Author: Graham Attwell

As I write this post, the newspaper headlines are focused on the record decline in value of tech shares, especially chip manufacturer Nvidia, following the release of the Open Source DeepSeek Large Language Model and platform.

Most of the extensive news coverage has focused on the tech business and the likelihood that it represents a bubble, especially for the Generative AI companies, OpenAi, Anthrpopic Google and the like. The other ,main focus has been geopolitical, with China having caught up with the USA in AI development.

For education, DeepSeek can be seen as good news. The domination of Generative AI by large tech companies has priced out public sector EdTech development. Here the big news is that DeepSeek is open source, freely available on the HuggingFace platform. The company is purely focused on research rather than commercial products – the DeepSeek assistant and underlying code can be downloaded for free, while DeepSeek’s models are also cheaper to operate than OpenAI’s o1.

As the Guardian newspaper reports, Dr Andrew Duncan, the director of science & innovation at the UK’s Alan Turing Institute, said the DeepSeek development was “really exciting” because it “democratised access” to advanced AI models by being an open source developer, meaning it makes its models freely available – a path also followed by Mark Zuckerberg’s Meta with its Llama model.
“Academia and the private sector will be able to play around and explore with it and use it as a launching,” he said.

Duncan added: “It demonstrates that you can do amazing things with relatively small models and resources. It shows that you can innovate without having the massive resources, say, of OpenAI.”
Its interesting to hear the motivation put forward by DeepSeek CEO, for open source. As Alberto Romero has written in his Algorithmic Bridge newsletter, in a rare interview for AnYong Waves, a Chinese media outlet, DeepSeek CEO Liang Wenfeng emphasized innovation as the cornerstone of his ambitious vision:

we believe the most important thing now is to participate in the global innovation wave. For many years, Chinese companies are used to others doing technological innovation, while we focused on application monetization—but this isn’t inevitable. In this wave, our starting point is not to take advantage of the opportunity to make a quick profit, but rather to reach the technical frontier and drive the development of the entire ecosystem.

DeepSeek has developed a completely different approach to the big tech companies who have focused on ever increasing use of hardware to scale Large language Models. And is so doing they have greatly reduced both the financial and the environmental cost of developing such technologies. This may well be a vindication of the policy of prioritising educational spending in China. Romero point out startups and universities can train top AI models and world-class human talent respectively in China. He says “DeepSeek—contrary to Google, OpenAI, and Anthropic—publishes a lot of papers on frontier research, architectures, training regimes, technical decisions, innovative approaches to AI, and even things that didn’t work.”

DeepSeek doesn’t solve all the problems associated with Generative AI. It doesn’t stop the so called hallucinations, it doesn’t solve the problems with bias and it is still only a productive language model. But through innovation DeepSeek has greatly reduced the need for vast amounts of power, has shown there are alternative approaches to demand for leakage amounts of capital investments and has provided an free and Open Source model for further research and innovation.

About the image

'Morning View' is part of the artist's series, 'Algorithmic Encounters': By overlaying AI-generated annotations onto everyday scenes, this series uncovers hidden layers of meaning, biases, and interpretations crafted by algorithms. It transforms the mundane into sites of dialogue, inviting reflection on how algorithms shape our understanding of the world.

I have been moaning lately about the quality of so called research and publications about education, learning and the. use of Generative AI. Well, the hype is showing no signs of dying down but there does seem to be some pretty good research beginning to emerge. And I understand it takes time to do research especially if you are trying to find out about the potential impact of AI on learning.

Anyway, one publication, not so much about formal education, but about the use of AI in work and its potential impact of employment, which I liked is a research article 'The unequal adoption of ChatGPT exacerbates existing inequalities among workers' by Anders Humlum and Emilie Vestergaard and published on December 30 of last year.

In the abstract they say:

We study the adoption of ChatGPT, the icon of Generative AI, using a large-scale survey linked to comprehensive register data in Denmark. Surveying 18,000 workers from 11 exposed occupations, we document that ChatGPT is widespread, especially among younger and less-experienced workers. However, substantial inequalities have emerged. Women are 16 percentage points less likely to have used the tool for work. Furthermore, despite its potential to lift workers with less expertise, users of ChatGPT earned slightly more already before its arrival, even given their lower tenure. Workers see a substantial productivity potential in ChatGPT but are often hindered by employer restrictions and a perceived need for training.

Somebody - and I cant remember who - usefully got Chat GPT to do a summary and published it on LinkedIn:

1. 41% of employees said they have used ChatGPT for work tasks.
2. Women are 16% less likely to ChatGPT for work than men.
3. Marketing professionals are the most likely to use ChatGPT (at 65%). Financial professionals are the least likely to use it (at 12%)
4. Less experienced and younger employees are more likely to use it. Every year of experience and age reduces likelihood of use by 0.6 & 0.7 percentage points.
5. More highly paid professionals are likely to use it.
6. Employees think ChatGPT can lead to big productivity gains in their job. They said that it could half the time to complete about 1/3 of their tasks. However many employees remain very uncertain about time savings from using the tech.
7. Despite these perceived time savings, employee regular use remains limited. For instance among employees who think it will save 1/2 the time in their job, only about 1/3 intend to use it.
8. Employees think ChatGPT can lead to big productivity gains in their job. They said that it could half the time to complete about 1/3 of their tasks. However many employees remain very uncertain about time savings from using the tech.
9. Despite these perceived time savings, employee regular use remains limited. For instance among employees who think it will save 1/2 the time in their job, only about 1/3 intend to use it.
10. Time saving may not lead to greater productivity. 37% of employees said they will not complete more tasks if ChatGPT can do it for them. 24% said they will devote more effort to using ChatGPT if it can save time.
11. The use of ChatGPT is mainly driven by individual worker initiative rather than company policy and systems.
12. Employees often face frictions in using ChatGPT. The limiting factors seem to be lack of training (42%) and company restrictions on use (32%). Restrictions on use was particularly high in the financial sector (82%). only 8% of employees reported fear of job loss as a reason for not using chat gpt.

I think the finding that the use of ChatGPT is mainly driven by individual worker initiative rather than company policy and systems nis interesting. It is reflected in our findings from the AI Pioneers project that most use is of GenAI in vocational education and training is mainly driven. by individual teacher initiative! But most research in learning or rather more commonly education, had focused on formal teaching and learning. But of course most people trying out GenAI are informal learners and there has been less insight into this.

About the image

This image is inspired by Virginia Woolf's A Room of One's Own. According to this essay, which is based on her lectures at Newnham College and Girton College, Cambridge University, two things are essential for a woman to write fiction: money and a room of her own. This image adds a new layer to this concept by bringing it into the Al era. Just as Woolf explored the meaning of “women and fiction”, defining “women and AI” is quite complex. It could refer to algorithms’ responses to inquiries involving women, the influence of trending comments on machine stereotypes, or the share of women in big tech. The list can go on and involve many different experiences of women with AI as developers, users, investors, and beyond. With all its complexity, Woolf’s ideas offer us insight: Allocating financial resources and providing safe spaces-in reality and online- is necessary for women to have positive interactions with AI and to be well-represented in this field.