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16 May 2025
LLM Benchmarks: Look Beyond the
Scores
LLM benchmarks like MMLU, GSM8K, HumanEval, TriviaQA, and BOLD are commonly used to
compare AI models, but their scores can be misleading. Companies may fine-tune
models on these tests, inflating results. What truly matters is how a model performs
on your specific tasks and data. High benchmark scores don't guarantee real-world
usefulness. Always test LLMs on your own use cases to find the best fit for your
needs.
LLM benchmarks help compare
how smart different
AI(LLM) systems are. popular benchmarks
often used to show how good a model:
- MMLU : The 'multiple-choice exam' for Al covering science, history, and more.
- GSM8K : Tests if Al can solve grade-school math problems step-by-step.
- HumanEval : Checks if Al can write working computer code.
- TriviaQA : Measures how many facts the Al remembers correctly.
- BOLD : Tests if Al treats different groups of people fairly.
These scores can be useful, but watch out! Many tech companies behind the models 'teach to the test', some directly finetune on the benchmark data. What matters more? How models perform on YOUR specific use cases with YOUR data. A model scoring 95% on MMLU might completely fail at understanding your industry's terminology.
Before choosing an LLM, always run your own evaluation with your own real-world scenarios. The best benchmark is how well it solves your problems, not how well it ranks on a leaderboard. A high score on some academic test doesn't mean it will help your bottom line. The best Al for you might not be the one with the flashiest benchmark numbers.
31 Mar 2025
Absence of Evidence ≠ Evidence of
Absence: A Critical Lens for Enterprise Al
In our work at Accelnomics helping companies navigate Al adoption, I often see a
critical thinking error: confusing "absence of evidence" with "evidence of absence."
Just because your Al project hasn't shown clear ROI yet doesn't mean it won't - you
might need better metrics or more time to see results. But there's a flip side too.
Some leaders hide behind this concept, continuing to fund Al initiatives that truly
aren't working. Finding this balance is what separates successful Al implementations
from costly experiments.
In our work at Accelnomics helping companies navigate Al adoption, I often see a
critical thinking error: confusing absence of evidence with evidence of
absence. Just because your Al project hasn't shown clear ROI yet doesn't
mean it won't - you might need better metrics or more time to see results. But
there's a flip side too. Some leaders hide behind this concept, continuing to fund
Al initiatives that truly aren't working. Finding this balance is what separates
successful Al implementations from costly experiments.
At Accelnomics, we've found that setting clear, practical success metrics from the start and allowing reasonable timeframes for evaluation leads to better decisions. Don't rush to judge your Al investments too quickly, but don't let the fear of missing out keep you invested in the wrong solutions either. The companies that master this distinction typically see 30% better returns on their Al investments.
At Accelnomics, we've found that setting clear, practical success metrics from the start and allowing reasonable timeframes for evaluation leads to better decisions. Don't rush to judge your Al investments too quickly, but don't let the fear of missing out keep you invested in the wrong solutions either. The companies that master this distinction typically see 30% better returns on their Al investments.
15 Mar 2025
Hero Culture
Many organizations celebrate employees who jump in at the last minute to fix crises.
These "heroes" are seen as invaluable-but in reality, they create more risk than
stability.
When companies rely on individual heroics instead of strong processes, failures keep
repeating because the root cause is never fixed. Problem detection gets delayed
since prevention isn't a priority, and the entire system becomes fragile, dependent
on a few key people to save the day.
Many organizations celebrate employees who jump in at the last minute to fix
crises. These heroes are seen as invaluable-but in reality, they create more risk
than stability.
When companies rely on individual heroics instead of strong processes, failures keep
repeating because the root cause is never fixed. Problem detection gets delayed
since prevention isn't a priority, and the entire system becomes fragile, dependent
on a few key people to save the day.
True success comes from preventing crises, not just fixing them. Organizations must shift from firefighting to foresight, empowering teams to solve problems early and using Al-driven automation to eliminate risks before they escalate.
Is your organization still rewarding last-minute heroics, or is it building Antifragile systems? We at Accelnomics believe Al can play a crucial role in this problem. Reach out to find out how.
True success comes from preventing crises, not just fixing them. Organizations must shift from firefighting to foresight, empowering teams to solve problems early and using Al-driven automation to eliminate risks before they escalate.
Is your organization still rewarding last-minute heroics, or is it building Antifragile systems? We at Accelnomics believe Al can play a crucial role in this problem. Reach out to find out how.
10 Mar 2025
Ergodicity and AI: Why Average
Success Stories Don't Guarantee Your Success
One of my favorite concepts is ergodicity. In simple terms, ergodicity means that
the average outcome across many parallel realities (like different companies
implementing the same solution) should equal the average outcome for a single entity
over time. When a system is non-ergodic, this equality breaks down - meaning what
works well on average might still lead to ruin for your specific business. But
here's the catch -most business situations aren't ergodic!
One of my favorite concepts is ergodicity. In simple terms, ergodicity means that
the average outcome across many parallel realities (like different companies
implementing the same solution) should equal the average outcome for a single entity
over time. When a system is non-ergodic, this equality breaks down - meaning what
works well on average might still lead to ruin for your specific business. But
here's the catch -most business situations aren't ergodic!
For your business, this means:
When adding Al to your company, don't just look at average success stories. What
matters is how it will affect YOUR specific business over time.
For example:
Smart Al implementation means:
The businesses that succeed with Al won't be those chasing the highest average performance, but those making sure their Al systems can handle unexpected problems without causing disaster.
Reach out if you are thinking of implementing Al solutions for your enterprise which can take up these shocks. At Accelnomics, we build resilient Al systems designed to withstand the unexpected.
For example:
- An Al customer service system might work great for 99% of cases but completely fail with your biggest client.
- An Al security tool might perform well day-to-day but miss a critical threat that causes major damage.
- An Al hiring tool that works for most companies could systematically filter out your ideal candidates.
Smart Al implementation means:
- Small before going big.
- Add safety limits that prevent major damage.
- Keep humans in the loop for important decisions (or all).
- Plan for worst-case scenarios, not just typical ones.
The businesses that succeed with Al won't be those chasing the highest average performance, but those making sure their Al systems can handle unexpected problems without causing disaster.
Reach out if you are thinking of implementing Al solutions for your enterprise which can take up these shocks. At Accelnomics, we build resilient Al systems designed to withstand the unexpected.
