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"Why wouldn't I just simply use ChatGPT?"
As for GPTs, two topics should concern everyone:
Both individually and combined, neither are sustainable nor acceptable for actual unsupervised use in everyday life.
"What is your logo? Why is it like that?"
It's a play on Neural Networks. In machine learning, the way we represent how a machine worked through its thinking is called a Neural Network map.
AI nodes (circles) imitate human neurons, AI edges (lines) imitate human synapses.
"What's the
Difference in AIs?"
est. 1950s
Thinking and reasoning resembling
human intelligence.
Popular Uses:
• Unsupervised Systems
• Essential Services
• Crucial Decisions
• Sensitive Domains
• Consistent Results
est. 1950s
Computational Linguistics
Popular Uses:
• Speech recognition
• Text classification
• Comprehension
• Language Generation
est. 1997
How an agent should take actions to maximize a reward
Popular Uses:
• Executes actions and evaluates results
• Remembers actions that produced good results
est. 2017
NLP with many parameters.
Popular Uses:
• Chatbots
• Solving Deterministic Problems
est. 2018
LLM + RL with additional service-ware built on top.
Popular Uses:
• Creating pictures
"Why start in
Huntsville, Alabama?"
Rocket City, USA is ranked a top city to live in that is growing at a breakneck pace that has a well established engineering presence saturated with government wisdom.
We believe this city in particular is poised for serious continued growth now and for the next 10-20 years, and Reason would like to be a contributor to that growth.
"Why philosophy / mental health?
Doesn't that seem too broad?"
Reason has a never before seen method that deconstructs and discretely relates the building blocks of virtue ethics for each person's individual journey through life into machine learning optimized by mathematics.
Newly illuminated relationships unlock never before seen insights and unprecedented capabilities to end users.
"Is it agentic?"
Yes and in a much richer fashion than GPTs.
GPTs are pattern based and inherently do not have traditional AI inputs and outputs, their agents can only feed something into the "black box" GPT and see what comes out the other side. This is where GPT's traceability capabilities ultimately stops.
True ML/RL agents have an entire landscape of inputs and outputs to work with, as there is no "black box" to tiptoe around. This affords an an advanced ability of statistical and mathematical analysis. Every input, function, and decision can be traced with our agents."
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