XuMiao
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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
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.
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.
Deterministic probability like Chaos or second law of thermodynamics imply the existence of incomplete information. This type of probabilistic reality might be subjective.
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.
It seems that a lot of Chinese doctors volunteer to consult on this coronavirus. Not sure whether they are helpful or not.
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.
By the way, economic concepts are statistics. Arguments are only meaningful on large number of samples or over long period of time.
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.
High level programming language is meant to communicate, not to dictate the computation. Python happens to be easy to understand by humans.
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.
Years ago, there is an app stumble upon. I always find it engaging. Hard to get bored.
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.
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.
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.
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.
Innovation should be protected with a capped value instead of a period of time.
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.