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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
13 min read

Approaching Ethical AI Design: an insider’s perspective

By Joanna Crown on Aug 2, 2021 3:00:00 PM

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

Women in Engineering

By Pippa Siret-Godfrey on Jun 22, 2021 9:19:00 AM

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

National Women in Engineering Day, June 23rd, is a fantastic opportunity to shine the spotlight on the need for a more inclusive and diverse workforce, but more importantly, it is also a chance to ignite the embers we know are lurking inside women and girls, of all ages and backgrounds, to show them that engineering is absolutely an option for them too.

We are proud to share the stories of two of our own trailblazers of AI innovation who have been defying stereotypes, knocking down barriers, and inspiring everyone around them, for as long as we can remember.

Topics: Engineering Careers
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
7 min read

Environmental impact and carbon cost of innovation: towards Green AI

By Dr. Alessandra Tosi on Mar 15, 2021 9:34:10 AM

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

AI In Government: Considerations for Ethics and Responsibility

By Nick Sherman on Mar 10, 2021 5:05:17 PM

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.

Topics: 3 Pillars Ethical AI Important Problems government public sector Scotland
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
5 min read

Software engineering as decision making

By Nicolò Andronio on Jan 1, 2021 6:26:00 PM

As software engineers we are called to make decisions. I would go as far as saying that most of our work revolves around it. From naming to choosing technologies, from picking the most appropriate conventions to outlining the best team process. Decision making is thus so deeply and subtly rooted in our craft that we often forget about its implications. What I want to explore today is the ability to settle for the lower end of the spectrum, i.e. choosing the simplest option and why you should give it more than a fleeting thought.

Topics: Engineering Careers
3 min read

Tapping into the hidden potential of text data

By Mind Foundry on May 27, 2020 1:48:48 PM

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
2 min read

Webinar: A business take on the data-science pipeline

By Dr. Alessandra Tosi on Apr 28, 2020 3:18:58 PM

In this webinar Dr. Alessandra Tosi goes through the steps of the data-science pipeline, with a particular focus on business applications. Dr. Tosi stresses the importance of correctly framing the business problem in terms of goals, value and success metrics, and how to translate this into the machine-learning framework.

Alessandra is a senior scientist and product owner, with a PhD in probabilistic machine learning. 

Topics: machine learning artificial intelligence in business data science Webinar
1 min read

Keeping Machine Learning Algorithms Humble and Honest

By Dr. Davide Zilli on Mar 3, 2020 11:26:47 AM

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.

Topics: First Principles Transparency 3 Pillars Ethical AI
3 min read

5 Reasons Why You Can Better Understand Your Data with AI

By Dr. Davide Zilli on Jul 17, 2019 2:25:00 PM

Many organizations still watch from the sidelines as competitors transform their understanding of data with AI. Why is this? 

Topics: artificial intelligence in business data AI
2 min read

Augmenting the MSL with AI in Life Sciences

By David Bennett on Jul 2, 2019 8:00:00 AM

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?

Topics: life sciences AI
4 min read

How to Use Machine Learning to Forecast Adverse Drug Reactions

By Mind Foundry on Jun 28, 2019 9:54:48 AM

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

The Layman's Guide to the Data Science Journey

By Mind Foundry on Jun 25, 2019 7:00:00 AM

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.

Topics: machine learning data science Continuous Meta-Learning 3 Pillars
2 min read

The Value of Machine Learning in Clinical Trials

By Mind Foundry on Jun 17, 2019 11:11:29 AM

Clinical research data plays an essential role in the pharmaceutical industry, but can also eat up resources in terms of time and money. However, by utilising machine learning in clinical trials, efficiency can be drastically improved, enabling faster and more cost-effective drug development and better patient outcomes.

Topics: machine learning life sciences
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
2 min read

AI in Life Sciences: from faster faster drug development to more personalised medicine

By Mind Foundry on Jun 11, 2019 1:55:37 PM

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.

Topics: life sciences 3 Pillars Important Problems Decision Intelligence
3 min read

What is Humanized Machine Learning and Why Should you Care?

By Paul on May 24, 2019 2:31:43 PM

While machine learning has been around for decades, it has swiftly become a major buzzword in recent years. Following in the footsteps of 'digital transformation', many businesses are struggling to comprehend the intrinsic value of machine learning and the ways in which it can be implemented successfully.

Topics: machine learning
5 min read

Bayesian Optimization can help Quantum Computing become a reality

By Dr. Alessandra Tosi on Jan 8, 2019 9:21:00 AM

Quantum computers have the potential to be exponentially faster than traditional computers, revolutionising the way we currently work. While we are still years away from general-purpose Quantum Computing, Bayesian Optimization can help to stabilise quantum circuits for certain applications. This blog will summarise how Mind Foundry Optimize did just that.

Topics: Bayesian Continuous Meta-Learning 3 Pillars
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
8 min read

Visualizing Black Box Optimization Problems in Machine Learning

By Dr. Alessandra Tosi on Oct 3, 2018 2:00:00 PM

Black box optimization in machine learning is a pretty common scenario. More often than not, the process or model we are trying to optimize does not have an algebraic model that can be solved analytically.

Topics: Bayesian
5 min read

Hyper-Parameter Tuning: What it is and why it’s natural

By Mind Foundry on Oct 1, 2018 1:59:00 PM

Hyper-parameter tuning can be observed in many everyday processes, but few people are aware of its existence. This blog aims to change that, and demonstrate its true value in data science.

Topics: machine learning artificial intelligence in business data science Bayesian

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