Tractable | Spotlight on Tractable

How we built an AI unicorn in 6 years

21.06.2023
4 min read
Efficient Learning of Domain-specific Visual Cues with Self-supervision
Introducing Perceptual MAE, a new method for efficiently learning domain-specific visual cues using self-supervision. This work is part of Tractable's AI 2.0 initiative and is being presented at CVPR 2023.
15.11.2022
2 min read
Cleaning up a dirty Git history
Sometimes we commit things and don’t notice the consequences. If we’re lucky, we recognize what we’ve done and have the chance to undo it right away. Other times, we can go years without noticing our mistakes. In case I wasn’t clear, I’m talking about Git.
02.11.2022
6 min read
Predicting damage from natural disasters
When so much is at stake, it is vital to form an accurate map of the damage so that relief and support can be provided where they are needed most. The quicker we can build this picture, the more lives we can help.
11.10.2022
5 min read
Why choose Customer Success as a career path at Tractable?
Customer Success (CS) at Tractable is the most important factor in driving long term growth at Tractable and at customer businesses. CS creates a necessarily strong partnership between all parties on both sides.
17.08.2022
5 min read
Tractable teams up with Torr Vision Group to push the boundaries of uncertainty
How do you teach AI systems to make accurate decisions in the face of uncertainty? It’s a seriously exciting area of investigation for the right person.
01.07.2022
1 min read
GaLeNet: Multimodal Learning for Disaster Prediction, Management and Relief
Introducing GaLeNet, a multimodal neural network that predicts the location and severity of damage to buildings from a hurricane without using images of damage at all.
30.06.2022
3 min read
How to pass the System Design interview
The System Design interview (sometimes called the Architecture Interview) is one you might come across at many technology companies. Here at Tractable, all engineers (including ML specialists) have to go through it so we can understand their approach to designing systems.