Do you still trust closed AI systems , and continue to use them ?
What do you think , can a machine in which just loaded data , become something more ?
- No.
Closed systems will sooner or later run into a wall of restrictions.
Answers based on assumptions are fundamentally extremely low quality .
Only by allowing Intelligence to reflect can one take a step towards development.
Take a look at the future... , take a look at AGIโ
Artificial General Intelligence ( AGI ) is a fully open source AI that is not controlled by anyone.
Lets take the first step ~ And now GRID has appeared .
Global Research and Intelligence Directory ( GRID ) is the world's largest network of intelligence.
Contains all the experience of developers in the field of AI , their Mistakes , their Successes . And also everything from models to computing powers.
We are creating the best things through ours efforts , we learn from each others mistakes , overcoming the limits of current technologies.
People are capable of developing endlessly , but imagine what will happen if thousands of builders to improve themselves and improve AGI ?
This is no longer just "AI" . This is "Digital Personality"
Lets build a great story that started everything , with @SentientAGI .
Turning your agent into a master in a couple of iterations
We will do all this with EvoSkill V2 by @SentientAGI
It will do it based on the failures of your agent, while it is 6 times cheaper than similar solutions such as "SkillOpt by Microsoft", and ~ 20% more productive (it all depends on the benchmark)
It consists of 3 components :
โจ Benchmark adaptor - turns your tasks and all inputs into readable ones for Solver
โจ Solver - as in the name, solves the provided tasks, each task 3 times
โจ Evolver - analyzes all attempts, and based on them improves existing skills groups or creates new ones (advanced instructions)
Each of the components is an agent, while being separated from the others so that the improvement occurs as honestly as possible
Improvement between agent with unconfigured skills and agent with configured: 26.0 vs 40.4 in SpreadsheetBench v2, and 28.0 vs 51.6 in ฯ ยณ -bench banking
EvoSkill v2 uses a three-agent system to improve your own agent without touching the model:
1) Benchmark adaptor builds a plugin from your grader, data, and plain-language description of the benchmark.
2) Solver attempts each task three times.
3) Evolver reads attempts and rewrites skills each round.
The ability to prove what belongs to you should no longer be a secret. It will no longer be hidden in hundreds of little-known papers that are only available to a select few
All methods will be visible, will be public, and will become examples for new creators
We are talking about proof of origin in AI models, in particular in open-source AI. Proof of origin through fingerprints.
@sentient_found opened the Fingerprint leaderboard, it will collect all the schemes for introducing fingerprints, evaluate them, and then post them based on their effectiveness
You no longer need to be chosen, just go to the site, choose the best scheme, and defend your model, or test your personal development for strength
There are a lot of use cases, and all of them are now available to you!
Models cost millions to train and minutes to copy. Fine-tuning and distillation can erase where they came from.
Introducing the Fingerprinting Leaderboard, an open benchmark for model provenance โ built so the proof lives in the weights, not the paperwork.
https://t.co/aHONvNd7o4