The few things I learned from this module- 1. What is LLM and Generative AI? 2. How are the growing interests by scientists, collaborators and editors increasing with the time? 3. what are the pros and cons of using Generative AI? 4. Upto what level and how Elsevier is allowing it's writers to use AI under human observation? 5. What are the regulations are being made in China, European Union and America.
Learning from LLM and Generative AI module
This title was summarized by AI from the post below.
More Relevant Posts
-
One thing I deeply love about the emerging AI world is that it consistently brings us back to philosophy. In the brilliant research paper conducted by Philipp Koralus he dives deeper into focusing more on AI's that ask better questions using the Socratic method... Not one's that just word vomit and answer to you. Read the full paper in the link below: https://lnkd.in/dRKuV5Tf
-
-
Do generative AI models have cultural leanings? A new study led by MIT Sloan’s Jackson Lu suggests they do. In examining two of the world’s most widely used generative AI models—OpenAI’s GPT and Baidu’s ERNIE—Lu and his colleagues found that the models’ responses shifted depending on the language of the prompt. Learn more at the link below. https://bit.ly/424kHRg Massachusetts Institute of Technology MIT Sloan School of Management
-
-
Step inside the 20th Anniversary of UNESCO’s Convention 2005. A panel of industry leaders came together to discuss cultural expression in the UK and what we can do to protect our sector from generative AI. Check out the highlights and read more ⬇️ https://lnkd.in/eSfRJPXw
-
True advancement comes from understanding where we add value and applying AI in ways that strengthen rather than imitate. This idea reaches beyond national strategy. It applies to how every organization & leader approaches technology. Wisdom in AI is not about possession. It is about alignment between human purpose & machine capability. That balance is at the heart of our Responsible AI Webinar on 10/22. Learn more here: https://lnkd.in/eH5Vhuxt
Dr. Murthy questions the rush toward AI nationalism and asks whether every country truly needs its own language model. He argues that progress depends on understanding strengths and applying AI where it adds real value rather than following trends. His perspective reflects the kind of thoughtful approach that guides our work at Soothsayer Analytics. To learn more about how we think about responsible and practical use of AI, join our upcoming Responsible AI Webinar on October 22nd:
-
Difference between generative ai and machine learning https://lnkd.in/ga-VEY49 Learn the key differences between generative AI and machine learning. Understand how they work, where they’re used, and why it matters in today’s AI-driven world.
-
-
Are you overwhelmed by the sheer volume and complexity of digital information in your cases? “Becoming a Digital Archaeologist by Gathering Early Case Intelligence” gives you a practical guide to mastering ECA and using powerful tools like sampling, entity extraction and generative AI. Unlock essential strategies in our latest blog: https://bit.ly/471hR2g
-
-
📂 Drowning in digital case data? You're not alone. Check out this quick guide on how to think like a digital archaeologist and dig up early case insights using tools like sampling, entity extraction, and generative AI. 💡 It’s all about working smarter, not harder. Read the blog here: https://bit.ly/471hR2g #LegalTech #ECA #SmartLitigation #GenerativeAI #EarlyCaseAssessment
Are you overwhelmed by the sheer volume and complexity of digital information in your cases? “Becoming a Digital Archaeologist by Gathering Early Case Intelligence” gives you a practical guide to mastering ECA and using powerful tools like sampling, entity extraction and generative AI. Unlock essential strategies in our latest blog: https://bit.ly/471hR2g
-
-
We just read the Nvidia+UMD's SATLUTION paper on building a better SAT solver https://lnkd.in/gNHS5CiJ Impressive claims but odd that they don't release their code despite building off of open-source SAT solvers. Also odd that the paper is littered with claims of self-evolution yet it hides this key fact in the appendix: "[agents] lacked sufficient domain-specific knowledge at the idea level, especially for nuanced aspects of SAT solving. To compensate, we incorporated human guidance in later stages by explicitly steering the agent’s “thinking“ process" How does one reproduce or trust such work?
In the systems seminar at UW this quarter, we are discussing papers that might help us design entire systems (beyond individual heuristics) using AI. Our current list (curated by Simon Peter) is https://lnkd.in/gRcqhigH What else should we be reading?
