“We want America to be inventing this stuff,” Eric Schmidt said during POLITICO’s summit on artificial intelligence. “Or at least the West.”
The U.S. needs an urgent national strategy on developing artificial intelligence technology to counter the rising competition from China, said former Google CEO Eric Schmidt, chair of the National Security Commission on Artificial Intelligence. Read More
Daily Archives: October 16, 2020
‘Less Than One’-Shot Learning: Learning N Classes From M<N Samples
Deep neural networks require large training sets but suffer from high computational cost and long training times. Training on much smaller training sets while maintaining nearly the same accuracy would be very beneficial. In the few-shot learning setting, a model must learn a new class given only a small number of samples from that class. One-shot learning is an extreme form of few-shot learning where the model must learn a new class from a single example. We propose the ‘less than one’-shot learning task where models must learn N new classes given only M < N examples and we show that this is achievable with the help of soft labels. We use a soft-label generalization of the k-Nearest Neighbors classifier to explore the intricate decision landscapes that can be created in the ‘less than one’-shot learning setting. We analyze these decision landscapes to derive theoretical lower bounds for separating N classes using M < N soft-label samples and investigate the robustness of the resulting systems. Read More
Deep reinforcement learning, symbolic learning and the road to AGI
Tim Rocktäschel on the TDS podcast
This episode is part of our podcast series on emerging problems in data science and machine learning, hosted by Jeremie Harris. Apart from hosting the podcast, Jeremie helps run a data science mentorship startup called SharpestMinds. Read More
What Does It Take To Scale An AI Company? Founders And Investors Share Their Insights
In recent years, it’s become increasingly clear that Artificial Intelligence (AI) startups can scale to become $1 billion-plus companies. When it comes to innovation at the early-stages, there is a pressing need to differentiate between hype and actual potential for scale and impact. Today, many startups claim to be innovating through the use of AI. Whilst some succeed, others fail to deliver upon their promise. How does one go about cutting through the noise and identifying the AI startups that have the most potential for scale?
Ask four key questions:
- Is the company solving a high-value use case in a specific domain?
- Does the team have deep domain expertise along with access to unique datasets and other assets?
- Does the team have deep technical, AI, and data expertise?
- Does the team have a commercial balance with expertise in selling and working with enterprises?
Rick's Cafe AI 