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6 min read

World Mosquito Day: Fighting Malaria with Machine Learning

By Dr. Davide Zilli on Aug 20, 2021 4:18:03 PM

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. 

Topics: 3 Pillars Ethical AI Important Problems Human • AI Collaboration
4 min read

The AI adoption paradox: can cautious adoption reap maximal benefits?

By Joanna Crown on Jun 10, 2021 4:06:16 PM

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?

Topics: 3 Pillars Ethical AI Human • AI Collaboration Decision Intelligence
4 min read

A route to more reliable, forward facing solutions with active Human • AI Collaboration

By Alistair Garfoot on May 27, 2021 4:00:00 AM

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.

Topics: Continuous Meta-Learning Ethical AI Important Problems Human • AI Collaboration Active Learning
3 min read

The future of AI in Health & Safety Is Now

By Gurinder Tamber on Feb 25, 2021 10:05:46 AM

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.

Topics: White Paper NLP 3 Pillars Health and Safety Important Problems Human • AI Collaboration construction
2 min read

What is Augmented Intelligence and Why  Should You Embrace It?

By Mind Foundry on Jun 13, 2019 8:00:00 AM

The internet can seem full of scare stories and science fiction, but what is augmented intelligence really going to do to transform our lives in the near future?

Topics: Augmented AI 3 Pillars Human • AI Collaboration
3 min read

How active learning can train machine learning models with less data

By Dr. Alessandra Tosi on Oct 9, 2018 2:00:00 PM

Even with low cost widely available cloud computing, it can take significant time and compute power to train machine learning models on large data sets. This is expensive and is often at odds with the net-zero carbon goals of many organisations today. Throwing more data at a problem isn’t always the best answer, and by using AI that is responsible by design, we can reduce these problems while maintaining performance. Active learning is one of the methods we use at Mind Foundry to achieve this.

Topics: machine learning data science Continuous Meta-Learning Human • AI Collaboration

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