define actions by Promise Theory train multiple neural nets to classify an action for a given input (train them differently to spice things up) take an environment for the agents to operate in (e.g. a 3d maze where collaboration is needed to escape) bind the agents interactions with a healthy dose of the wave collapse algorithm
(I forget exactly but I’m pretty sure this is from an Alan Watts lecture). A farmer needs some help around his farm. He puts up a sign in town, asking for someone with general skills to help around the farm. A gent arrives two days later with his toolkit, the farmer welcomes him and tells him there are some broken fences on the north side of his farm. The gent heads over to the area, spends the day fixing the fences, and comes back in the evening to tell the farmer it’s done.
This is an interesting one as I’d thought it was quite academic, with limited utility. Then I saw these graphs Error per epoch This graph shows the error per epoch of training a model on the data as is We can see that it takes around 180-200 epochs to train with a learning rate (eta) of 0.0002 or lower. Now compare it to this one Here we see the training takes around 15 epochs with a learning rate of 0.
From Green Mars by Kim Stanley Robinson.
Next I’m looking at the Adaline in python code. This post is a mixture of what I’ve learnt in my degree, Sebestien Raschka’s book/code, and the 1960 paper that delivered the Adaline Neuron. Difference between the Perceptron and the Adaline In the first post we looked at the Perceptron as a flow of inputs (x), multiplied by weights (w), then summed in the Aggregation Function and finally quantised in the Threshold Function.
The text discusses the development and significance of the Adaline artificial neuron, highlighting its introduction of non-linear activation functions and cost minimization, which have important implications for modern machine learning.
The author shares personal reflections on self-kindness and positive thinking as tools for finding peace amid societal challenges.
My wife and I have 3 main concerns with our daughters use of phones and Social Media what her videos and posts can be used for. That includes both the companies and any who has access (making fake videos in their likeness) loss of critical thinking addiction and the infinite scroll So we’ve got these 4 guidelines in place: phone off at 20h 30 minutes reading each night creative session per week meditation (2*5 minutes per week) I’ve also gone through these two posts with my 13 year old daughter.
Had a nice exchange about Agency with Paul Burchard on LinkedIn this morning. My thinking goes towards Agency being a secondary characteristic, definition even, of what we see as a result of senses, perception, intelligence, and consciousness. Those primary characteristics are from the Buddhist 5 Aggregates (physical form, senses, perception, mental activity, and consciousness). It was a great exchange, helped me clarify and link my thinking to yesterday’s post on the Perceptron.
This post looks at the Percepton, from Frank Rosenblatt’s original paper to a practical implementation classifying Iris flowers. The Perceptron is the original Artificial Neuron and provided a way to train a model to classify linearly separable data sets. The Perceptron itself had a short life, with the Adaline coming in 3 years later. However it’s name lives on as neural networks have, Multilayer Perceptrons (MLPs). The naming shows the importance of this discovery.
This is not normal nor is it ok. Meta is now the pervy old man you have to teach your kids to avoid.
transparency.meta.com/en-gb/pol…
#BeingHuman #ResponsibleAI
Nice opening. Looking forward to reading more!
Nous pouvons et devons bâtir l’intelligence artificielle au service des femmes et des hommes, compatible avec notre vision du monde, dotée d’une gouvernance large, en préservant notre souveraineté.
We can and must build artificial intelligence to serve women and men, compatible with our vision of the world, with broad governance, while preserving our sovereignty.
First test with a “reasoning” model, pleasantly surprised.
Not sure how to integrate it into my workflow though, there’s a big response!!
How do humans decipher reward in an uncertain state and environment?
Imitation seems the most likely, supported by extended solitude usually leading to a depressed state.
Feels like a question to run a human Monte Carlo Tree Search on!
#BeingHuman #ReinforcementLearning #InverseReinforcementLearning
If I could answer any question in science, I’d find out what involvement the neurons in our heart and gut have in decision making and how we view ourselves.
What about you?
#BeingHuman #ThatsNotAWeekendProject 🙃
intro Maths, computation, the mind, and related fields are a fascination for me. I had thought I was quite well informed and to a large degree I did know most of the science in more traditional Computer Science (it was my undergraduate degree…). What had slipped me by was reinforcement learning, both its mathematical grounding and value of application. If you’ve come from the previous post ([RL Series 1/n] Defining Artificial Intelligence and Reinforcement Learning) you know I’ve said something like that already.
I read an insightful comment within the current wave of LLM Reasoning hype. It has stuck with me. At least two reasons: It reminded me of my view that AGI is already here in the guise of companies It’s also a valid answer as to why I meditate and why Searle’s Chinese Room is mainly wrong Back to the comment, paraphrased it said: “the uncomfortable truth that these reasoning models show us that a lot of activities that we thought need human reasoning to complete simply need functional predictions”
intro I’m learning about Reinforcement Learning, it’s an area that has a lot of intrigue for me. The first I recall hearing of it was when ChatGPT wes released and it was said Reinforcement Learning from Human Feedback was the key to making it so fluent in responses. Since then I’m studying AI and Data Science for a Masters so with that I’m stepping back to understand the domain in greater detail.
I’ve recently dug into Temporal Difference algorithms for Reinforcement Learning. The field of study has been a ride, from Animals in the late 1890s to Control Theory, Agents and back to Animals in the 1990s (and on). It’s accumulated in me developing a Q-Learning agent, and learning about hyperparameter sweeps and statistical significance, all relevant to the efficiency of Off-policy learning but topics for another day. I write this as it took a moment for me to realise what off-policy learning actually is.
A Theory for Emergence of Complex Skills in Language Models Skill-Mix: a Flexible and Expandable Family of Evaluations for AI models www.quantamagazine.org/new-theor… youtu.be/fTMMsreAq… related to the authors Arora, S arxiv.org/search/cs Was that Sarcasm?: A Literature Survey on Sarcasm Detection Can Models Learn Skill Composition from Examples? Instruct-SkillMix: A Powerful Pipeline for LLM Instruction Tuning Goyal, A arxiv.org/search/cs Learning Beyond Pattern Matching? Assaying Mathematical Understanding in LLMs Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving