What It Really Takes to Build An AI System: It’s more complicated than many think
There’s a saying ‘the last 20% of the work takes 80% of the time’ and nowhere is that more true than AI systems.
Machine Learning
Natural Language v. Regex: The Context wars
Regexes are highly effective in the perfect world of computer data, but unfortunately the real world is much more complicated.
Liability & AI Malfunction: An AI System Developer’s Perspective
AI is rapidly being deployed around the world with few to follow. Along with the complexity of creating the technology, there remain many unanswered legal questions.
Accelerating Tensorflow Lite with XNNPACK
The new Tensorflow Lite XNNPACK delegate enables best in-class performance on x86 and ARM CPUs — over 10x faster than the default Tensorflow Lite backend in some cases.
NVIDIA DALI: Speeding up PyTorch
Some techniques to improve DALI resource usage & create a completely CPU-based pipeline.
The Four Pillars of Privacy-Preserving AI
We introduce the four pillars required to achieve perfectly privacy-preserving AI and discuss various technologies that can help address each of the pillars.
Why is Privacy-Preserving NLP Important?
A number of people ask us why we should bother creating NLP tools that preserve privacy. Apparently not everyone spends hours thinking about data breaches and privacy infringements.