Who are Faculty?

On 7th February we rebranded ASI Data Science and SherlockML to become Faculty. We have always been a community and have always worked to foster the human and machine capabilities to make artificial intelligence (AI) real. Bringing all our products and services under one brand, we felt that the Faculty name perfectly represented our character, and also our purpose.

While we now have a different name, we remain committed to the same values we have had since we began: to seek truth – trying to establish how the world really works; to execute pragmatically – with a bias to action and never letting analysis become paralysis; to foster talent – being the best place to learn how to make AI real; and to invert – always invert.

What are we up to in 2019?

With a new brand that better represents us and what we do, we are excited about 2019.

We’re still growing rapidly, and delivering an increasing number of AI projects internationally, as well as in the UK. Among the diverse range of projects we have running right now, one I’m really looking forward to given my own career history is the AI review for the Government that will inform the future shape of public services. In fact, Sana Khareghani, Head of the Office for Artificial Intelligence, spoke about our partnership last week at our launch event, and how with the right principles the partnership between human and machine will elevate public services to a new level of performance.

We are already seeing the death of the ‘proof-of-concept’ – a welcome development as AI matures in 2019 to the point where most firms expect to continue to the operational deployment of a machine learning (ML) model rather than marvel at its theoretical benefits within an innovation lab.

Getting models operational is hard work, but it brings huge value. Our Faculty Platform remains the foundation for the rapid deployment of most of our data science models, and during 2019 we will continue to mature its range of features and integrations. The next feature we will be releasing shortly is to help teams manage experiment-tracking with different ML models. Once a team has built a model, this feature will allow them to systematically track the performance and impact of different model settings to help them optimise model performance. The team will write more on that soon.

While helping organisations to apply AI in the real world, we also need to ensure it is deployed responsibly. We have our own research lab, and through our partnerships with leading institutions our research work on AI Safety is continuing to bridge the gap between cutting edge techniques and practical applications. We look forward to publishing new papers and applying those techniques in the real-world to provide practical support for organisations looking to increase their assurance and confidence in the models they are using.

How can you work with Faculty?

To make AI real we help organisations work out what they could do with AI, and then help them to do it. This requires three things: the right strategy, the right skills, and the right software. We help with all three, and that makes us pretty unique.

Whether we’re working with our clients to deliver AI for Executives training, deploying bespoke ML algorithms to solve business problems, or installing Faculty Platform into client infrastructure to raise data science productivity, we’re committed to establishing the confidence and independence of our clients with perhaps the most exciting and radical technology of the twenty-first century.

Over the next few weeks, we’ll be exploring our skills, strategy and software offering in a series of blogs from some of our experts, and how these fit with the broader AI trends and strategies shaping the industry.

If you’d like to stay in touch, subscribe to our newsletter below for regular updates, or come along to our next Demo Day to hear about the latest Fellowship projects.

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