Thanks to those who were able to join us last week for our first AI event with InstaDeep. This, the first of four events, looked at Scaling AI in the Enterprise and helping business leaders to realise the potential of introducing and scaling AI in their businesses.
The evening was split into two sessions; the first containing three lightning talks from Gary Richardson, MD of Emerging Tech at 6point6, Dr. Marek Barwinski, Head of Machine Learning at InstaDeep and Prof. Hani Hagras, Chief Science Officer at Logical Glue. These talks were followed by a panel discussion with Gary Richardson, Dr Marek Barwinski and Dr. Rebecca Pope, Head of Data Science & Engineering at KPMG UK, moderated by Isha Chander, Marketing & Sales Director at 6point6.
Below is a short summary of each of the lightning talks and the panel discussion.
If you search for the term ‘feature stores’, what you’ll find is, well, not a lot. That’s because feature stores, while growing in prominence and importance, are yet to be recognised for the vital role that they play in your data strategy. They should form the cornerstone of your enterprise-wide machine learning ambition. In a world of increasing data governance and with data monetisation in mind, it’s important to understand exactly what they are and why they should matter to your organisation.
Made up of precomputed features, feature stores create highly curated data to feed into machine learning algorithms — so they represent the computed end of the Data Lake.
Feature stores enable highly curated data and consistent training data sets for machine learning. This offers full traceability from the data source to the final outcome. You can read more about feature stores on Medium.
User profiles in business often come from human defined clusters and tags. In AI-first personalisation products, they come from aggregating user behaviour events and using them to train a predictor of future user actions and associated business metrics. This way we create a positive feedback loop between recommendation quality and user understanding. To bootstrap AI into your business you need to store business metrics and decisions into logs, train a first decision agent mimicking existing approaches, introduce automated decisions with human in the loop, and track the differences and outcomes in order to improve the model. The right way to combat bias in decision-making is by constructing a neural architecture that bottlenecks the internal representation such that antidiscrimination protected features cannot be reconstructed. Finally, transfer learning allows for re-using the architecture from expensive pretrained models which can save time and effort when applying it to your problem.
There has been a huge increase in the amount of digital information being generated, stored and available for analysis. The use of complex AI algorithms like Deep Learning can result in a lack of transparency to create ‘black/opaque box’ models meaning we cannot tell why a system made a decision, they just provide an answer. Enterprises, especially those in financial services, need explainability. This requires transparency, causality, fairness, and safety. This requires computational intelligence which involves developing computational models that try to mimic nature in problem-solving. Fuzzy Logic Systems attempt to mimic human thinking, but not like neural systems do, rather it focuses on the approximate, guestimating side of the brain. This will help to scale AI to make more decisions in an organisation.
The panelists discussed how to go from proof to production with AI successfully, and the hardening of feature stores was mentioned as a key factor. Furthermore, the panelist discussed cultural barriers to installing AI in the enterprise and how one needs to be more respectful of an organisation’s capabilities and goals. Ethics was also emphasised as an important developing factor in how AI affects issues such as climate change or agriculture. Finally, the panellists agreed that real-life applications of AI are indeed an exciting arena; with diagnostic imaging within medicine highlighted as a particular area that AI can help with, as it contributes significant value to an issue that will affect everyone.