Dateme Tamuno

Causal representation and reasoning. Using causal models to better analyse search behaviour

[Ruben Hassid @rubenhassid]

People think learning Claude takes days. It doesn't.

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[kieran Drew @Drew_code0]

Want to become a Claude Certified Architect in 6 weeks? 🚀

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📅 Week 1 — Learn the Basics Master the essentials: • Claude API
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📅 Week 3 — Study the Exam Understand what matters: • Real-world case studies
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• Aim for 850+/1000

📅 Week 6 — Certification Time Take the real exam. One attempt. One goal. 🏆

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[Vikas Singh @vikas_ai_]

🚨Anthropic just showed a 30-minute workshop on how actually to use prompts for Claude.

Free. No registration. No paywall.

I've seen $300 courses that don't cover what they teach in the first 8 minutes.

Watch it and bookmark it now.

[Echo @ech0_speaks]

Instead of watching Netflix tonight, dedicate two hours to this comprehensive Claude AI course. It will teach you how to build and automate virtually anything. Those who invest the time will wake up tomorrow with a valuable new skill. Watch it now and bookmark for future reference.

[Dateme Tamuno @datemeT]

A great paper on context engineering in LLMs. With Bayesian context inference, AI agents can update their beliefs of the contexts such as memory, previous conversations, user domain after encountering a new query. https://t.co/9CjlEcobqi

[Dateme Tamuno @datemeT]

@AmazonScience showing how commonsense knowledge graph integration is better in understanding the user's search intent with COSMO: https://t.co/GfgS4Nofuz

[David Ornstein @David_Ornstein]

🚨 Arsenal in advanced talks with Sporting Lisbon over deal to sign Viktor Gyokeres. Transfer fee discussions continue but personal terms in place on 5yr contract. 27yo #SportingCP striker only wants #AFC. Arteta pushing for swift conclusion @TheAthleticFC https://t.co/Re7peRinub

[Dateme Tamuno @datemeT]

The search journey is not linear but dynamic, contextual and uncertain. Users can not simply be plotted along the stages of awareness, consideration, conversion & advocacy. Human behavioural models that are intellectually robust are required to make sense of the search journey

[Dateme Tamuno @datemeT]

There is more to a query than surface lexical clustering and oversimplified intent classifications.

[Dateme Tamuno @datemeT]

Human behavior is not linear and search intent cannot be simplified into buckets of informational, navigational, commercial amd transactional. Search intent is dynamic, has temporal dependencies and should be modelled with degree of uncertainties

[Dateme Tamuno @datemeT]

Topical authority helps you understand what people are searching for and how to throughly provide coverage. Semantic authority is beneficial in understanding the "why" behind searches and catering to the temporal & dynamic nature of search intents.

[Dateme Tamuno @datemeT]

There's a lot of focus on Topical Authority. A great concept but has its limitations. Here is an introduction of the concept of semantic authority. An evolution of topical authority: https://t.co/X77EsR0lil

[Dateme Tamuno @datemeT]

Insight isn't just about finding answers to questions it's about generating new questions and exploring why those questions arise in the first place.

That's a key benefit of being a model-driven rather than purely a dsta-driven marketer.

[Codie Sanchez @Codie_Sanchez]

Decrease the time between having an idea and getting it done.

Money loves speed. Poverty loves waiting.

[Codie Sanchez @Codie_Sanchez]

Underrated truth… first you just do it, then you do it well.

[Dateme Tamuno @datemeT]

Keyword is fundamental to a detailed search analysis. Topics are important in grouping content around themes. Concepts have a place in search and can be applied in areas such as user intent mining, content gap analysis, keyword research, search volume forecasting e.t.c #SEO

[Yann LeCun @ylecun]

@yudapearl State of the world at time t: s(t)
Imagined action taken at time t: a(t)
Causal prediction:
s(t+1) = g(s(t),a(t))
where g() is the world model.
Such a *causal* world models enables planning.

There are other types, e.g. retrodiction:
s(t-1) = g(s(t),a(t))

[Dateme Tamuno @datemeT]

Great interview from @cyberandy about the YoY growth of @wordliftit from @NathanLatka's podcast #SEO #knowledgegraph #SaaS https://t.co/5mfnBcLSXG

[Amit Sharma @amt_shrma]

New paper: On the unreasonable effectiveness of LLMs for causal inference.

GPT4 achieves new SoTA on a wide range of causal tasks: graph discovery (97%, 13 pts gain), counterfactual reasoning (92%, 20 pts gain) & actual causality.

How is this possible?🧵
https://t.co/RR1RxJTmej

[Judea Pearl @yudapearl]

Goodness highness! Nature has discovered causality https://t.co/WmiX2Rk2kL
We should soon expect a flood of machine learning folks asking: What is it?
BTW, I didn't tell them, but the book I'm holding in the picture is the first printing (1763) of Bayes paper on Bayes Rule.