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.
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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
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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?
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AI has the potential to help us tackle the problems associated with climate change and the warming of our Earth. The closer we get to the precipice, the greater the urgency. This has helped fuel tremendous growth in AI projects throughout government and the public sector, where AI is being used to make more accurate climate change predictions or to intelligently power the infrastructure that could support lower emissions on a global scale.
Amidst all this enthusiasm, the one thing often being left out of the conversation is the carbon cost of these compute-intensive solutions. At best, the adoption of AI might be slowed down because people hadn't adequately considered the cost (financial or environmental) of the solution required. At worst, it could accelerate the warming of our planet.
This is why it is so important to develop a Green AI technology: a technology that takes into account energy-efficiency as an important evaluation metric.
Topics: 3 Pillars Ethical AI government Green AI
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.
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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.
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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.
Topics: Data preparation NLP Text Analysis 3 Pillars
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Today in so many industries, from manufacturing and life sciences to financial services and retail, we rely on algorithms to conduct large-scale machine learning analyses.
They are hugely useful for problem-solving and beneficial for augmenting human expertise within an organization. But they are now under the spotlight for many reasons – and regulation is on the horizon. Gartner projects that four of the G7 countries will establish dedicated associations to oversee artificial intelligence and ML design by 2023.
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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
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For the past five years, data science has been praised as a technology that can unlock new applications and hidden insights for organisations. However, today it is struggling to live up to expectations.