THE WORLD’S MOST COMPLEX AI MODELS
Companies reliant on AI do not have adequate tools for anticipating and adapting how their models will behave when exposed to complex non-deterministic data environments.
So we made one.
95% of the decisions we make are driven by “System 1” thinking. This is our brains’ effortless, unconscious response to stimuli. This type of decision-making is fast and intuitive, but it’s also prone to error.
This is because “System 1” bypasses the rational and analytical processes of “System 2” thinking and the prefrontal cortex, resulting in unconscious cognitive bias.
AI built by humans magnifies this bias at an exponential scale. It’s led to racist virtual assistants, self-driving cars that ignore child pedestrians, and catastrophic misinformation campaigns.
If AI is going to make decisions that affect people’s lives, it needs to be free from prejudice.
But humans cannot remove biases they are not aware of. A symbiotic relationship with AI needs to exist to alert creators to misinformation that is blocking progress.
Metacognitive is the prefrontal cortex of AI, adding “System 2” scrutiny back into the decision-making process to enable AI and humans to take the next leap forward.
what makes metacognitive
AI is not ready for human environments – yet.
This is because AI is great at specific tasks. However, it can’t transfer that knowledge to new or unpredictable situations and respond appropriately or reliably.
Metacognitive is the only tool available that supports ML models to overcome this challenge by creating an automatic feedback loop between users and operators to constantly improve accuracy.
The result? Dependable ML models that adapt to repeated uncertainties with transparency, and continuously upgrade performance by reporting areas that need improvement.
An AI system that’s
kept in the loop
If data is bad, or an ML model is uncertain, Metacognitive alerts operators and shares a report explaining what has caused the issue. This information can then be used to integrate a specific response into the model pipeline, creating a feedback loop. So the next time Metacognitive triggers an alert – rather than the ML model taking the most “accurate” decision it can with bad data – the ML model will take a more preferable decision set by the operators.
OF AI MODELS
Our vision is to create a catalyst for the realisation of a reliable human AI interface
The potential of AI cannot be understated. Its business value is forecast to reach $1.59 Trillion by 2030, and there are game-changing use cases that will significantly improve everything from entertainment to e-commerce, and trading to technology. There is hope that AI will transform whole industries all over the world.
But AI advancement won’t happen and shouldn’t happen unless AI is made reliable, transparent, and accountable to users.
Current AI isn’t, Metacognitive is.
We want to provide the catalyst that can turn the AI we all hope for into a reality.
We’re happy to answer any questions you might have.