A common assumption is that we need significant amounts of data in order to do deep learning. Many companies wanting to adopt AI find themselves stuck in the “data gathering” phase and as a result delaying the use of AI to gain competitive advantage in their business. But how much data is enough? Can we get by with less?

In this talk we will explore the impact on our results when we use different amounts of data to train a classification model. It is actually possible to get by with much less data than we might expect. We will discuss why this might be so, in which particular areas this applies, and how we can use these ideas to improve how we train, deploy and engage end-users in our models.

Gradient descent continues to be our main work horse for training neural networks. One recurring problem though is the large amount of data required. Meta learning frames the problem not as learning from a single large dataset, but learning how to learn from multiple related smaller datasets. In this talk we'll first discuss some key concepts around gradient descent; fine-tuning, transfer learning, joint training and catastrophic forgetting and compare them to how simple meta learning techniques can make optimisation feasible for much smaller datasets.

The future is here. Quantum computers are becoming an increasing reality, and with them the potential for quantum machine learning applications. What does a AI with quantum computers look like? What kind of problems can we hope to tackle with this new capability, that we can’t presently? Are there any caveats? In this talk we will learn what quantum computers are, and the kinds of scenarios in which we can expect to see quantum machine learning used in, the limitations, and what the field is working on.