To create Grokipedia, XAI trained a version of Grok to be "maximally truth seeking" and skilled at cogent analysis. They then tasked this AI with cycling through Wikipedia's top million articles, using the entire internet to add, modify, and delete information to improve accuracy and context.
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To maintain quality, 6AM City's AI newsletters don't generate content from scratch. Instead, they use "extractive generative" AI to summarize information from existing, verified sources. This minimizes the risk of AI "hallucinations" and factual errors, which are common when AI is asked to expand upon a topic or create net-new content.
Building an AI application is becoming trivial and fast ("under 10 minutes"). The true differentiator and the most difficult part is embedding deep domain knowledge into the prompts. The AI needs to be taught *what* to look for, which requires human expertise in that specific field.
Wikipedia was initially dismissed by academia as unreliable. Over 15 years, its decentralized, community-driven model built immense trust, making it a universally accepted source of truth. This journey from skepticism to indispensability may serve as a blueprint for how society ultimately embraces and integrates artificial intelligence.
The early focus on crafting the perfect prompt is obsolete. Sophisticated AI interaction is now about 'context engineering': architecting the entire environment by providing models with the right tools, data, and retrieval mechanisms to guide their reasoning process effectively.
When prompted, Elon Musk's Grok chatbot acknowledged that his rival to Wikipedia, Grokipedia, will likely inherit the biases of its creators and could mirror Musk's tech-centric or libertarian-leaning narratives.
Training models like GPT-4 involves two stages. First, "pre-training" consumes the internet to create a powerful but unfocused base model (“raw brain mass”). Second, "post-training" uses expert human feedback (SFT and RLHF) to align this raw intelligence into a useful, harmless assistant like ChatGPT.
Unlike consumer chatbots, AlphaSense's AI is designed for verification in high-stakes environments. The UI makes it easy to see the source documents for every claim in a generated summary. This focus on traceable citations is crucial for building the user confidence required for multi-billion dollar decisions.
Unlike chatbots that rely solely on their training data, Google's AI acts as a live researcher. For a single user query, the model executes a 'query fanout'—running multiple, targeted background searches to gather, synthesize, and cite fresh information from across the web in real-time.
Identify an expert who hasn't written a book on a specific topic. Train an AI on their entire public corpus of interviews, podcasts, and articles. Then, prompt it to structure and synthesize that knowledge into the book they might have written, complete with their unique frameworks and quotes.
To move beyond simple engagement signals, Elon Musk's X is deploying Grok to read and understand up to 100 million posts per day. The AI will categorize and match content to individual users, a personalization task he says is impossible for humans to perform at scale.