August 20th is World Mosquito Day. A day to raise awareness about the diseases carried by mosquitoes and highlight the scientific innovations that are emerging to help us reduce the suffering caused by the world’s second most deadly animal.
6 min read
13 min read
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.
Topics: 3 Pillars Ethical AI Important Problems
4 min read
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.
14 min read
Decisions made by governments and other public sector organisations affect the lives of large numbers of people in profound ways every day. If considerations for ethics and responsibility are not made during the processes for designing, building, and implementing a solution with AI, unintended and unanticipated far-reaching consequences can be felt.
3 min read
Though the world is rapidly evolving with advances in AI and other technologies, the H&S sector is playing catch-up. It is relatively under-digitised, less connected, and has sometimes been slow to embrace meaningful digital transformation. It’s a highly competitive industry, on the cusp of a revolution driven by wide scale adoption of AI.
Innovative stakeholders are finding new ways to use AI throughout their workflows right through to the job site. The most meaningful examples are the ones that improve human outcomes while making proper considerations for ethics and responsibility.
4 min read
When patients suffer unintended reactions to medicines, it can be both dangerous for the individual and costly to society. However, what if medical professionals could use machine learning to forecast adverse drug reactions (ADRs) and minimise risks to patients?
Topics: life sciences 3 Pillars Important Problems
2 min read
When we consider the role of artificial intelligence in life sciences, it already has a proven track record as an effective tool to support pharmaceutical research. However, companies are also moving fast to explore the transformational impact it can have on their commercial operations, such as sales and marketing.