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 ...

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):A great tool to estimate how much VRAM your LLMs actually need.Alter the hardware config, quantization, etc., it tells you about:- Generation speed (tokens/sec)- Precise memory allocation- System throughput, etc.No more VRAM guessing! https://t.co/FlaeMVaWmK ...

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]

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/rKzvXmB8T0 ...

Hello everyone. Uh I'm Laurentia. I'm a uh well I work on model behavior.But today I'm excited to talk to you today about the science of AI style. But before we get into that, a little bit about me. I'm actually a librarian by trade.I went to library school after learning that librarians work at Google on their search team. Uh, librarians help people access information and I wanted to help people access information on the internet. It's kind of cool.I still get to do that today, but grad school was ages ago ...