There was an interesting debate on Novara Media between Michael Walker and James Meadway, which explored AI from a different perspective than is usually aired, certainly than in print and broadcast media. It involved the economic realities of AI.
Michael Walker is the Contributing Editor at Novara Media, and one of the better presenters of complex issues.
Meadway is one an astute economist, the director of the Progressive Economy Forum his podcast, Macro Dose, can be found at patreon.com/Macrodose
The Debate gets twisted
Walker was framing the debate from Hinton's much publicised resignation from Google, and his subsequent warning. The debate that followed was soon to change direction.
Walker: Geoffrey Hinton, a computer scientist at the University of Toronto, is considered one of the fathers of artificial intelligence (AI). He helped develop neural networks, a type of machine learning that mimics the way the human brain works. Neural networks are now used in a wide range of applications, from self-driving cars to facial recognition software.
In a recent interview with The New York Times, Hinton expressed his concerns about the future of AI. He believes that as AI systems become more powerful, they pose an increasing threat to humanity.
"Look at how AI was five years ago," Hinton said. "Now take the difference and propagate it forwards. That's scary."
Hinton is particularly worried about the potential for AI to be used to create fake news and propaganda. He believes that AI-generated content could be so realistic that it would be difficult for people to tell what is real and what is fake. This could have a devastating impact on democracy and society as a whole.
Hinton is also concerned about the potential for AI to be used to create autonomous weapons. He believes that such weapons could be used to kill without human intervention, and that this could lead to a new era of warfare.
"I fear a day when truly autonomous weapons, those killer robots, become reality," Hinton said.
Hinton's concerns are shared by many other experts in the field of AI. However, there is no consensus on what to do about the problem. Some experts believe that we need to develop international regulations to govern the development and use of AI. Others believe that we need to focus on developing ethical guidelines for the use of AI.
Whatever we do, it is clear that we need to start thinking about the potential risks of AI now. If we don't, we could be in for a very dangerous future.'
In a recent interview with The New York Times, Hinton expressed his concerns about the future of AI. He believes that as AI systems become more powerful, they pose an increasing threat to humanity.
"Look at how AI was five years ago," Hinton said. "Now take the difference and propagate it forwards. That's scary."
Hinton is particularly worried about the potential for AI to be used to create fake news and propaganda. He believes that AI-generated content could be so realistic that it would be difficult for people to tell what is real and what is fake. This could have a devastating impact on democracy and society as a whole.
Hinton is also concerned about the potential for AI to be used to create autonomous weapons. He believes that such weapons could be used to kill without human intervention, and that this could lead to a new era of warfare.
"I fear a day when truly autonomous weapons, those killer robots, become reality," Hinton said.
Hinton's concerns are shared by many other experts in the field of AI. However, there is no consensus on what to do about the problem. Some experts believe that we need to develop international regulations to govern the development and use of AI. Others believe that we need to focus on developing ethical guidelines for the use of AI.
Whatever we do, it is clear that we need to start thinking about the potential risks of AI now. If we don't, we could be in for a very dangerous future.'
Meadway was having none of this line of discourse. Instead he stressed that we need to examine the problems that we are currently experiencing, arguing that the economics of resourcing AI development would have significant impacts in delaying any AGI like capable machine.
Meadway further indicated that the trade papers of the IT industry are demonstrating the increasing costs of AI and the scarcity of resources that's beginning to accrue. Here are just some snippets from articles that expand upon this point:
'AI consumes more energy than traditional types of computing, and just one model's training can consume more electricity in a year than 100 US homes do.
According to a study article released in 2021, training GPT-3, a single general-purpose AI software that can generate language and has numerous applications, required 1.287 gigawatt hours, or roughly the amount of electricity used by 120 American houses annually. According to the same report, such training produced 502 tons of carbon emissions, or roughly 110 US automobiles' worth in a year. Although training a model has a significant upfront power cost, researchers found that in some circumstances it only consumes around 40% of the power used by the real use of the model, with billions of applications for popular programs flooding in. Also, the models are growing larger. The 175 billion parameters, or variables, that the AI system has learnt through training and retraining are used in OpenAI's GPT-3. Only 1.5 billion were consumed by its predecessor.'
Forbes, published March 22, 2023 Green Intelligence, why data and AI must become more sustainable
Generative AI has been costly to build, but particularly to train for Alignment. Legal costs may also begin to accrue, quite rapidly too. What's been very apparent over the last 18 months or so is that these costs are hitting the share values of the major players. Hence the large number of layoffs by the tech giants we've seen recently. The real profits, over this time has been with the hardware companies.
And in dealing with those terrible things, we can start to say "well, how can we put under some sort of public supervision and control the development of these particular data techniques and these machine learning techniques?"
I think when George said this, he was kind of right. It's a process of competition. Google is scared of Microsoft, so they have to charge at this and put loads of money and time and infinite resources into it. The process of competition is driving this in a chaotic way.
What can we do that starts to put some public control, some democratic accountability, as much supervision as we can? We can have a serious society-wide conversation about how we start to use these things. We can potentially use them for all sorts of really good, exciting purposes. The invention of new drugs, the invention of far more efficient ways to use the resources we have, is something that AI and big data could be particularly good at.
We can do that if we start to get in our heads that we need to challenge the structures that are investing in and applying and using these new technologies. And that means, at the very least, thinking about regulation now. Not because of the existential risk, which I can write a kind of science fiction story about how this might happen in the future. I've tried to give you a sort of economics version of why this is quite implausible given the technologies that we have.
But what we really have to deal with is that issue of corporate power. The fact that decisions over these things are made by a really quite a limited number of institutions globally, with very limited real public control and oversight. And if we don't start to deal with this, then the kind of problems we saw with the development of things like Facebook and all the other big tech, big data companies from the early 2000s onwards, they will reproduce themselves in a much more dramatic and unpleasant fashion through machine learning and the application of machine learning.
That to me seems like something to really focus on now. And potentially then think about existential risks, rather than the immediate existential risk of "oh my God, we'll have a computer that will run off and kill us all." The problem is capitalism. The problem isn't the computer directly.'
Trade Algo published The Carbon Footprint Of Artificial Intelligence Is On The Rise March 9, 2023
According to a study article released in 2021, training GPT-3, a single general-purpose AI software that can generate language and has numerous applications, required 1.287 gigawatt hours, or roughly the amount of electricity used by 120 American houses annually. According to the same report, such training produced 502 tons of carbon emissions, or roughly 110 US automobiles' worth in a year. Although training a model has a significant upfront power cost, researchers found that in some circumstances it only consumes around 40% of the power used by the real use of the model, with billions of applications for popular programs flooding in. Also, the models are growing larger. The 175 billion parameters, or variables, that the AI system has learnt through training and retraining are used in OpenAI's GPT-3. Only 1.5 billion were consumed by its predecessor.'
Forbes, published March 22, 2023 Green Intelligence, why data and AI must become more sustainable
'Sanjay Podder, managing director and global lead of technology sustainability innovation at Accenture, says that the exponential growth in data and its increased energy demand could actually counteract and impede our global progress on climate change.
Right now, the AI community has adopted a “bigger is better” attitude regarding data and artificial intelligence – but that approach threatens to inflict major environmental damage in the future.
Tech experts will need to expend greater and greater amounts of energy to build increasingly larger models, with decreasing improvements in performance.'
A financial arms race, generating hype to generate profitability
NVIDIA, who make the GPUs that run AI models, that are financially benefitting from the AI hypesters. The costs of the hardware to develop, maintain and run LLMs has been staggering. One of the reasons why Google recently announced the development of it's chips, so has Microsoft
Share prices of the major players over a one year period.
Share prices of the major players over a one year period.
Meadway concludes
Meadway: 'It's a bit like climate change. We can say "wow, what if this terrible thing happens in the future?" But it's already happening. These terrible things are already happening. We should deal with the terrible things that are already happening.And in dealing with those terrible things, we can start to say "well, how can we put under some sort of public supervision and control the development of these particular data techniques and these machine learning techniques?"
I think when George said this, he was kind of right. It's a process of competition. Google is scared of Microsoft, so they have to charge at this and put loads of money and time and infinite resources into it. The process of competition is driving this in a chaotic way.
What can we do that starts to put some public control, some democratic accountability, as much supervision as we can? We can have a serious society-wide conversation about how we start to use these things. We can potentially use them for all sorts of really good, exciting purposes. The invention of new drugs, the invention of far more efficient ways to use the resources we have, is something that AI and big data could be particularly good at.
We can do that if we start to get in our heads that we need to challenge the structures that are investing in and applying and using these new technologies. And that means, at the very least, thinking about regulation now. Not because of the existential risk, which I can write a kind of science fiction story about how this might happen in the future. I've tried to give you a sort of economics version of why this is quite implausible given the technologies that we have.
But what we really have to deal with is that issue of corporate power. The fact that decisions over these things are made by a really quite a limited number of institutions globally, with very limited real public control and oversight. And if we don't start to deal with this, then the kind of problems we saw with the development of things like Facebook and all the other big tech, big data companies from the early 2000s onwards, they will reproduce themselves in a much more dramatic and unpleasant fashion through machine learning and the application of machine learning.
That to me seems like something to really focus on now. And potentially then think about existential risks, rather than the immediate existential risk of "oh my God, we'll have a computer that will run off and kill us all." The problem is capitalism. The problem isn't the computer directly.'
It was a sophisticated argument from Meadway: acknowledging the issues we currently face, including the lack of public accountability, the sorry state of regulation, economic and environmental constraints, alignment issues, and the intractable problem of capitalism.
Watch the full debate.
Watch the full debate.
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