of AI, ML and Data Engineering.
One of the regular features of InfoQ are the trends reports, which each focus on a different aspect of software development. These reports provide the InfoQ readers and listeners with a high-level overview of the topics to pay attention to this year.
InfoQ AI, ML and Data Engineering editorial team met with external guests to discuss the trends in AI and ML areas, and what to watch out for the next 12 months. This podcast is a recording of this discussion where panelists discuss how the innovative AI technologies are disrupting the industry.
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