Drop a sample Subject, Scene and/or Style image and the AI creates a more detailed text description of each element and then recombines them. This takes away some of the hassle of creating prompts for a new AI image.
Oh – and here is your audio “deep dive” from NotebookLM.
Hello, my friends. If you, like me, have ever gazed into the cosmos of thought and marveled at the boundless intersections of science, technology, and human understanding, then you’re in for a journey. Today, we embark on a thoughtful exploration inspired by the writings of Jennifer L. Adams—a thinker deeply entrenched in the realm of higher education, where technology and learning converge like celestial bodies in orbit. Her central question is both provocative and profound: Is artificial intelligence truly that different from how our own minds work?
Such a question beckons us to consider the intricate dance of memory, intelligence, and pattern recognition, and to marvel at their manifestations both natural and artificial. Adams begins her inquiry not in a laboratory or lecture hall, but in a bathtub—a setting both humble and evocative, echoing Archimedes himself. She watches whirlpools form and dissipate, contemplating the microscopic life swirling in these temporary eddies. Her curiosity takes her to a surprising discovery: slime mold.
Ah, slime mold—a single-celled organism seemingly so simple, yet capable of navigating mazes and anticipating environmental changes. Imagine: a creature with no brain, no nervous system, no neurons, yet it remembers. Its memory, Adams suggests, may be chemical, a fundamental organization of matter with purpose. It is here, in this primal intelligence, that we are invited to see echoes of artificial intelligence.
Adams draws a parallel to large language models (LLMs), like GPT-4. These models, too, operate without consciousness, yet they predict patterns and generate responses so human-like that they often blur the line between machine and mind. Consider this: when tasked with responding to a zoo worker’s query, the AI adapts, contextualizes, and personalizes its response. It mirrors the dynamic complexity of thought, much as the slime mold mirrors memory.
But Adams doesn’t stop at algorithms. She speculates on the broader implications of intelligence—animal, artificial, and human. She recounts the intricate songs of whales, passed down through generations, a kind of aquatic epic encoded in soundwaves. Could their communication represent an organic language model, evolved naturally and independently of human cognition? What might these songs tell us about their history, their emotions, their view of the universe?
This thought invites an even deeper question: if intelligence emerges in myriad forms—from the chemical traces of slime molds to the silicon networks of AI—what truly defines intelligence? Is it memory? Pattern recognition? Adaptability? Or something ineffable, like the capacity for wonder or the ability to ask questions about existence itself?
Adams provocatively ties this inquiry back to the classroom, to the very essence of learning. Imagine a world where AI personalizes education for every learner, a virtual tutor attuned to the unique pathways of each student’s mind. Yet here, she invokes a cautionary principle: the Prime Directive from Star Trek, a reminder that with great power comes great responsibility. How do we harness AI to amplify human potential without losing what makes learning an inherently human endeavor?
The bathtub becomes a metaphor for our role in this vast experiment. As Adams muses about pulling the plug, ending the microcosmic swirl of life, we are reminded of the fragility of discovery, the delicacy of choice. How we engage with AI, how we integrate it into education, and how we define its role in our society will shape not only our future but our very understanding of intelligence itself.
So, as we stare into the starry vastness of possibility, let us ponder: What if AI is not merely a tool, but a mirror? A mirror reflecting our own creativity, our capacity for connection, our endless curiosity? And in that reflection, perhaps we might better understand ourselves—not as isolated beings, but as part of a vast and intricate cosmos, forever learning, forever exploring.
Stay curious, my friends. The universe awaits.
There is a bit of fiction within the Star Trek universe, that really made an impact of me. It is tied into the prime directive:
This is a directive that is often dismissed in the heat of an episode (particularly by Captain Kirk)… but in one movie, Star Trek II: The Wrath of Khan, it shows up as a crew of a science vessel is having a damn hard time finding a planet that has absolutely zero life on it. This concept, when we live in world view where people wonder if life exists anywhere else at all in the universe, was extremely captivating to me.
So I sat in the bath this morning, and watched as tiny and fleeting whirling vortexes formed when i lower my arms quickly into the water. I wondered if there was microscopic life in the water that has been doing it’s thing in this newly created body of water – maybe organizing, reproducing, eating – that had just been swirled into a chaotic storm. I wondered, if this microscopic stuff, might have thought something like “what the?” as everything was displaced around it. Which made me think of language.
I asked Alexa (yes I have an device in my bathroom – one with a nice speaker of course): What is the most primitive life form that we know has a memory. I asked this, because I thought that proving one has memory, proves some organization of thought – at least to me. I’ll accept arguments of course.
So the answer is slime mold; which to be honest is a much more primitive life form than I was expecting the answer to be. Turns out that these single-celled organisms can remember their way through mazes, and anticipate changes in temperature and light if they happen in a pattern. They do not have brains or even a nervous system. Other tiny primitive organisms that show memory include bacterias, ciliates and other more complicated, yet distinctly un “animal” like, plants and fungi.
So, AIs are using what is called a Large Language Model to appear intelligent. These break down vast amounts of training data to analyze what tiny bit of language is the most likely bit to come next. For example, if I write:
Hello, how are
Just as your phones some predictive text so you don’t have to text as many letter – you can see the most likely word could be
you?
If you are told that the person speaking is a 35 year old person who works at the zoo taking care of parrots and they are speaking to there coworker as they change shifts (changing the query) you might still guess
you?
or you might think
the parrots?
Of course, GPT 4o is a blabber mouth by default so when I ask it to finish the sentence
Hello, how are
It says
you doing today? I hope you’re having a great day! 😊
When I give it the query that I mentioned about, with the speaker being a parrot caregiver at the zoo, it responds:
you? I hope your shift went smoothly—any updates or things I should know about the parrots today?
Demonstrating its politeness and verbosity.
Anyway, back to my bath, I am scrubbing up, thinking about whether I think, therefore, I am – only because I have a runaway LLM running inside of my head. Blah blah blahing at me – and I have a chemical system that remembers things. I remember myself blabbing. I remember conclusions I have come to. I remember patterns. I remember grudges and fondnesses. (sometimes I can’t quite remember why I like or dislike someone – but I know I do. Similarly, I might not remember what happens in a book, but I can tell you if I really liked it. This will come in handy for retirement rereads.)
I remember me.
“If You Think You Can Hold a Grudge, Consider the Crow” New York Times Article.
[side note – did you know you can read the NYTimes as a person with an .edu email address?]
Do Animals who use sounds to communicate use a small or midsized language model? To be sure, a chemical one. As we as humans may be using a chemical LLM?
Did you hear about the whales yet?
I’m going somewhere with all of this.
I promise. But I won’t be finishing these thoughts in today’s blog.
Maybe my LLM can interact with your LLM and we can make some new ideas together?
Oh – and to tie things up a bit. If I had never drained my bath, would something eventually have crawled out of it, wondered about the planet and started a new civilization? We will never know, because similar to what Captain Kirk may have done, I pulled out the drain when I was finished.
A strawberry with two R’s
https://chatgpt.com/share/50375a02-92cf-4be3-875b-cc8c0a74af21
Here is the chat:
It sounds like you’re describing a system that prioritizes deep analysis and precision in understanding and defining words. Such a system would be highly effective at exploring nuances in language, ensuring that every detail is carefully considered before providing an answer. This approach might involve cross-referencing multiple sources, analyzing context, and even considering the historical evolution of a word’s meaning to provide the most accurate and insightful response. Would you like to dive into a specific word or concept with this level of detail?
4o
The word “strawberry” has 10 letters in total. Here’s the breakdown of how many times each letter appears:
So, the letter counts are:
4o
The word “berry” has 5 letters in total. Here’s the breakdown of how many times each letter appears:
So, the letter counts are:
4o
The word “straw” has 5 letters in total. Here’s the breakdown of how many times each letter appears:
So, the letter counts are:
4o
When you combine the letters in “straw” and “berry,” you get “strawberry,” which we’ve already analyzed. Here’s the breakdown of how many times each letter appears in the combined word:
So, the combined letter counts for “straw” and “berry” are:
4o
The breakdown of the word “strawberry” (which is “straw” + “berry”) is as follows:
So the letter counts are:
This matches the analysis of combining “straw” and “berry.”
4o
You’re right—I made an error earlier in the conversation. My initial breakdown of “strawberry” incorrectly listed the count of certain letters. The correct breakdown should indeed be:
Both the initial breakdown and the later one should have matched. Thank you for pointing that out.
Highlights:
Anonymous access to popular AI models, including GPT-3.5, Claude 3, and open-source Llama 3 and Mixtral.
‘DuckDuckGo AI Chat is an anonymous way to access popular AI chatbots – currently, Open AI’s GPT 3.5 Turbo, Anthropic’s Claude 3 Haiku, and two open-source models (Meta Llama 3 and Mistral’s Mixtral 8x7B), with more to come. This optional feature is free to use within a daily limit, and can easily be switched off.
I’ve been using this tool since it became publically available to mess around with, and boy oh boy is it a game changer. For me, I am trying to wrap my head around how this forms the future of education. I’m not as concerned about how we will stop cheating – but of course that is the immediate question that many of you will have. Rather than paraphrase what is already out there; I’m going to share with you a recent response that I think sums it up nicely from the EDUCAUSE Connect listserv:
“There are a few responses to ChatGPT (besides panic) that I’ve seen and walked folks through, and it kind of goes by what level of intervention you want to work with.
By tool:
By question:
By activity:
Long term, I think it’s going to take a lot of thinking about what it means to study and assess learning in the age of the chatbots.
As a fun test, I also gave the prompt “What should instructors do to prevent cheating using ChatGPT?” to ChatGPT, and this is the response it gave:
ChatGPT is a chatbot that uses natural language processing (NLP) to generate responses based on a given prompt. It could potentially be used to cheat on exams or assignments by generating answers to questions. Here are some strategies that instructors can use to prevent cheating using ChatGPT or other similar tools:
Overall, it is important for instructors to be proactive in preventing cheating, including the use of chatbots or other AI tools, and to take appropriate action if they suspect that a student has cheated.
Best,
Dan
—
Daniel Cannity (he/him)
Team Lead, Instructional Design, Engagement, and Support (IDEAS)
University of Massachusetts Amherst
e: dcannity@umass.edu”
I too actually asked the ChatAI about running submissions through plagiarism checkers and got some interesting answers as well. Also, I love an AI that has been designed to detect whether a writing was written by another AI. It is all very interesting. Hold on to your seats and enjoy this rapidly changing world!