Decisions like these are profoundly personal. Whether you choose the rigorous path of a Ph.D. or the dynamic world of entrepreneurship, the key is aligning with what resonates with your spirit, skills, and aspirations.
@whats_ai
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Learning, Knowledge Sharing, and Career Growth in Tech
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• Those who enjoy learning and improving for long-term projects. • Individuals who derive satisfaction from sharing knowledge. • People who aspire to work in big companies that value formally recognised titles.
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Academic Career Paths in Research and Teaching Excellence
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• Individuals who love deep research. • Aspiring professors. • People who are okay with working on complex problems for long period. • Those who value contributing to a vast body of knowledge. • Individuals considering a career in academia or teaching.
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Who Should Consider a PhD After Dropping Out
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Who I think is a Ph.D. for… after dropping out of one I recently shared a video about my PhD, or rather, why I decided to quit. Here are my thoughts on who should consider doing one.
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LLMs Need User Control for Safe Powerful Applications
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LLMs are not yet generalists; they work best under user control. As they evolve, use them specifically for safe, shared, and powerful applications. Harness the AI's usefulness while mastering it.
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LLMs Develop Apps Through Agent-Based Models and Strategy
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LLMs can develop code-based apps, manage complex tasks, and construct business models with proper strategy. MetaGPT and Baby AGI models use distinct agents, guided by human SOPs for precision for the former.
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LLMs Support Writing Coding and Skill Acquisition Tasks
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Whether it's a business strategy AI or one based on Plato's works, LLMs provide invaluable support in tasks like writing, coding and skill acquisition.
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AI Tutor Built with RAG and GPT-4 Technology
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We created an AI tutor from existing documents using Retrieval Augmented Generation (RAG) and GPT-4. This method ensures answers stay within our dataset, enhancing accuracy.
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Understanding Large Language Models Beyond Training and Fine-tuning
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Comprehending large language models (LLMs) like GPT-4 or Llama 2 extends beyond training and fine-tuning, encompassing their impact and uses. It's more than coding skills, as even non-coders can benefit and add value.
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Detecting Hallucinations in Language Models via Explainability Methods
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Explainability, demonstrated via methods like saliency maps, elucidates a model's decision pathway, pinpointing its focus area. It shows the model failed not from misidentification but incorrect feature attention. How to detect this with language models and hallucinated facts?