On 16th July, fresh after England’s clean defeat to Argentina, OPIG took to the high seas. The OPIGlets pooled our resources and procured punts from many different berths. Organised by Admiral Alexander Hasson, we departed from the Cherwell boathouse and shipped out upriver to the Vicky Arms.
In research, it can be tough even for experts to tell fact from fiction. This is especially true for more speculative fields like AI. It can feel like every model is “State-of-the-Art” and that AGI (whatever that means) will be “6 months away” for at least the next 6 months. How do we know we can trust these people?
Visionaries sell a fringe idea of the future, usually with strong self-belief and charisma. Con men are visionaries who don’t believe what they’re saying. It’s hard to tell the difference because we can’t read minds and can’t see the future.
I solved this by adapting the insightful “New Political Compass” from Harper O’Connor, an American political YouTuber. Harper found a similar problem. He’s active in local politics and has to decide who he should build alliances with to tackle certain issues. Interestingly, the “left-right” axis wasn’t a helpful guide. Most people he canvassed were reasonable but didn’t follow politics closely enough to have a robust ideology. He realised that someone’s psychology can be more important than their stated ideology.
Therefore, it was more useful to ask: Am I talking to a reasonable person? Here, I expand his framework into the Belief Compass (or the “Stinkometer”). We can answer our question by answering three simpler ones:
Continue readingThis month we said Goodbye to a few OG members of OPIG 🙁 among them was one of my favourites, Lucy! (should I apologise to the others?)
Lucy did some amazing work on improving output of generative models during her time in OPIG. One of her recent works involved increasing the plausibility of 3D molecular diffusion models using distorted training data. Check it out here.
Early in her PhD she worked on PointVS with Jack Scantlebury. PointVS is a machine learning scoring function that predicts protein-small molecule binding affinity by learning actual binding physics rather than dataset biases.
Word on the street is she also has some secret works in the making…
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One of the great delights in this life is pointless optimisation. Point-ful optimisation has its place of course; it is right and proper and sensible, and, well, useful, and it also does, when first achieved, yield considerable satisfaction. But I have found I soon adjust to the newly more efficient (and equally drab) normality, and so the spell fades quickly.
Not so with pointless optimisation. Pointless optimisation, once attained, is a preternaturally persistent source of joy that keeps on giving indefinitely. Particularly if it involves acquiring a skill of some description; if the task optimised is frequent; and if the time so saved could not possibly compensate for the time and effort sunk into the optimisation process. Words cannot convey the triumph of completing a common task with hard-earned skill and effortless efficiency, knowing full-well it makes no difference whatsoever in the grand scheme of things.
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There comes a time in every OPIGlet’s life –all too often, if you ask me– when one is asked to write a blog post. Oh, what frightful fate to befall one! What topic to select? To what end? Something surprising? Something insightful! Something useful, at least.
Not today. Today, dear reader, we address instead the singularly most enduring, deeply seated, fervently felt yearnings of the computational scientist – a fervour woven from every fibre of their being, a longing so forceful it rends their very heart, a passion that burns with the fire of a thousand suns.
Continue readingAt the recent OPIG retreat, I was tasked with writing the pub quiz. The quiz included five rounds, and it’s always fun to do a couple “how well do you know your group?” style rounds. Since I work with Transformers, I thought it would be fun to get AI to create Haiku summaries of OPIGlet research descriptions from the website.
AI isn’t as funny as it used to be, but it’s a lot easier to get it to write something coherent. There are also lots of knobs you can turn like temperature, top_p, and the details of the prompt. I decided to use Meta’s new Llama 3.2-3B-Instruct model which is publicly available on Hugging Face. I ran it locally using vllm, and instructed it to write a haiku for each member’s description using a short script which parses the html from the website.
Continue readingAs a long-standing social sec of the OPIG research group, the key part of the job is to ensure that the event name includes some kind of pun around OPIG (and then maybe organise something). Notable ones include OPIGmas, the annual Christmas party that I am neglecting to organise at the moment, and O’Punting, our annual punting trip. For the next generation of social secs, I have proposed some new activities with even worse names.
Continue readingReproduced below is the introductory passage from a psycholinguistics paper, published in the mid-nineties. Riveted, as I’m sure you are, having just read that banger opening line to my blog post, humour me and read on; I promise it gets interesting.
The Olympic Games may have come and gone, but like me, I’m sure you’re all wondering which Olympic sport your favourite drug discovery tool would compete in. Fear not, I have taken it upon myself to answer this pressing question. In this blogpost, we’ll match some of the most popular tools in our field with their Olympic counterparts. Before we begin, let me clarify that I’m using the term ‘tool’ rather loosely here; I’ve included a variety of resources. I don’t claim these to be the most popular, just the ones I thought were most sport like.
RDKit: Athletics. I’m biased, but we must start with the big one. Like track and field events at the heart of the Olympics, RDKit is at the centre of many other tools in our field. It’s versatile, essential, and it’s hard to imagine our work without it. RDKit does it all.
“For sale: baby shoes, never worn.” ~ Ernest Hemingway??
This is a six-word story famously misattributed to Ernest Hemingway. According to Wikipedia, this story first appeared in 1906, when Hemingway was 7 years old, and later attributed to him in 1991, 30 years after his death. So, no chance it was his.
Regardless of its origin, I found this type of story very creative.
In this blog post, as the title says, I will dare to push the boundary to present 5-word stories on the topic of AI taking over the world, BUT with a humorous spin.
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