As machine learning (ML) capabilities advance, and with the advent of widely available low-cost cloud computing, AI will inevitably be applied to a wider range of more challenging problems, including those that affect the outcomes for millions of individuals throughout society. In high impact, complex settings, it simply isn’t realistic to train a model up front with a single batch of training data and expect it to perform well in all possible scenarios - such a naïve approach will almost certainly fail to capture some of the underlying nuance and edge cases of the situation, leaving gaps in performance and risk of failure during use. Active learning provides a promising way around the issue, empowering the AI to learn from human teachers in uncertain or novel settings and on new data. This architecture allows human experts to impart knowledge gradually as and when they become aware of AI shortcomings, improving performance through teaching and demonstration.
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For the past five years, data science has been praised as a technology that can unlock new applications and hidden insights for organisations. However, today it is struggling to live up to expectations.
5 min read
Quantum computers have the potential to be exponentially faster than traditional computers, revolutionising the way we currently work. While we are still years away from general-purpose Quantum Computing, Bayesian Optimization can help to stabilise quantum circuits for certain applications. This blog will summarise how Mind Foundry Optimize did just that.
3 min read
Even with low cost widely available cloud computing, it can take significant time and compute power to train machine learning models on large data sets. This is expensive and is often at odds with the net-zero carbon goals of many organisations today. Throwing more data at a problem isn’t always the best answer, and by using AI that is responsible by design, we can reduce these problems while maintaining performance. Active learning is one of the methods we use at Mind Foundry to achieve this.