#putative

adjective | pu·​ta·​tive | \ ˈpyü-tə-tiv \ 

commonly accepted or supposed

Kaya Scodelario as Carina Smyth by ambrose

Building a Fairer Economy: The Power of Cooperation

TL;DR: Cooperative businesses and worker ownership are key to creating more equitable economies by empowering individuals and fostering community resilience.

The Heart of the Matter

Cooperative businesses and worker ownership aren’t just buzzwords—they’re game-changers. In a world where inequality is rampant, these models offer a beacon of hope. They empower workers, distribute wealth more fairly, and build stronger communities. It’s not just about making money; it’s about making a difference.

Why Cooperatives Matter

Cooperatives are businesses owned and run by and for their members. They prioritize people over profit, ensuring that everyone has a say in decisions. This democratic approach means profits are shared among members, not funneled to distant shareholders. It’s a model that values fairness and transparency.

Empowerment: Members have a voice and a vote.

Equity: Profits are shared, reducing income inequality.

Resilience: Communities thrive when everyone has a stake.

Evidence of Success

Look around, and you’ll see cooperatives thriving. Take Mondragon Corporation in Spain, a federation of worker cooperatives that’s been around since 1956. It’s a shining example of how worker ownership can lead to economic success and social wellbeing. Closer to home, the Evergreen Cooperatives in Cleveland are revitalizing neighborhoods by creating jobs and keeping wealth local.

Addressing Criticisms

Some say cooperatives can’t compete with traditional businesses. But that’s a myth. Cooperatives often outperform their conventional counterparts because they’re more adaptable and focused on long-term sustainability. Sure, they face challenges—what business doesn’t? But their commitment to community and collaboration gives them a unique edge.

How You Can Get Involved

Want to support cooperative businesses? Here are a few ways to get started:

Shop at co-ops: Choose cooperative grocery stores, credit unions, and other member-owned businesses.

Spread the word: Share stories of cooperative success on social media.

Invest in cooperatives: Look for opportunities to support co-op startups or expansions.

A Glimpse of Hope

Remember the story of the community garden in Detroit? Residents came together to transform vacant lots into thriving green spaces, providing fresh produce and fostering a sense of community. It’s a testament to what people can achieve when they work together for the common good.

Conclusion

Artificial Intelligence systems are often touted as the panacea for a myriad of problems, yet they frequently stumble when faced with the harsh reality of distributional shift. This phenomenon, where the training distribution ( P ) diverges from the test distribution ( Q ), is not just a minor hiccup but a fundamental flaw that can lead to catastrophic prediction failures. The mathematical tools to quantify this gap—KL divergence, Wasserstein distance, and total variation distance—offer a rigorous framework for understanding the depth of the problem.

KL divergence, a measure of how one probability distribution diverges from a second, expected probability distribution, is often used to quantify the difference between ( P ) and ( Q ). However, it assumes absolute continuity and can become infinite if ( Q ) assigns zero probability to any event that ( P ) considers possible. This limitation is where Wasserstein distance steps in, providing a more robust measure by considering the cost of transporting probability mass from one distribution to another. Total variation distance, on the other hand, offers a simpler, albeit less nuanced, measure of the maximum discrepancy between the probabilities assigned by ( P ) and ( Q ).

The implications of these distributional mismatches manifest through covariate shift, prior probability shift, and concept drift. Covariate shift occurs when the input distribution changes but the conditional distribution of outputs given inputs remains the same. Prior probability shift involves changes in the distribution of the output labels themselves, while concept drift refers to changes in the underlying relationship between inputs and outputs. Each of these shifts can independently or collectively lead to significant prediction errors, undermining the reliability of AI models.

Importance weighting is a common technique employed to address these shifts by re-weighting the training samples to better reflect the test distribution. However, this approach falters when the likelihood ratio ( \frac{dP}{dQ} ) is unbounded, leading to unstable estimations and exacerbating the very prediction errors it seeks to mitigate.

Adversarial domain adaptation, a more sophisticated approach, attempts to bridge the distributional gap through game-theoretic minimax optimization. By training a model to perform well across both domains, it seeks to learn domain-invariant representations. Yet, even this method is not foolproof. Learned representations often retain domain-specific information, detectable through metrics like maximum mean discrepancy, which measures the difference between distributions in a reproducing kernel Hilbert space.

A recent AI debacle, where a high-profile autonomous vehicle project failed to adapt to new driving environments, underscores the gravity of these issues. The project’s reliance on domain adaptation techniques proved insufficient as the vehicles struggled with unexpected road conditions, highlighting the persistent challenge of domain-specific information leakage.

Word of the Day… not actually daily, but whenever I encounter one I think worth sharing and there are not too many on my landing page.  Words have always fascinated me, and I am a fan of all types of word play, especially puns.  I have a hard time not looking up a word that I am unfamiliar with or not sure of the definition. I like those authors that stretch me by throwing in unexpected terms. …

word of the day: Putative

this word's image on the Merriam Webster word of the day block is of fucking Santa, and that's either a Christmas thing or it's a representation.

anyway, as the image says it means that we assume something has existed or exists. man, i wonder why this is so close to Christmas.

this word is, well, usable, but not in any way that makes sense. it's easier to say that something either doesn't exist or just to ignore that fact.

not much i can say about this word. guess it has a...

...PUTATIVE USE!

final thoughts, too uncommon, too specific, not descriptive enough to be of any use, terrible word.