embedded machine learning research engineer - georgist - urbanist - environmentalist
Yeah, this is the one piece a lot of people miss: in any decently competitive market, individual firms have effectively zero power to set prices; they must instead accept the prices determined by the market.
Knowing that, the solution to that sort of corporate BS, then, is to ensure markets are competitive by busting monopolies, lowering barriers to entry, and getting money out of politics to reduce the effect of lobbying.
Sounds similar to some of the research my sister has done in her PhD so far. As I understand, she had a bunch of snapshots of proteins from a cryo electron microscope, but these snapshots are 2D. She used ML to construct 3D shapes of different types of proteins. And finding the shape of a protein is important because the shape defines the function. It’s crazy stuff that would be ludicrously difficult and time-consuming to try to do manually.
Exactly. I rode an ebike one summer to commute to an internship. The sweat factor alone meant I never would have done that by regular bike, as I would’ve arrived at the office sweating like a pig.
On the other hand, a car has far greater maintenance costs. The car has license, insurance, maintenance, gas, parking, etc., whereas an ebike is basically free in comparison. Electricity to power an ebike is pennies, and maintainance is a few basic tools and a new tire or inner tube on occasion.
With all the money saved, you can just rent a car for the handful of days the ebike genuinely is not sufficient.
Exactly. I’d honestly rather have paid social media than engagement algo and ad-driven social media. When your algorithms chase engagement over all else, it leads to real harms, like the youtube alt-right pipeline. Fediverse ain’t perfect, but I like that there’s no engagement-chasing algorithm, no ads, just donations.
Me, who’s not in web dev:
Okay, so that’s enough about the past. What about now?
Well, monopolization of land and housing via the housing crisis has done tremendous harm:
In 2015, two talented professors, Enrico Moretti at Berkeley and Chang-Tai Hsieh at Chicago Booth, decided to estimate the effect of shortage of housing on US productivity. They concluded that lack of housing had impaired US GDP by between 9.5 per cent and 13.5 per cent.
In a follow-up paper, based on surveying 220 metropolitan areas, they revised the figure upwards – claiming that housing constraints lowered aggregate US growth by more than 50 per cent between 1964 and 2009. In other words, they estimate that the US economy would have been 74 per cent larger in 2009, if enough housing had been built in the right places.
How does that damage happen? It’s simple. The parts of the country with the highest productivity, like New York and San Francisco, also had stringent restrictions on building more homes. That limited the number of homes and workers who could move to the best job opportunities; it limited their output and the growth of the companies who would have employed them. Plus, the same restrictions meant that it was more expensive to run an office or open a factory, because the land and buildings cost more.
And that is just one form of rent-seeking. Imagine the collective toll of externalities (e.g., the climate crisis), monopolistic/oligopolistic markets such as energy and communications, monopolization of valuable intellectual property, etc.
So I would tend to say that — unless we change our policies to eliminate the housing crisis, properly price in externalities, eliminate monopolies, encourage the growth of free and open IP (e.g., free and open-source software, open research, etc.), and provide critical public goods/services such as healthcare and education and public transit — we are on a trajectory for AI to be Gilded Age 2: Electric Boogaloo. AI merely represents yet another source of productivity growth, and its economic spoils will continue to be captured by the already-wealthy.
I say this as someone who works as an AI and machine learning research engineer: AI alone will not fix our problems; it must be paired with major policy reform so that the economic spoils of progress are felt by all, not just the rich.
Joseph Stiglitz, in the same essay I referred to earlier, has this to say:
My analysis of market models suggests that there is no inherent reason that there should be the high level of inequality that is observed in the United States and many other advanced countries. It is not a necessary feature of the market economy. It is politics in the 21st century, not capitalism, which is at fault. Market and political forces have, of course, always been interwined. Especially in America, where our politics is so money-driven, economic inequalities translate into political inequality.
There is nevertheless considerable hope. For if the growth of inequality was largely the result of inexorable economic laws, public policy could do little more than lean against the wind. But if the growth of inequality is the result of public policy, a change in those policies could lead to an economy with less inequality, and even stronger growth.
Exactly. I work in AI (although not the LLM kind, just applying smaller computer vision models), and my belief is that AI can be a great liberator for humanity if we have the right political and economic apparatus. The question is what that apparatus is. Some will say it’s an inherent feature of capitalism, but that’s not terribly specific, nor does it explain the relatively high wealth equality that existed briefly during the middle of the 20th century in America. I think some historical context is important here.
During the Industrial Revolution, we had an unprecedented growth in average labor productivity due to automation. From a naïve perspective, we might expect increasing labor productivity to result in improved quality of life and less working hours. I.e., the spoils of that productivity being felt by all.
But what we saw instead was the workers lived in squalor and abject poverty, while the mega-rich captured those productivity gains and became stupidly wealthy.
Many people at the time took note of this and sought to answer this question: why, in an era over greater-than-ever labor productivity, is there still so much poverty? Clearly all that extra wealth is going somewhere, and if it’s not going to the working class, then it’s evidently going to the top.
One economist and philosopher, Henry George, wrote a book exploring this very question, Progress and Poverty. His answer, in short, was rent-seeking:
Rent-seeking is the act of growing one’s existing wealth by manipulating the social or political environment without creating new wealth.[1] Rent-seeking activities have negative effects on the rest of society. They result in reduced economic efficiency through misallocation of resources, reduced wealth creation, lost government revenue, heightened income inequality,[2] risk of growing political bribery, and potential national decline.
Rent-seeking takes many forms. To list a few examples:
George’s argument, essentially, was that the privatization of the economic rents borne of god-given things — be it land, minerals, or ideas — allowed the rich and powerful to extract all that new wealth and funnel it into their own portfolios. George was not the only one to blame these factors as the primary drivers of sky-high inequality; Nobel-prize winning economist Joseph Stiglitz has stated:
Specifically, I suggest that much of the increase in inequality is associated with the growth in rents — including land and exploitation rents (e.g., arising from monopoly power and political influence).
George’s proposed remedies were a series of taxes and reforms to return the economic rents of those god-given things to society at large. These include:
Land value taxes are generally favored by economists as they do not cause economic inefficiency, and reduce inequality.[2] A land value tax is a progressive tax, in that the tax burden falls on land owners, because land ownership is correlated with wealth and income.[3][4] The land value tax has been referred to as “the perfect tax” and the economic efficiency of a land value tax has been accepted since the eighteenth century.
A Pigouvian tax (also spelled Pigovian tax) is a tax on any market activity that generates negative externalities (i.e., external costs incurred by the producer that are not included in the market price). The tax is normally set by the government to correct an undesirable or inefficient market outcome (a market failure) and does so by being set equal to the external marginal cost of the negative externalities. In the presence of negative externalities, social cost includes private cost and external cost caused by negative externalities. This means the social cost of a market activity is not covered by the private cost of the activity. In such a case, the market outcome is not efficient and may lead to over-consumption of the product.[1] Often-cited examples of negative externalities are environmental pollution and increased public healthcare costs associated with tobacco and sugary drink consumption.[2]
Severance taxes are taxes imposed on the removal of natural resources within a taxing jurisdiction. Severance taxes are most commonly imposed in oil producing states within the United States. Resources that typically incur severance taxes when extracted include oil, natural gas, coal, uranium, and timber. Some jurisdictions use other terms like gross production tax.
such as in the Norwegian model:
The key to Norway’s success in oil exploitation has been the special regime of ownership rights which apply to extraction: the severance tax takes most of those rents, meaning that the people of Norway are the primary beneficiaries of the country’s petroleum wealth. Instead of privatizing the resource rents provided by access to oil, companies make their returns off of the extraction and transportation of the oil, incentivizing them to develop the most efficient technologies and processes rather than simply collecting the resource rents. Exploration and development is subsidized by the Norwegian government in order to maximize the amount of resource rents that can be taxed by the state, while also promoting a highly competitive environment free of the corruption and stagnation that afflicts state-controlled oil companies.
Citizen’s dividend is a proposed policy based upon the Georgist principle that the natural world is the common property of all people. It is proposed that all citizens receive regular payments (dividends) from revenue raised by leasing or taxing the monopoly of valuable land and other natural resources.
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This concept is a form of universal basic income (UBI), where the citizen’s dividend depends upon the value of natural resources or what could be titled as common goods like location values, seignorage, the electromagnetic spectrum, the industrial use of air (CO2 production), etc.[4]
In 1977, Joseph Stiglitz showed that under certain conditions, beneficial investments in public goods will increase aggregate land rents by at least as much as the investments’ cost.[1] This proposition was dubbed the “Henry George theorem”, as it characterizes a situation where Henry George’s ‘single tax’ on land values, is not only efficient, it is also the only tax necessary to finance public expenditures.[2] Henry George had famously advocated for the replacement of all other taxes with a land value tax, arguing that as the location value of land was improved by public works, its economic rent was the most logical source of public revenue.[3]
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Subsequent studies generalized the principle and found that the theorem holds even after relaxing assumptions.[4] Studies indicate that even existing land prices, which are depressed due to the existing burden of taxation on labor and investment, are great enough to replace taxes at all levels of government.[5][6][7]
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Train a neural network to detect non-primes. The more data you give it, the more accurate it is!
Train a neural network to detect non-primes. The more data you give it, the more accurate it is!
The raison d’être for RISC-V is domain-specific architecture. Currently, computational demands are growing exponentially (especially with AI), but Moore’s Law is ending, which means we can no longer meet our computational demands by scaling single-core speed on general-purpose CPUs. Instead, we are needing to create custom architectures for handling particular computational loads to eke out more performance. Things like NPUs, TPUs, etc.
The trouble is designing and producing these domain-specific architectures is expensive af, especially given the closed-source nature of computer hardware at the moment. And all that time, effort, and money just to produce a niche chip used for a niche application? The economics don’t economic.
But with an open ISA like RISC-V, it’s both possible and legal to do things like create an open-source chip design and put it on GitHub. In fact, several of those exist already. This significantly lowers the costs of designing domain-specific architectures, as you can now just fork an existing chip and make some domain-specific modifications/additions. A great example of this is PERCIVAL: Open-Source Posit RISC-V Core with Quire Capability. You could clone their repo and spin up their custom RISC-V posit chip on an FPGA today if you wanted to.