Helpful resources for generative AI newbies in academia

If you are an academic (including a PhD student) curious about the potential of generative AI (GenAI) in a university context, but not sure where to start, here are some helpful resources. About the technology Research shows that understanding GenAIs’ strengths and weaknesses is crucial to enable a critical approach to its use. So, my first set … Continue reading Helpful resources for generative AI newbies in academia

Thoughts on the privacy threats and personalisation opportunities of qualitative inference with large language models

I have come across the paper entitled “Beyond Memorization: Violating Privacy Via Inference with Large Language Models”, authored by Robin Staab, Mark Vero, Mislav Balunović and Martin Vechev. Staab and his team investigated “whether current LLMs could violate individuals' privacy by inferring personal attributes from text”. Using prompts and techniques that, to me, seem quite … Continue reading Thoughts on the privacy threats and personalisation opportunities of qualitative inference with large language models

It’s not because a dataset is big that it will be good. And it is not because we used a sophisticated algorithm that the decision will be fine

These are the notes from a talk that I delivered, recently, about the importance of data quality, and how to assess it. https://www.slideshare.net/slideshow/embed_code/key/1ect9VUVbgOPt7 In my talk, I started by noting the critical role of data as a source of insight and, subsequently, as an enabler of service automation. Then, went on to note that data … Continue reading It’s not because a dataset is big that it will be good. And it is not because we used a sophisticated algorithm that the decision will be fine

The handful of datasets that rule our lives

There are numerous examples of how the datasets that are used to train the algorithms that rule our daily lives are biased. For instance, tools that automatically translate professional titles tends to follow gender stereotypes: males are doctors while nurses are females. There is also bias against faces of females and faces of people of colour. But if these biases are … Continue reading The handful of datasets that rule our lives

Artificial Intelligence vs household product safety

Apparently, autonomous robotic vacuum cleaners (i.e., Roombas) and dog poos don’t mix well. I had no idea as I have neither a Roomba nor a dog; but I have, now, learned that this is a common problem faced by pet owners, as reported in this 2016 article in The Guardian. Image source Maybe I should … Continue reading Artificial Intelligence vs household product safety

The easiest, safest, fastest way to save someone’s life

A couple of weeks ago, I came across a paper where the authors had used machine learning to discover the best predictors of blood donations. Why was this an important application? Because blood donations save lives; and because, despite its importance, blood harvesting is, usually, a not for profit venture. Thus, any insight that can … Continue reading The easiest, safest, fastest way to save someone’s life

The potential and limitations of AI in home care – the users’ view

This week, the English parliament approved a new “health and social care” tax, corresponding to an increase in National Insurance contributions from 12% to 13.25% of salary (i.e., a whopping 10.4% increase!!). This increase is to pay for the home care needs of older people, disabled citizens, and others with high care needs. That is, for carers … Continue reading The potential and limitations of AI in home care – the users’ view

Using machine learning to identify learners at risk, and develop targeted interventions

Education is linked to higher salaries, increased job satisfaction, and better health outcomes. It prepares learners to tackle complex societal problems and can address regional skills’ gaps. Thus, being able to identify leaners at risk of not progressing on their studies, or even dropping out of their courses, is of critical importance for the learners … Continue reading Using machine learning to identify learners at risk, and develop targeted interventions

Understanding and solving opacity in algorithms

One of the key challenges presented by algorithms is its opacity – that is, the inability to see how the algorithm produced a specific output. For instance, the ability to see how a search engine algorithm ranks content; how credit rating algorithm ranks the characteristic of potential borrowers; or, how a self-driving algorithm ranks external … Continue reading Understanding and solving opacity in algorithms

New paper: Leveraging machine learning in the global fight against money laundering and terrorism financing: An affordances perspective

There is a lot of enthusiasm about the potential of artificial intelligence in general, and machine learning in particular, to solve just about any problem on Earth. Thus, a special issue of the Journal of Business Research is looking at the potential of those technologies to meet the United Nations 17 Sustainable Development Goals; and … Continue reading New paper: Leveraging machine learning in the global fight against money laundering and terrorism financing: An affordances perspective