XuMiao

The claim that “tourism has little financial benefits to the local economy in Venice” is debatable and context-dependent. Here's a detailed breakdown addressing both why the claim may be true in some aspects, and why it may be misleading or false in others.

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Arguments Supporting the Claim:

1. High Leakage of Tourist Revenue

Much of the tourist spending in Venice ends up outside the local economy:

Many hotels, cruise lines, and travel agencies are owned by foreign or non-local entities.

Revenue often flows to large tour operators, not to Venetians themselves.

Day-trippers (especially cruise passengers) spend very little per capita.

2. Overtourism and Cost Externalization

The externalities of mass tourism (e.g. garbage collection, water bus crowding, maintenance of ancient infrastructure) are borne by the municipality and residents, not by tourists.

The economic cost of wear and tear on fragile historical structures is immense and undercompensated.

3. Loss of Local Businesses and Services

Traditional shops and services (bakeries, fishmongers, schools) are being replaced by souvenir shops and Airbnbs, which often serve short-term tourists.

This creates a "hollow economy" where real life becomes unviable for locals.

4. Depopulation and Real Estate Inflation

Real estate is increasingly purchased by investors for short-term rentals, pushing locals out and reducing residential density.

Venice’s population has dropped from ~175,000 in 1950s to under 50,000 today in the historic center.

5. Low Multiplier Effect

Much of the employment created is low-paid, seasonal, precarious, and lacks career development.

Limited reinvestment into the community fabric (education, public health, sustainable infrastructure).

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Counterarguments (Why Tourism Still Brings Economic Benefit):

1. Tourism Is a Major Employer

A significant portion of Venetian jobs is in hospitality, transport, and retail, all tied to tourism.

Completely removing tourism would collapse the current local job market.

2. Tax Revenues

The city imposes tourist taxes (tassa di soggiorno) on accommodations and more recently, even entrance fees for day-trippers.

These can help fund infrastructure and conservation—if well-managed.

3. Export Substitute

Venice doesn’t have a diversified industrial base. Tourism is one of the few export-equivalent services Venice can offer due to its geographic isolation and fragile ecosystem.

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Conclusion

While tourism contributes significantly in gross economic terms, the net local financial benefit is undermined by:

revenue leakage,

rising costs of living,

poor job quality,

and infrastructure stress.

Thus, the statement is partially true: mass tourism as currently structured in Venice is unsustainable and offers diminishing marginal returns to locals, especially compared to the burdens it imposes

Algebra geometry view makes sense to me. Considering ML as a learning to approximate scheme algorithm. Tensor representation is similar to the SDP trick achieving the optimal max-sat approximation. The difference is that DL approximates from inside of the high dimensional space (concave) while SDP approximates from outside (convex). The later one turns into polynomial algorithm, but the former one remains NP-hard. The success of DL just proved that there is a long way to go for P equals NP. Whenever we figure that out, symbolic approach and Tensor approach will merge.

US was wiser than others because we were able to look at our mistakes and fix them admirably.

It's only a weak character who tries to demonize others in exchange for self-comforting. Sadly, I'm seeing more and more people doing that.

To be honest, many potential murders sacrifice themselves so much that I can live safely. I really should have thanked them for not killing me.

From the physics point of view, an intelligence is to keep the universe running in the perfect order, no waste of energy to increase the temperature. All accurate predictions align our current states to the future. If we look deep enough, we always find information coming from the past and the future. The only trouble is that our computation power is bounded, and computing itself generates heat waste.

Human always feel that we have free will to design. It's likely a mistake in our intelligence unless the universe is infinitely dimensional where we can process the energy flow in any way we want.

If quantum reality is objective, then probabilistic reality is objective.

Deterministic probability like Chaos or second law of thermodynamics imply the existence of incomplete information. This type of probabilistic reality might be subjective.

This is a review paper. It's long. Past the first sentence I find it readable and well organized. It mentions some of the work on interpretability I'd expect to see, Finale-Velez, Rudin, Wallach, and LIME, but does not appear to mention Shapley. The bottom line conclusion is "In the end, the important thing is to explain the right thing to the right person in the right way at the right time." That's both an obvious truth and a differentiating mindset in research-first space. It's worth a skim.

People want to know whether some mathematical formulas can work. Then how do they work? Then what can make them work in a different way.

Explanability or interpretability leads to controllability at the end.

I rather see NNs with semantic meanings instead of semantic meanings from NNs. If human would like to control NNs, why not make them meaningful modules that can be composed like a regular program.

For example, instead of using CNNs or RNNs, we simply make a model by stating the definition:

Jaywalk :: (p: Person, scene: Image) := p in scene & exist s: Street in scene, walk_cross(p, s) in scene & not exist z: ZebraCross in scene, inside(p, z) in scene

Here predicates, walk_cross and inside, are neural network modules that might be used in many different problems. We can identify cases where the model make wrong predictions and modify the definition accordingly.

This is much human friendly development than tweaking parameters. After all, not everyone is fond of programming in NNs directly.

https://promo.guahao.com/en/global/pneumonia/?_cp=yhcbz0315

It seems that a lot of Chinese doctors volunteer to consult on this coronavirus. Not sure whether they are helpful or not.

Exactly.

a+b should be the same as a.add.b

A high level programming language should be designed for communication instead of dictating the computations.

In terms of the statistical learning framework, the Bayesian optimal classifiers are NP hard to find under distribution free conditions. The models have rather large confidence intervals to compare with each other. Scientific methods can only lead you to a certain level of certainty, the rest are purely subjective.

If they don't buy it from the store, they have to make it themselves. Everybody are satisfied in the transaction because they played well on this trivial zero sum game instead of falling into a negative sum swirl.

By the way, economic concepts are statistics. Arguments are only meaningful on large number of samples or over long period of time.

Supervised learning algorithms assume that the input data are iid of the future. This is not valid in most of the real applications. The observation that we see men more than women in programming does not necessarily generalize to the future. That's why online learning provides an exploitation vs exploration mechanism to minimize the bias in the hindsight. In many applications, people just forgot about this simple strategy and blame the bias caused by supervised learning to the black box model.

Of course, black box AI itself is not the right solution. As more and more cross domain multitask settings emerge, open box AI will gradually take off. It is about compositional capability like functor and monad in functional language. Explanable or not is just a communication problem which is parallel to the ultimate intelligence problem. It is very possible that human intelligence is bounded.

The fear is that jobs left to humans are those low level ones. Technology divide up the society into elites who control machines and labors who is controlled by machines. And gradually, the elites become less and less until the day we humans are all batteries.

Python does have a lot of problems, but it's so far the most flexible language like English.

High level programming language is meant to communicate, not to dictate the computation. Python happens to be easy to understand by humans.

Most of human knowledge are represented in logic. Semantic web is designed from description logic. A less powerful logic than first order logic.

It seems odd to me that they ended up computing the average of the word vectors which is way too simple to represent meaning. Similarity metric over this semantic vector is not accurate.

HK has been in a bad economic situation for many years. Without mainland China's support, it's even worse. The tension in the HK society is at the breaking point. Young generation has less opportunities to improve their life quality.

People who support CCP believe that it is the greedy capitalism caused the problem. Implementation of the CCP regime can rebuild HK. People who support democracy believe that it is the CCP iron fist caused suffocating.

No one is backing down. It ended up with a civil war.

Humans repeat their mistakes generation after generation.

I like the Capsule idea too. In some way, capsule network is very similar to sparse attention network. It's just the attention normalization is different. Attention is normalized on the inputs, the capsule is normalized on the output. Potentially capsule can yield much cleaner patterns, while patterns generated by attention networks can be overlapping. It's just that capsule is much harder to solve.

I prefer a user driven random walk. Like a multi-arm bandit over a hierarchical graph instead of a stuck-in-local-minimun + noise recommender. But no one does it.

Years ago, there is an app stumble upon. I always find it engaging. Hard to get bored.

I am not sure that I get the definition of skill right. Does classifying hot dog or not count as a unique skill or not? Or does object detection count as one skill?

Intelligence is hard to measure. It's personal and contextual. Even the IQ test for human is incomplete and inaccurate.

I would be really impressed if AI can provide a better theory than the multi-verse to explain the quantum mechanics. I consider that the moment of true AI.

Instead of overfitting , it's more related to exploitation vs exploration. We see more men related to programming might be just that women are not given opportunities to explore the programming as a career.

When AI makes a decision, right now, people only uses the probability output. Hiring A has .6 probability while hiring B has .4. then we will hire A instead of B. However, if we consider the confidence intervals, the decision might not be that clear. Say +/- .5 to hire A but .2 to hire B. If exploration is considered too, very likely that we will give B a chance.

AI is in the realm of probabilistic decision making, while normal people don't follow. The bias is not from the training side. It's the decision making process incorporating AI should change.

Agree. Most of program languages are context free. Human language is mostly context dependent. The auto completion and auto suggestion are the tools to close the gaps of the user experience.

Moreover, human communication is continuous and conversational. Programming is not. Most of existing code editors are not designed to have a conversation. Jupyter notebook is close but not there yet. I bet with a conversational agent style code editor plus a good auto suggestion and auto completion feature, we don't need to invent technology to use natural language to communicate with a machine. We just need a well designed formal language with precise and concise syntax.

Some people write news conforming to Google translation's performance just to make sure the story can be auto translated to many other languages. Most of these stories have pretty normalized vocabulary. This is how much human can adapt to the new world.

One day when the machine intelligence surpasses human intelligence, we will all speak in Python or whatever the most popular among machines.

It's always a small group of people apply forces on the rest. In communism, it's the party members. In capitalism, it's the board of directors. communism prioritizes fairness. capitalism prioritizes freedom. People who want fairness hate capitalism. People who want freedom hate communism. Too much fairness but too little freedom bankrupts communism: people die in a fair manner. Too much freedom but too little fairness crashes capitalism: people die from voluntarily killing each other. Stop feeling superior to anyone else, but start acting. Western society has a lot of fairness problems to solve. Let's start solving them.

Good comment. A truly ideological person with high moral ground should help instead of blame. Media's double standard is a deal with evil.

USA has been a dictator around the world for a century. So many unethical wars involved. So many radical movements are cultivated by US propaganda. Now these fear fed propaganda are dividing itself and destroying the future. People who are blind to all these are the ones full of fake Democracy ideology.

This is a less studied senario for time series research: contextual forecasting. With enough contextual information, forecasting doesn't need to squeeze the information from its own history that hard.

CNN is essentially a FT with learnable kernels.

Relational algebra didn't evolved into a relational programming language. SQL is merely a query language. The recursion is horribly done and there is no type systems. Imagine this, Friends(a: Person, b: Person) defines the relationship and the foreign keys at the same time. It makes the reasoning easier too. mary.Friends.Friends get all friends of friends of mary who is a Person. SQL requires you to write a lot of joins to achieve this. Error prone coding experience. That is why there are ORMs which end up with half baked solutions.

In fact, logic programming language and SQL should consolidate into a relational programming language. Every thing we write as a program, automatically supports persistent and distributed storage. It can also support probabilistic computation to have machine learning involved. Then we will have a complete data driven software solution.

Unfortunately, right now, we cook everything up with SQL, python, operational DBs, analytics DBs, Spark, Tensorflow.

It could have been a better place.

Patent system was not meant for 21st century. Many drugs re-patented to profit higher, e.g., Albuterol.

Innovation should be protected with a capped value instead of a period of time.

Human knowledge is built upon the high level logical relations. For example, 3-body problem. The equations over the variables are always right although no one can predict them accurately using those equations.

Current black box AI does not learn such a high level logical relation although it might predict motions the most accurate.

High level logical relations likely generalize to other domains. Low level prediction models are sensitive to the distributions of the data and hardly generalizable cross domain.

Perhaps we need a hybrid system to combine both abstract logical reasoning and semantic tensor computations.