Cassie Kozyrkov shared:
Your Creative Edits = ✅ Copyrightable
Your Creative Edits + AI Output = ✅ Copyrightable
Unedited AI Output = ❌ Not Copyrightable
Your Prompts + Unedited AI Output = ❌ Not Copyrightable
Here’s the actual text from their published PDF: https://lnkd.in/ev2bv2Hv
Warren Powell just posted “The emergence of DeepSeek“: How could the Chinese do this so quickly? Because the technology is not that hard… I am not minimizing what OpenAI and the other LLM developers have created, but the core technology is a neural network, which is well understood by a large community. But let’s face it, compare this to inventing a breakthrough battery, creating fusion power, curing Alzheimers, or making good decisions over time for complex systems in transportation, energy, and health. Making good decisions over time, under uncertainty, is on an entirely different level in terms of analytics. Link Comment
Kurt Cagle just shared his experience of using an LLM tool for building a knowledge graph. He asked DeepSeek to “Generate a list of all of the object types that may be relevant to running a pizza shop” and after some prompts received a quite comprehensive ontology. He concluded: “After a few years of working with both LLMs and KGs, I’m still not convinced that an LLM can act as a broad-scale knowledge graph out of the box (there are still some big unresolved issues about the limitation of latent spaces and the mapping of narrative to conceptual models, for instance). However, as a tool for building knowledge graphs, an LLM can dramatically reduce both the complexity of constructing a knowledge graph and make it easier to test and visualize what that knowledge graph is capable of.” Link
David Pidsley from Gartner posted on LinkedIn: “Centralizing AI agent management under IT could stifle adaptive governance and innovation by focusing too much on operational “how” rather than strategic “why.” Instead, decision-making authority should remain distributed across business units, ensuring alignment with customer needs and strategic goals.“
“AI agents should not be viewed as “workers” or “interns” but rather as high-agency decision systems—designed to automate or augment specific decision model, executed and monitored for self-improvement.”
“A design principle for AI agents should be decision-centricity. AI agents are more akin to “the new apps” than human employees, and “managing” them requires a product management approach, not a talent management framework.” Link
In 2017 Prof. Bob Kowalski, the famous expert in Logical AI including Prolog, presented “Logic and AI” at the joint session of DecisionCAMP and RuleML+RR. It is interesting to hear his recent thoughts about today’s symbolic and sub-symbolic AIs – here is the link.
Sam Burrett posted today: Insurance policies are a nightmare for most consumers. It’s hard to know what’s covered and what isn’t. Could AI help? The Stanford CodeX team tested GPT4o’s ability to turn an insurance policy into code, which could be queried in a simple question/answer format. They found GPT4 produced tangled code and misunderstood key provisions.
However, OpenAI’s new ‘reasoning’ model was much better. o1-preview correctly encoded and insurance policy and hit 83% accuracy on coverage questions. Is that good enough for the real world? Not yet.
But as the researchers say: “We are on the cusp of an exciting era where AI can make legal solutions more accessible by applying human-like thinking, including planning and reasoning.” That’s exciting. Link
“Gartner predicts by the end of this year, 60% of analytics and business intelligence (ABI) platforms will claim to enable decision intelligence (DI), but only 10% will have a decision-centric UI to model and track decisions.” Link
An interesting event “Bridge: AI+ORMS” will occur on February 25-26, 2025 as a part of the 39th Annual AAAI Conference on Artificial Intelligence in Philadelphia USA. “The goal of this bridge program is to unite AI and OR/MS practitioners and researchers to improve trustworthy decision-making in key socio-technical areas such as supply chains, healthcare, crisis management, homeland security, robotics, wildlife conservation, medicine, transportation, and finance. It aims to equip them with better tools by familiarizing them with each other’s techniques and domains and bring the disciplines together to advance the research and application at the intersection of AI and OR/MS so as to improve decision-making.” Link
Here is the post of Rafael Brown:
Isaac Asimov: “There is a cult of ignorance in the United States, and there has always been. The strain of anti-intellectualism has been a constant thread winding its way through our political and cultural life, nurtured by the false notion that democracy means that ‘my ignorance is just as good as your knowledge.”‘
Generative AI isn’t democratizing anything creative. Far from it, Generative AI is democratizing laziness and stupidity. We didn’t have any shortage of either prior to generative AI. Actual writing is thinking put onto paper (or electronically or digitally). I was taught long ago by English & Literature teachers that writing was a way of composing one’s thoughts. Writing, or other compositions, regardless of medium, are essential to working out, mapping out, interrogating, and comprehending complex ideas, including one’s own thoughts. Don’t miss comments. Here is the most scary one.
The latest AI buzz word: “Agentic AI”.
“AI” has become such an empty phrase, and adding adjectives such as “agentic” simply adds more jargon.
When referring to software, can we please use terms that clearly describe products that businesses can actually purchase, with well-defined functionality? Link
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