Core Insights - Rocket Doctor AI Inc. has engaged Fundamental Research Corp. (FRC) for analyst coverage, aiming to enhance its visibility and credibility in the market [1][3] - FRC is a well-established independent research firm with a 22-year history, covering over 750 companies and recognized for its high-quality research [2] - The agreement includes a comprehensive financial analysis, an Independent Analyst Rating, and ongoing updates over an 18-month period, with a fee of CAD$35,000 paid to FRC [3] Company Overview - Rocket Doctor AI Inc. provides AI-powered healthcare solutions designed to improve access to quality healthcare throughout the patient journey [4] - The company’s technology includes the Global Library of Medicine (GLM), a decision support system developed with input from numerous physicians globally [4] - Rocket Doctor AI has empowered over 300 MDs to manage more than 700,000 patient visits, facilitating the launch of virtual or hybrid practices [5] Technology and Impact - The technology reduces administrative burdens, allowing for more meaningful interactions between physicians and patients [6] - The company focuses on underserved, rural, and remote communities in Canada, as well as supporting Medicaid and Medicare patients in the U.S. [6] - By leveraging advanced AI and connected medical devices, Rocket Doctor AI aims to redefine modern healthcare, making it more scalable and equitable [6]

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):Finally, researchers have open-sourced a new reasoning approach that actually prevents hallucinations in LLMs.It beats popular techniques like Chain-of-Thought and has a SOTA success rate of 90.2%.Here's the core problem with current techniques that this new approach solves: https://t.co/eJnOXv0abS ...

Finally, researchers have open-sourced a new reasoning approach that actually prevents hallucinations in LLMs.It beats popular techniques like Chain-of-Thought and has a SOTA success rate of 90.2%.Here's the core problem with current techniques that this new approach solves:We have enough research to conclude that LLMs often struggle to assess what truly matters in a particular stage of a long, multi-turn conversation.For instance, when you give Agents a 2,000-word system prompt filled with policies, tone r ...

Experimenting with the math and data behind large language models helped me understand how AI “thinks.” I wish everyone had the chance to do the same, writes WSJ software engineer John West. https://t.co/skWMrHRBxm ...

LLM Resource Estimation Tool - A tool helps estimate VRAM needs for LLMs [1] - The tool allows hardware configuration and quantization adjustments [1] - It provides information on generation speed (tokens/sec), precise memory allocation, and system throughput [1] Key Features - Eliminates VRAM guessing [1]

A new study shows large language models can mirror human purchase intent with near-survey accuracy—hinting at a future where synthetic shoppers replace real ones in market research. https://t.co/45LbG3WKbH ...

Experimenting with the math and data behind large language models helped me understand how AI “thinks.” I wish everyone had the chance to do the same, writes WSJ software engineer John West. https://t.co/9ldAeQiRib ...

Core Insights - Large Language Models (LLMs) like ChatGPT are being misused in professional settings, leading to inefficiencies and risks [1][2] Mistakes and Solutions Mistake 1: Treating LLMs Like Search Engines - LLMs are not fact-checking tools and can produce fabricated information, leading to serious consequences [3][4] Mistake 2: The "Copy, Paste, Send" Disaster - Using LLM output without human review can perpetuate biases and require more time to correct than creating original content [4][5] - Example incidents include a law firm submitting fake legal cases and Air Canada being forced to honor a non-existent policy generated by a chatbot [5] Fix for Mistake 1 and 2 - LLMs should be used to create first drafts, with human expertise added for finalization [6] Mistake 3: Sharing Sensitive Information - A significant 77% of employees admit to inputting confidential data into public LLMs, risking data breaches and regulatory violations [7][8] Fix for Mistake 3 - Organizations should establish clear policies against entering confidential data into public LLMs and invest in secure AI solutions [9] Mistake 4: Using LLMs for All Tasks - LLMs are not suitable for complex reasoning or specialized tasks, which can lead to decreased productivity [10][11] Fix for Mistake 4 - It is essential to use the right tools for specific tasks, recognizing the limitations of LLMs [11] Conclusion - LLMs should be viewed as powerful assistants that require human oversight to maximize their potential and minimize risks [12]

Technology & AI Understanding - The article discusses how experimenting with the mathematics and data behind large language models can help individuals understand how AI "thinks" [1] - The author, a WSJ software engineer, suggests that everyone should have the opportunity to explore the inner workings of AI [1]

Model Style Definition & Importance - Model style encompasses values (what models should/shouldn't do), traits (curiosity, warmth, conciseness), and flare (emojis, m-dashes), which together form demeanor [8] - Style matters because it shapes user experience, influencing how people perceive and trust the model, shifting usage from simple search to collaboration [9][10][11] Model Style Development - Model style is primarily set by pre-training (corpus defining knowledge and voice), refined by fine-tuning (adding tone, guardrails), and shaped by user prompts and app settings [12][13][16] - User prompts significantly influence model response style, with personalization features like memory further tailoring the style over time [14][15] Challenges & Considerations - Consistency in style is a major challenge because large language models approximate patterns rather than execute rules, making alignment difficult [27][28][31] - The company balances maximizing user autonomy and freedom with minimizing harm, setting default behaviors that users and developers can override within safety policies [23][24][25] - There is no single style that works for all users; the company aims to provide choice and flexibility for models to adapt to different contexts and needs [26][27] Future Directions - The company is focused on steerability, aiming to improve how well models follow customization requests for managing traits and flare [34][35] - The company aims to improve contextual awareness, enabling models to shift tone appropriately based on the user's context [36] - The company prioritizes AI literacy and accessibility, striving to make style management simple and intuitive for all users [37]