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The International Day of Women and Girls in Science, supported by the UN, is a fantastic opportunity to acknowledge the inspirational contributions of women across the globe in science and technology, who continue to break down stereotypes and pioneer gender equality in a traditionally male-dominated industry. We have the chance to inspire and encourage girls and women of all ages, backgrounds and abilities to discover their potential within science and become leaders in an exciting and innovative field.

To celebrate this important day, we are proud to showcase some of the extraordinary women at Mind Foundry who are making significant contributions to science and technology as they share their journeys so far and the advice they would give to others. Including:

Poppy, a Lead Research Scientist at Mind Foundry, leading the Defence & Security delivery teams to develop innovative solutions to complex problems.

Hannah, the Chief of Staff to the CEO at Mind Foundry, where she interacts with all aspects of the business from partnership building, sales operations, marketing, and strategy, with an additional focus to drive alignment.

Alessandra, a Senior Research Scientist at Mind Foundry, with a PhD in probabilistic Machine Learning and on a mission to push the boundaries of AI by integrating software solutions and original research. 

Harriet, a Project Manager at Mind Foundry, where she is responsible for managing a portfolio of Machine Learning projects in the Insurance sector and Product Manager of The Mind Foundry Academy.

Change is hard. We're creatures of habit by nature, and despite our best efforts, we often struggle to avoid repeating entrenched behaviours that take up much of our time and energy. It’s altogether too easy to find yourself coasting along the same path with no signs of improvement on the horizon. However, it doesn’t have to be this way. Setting clearly defined goals, ones with simple metrics to measure completion and success, is one of the most effective ways to break the cycle and set yourself on the path to change. This idea forms the bedrock of our goals process at Mind Foundry, where our amazing team of scientists, engineers, and designers work tirelessly every day to push the boundaries of human-AI collaboration. In doing so, they prove that our value is not just in the technology we create but in the people that create it. To that end, we wanted to share the stories of 3 members of our team and how they used our SMART Goals framework to take on some exciting and inspiring challenges outside of work.  

Mind Foundry recently hosted 'Defining Ambitions: The Future of AI in Public Sector', an invaluable virtual learning exchange for the public sector. Delivered in partnership with GovNewsDirect, the session's objective was to explore the possibilities for AI innovation within public services. It is fair to say that many organisations are only just beginning to use AI and machine learning; although growing, the number of organisations that rely on it as a core part of their operations is still relatively small, but those that do are drastically outperforming their peers. So, what does the future of AI hold in public sector organisations?

In June 2019, the government launched a call for evidence on the proposed framework of a strategy around data usage in the UK, receiving over 100 responses. At the same time, the government undertook a programme of roundtables, workshops, and webinars, to ascertain the parameters, objectives, and evidence supporting what would become the National Data Strategy. 

It’s no longer enough to measure model accuracy. Ethics, explainability, and wider performance are key KPIs that must be considered as equally crucial priorities.

I would be surprised to find anyone who works in the tech sector, especially if they’re working with data, who hasn’t seen a significant emphasis on ethical applications of AI, or “doing AI ethically” (even the Pope has got involved!). Conferences, research, blog posts, videos, thought-starters are all - quite rightly - honing in on arguably one of the most important considerations of the 21st century: how do we build AI to the benefit of humankind?

To some aspiring to answer this question, this might signify decades’ worth of research. To others, it’s millions of hours of person-time in algorithmic design or troubleshooting software. The responsibilities to getting this right extend beyond this to policy, regulation, education, investment… the list goes on.

But the list isn’t the only thing that goes on; as I’m writing this, thousands to millions of companies around the world are grappling with adopting AI right at this very moment. They don’t have decades or even years to play with… they need it now. As I mentioned in a previous blog post, there’s a race on to get the most out of AI adoption before it’s too late. Currently, in the UK alone there are over 1400 high-growth AI startups and scale-ups, and this doesn’t even count the vast swathes of commercial and public sector adopters outside of the AI industry. This is a real challenge.

So how can you do it ethically? Or responsibly? Or is that even technically possible right now? Let me answer by addressing some of the most common questions that we’re posed at Mind Foundry.

Though the numbers fluctuate slightly year to year, only about 10% of the engineering workforce in the UK are female.

In the race to adopt AI, there is a flurry of activity happening in boardrooms and technical teams across the country. AI, which even a few years ago seemed to be the preserve of a vanguard of highly innovative companies, has suddenly become a prerequisite for organisations in every sector. Perhaps the stern warning from McKinsey’s 2019 report is ringing in their ears, that “Front-runners [...] could increase economic value by about 120 per cent by 2030” whereas “Laggards, who adopt AI late or not at all, could lose about 20 per cent of cash flow”.

It appears easy, then, to stay ahead of the curve and reap the financial benefits you need to adopt AI. Yet, according to MITSloan 2020 AI Global Executive Study, it’s not quite that simple, and only 10% of companies are obtaining significant financial benefits from AI technologies. 

So, why is that the case?

Machine Learning has become more and more popular. It's become accessible to a wider variety of users. And it is getting better and better at handling data - whether big or small. However one thing remains true about the data used in analysis - it is, most of the time, structured. Regardless of whether the data is stored in Excel spreadsheets, relational databases or big data repositories, it is structured.  That is to say,  it comes in the form of columns and rows of numbers, categories or labels. 

By engaging with the right medical experts as early as possible in a drug’s lifecycle, pharmaceutical companies can gain enduring competitive edge. However, despite medical affairs departments continually undergoing profound change and growth, studies show 50% of new drug launches fail to meet company expectations. Could embracing AI in life sciences help to tackle this trend?