Technology for machines
that learn on their own

There is no need for a separate training phase

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Main benefits of our technology

The traditional approach to ML

Standard procedure is often a struggle when you need to collect a dataset in advance, hire and manage a team to look after your AI models, evaluate their performance, and re-train regularly to adapt to an ever-changing environment.

Collect the dataset

Train the model

Evaluate the results

Re-train to optimize

We offer an alternative!

“Deep learning is an amazing technology and hugely useful in itself, but in my opinion it’s definitely not enough to solve AI, [not] by a long shot”

Demis HassabisDeepMind CEO

“My view is throw it all away and start again, I suspect that means getting rid of back-propagation.(…) We clearly don’t need all the labeled data.”

Geoff HintonThe Godfather of Deep Learning

“We need systems that can handle changes and do continual learning, lifelong learning and so on (…) This is a long-standing goal for ML”

Yoshua BengioDeep Learning pioneer

In ML we distinguish

the following types of learning:

Our Focus

Continual Learning

Use a pre-trained model, then adapt it in real-time with data coming through available sensors

Combine self-supervised, unsupervised, and reinforcement learning together to evaluate what and how to learn and how to drive perception and actions.

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Supervised learning

Collect the dataset, provide correct answers, and optimize the model. But what if you don’t have the necessary data? Don’t have resources to label all of it? And a team to maintain it all the time?

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Reinforcement learning

Define a target and measure the success rate to guide action. A very good way to direct AI behavior in games and simulations where the objective is clear (e.g. win the game, get the most points possible, get to the destination).

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Unsupervised learning

Analyze the internal relations and similarities between available data samples, and learn to reconstruct them. Useful to some applications, but without any base to rely on – it is hard to use it successfully on its own.

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Case studies

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Maciej WolskiCEO

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