We have become enthralled by the technical wonders of our age - to the exclusion of all else. Whether it's dodgy epidemiology models or dodgy climate predictions, the algorithm has become the Great White Hope of the failing West - as evinced by the WEF.
There are good reasons to believe that we're heading for a period of global cooling over the next couple of decades and if that turns out to be right, how will the catastrophists handle it?
As one deeply and daily involved "in the AI" industry, it's all bollocks. Skynet is still a long way off. For managing huge relatively simple datasets and sussing out potential meaningful insights from patterns it's invaluable. But it's still GIGO, just on a massive scale now.
We're a long way from anything reading human thoughts without even a rudimentary understanding of consciousness.
Garbage In, Gospel Out has always been the motto of the programmer in the street. Funny thing is that even though people can make a system smart enough to win Jeopardy, I still don't seem to be able to get a correct electricity bill.
Anyone who claims that a purported computer game - er, sorry - climate simulation of an effectively infinitely large open-ended non-linear feedback-driven (where we don’t know all the feedbacks, and even the ones we do know, we are unsure of the signs of some critical ones) chaotic system – hence subject to inter alia extreme sensitivity to initial conditions, strange attractors and bifurcation – is capable of making meaningful predictions over any significant time period is either a charlatan or a computer salesman.
Ironically, the first person to point this out was Edward Lorenz – a climate scientist.
Lorenz’s early insights marked the beginning of a new field of study that impacted not just the field of mathematics but virtually every branch of science–biological, physical and social. In meteorology, it led to the conclusion that it may be fundamentally impossible to predict weather beyond two or three weeks with a reasonable degree of accuracy.
Some scientists have since asserted that the 20th century will be remembered for three scientific revolutions–relativity, quantum mechanics and chaos.
You can add as much computing power as you like, the result is purely to produce the wrong answer faster. But for some climate “scientists” I suppose it pays the mortgage…
Weather forecasting and climate change modelling are in fact quite different. With a weather model, the outcome is critically dependent upon the initial conditions - hence the need to collect as much data as possible in order to reduce error in the forecast. Climate forecasting is a boundary value problem. This article explains it very well:
"Climate science has the opposite problem. Using pretty much the same model as for numerical weather prediction, climate scientists will run the model for years, decades or even centuries of simulation time. After the first few days of simulation, the similarity to any actual weather conditions disappears. But over the long term, day-to-day and season-to-season variability in the weather is constrained by the overall climate. We sometimes describe climate as “average weather over a long period”, but in reality it is the other way round – the climate constrains what kinds of weather we get.
For understanding climate, we no longer need to worry about the initial values, we have to worry about the boundary values. These are the conditions that constraint the climate over the long term: the amount of energy received from the sun, the amount of energy radiated back into space from the earth, the amount of energy absorbed or emitted from oceans and land surfaces, and so on. If we get these boundary conditions right, we can simulate the earth’s climate for centuries, no matter what the initial conditions are. The weather itself is a chaotic system, but it operates within boundaries that keep the long term averages stable. Of course, a particularly weird choice of initial conditions will make the model behave strangely for a while, at the start of a simulation. But if the boundary conditions are right, eventually the simulation will settle down into a stable climate. (This effect is well known in chaos theory: the butterfly effect expresses the idea that the system is very sensitive to initial conditions, and attractors are what cause a chaotic system to exhibit a stable pattern over the long term).
At first sight, numerical weather prediction and climate models look very similar. They model the same phenomena (e.g. how energy moves around the planet via airflows in the atmosphere and currents in the ocean), using the same computational techniques (e.g., three dimensional models of fluid flow on a rotating sphere). And quite often they use the same program code. But the problems are completely different: one is an initial value problem, and one is a boundary value problem."
Unfortunately, it is written by global warming fanatic Steve Easterbrook who then goes on to make the ridiculous claim that this is why meteorologists, who use weather models, often end up becoming climate deniers! No, the real reason is that the weather models, though fallible, are often very useful and accurate in the short term, whereas climate models are mostly useless because scientists have completely failed to correctly qualify and quantify the boundary conditions!
Concerning the chaotic, non-linear nature of climate and hence its lack of long term predicability, may I draw your attention to the following IPCC reports:
Oh, from the post... “ I ask you, is a machine better at reading my mind than my dog?” Great question to show their absurdity, and illustrate mysterious aspects to true sentient intelligence, demonstrated by our canine friends.
AFAICT there is zero evidence of actual AI. ( by this I mean there is zero evidence of volition, and there is alway proof of
a complete lack of discrimination or purpose.)The best car auto pilots have no “want” to avoid an accident, they have zero introspective understanding of their own purpose, so if something outside their programmed safety recognition protocols appears in front of them, they will barrel straight into it, without the least compunction or hint of regret. In this they do something no human or animal would do.
Models are very often better reflections of the modeler than they are of that which is modeled.
‘AI’ isn’t going to predict the climate -- non-linear, chaotic systems are more sensitive to initial conditions than current technology is capable of measuring those initial conditions.
Current ‘climate models’ are to a greater or lesser degree, nothing more than a technique for adding a patina of legitimacy to the policy preferences (or purchased conclusions) of the modelers. There’s nothing about ‘AI’ that improves on that in any qualitative fashion. It’s still garbage in, garbage out.
The understanding of the functioning of the brain in relation to the mind is even more crude and childlike than the understanding of antibodies and immunity.
I think ‘AI’ is probably valuable research because it will help develop new statistical techniques, and may help automate some mundane tasks, but if the state of the art is reflected in the Musk robot, which had to be carried onto the stage and which had zero interaction with its surroundings, or in my robot vacuum cleaner, which is confused by the rug being at slight angle to the wall, then ‘AI’ is vastly oversold.
We have become enthralled by the technical wonders of our age - to the exclusion of all else. Whether it's dodgy epidemiology models or dodgy climate predictions, the algorithm has become the Great White Hope of the failing West - as evinced by the WEF.
There are good reasons to believe that we're heading for a period of global cooling over the next couple of decades and if that turns out to be right, how will the catastrophists handle it?
As one deeply and daily involved "in the AI" industry, it's all bollocks. Skynet is still a long way off. For managing huge relatively simple datasets and sussing out potential meaningful insights from patterns it's invaluable. But it's still GIGO, just on a massive scale now.
We're a long way from anything reading human thoughts without even a rudimentary understanding of consciousness.
Garbage In, Gospel Out has always been the motto of the programmer in the street. Funny thing is that even though people can make a system smart enough to win Jeopardy, I still don't seem to be able to get a correct electricity bill.
Anyone who claims that a purported computer game - er, sorry - climate simulation of an effectively infinitely large open-ended non-linear feedback-driven (where we don’t know all the feedbacks, and even the ones we do know, we are unsure of the signs of some critical ones) chaotic system – hence subject to inter alia extreme sensitivity to initial conditions, strange attractors and bifurcation – is capable of making meaningful predictions over any significant time period is either a charlatan or a computer salesman.
Ironically, the first person to point this out was Edward Lorenz – a climate scientist.
Lorenz’s early insights marked the beginning of a new field of study that impacted not just the field of mathematics but virtually every branch of science–biological, physical and social. In meteorology, it led to the conclusion that it may be fundamentally impossible to predict weather beyond two or three weeks with a reasonable degree of accuracy.
Some scientists have since asserted that the 20th century will be remembered for three scientific revolutions–relativity, quantum mechanics and chaos.
http://news.mit.edu/2008/obit-lorenz-0416
You can add as much computing power as you like, the result is purely to produce the wrong answer faster. But for some climate “scientists” I suppose it pays the mortgage…
Weather forecasting and climate change modelling are in fact quite different. With a weather model, the outcome is critically dependent upon the initial conditions - hence the need to collect as much data as possible in order to reduce error in the forecast. Climate forecasting is a boundary value problem. This article explains it very well:
"Climate science has the opposite problem. Using pretty much the same model as for numerical weather prediction, climate scientists will run the model for years, decades or even centuries of simulation time. After the first few days of simulation, the similarity to any actual weather conditions disappears. But over the long term, day-to-day and season-to-season variability in the weather is constrained by the overall climate. We sometimes describe climate as “average weather over a long period”, but in reality it is the other way round – the climate constrains what kinds of weather we get.
For understanding climate, we no longer need to worry about the initial values, we have to worry about the boundary values. These are the conditions that constraint the climate over the long term: the amount of energy received from the sun, the amount of energy radiated back into space from the earth, the amount of energy absorbed or emitted from oceans and land surfaces, and so on. If we get these boundary conditions right, we can simulate the earth’s climate for centuries, no matter what the initial conditions are. The weather itself is a chaotic system, but it operates within boundaries that keep the long term averages stable. Of course, a particularly weird choice of initial conditions will make the model behave strangely for a while, at the start of a simulation. But if the boundary conditions are right, eventually the simulation will settle down into a stable climate. (This effect is well known in chaos theory: the butterfly effect expresses the idea that the system is very sensitive to initial conditions, and attractors are what cause a chaotic system to exhibit a stable pattern over the long term).
At first sight, numerical weather prediction and climate models look very similar. They model the same phenomena (e.g. how energy moves around the planet via airflows in the atmosphere and currents in the ocean), using the same computational techniques (e.g., three dimensional models of fluid flow on a rotating sphere). And quite often they use the same program code. But the problems are completely different: one is an initial value problem, and one is a boundary value problem."
Unfortunately, it is written by global warming fanatic Steve Easterbrook who then goes on to make the ridiculous claim that this is why meteorologists, who use weather models, often end up becoming climate deniers! No, the real reason is that the weather models, though fallible, are often very useful and accurate in the short term, whereas climate models are mostly useless because scientists have completely failed to correctly qualify and quantify the boundary conditions!
http://www.easterbrook.ca/steve/2010/01/initial-value-vs-boundary-value-problems/
Concerning the chaotic, non-linear nature of climate and hence its lack of long term predicability, may I draw your attention to the following IPCC reports:
14.2.2 Predictability in a Chaotic System
https://www.ipcc.ch/site/assets/uploads/2018/03/TAR-14.pdf
And
7.7 Rapid Changes in the Climate System
https://www.ipcc.ch/site/assets/uploads/2018/03/TAR-07.pdf
Thanks David. Will read with interest.
If AI predicts global warming, then its been programmed to predict it.
Tell me, how much sunspot and ice core data did they feed it? None I bet money on it.
Well they said it was based on the models, so yes, they are programmed to predict about 300 percent more warming then the observations show.
https://open.substack.com/pub/anderdaa7/p/global-warming?utm_source=direct&r=slvym&utm_campaign=post&utm_medium=web
Oh, from the post... “ I ask you, is a machine better at reading my mind than my dog?” Great question to show their absurdity, and illustrate mysterious aspects to true sentient intelligence, demonstrated by our canine friends.
AFAICT there is zero evidence of actual AI. ( by this I mean there is zero evidence of volition, and there is alway proof of
a complete lack of discrimination or purpose.)The best car auto pilots have no “want” to avoid an accident, they have zero introspective understanding of their own purpose, so if something outside their programmed safety recognition protocols appears in front of them, they will barrel straight into it, without the least compunction or hint of regret. In this they do something no human or animal would do.
Dogs are amazing!
So if its going to happen anyway, we can stop all the net zero nonsense now, because it makes no difference!
Models are very often better reflections of the modeler than they are of that which is modeled.
‘AI’ isn’t going to predict the climate -- non-linear, chaotic systems are more sensitive to initial conditions than current technology is capable of measuring those initial conditions.
Current ‘climate models’ are to a greater or lesser degree, nothing more than a technique for adding a patina of legitimacy to the policy preferences (or purchased conclusions) of the modelers. There’s nothing about ‘AI’ that improves on that in any qualitative fashion. It’s still garbage in, garbage out.
The understanding of the functioning of the brain in relation to the mind is even more crude and childlike than the understanding of antibodies and immunity.
I think ‘AI’ is probably valuable research because it will help develop new statistical techniques, and may help automate some mundane tasks, but if the state of the art is reflected in the Musk robot, which had to be carried onto the stage and which had zero interaction with its surroundings, or in my robot vacuum cleaner, which is confused by the rug being at slight angle to the wall, then ‘AI’ is vastly oversold.