Another Message Board
Post comments on any topic. Civil discussion and no coarse language please. Side discussions and idees fixes to the sandpits, please.
I’ve moved my irregular email news from Mailchimp to Substack. You can read it here. You can also follow me on Twitter @JohnQuiggin
I’m also trying out Substack as a blogging platform. For the moment, I’ll post both at this blog and on Substack.
John Quiggin 
The catastrophic flooding and re-flooding that has occurred so far, and is likely to continue in the short term, further exacerbates the ultra-tight rental market. That much is clear. I have been wondering whether it will also have a substantial impact on the investment property market, for as people try to restock white goods, furniture, and renovate/repair flooded housing stock, the ongoing supply chain issues and the destruction of agricultural crops pretty much guarantees a surge in the rate of inflation, and that’s likely to force the Reserve Bank to increase interest rates by a substantial margin. None of this looks good for the economy.
In a related question, I was wondering about the use of houses and apartments as short-term rentals, and how the tax system deals with them. It sure seems that a lot of these houses/apartments are vacant for all but a couple of weeks a year, maybe a month; do these “investors” just use the property as short-term rental so they can claim an income on it for negative gearing? Assuming that is the case, what happens to those investors if they now have flooded stock on which they are servicing interest-only loans? Seems to me that there could be another property value risk embedded in this. I haven’t seen hard data to know to what extent there is such exposure, but given the known tourist towns and cities that have been hit with serious flooding, I would have thought there is some exposure. Given the already long delays in getting house renovations and the supply-side issues for building materials, and the inflation effects that are entrenched, it seems highly unlikely that repair work on flooded properties can possibly be done in a reasonable time frame.
Don, good questions. I too would like answers.
The JQ AI. Nefarious or nice?
Your OP JQ, where you used Jasper to generate text, as a new toy which is as I would have done,, belies the economic and social effects in the not too distant future of AI. Like getting AI to say “hello world”.
The article below is about the (seemingly minor) spat over image “degeee of transformation” in the US Supreme Court case will have unintended consequences imo for AI.
And this SCOTUS decision will have consequences imo for you JQ or economics students, knowledge, and my child, society and Elon Musk’s Open AI and Google’s AI Deep Mind -AI training sets – output, in the not too distant future.
I am now able to:
1) scrape all JQ’s face images
2) all moving images
3) all written and spoken works
4) all lesson 1, 2, and market and alternate economics
5) all philosophy
The above training set would enable me, in combination of news and current affairs and a loosely defined outline as input prompt, to generate JQ-esque articles, and revise and express those articles nuanced to my preferred end game. With a deep fake video too.
Only private capitalism is able to do this now, as the costs are just too high to train AI.
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“The degree of transformation is the unaddressed problem.” From:
“SCOTUS: Meaningfully Transformative v. Recognizably Derivative?”
https://thepatronsaintofsuperheroes.wordpress.com/2022/10/10/scotus-meaningfully-transformative-v-recognizably-derivative/
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Open AI “was” to be free for all to use. It has now been monetized to a degree.
No one in the future – now imo – has the money, infrastructure or compute time to collect enough data and generate a training set able to compete with OpenAI or Google AI, as Scott Alexander writes below “AIs need lots of training data (in some cases, the entire Internet).”.
In the sci-fi novel Snow Crash, those who are lower on the socioeconomic ladder appear as poor facsimiles in the metaverse, compared to the rich and powerful. Poor – easy to spot. Rich & powerful – assume any guise or stance conceptually or digitally.
And place “Undetectable backdoors…” in machine learning models as Hiro Protaganust did in Snow Crash.
https://pluralistic.net/2022/04/20/ceci-nest-pas-un-helicopter/#im-a-back-door-man
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And the development of AI and safety are now like letting Exxon or Shell determine both development and safety, as Scott Alexander writes in;
“Why Not Slow AI Progress?Machine Alignment”
Aug 8 2022
“The Broader Fossil Fuel Community
“Imagine if oil companies and environmental activists were both considered part of the broader “fossil fuel community”. Exxon and Shell would be “fossil fuel capabilities”; Greenpeace and the Sierra Club would be “fossil fuel safety” – two equally beloved parts of the rich diverse tapestry of fossil fuel-related work.
“This is how AI safety works now. AI capabilities – the work of researching bigger and better AI – is poorly differentiated from AI safety – the work of preventing AI from becoming dangerous. Two of the biggest AI safety teams are at DeepMind and OpenAI, ie the two biggest AI capabilities companies.”…
…
“Or: what about limits on something other than research? AIs need lots of training data (in some cases, the entire Internet). Whenever I post an article here, it’s going into some dataset that will one day help an AI write better toothpaste ads. What if I don’t want it to do that? Privacy advocates are already asking tough questions about data ownership; these kinds of rules could slow AI research without having to attack companies directly. As a sort of libertarian, I hate blah blah blah same story.
“Or what about standards?”…
…
https://astralcodexten.substack.com/p/why-not-slow-ai-progress
Any comment JQ and readers?
I would appreciate JQ, a well thought, considered personal and economic consequences series of threads on the potential of above into the future. Please.
JQ see kindred spirit below as Ganna Pogrebna references you, and features in this Nature article.
Re:- AI, social media, data harvesting, inequality & externalities, and disaster warnings. Trickle up as per usual. Rural subsidising city.
Interesting finding for inequality via mobile phones and data “”According to Ganna Pogrebna, executive director of the Artificial Intelligence and Cyber Futures Institute at Charles Sturt University in Bathurst, New South Wales, those living in remote areas are potentially more exposed to the technology’s dangers than their urban counterparts, but are being neglected by research.”.
Data reverse colonialism – see Africa penetration, and trickle up – rural to city – equity. I may now dump Android and go to back to Apple, the walled garden… “Android phones give Google 20 times more data than iPhones send to Apple. Android phones dominate in more rural countries such as those in Africa where 87.22% of the population use Android phones.”
Where is the competition?
How do I weigh up this choice? Do the costs of a +$1,000 new phone 2 years before a usual phone upgrade, outweigh the benefits and contribute to balancing equality / equity? The data snatcher – Google – is an emotive factor.
Is disaster warning and knowledge going to be taken over by social media data harvesting and dearth of data laws. Then they have the potential to usurp say the Australian Broadcasting Corp “our ABC”, and our Bureau of Meteorological sercices monetized and subsumed to private corporations. And benefit nor balanced berween rural and city.
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From:
“The rural areas missing out on AI opportunities
“Behavioural data scientist Ganna Pogrebna believes the AI revolution is overlooking remote communities.
…
“One of these potential discrepancies is the way in which AI relates to urban and rural communities. According to Ganna Pogrebna, executive director of the Artificial Intelligence and Cyber Futures Institute at Charles Sturt University in Bathurst, New South Wales, those living in remote areas are potentially more exposed to the technology’s dangers than their urban counterparts, but are being neglected by research.
…
“Rural communities are largely missing out on the benefits of data-driven research and that’s a big shame because AI has the potential to improve country life. I’m based in rural Australia where we often face flooding and forest fires; there are projects going on at the moment that seek to use AI to advance disaster management in remote communities.
“Algorithms are mining social-media posts to learn from the language being used and the pictures being shared to deduce whether flooding is happening and to what extent. This can then be used to predict which areas might be flooded next and how badly. It can give us several hours’ head-start in rural areas where resources are stretched.
…
“Are you optimistic that this AI regional-urban gap will ever be closed? What can be done to solve or improve the situation?
…
What are the consequences of this inequality in data gathering? Doesn’t it benefit rural areas?
…
How would AI improve rural communities if they were able to better access the technology?
…
Are you optimistic that this AI regional-urban gap will ever be closed? What can be done to solve or improve the situation?
…
https://www.nature.com/articles/d41586-022-03212-7
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JQ you are noted #1 and referenced by Ganna Pogrebna.
A kindred academic of prospect theory I suspect.
“Testing for independence while allowing for probabilistic choice”
Graham Loomes &
Ganna Pogrebna
Journal of Risk and Uncertainty volume 49, pages 189–211 (2014)
https://link.springer.com/article/10.1007/s11166-014-9205-0
And here…
“Report NEP-UPT-2006-03-05”
“The following items were announced in this report:
“Pavlo Blavatskyy & Ganna Pogrebna, 2006. “Loss Aversion? Not with Half-a-Million on the Table!,” IEW – Working Papers 274, Institute for Empirical Research in Economics – University of Zurich.
“Robert G. Chambers & John Quiggin, 2005. “Comparative Risk Aversion for State-Dependent Preferences,” Risk & Uncertainty Working Papers WP5R05, Risk and Sustainable Management Group, University of Queensland.
…
https://ideas.repec.org/n/nep-upt/2006-03-05.html
Per Carbon Monitor, global CO₂ emissions:
* For the first 8 months of 2022: 98.97 Mt CO₂ / day average
* Compared with 2019: _ +2.9%
* Compared with 2020: +11.6%
* Compared with 2021: _ +2.2%
https://carbonmonitor.org/
Funding the man not the ball.
Will they wake up?
Is there an economic term for such poor available funds of pool investment?
Maybe this is quasi capital.
Or the opportunity cost of stupid and dogma.
Will this be on Truth Social and Fox?
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“Trump Spent 91 Cents to Raise Each Dollar as Troubles Mounted
“Trump spent $22 million to raise $24 million, filing shows
“SMS, digital ads and lists ate up most of third-quarter haul
https://www.bloomberg.com/news/articles/2022-10-16/trump-spent-91-cents-to-raise-each-dollar-as-troubles-mounted
Robot & AI job change priors update. This article updated my negative numbers. Detailed, specific industries not averages.
2019 -2029 Surgeons deline by 2%, …”By contrast, the number of public relations specialists is projected to grow more than 7 percent between 2019 and 2029.” Groan. (PR needs a proxy linked to slow down of global warming mitigation efforts. Say the PRAGW Drag Coefficient.)
Avoids the Norbert Wiener effect which is where my priors lay; “2 Norbert Wiener wrote, “It is perfectly obvious that [“automatic machines”] will produce an unemployment situation, in comparison with which the present recession and even the depression of the thirties will seem a pleasant joke.” See Wiener”
I hope this proves to be correct. Certainly a very detailed analysis and basis for calibration in future as “Omitting considerations like scale effects, job redefinition, and job variety, or mentioning them only in passing, assumes by default that the effects of technological substitution are stronger than the effects of economic growth and other offsetting variables that operate to maintain or increase jobs in affected occupations.15 As will be seen, this is not necessarily a safe assumption. Nevertheless, it should be noted that nothing in this article should be interpreted as minimizing the number of jobs lost, the hardships experienced by workers affected by job loss, or the implications of the changing occupational composition for inequality and economic opportunity more generally.”
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Above quotes from:
“Growth trends for selected occupations considered at risk from automation
JULY 2022
Monthly Labor Review(MLR) from the U.S. Bureau of Labor Statistics (BLS), within the U.S. Department of Labor.
“Occupations considered highly susceptible to automation
“Tables 2 and 3 show recent and projected trends for 27 occupations that have been used as illustrations in widely cited works on the effects of robotics and AI on employment.23 … “In both tables, occupations in the top panel are closer to “pure” cases in which technological drivers of employment are more likely to be exclusively robotics and AI.”
…
Aggregate results
…”In other words, these occupations grew in both absolute and relative terms since 2008 and are expected to continue to do so. According to the OEWS database, these occupations also grew in absolute size from 1999 to 2009, but their relative share remained flat at 8.8 percent of jobs during this period. Table 3 shows this group grew somewhat faster than projected from 2008 to 2018 (13.9 percent real growth versus 8.7 percent expected growth) and is projected to continue to grow faster than average for 2019 to 2029. Therefore, neither recent data nor BLS projections suggest automation is a serious issue for this group overall, though individual occupations may face greater risks.”
…
“Detailed occupations newly affected by robotics and AI
“Reading four key works on automation, one finds 11 occupations used as illustrations whose employment levels were likely to be relatively unaffected by previous waves of computing.25 …
“For none of the occupations in this section do the employment projections to 2029 or observed changes for the 2008–18 period fit the pattern of large-scale job loss suggested by the automation literature. ”
…
“Personal financial advisors
“Recent works on automation note that algorithms can provide personalized financial advice.26 … “However, the projections anticipate that the demand for personal financial advisors will continue to increase, more than offsetting the anticipated labor-saving effects of AI-driven advising systems
“Interpreters and translators
…
“Surgeons, except ophthalmologists
…”BLS projects the number of surgeon jobs will decline by 2 percent between 2019 and 2029, a significant moderation compared with the 30-percent decline from 2008 to 2018, which was probably not due to robot adoption, and a marked departure from the 17-percent increase that had been projected for the 2008–18 period.”
– Fast food and counter workers
…”However, BLS projections to 2029 foresee 11.4-percent growth in this occupation, following nearly 30-percent increase for the 2008–18 period and 35-percent growth for the 1999–2009 period.”
– Laborers and freight, stock, and material movers, hand
…”projections anticipate the occupation will grow 4.2 percent between 2019 and 2029, a slowdown from the remarkable 27.5-percent growth over the 2008–18 period that may have reflected the growth of e-commerce.”
– Lawyers and paralegals and legal assistants
…”59 By contrast, Professors Dana Remus and Frank Levy find lawyers’ tasks are much more diverse than recognized in these writings, and only about 4 percent of billed hours are spent on document review, which is the only task strongly susceptible to automation.60
…
– News analysts, reporters, journalists and public relations specialists
…”The projections anticipate news-related jobs will decline by 11.2 percent between 2019 and 2029, . .. “Nevertheless, this occupation is likely an example in which AI may automate certain tasks, like composing certain kinds of press releases, without substituting for enough tasks within the occupation to meaningfully decrease employment.”
– First-line supervisors of retail sales workers, retail salespersons, counter and rental clerks, and cashiers
…”and the bundling of checkout tasks into retail salespersons’ jobs, none of which rely heavily on AI. Cashiers are projected to decline by 7.4 percent between 2019 and 2029 after growing by 2.8 percent (2008–18) and 8.8 percent (1999–2009).”
Stockers and order fillers
…”this may be another example in which job diversity within an occupation limits the potential for technological substitution.66 BLS projects this occupation will grow 0.7 percent between 2019 and 2029,”
“Occupations losing the most jobs, 2008–18
…” However, the 4.4 million jobs lost represent only 3.8 percent of all jobs in 1999 and would have added only 3.3 percent to the total in 2018 had they not been lost. Even though the percent losses within these occupations were similar to those foreseen in the recent automation literature, they were quite atypical, so their effect on overall employment was much smaller than the magnitude of their decline might suggest. This is not intended to minimize the number of jobs lost, the hardships experienced by workers affected by them, or the implications of the changing occupational composition for inequality and economic opportunity more generally. However, the incidence of these dramatic declines is more than an order of magnitude smaller than the 47-percent potential job losses commonly cited in the automation literature.”
“High-technology occupations
“Finally, consideration of the new technology raises the question of the jobs they create, as well as replace. Tables 5 and 6 show trends in science, technology, engineering, and mathematics-related (STEM) jobs. The projections anticipate that computer-related jobs will increase 11.5 percent between 2019 and 2029. However, this may be an underestimate,” … “Nevertheless, these two occupations together accounted for only 76,000 jobs in 2019 and were projected to grow to 101,000 jobs by 2029.”
…
Conclusion
…” However, these occupations did not exhibit any general tendency toward notably rapid job loss in the first half of this period (2008–18) and are not projected to experience such losses in the second half (2019–29). Occupations that did decline were mostly those that were vulnerable to previous waves of computing technology and other trends, such as offshoring, rather than occupations newly susceptible to automation due to AI and advanced robotics. “… ” None of this is to minimize the hardships experienced by displaced workers. However, rapid leaps in technology in the early 2010s prompted many to envision a future scenario of massive disruption. This article has examined specific occupations that are most favorable to the automation thesis and found little support for this view. It is entirely possible that robotics and AI are simply another in a long line of waves of innovation whose effects on employment will unfold at rates comparable to those in the past.”
“Appendix: The distribution of occupational sizes
Related Articles
– Projections overview and highlights, 2020–30,Monthly Labor Review, October 2021.
– Technology may disrupt occupations, but it won’t kill jobs, Monthly Labor Review, February 2016.
https://www.bls.gov/opub/mlr/2022/article/growth-trends-for-selected-occupations-considered-at-risk-from-automation.htm
Above BLS study “Growth trends for selected occupations considered at risk from automation” via;
nytimes
com/2022/10/07/opinion/machines-ai-employment.html
Puhh…. remember~ 2001-2003 when many it people where unemployed and the prognosis was that most programing will also be automated to a point that the subject specialists would do most of the qualified work with easy to use end user sort of programing interfaces- or outsourced as a lowpaid job to india.
My labour market recommendation for maximum risk adjusted return remains to become a dentist (at least half serious about that). Not that my prognosis for radiologists would be particular bad – the risk is rather in managing to get to the degree, maybe capex … regulation has a habit to take a long time to catch up with reality in such cases. Flight controlers should also do great regarding risk adjusted return despite some serious probability of not realy beeing needed in the near future anymore.
Brown shift or Green shift. Depends on your perspective. The Sami & raindeer sacrificed for the green revolution. Even with a “zero emissions” copper mine.
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“It seems like everything will be destroyed and then they call it a green shift.”
“Lessons from the Arctic about an Indigenous Voice to Parliament
…
“A plan to mine Norway’s largest deposit of copper in the Nussir Mountains, not far from the summer reindeer pasture’s he’s just migrated from, promises to bring new jobs to the country’s remote north. According to the company behind the project, it will also set an environmental benchmark as the world’s first zero-emissions mine.
“But the project’s green credentials have done little to sway Nils Mathis, even as the Arctic warms at a rate nearly four times faster than the rest of the planet. “The ‘green shift’ is not a green shift for us,” he says. “More like a brown shift, from green to brown. It seems like everything will be destroyed and they call it a ‘green shift’.”
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https://www.abc.net.au/news/2022-10-20/sami-parliaments-indigenous-voice-foreign-correspondent/101512762
Paid parental leave for those on combined incomes of $350,000. Childcare subsidies for those earning up to $400,000. And, of course, retaining those tax cuts for those on high incomes.
Labor is taking care of the oppressed Australian working class.
https://www.theage.com.au/politics/federal/couples-earning-350k-to-get-parental-leave-benefits-in-family-friendly-budget-20221021-p5brr4.html
Climate reparations please.
“it also seems to add to the unfairness of this situation.”
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“In the climate crisis, vulnerable countries bear the least responsibility
…
“It’s important to notice that “readiness” here means “readiness to make effective use of investments for adaptation actions thanks to a safe and efficient business environment.”
“This score does not really reflect how well a country is prepared to fight the consequences of climate change. Instead it says how well a country can make use of investments because of its economic situation, social conditions, and stability of governance.
“While this helps to direct effective investments for climate adaptation, it also seems to add to the unfairness of this situation. Many countries with high vulnerability are also less economically and politically stable, which leads them to be rated lower for investment.
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https://blog.datawrapper.de/climate-risk-readiness-responsibility/