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Will AI replace tech jobs or will your job evolve? Is it too late to switch to a career in AI/ML? If not, where do you start? What AI skills should you build?
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Will AI replace tech jobs or will your job evolve? Is it too late to switch to a career in AI/ML? If not, where do you start? What AI skills should you build?
More details included about the secretive safety start up. Mostly we get to see who is on the team and working to help this venture. The stated goal is to “understand the universe”. We are interested to see how this plays out.
Clibrain, a Spanish AI startup, has launched Lince Zero, a large language model optimized for Spanish. The model, available under open-source license, is aimed at delivering enhanced performance for Spanish language queries.
Shutterstock is expanding its partnership with OpenAI, providing data to train AI models in return for advanced tech access. The move comes despite industry debates over generative AI and copyright issues.
This study proposes a method to improve the functionality of chatbot systems like ChatGPT by using ontology-driven structured prompts that allow for more effective information retrieval and response generation. By showcasing its effectiveness in the Ukrainian language within rehabilitation, the study illustrates that this approach is adaptable and can be applied to a variety of other large language models.
This paper presents CAT-ViL, a deep learning tool that can provide detailed answers and visualize them on surgical videos, serving as a valuable resource for medical students and junior surgeons.
A new automated system, Text2Cinemagraph, can transform text descriptions into moving artistic images called cinemagraphs, even when the text includes imaginary elements and stylistic details.
a16z released an AI companion app starter kit.
GPT Researcher is an autonomous agent designed for comprehensive online research on a variety of tasks.
This repo holds the code, demos, and logs for Reflexion: Language Agents with Verbal Reinforcement Learning.
This blog post discusses the "Picks & Shovels" behind Generative AI leaders like OpenAI. The post covers topics like foundational models, compute, frameworks, orchestration & vector databases, fine-tuning, labeling, synthetic data, AI observability, and model safety.
Bill Gates discusses the real but manageable risks of AI, making the case that we’ve successfully mitigated the risks from past innovations.
A profile into Anthropic’s efforts to simultaneously balance competition with a desire to build safe AI models.
Mike Loukides argues that the only thing to fear is failing to make the transition to AI-assisted programming.
This article discusses the commercial ecosystem that is emerging around data in the age of AI. It argues that new pricing and usage models, compliance and data rights, and data marketplaces are needed to update the current system.