Bruce King’s Post

https://lnkd.in/gC9bUbaK This article highlights a significant limitation to research - solutions sell. Be it a genuine commitment to solving scientific problems or maintaining a scientific ego, publishers seek positive results. Distinguishing "positive results" from "negative results" can become ambiguous, and unfortunately, the author does not provide definitions. Let's dive in. 🤓 "Positive results" often refer to results that confirm a hypothesis, whereas "negative results" usually refute that hypothesis. However, this nomenclature infers a "good and bad" dynamic. Those of us who study nuance understand that the relationship between "good" and "bad" is, well, nuanced. It's easy to fall victim as authors. While I agree that the shift towards "negative data" publication is useful, at the very least, we must find means to publish in the meantime. As authors, we can own our fate by re-contextualizing data for publication by these means: 1. Experimentation. Results propagate questions. In other words, whether a result confirms or refutes a hypothesis, a follow-up question can be manufactured. We may just have to change our biases. The most straightforward approach may, therefore, be to find a useful lens and execute a new experiment. (Psst - this is a great time to execute humility and incorporate collaboration. Ask your community for help!) 2. Text. Negative results are not negative by nature. Instead, the results are likely considered out of context. It may be that the interpretation and discussion of the results need reworking with new considerations in mind. Instead of "ABC does not cause XYZ," reworking the text to "XYZ occurs independent of ABC" could read as more "positive." This especially applies to hypotheses and titles of papers. Lingual restructuring becomes easier over time and with more experience, so it is never too early to begin practicing. Be careful not to simply convert active to passive voice. 3. Structure. In academia, experiments are often not performed in the order they are presented to the reader. It is ultimately the author's duty to decide the order in which data is presented. This ties directly into contextualization. Is your justification for performing the study better used as an interpretation? In other words, can the vantage point of the experiments be changed for the sake of publishing the results? It's important to note that none of these suggestions involve data or statistical manipulation. The goal of each of these suggestions is not to manipulate; rather, it's to publish experiments to help each other promote knowledge. Happy writing!

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I have been diving deep into systems science lately and I just came across Nexus: A Brief History of Information Networks. This book explores how information networks have evolved—from ancient systems to today’s digital age. It’s very fascinating to see how these connections have shaped the world we live in.

From artificial-intelligence algorithms to zebrafish, this book take a precautionary approach to assessing how sentient such entities are. https://lnkd.in/d7XpwQ4E

Let me know how this works as a blurb for my book Extinction Event: Extinction Event starts with a middle-aged guy known as LP going back to school to study music only to discover a massive, global shift that will happen as a result of a sentient artificial intelligence known as GAIA. Through the book, we meet friends and foes of LP and various villains trying to prevent human progress with issues like climate change, animal extinction, political issues and more. The book ultimately forces the reader to realize that we're all on a very dangerous path and whether or not AI will save us or destroy us. The book was written specifically for the fast-growing #climatefiction category, but can also be described as a #thriller, #mystery and #dystopian fiction.

Using Vague Language About Scientific Facts Misleads Readers https://lnkd.in/dmVZt75s

LLM has a “word” model and not a “world” model. Humans have world model aka mental model, cognitive model. It’s formation is partly innate and partly based on learning while interacting with the world. It exists in our long term memory at various levels of abstractions. It’s not necessarily an accurate representation of the world. Aspects of reality captured depend on the goal of the individual and the task to be performed. Our world model is personalized based on our values and goals. Our world model enables to do the following 1) Reasoning 2) Causal reasoning 3) Planning 4) Counterfactual analysis 5) Predicting 6) Problem solving Our world model plays a key role in our perception of the world. The brain uses the world model in a predictive generative process for the next perception There are unfounded claims that LLM has a world model with all the characteristics of our world model. These claims are typically based on anecdotal examples. The very compelling illusion of world model in LLM is created due to the extraordinary mimicking ability of LLM. #ai #world #model #cognitive #mental #reasoning #causal #planning #prediction #llm

Shape: The Hidden Geometry of Information, Biology, Strategy, Democracy, and Everything Else