Joachim Landström (@J_Landstroem) header
Joachim Landström (@J_Landstroem) profile picture

PhD in Market-based Financial Accounting/Finance (e.g. valuation, capital market reactions), Ex board member https://nitter.gq/t.co/8g1AsWkFHZ

Uppsala, Sverige
Joined October 2016
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This may spell difficulties for OpenAI and Anthropic. Radical new way for language models seem possible.
Yann LeCun was right & nobody in this industry wants to say it out loud. 109 million parameters beat 72 billion. He has spent years saying scale is not the thing that makes a model understand the world. Meta bet the other way. He left in November and started his own lab in Paris. Last Thursday he published the proof. The theory everyone has been running on since 2024 is that if you train models big enough, on text, on images, on sound, they all end up with the same picture of reality inside them. Size does it. Nobody ever proved that part. Randall Balestriero and LeCun proved it. And the proof says size was never doing the work. What does the work is which two things you show a model together. A photo and the caption of that photo are two views of one thing. Models trained on pairs like that get there fast. Two paragraphs pulled from the same document are not two views of one thing. Models trained on those get there slowly and expensively. Next word prediction is the second kind. That is what every chatbot in the world runs on. In their tests the first method reached the same understanding after 21.6 million examples. Next word prediction needed 900 million. And a 109 million parameter text model ended up closer to an image model's view of the world than a 72 billion parameter language model was. 660 times smaller.
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Automatization of mundae searching for relevant journal articles, here in the top-10 finance journals. 21,154 articles from first sweep. Down to 4,609 in 2nd sweep, and to 143 articles 3rd and final LLM-sweep saved as bibtex. Now I have a manageable article count to filter myself
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Notes: (i) This broad sweep would not be possible w/o an Agent. (ii) Deilvery is a complete bibtex file incl abstract + ranking on relevance for my project + URLs to full text article on publisher's site, so it's easy for me to scan the articles in the final sweep.
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Joachim Landström retweeted
Lots of debate this week on whether journals should ban AI in peer review. We actually tested this in our new preprint, and the results were pretty clear to me. For context, I've reviewed for most of the big journals and evaluated 1,000 human reviews as an editor. I love to read and evaluate science. TLDR: From now on I won't do any peer review without AI. For clinical trials, I'd even call human-only review dangerous 🧵
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Think of trying to sift through this raw list of published articles manually to find potential articles for manual eval...
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Robotization of research.
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Joachim Landström retweeted
This paper answers why confident idiots run the world. Cornell University published a psychological paper that explains every terrible boss, every loud influencer, and every broken corporate hierarchy. It’s called “Unskilled and Unaware of It.” they tested participants across logic, grammar, and humor to measure the gap between actual performance and self-perception. And the results are wild. participants scoring in the bottom 12th percentile consistently believed they were operating in the 62nd percentile. they were not lying. they were literally incapable of recognizing their own incompetence. the paper revealed a brutal paradox: the exact skills required to master a domain are the exact same skills required to accurately evaluate your own competence in that domain. if you lack the skill, you inherently lack the cognitive capacity to realize you are failing. this creates a dangerous systemic loop. the incompetent are blessed with absolute, blinding certainty. their brain protects them from their own ignorance, allowing them to speak first, demand authority, and never second-guess their decisions. meanwhile, the actual experts suffer the exact inverse. top performers in the study consistently underestimated their rank. because a task was easy for them, they falsely assumed it was easy for everyone else. they hesitate. they obsess over nuance. they assume they have nothing unique to add. we are biologically wired to confuse confidence with competence. the loudest person in the room gets promoted, gets funded, and gets the microphone, not because they know what they are doing, but because they are physically incapable of seeing what they don't. the only defense mechanism is building systems that stop rewarding absolute certainty and start demanding proof of work.
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This seems pretty cool. Looks as if I actually can use my LLM to search for references (and get them) using an Elsevier API: dev.elsevier.com/api_docs.ht… Should both improve coverage and time-spent on lookup of articles. Seem possible to also download them. (I will explore this)
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Am working on delivering a paper on "Macro Determinants of the Price-Earnings Ratio". I'd like to share this. It's old, it's famous, and for all lovers of PE-ratios, it should be sobering (it's the epsilon t in the table). The table shows the R2 for 1, 3, 10 years.
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And extra credit goes to the one who can guess which paper the table comes from.
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One thing to note here: there could be many, but I restrict myself to one. The paper studies ex post PE ratios, not ex ante. This is, of course, a major drawback.
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Angående dagens debacle vid #SBB s extra bolagsstämma. Bolaget behöver arbeta på att återfå investerarnas trovärdighet. Det är ett komplext bygge som börjar med att se över styrelsen, sedan VD, och resten av ledningen. Endast då tror jag att bolaget kan få med sig småägarna.
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Lite typos men det får ni tåla 😆
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Det finns en anledning till varför 'sin stocks' brukar gå bättre än dess motsats.
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