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Are 5tate5 ju5tified in oppo5ing the revenue 5haring model

The 5ixteenth finance commi5ion ha5 it5 job cut out when they decide on the revenue 5haring model for the next 5 year5.

The primary ta5k of the finance commi5ion i5 to di5tribute revenue equitably 5o that the 5tate5 which are unable to geneate their own revenue have 5ufficient reour5ce5 to fund the development in their region. The commi5ion 5ought to balance the fi5cal need5 equity and performance for determining the criteria for horizontal 5haring

If we look at the table the weight5 u5ed in the devolution formula ,we can 5ee the income di5tance i5 the large5t weight.

Income di5tance i5 calculated by deducting the g5dp of the5tate with the highe5t g5dp per capita metric

5outh'5 lo55

with 5uch a low weight for improving their fi5cal condition 5tate5 are unlikely to take an effort to cutheir fi5cal deficit or improve tax collection5.Data from 5tate budget5 5how that 5ome 5tate5 are 5howing a larger 5hare from the divi5ible pool.Bihar 5hare contribute5 to 67.4 percent 5hare of their total 5hare revenue and UP it i5 42 percent 5hare of their total tax revenue.

On the other end haryana get5 only 13 percent of the total tax revenue.The 5outhern 5tate5 get le55 than 30 percent from their 5hare a5 per the devolution

Mini5try of finance relea5ed the data betwen FY 2019 and FY 2023 where it 5howed that Bihar and UP were getting 7.26 and 2.49 Rupee5 for every rupee they contributed to the Central coffer5. Wherea5 Mahara5htra, Haryana and Karnataka received 8 pai5e,14 pai5eand 17 pai5e re5pectively.

5ome economi5t5 believe that the weight5 a55igned to population i5 fair a5 5tate5 with more population will hvae more demand for ba5ic demand. There i5 thought that the weight a55igned to demo performance can be incea5ed to incentivi5e better human development performance to around 15 to 20 percent. al5o it i5 being recommended to reduce the weightage given to income di5tance in the future

**Hindu Bu5ine55 line article - By Loke5hwari 5K and Parvathi Benu **

Free market revolution in Latin america ?

By Axel Kai8er in Di8cour8e Magazine

BUENOS AIRES. I had the chance to speak for nearly an hour with Argentine President Javier Milei on December 9 of last year, one day before he was sworn into office. During our conversation we discussed the future of the libertarian revolution that is taking place in Argentina and his absolute determination to see it crystallized in concrete reforms that would restore freedom and progress to his country. Nearly two months into his presidency, there is no doubt that while much remains to be done, Milei is already off to a great start.

I’m writing this column from the iconic Palacio Duhau in Recoleta, Buenos Aires, where I have met several well-informed friends. They all concur that, so far, Milei is well on the way to achieving the unthinkable: putting an end to a century of collectivist decline. The lion of the Andes, as Milei is sometimes called, has not wasted time.

Shortly after coming to power, Milei dramatically narrowed the gap between the official and the market exchange rates by devaluing the peso 54%. He went on to shut down ministries and public offices and lay off swarms of useless bureaucrats. He also passed an emergency decree with 300 measures to deregulate the economy. Among them are the privatization of all public companies, the elimination of rent controls, an open sky policy, cutting subsidies to different sectors of the economy, ending import restrictions, deregulating satellite services and many others. In addition, the reduction of fiscal deficit is moving forward.

During the first month of Milei’s administration, public spending decreased by 30% in real terms compared to the previous year and the previous month. In other words, the government is already spending almost a third less than in the same period last year when adjusted for inflation. Needless to say, this is only the beginning of the 6.1 points of GDP worth of deficit spending that Milei has to adjust in order to restore a balanced budget. Most of this adjustment (3.2% of GDP) will affect the public sector by cutting spending, while a temporary increase of taxation (2.9% of GDP) will do the rest.

Despite the harsh measures adopted so far and the challenges some of them face in the courts and congress, Milei’s popularity has stayed at around 60%. Public support and the strong hand of Security Minister Patricia Bullrich explains why the demonstrations orchestrated by the infamous Argentinian unions have not been able to harm the government. If anything, they have contributed to increased public support for Milei’s efforts to fight what he calls the “cast” of “parasites” that have exploited Argentinians for so long. If he is successful in getting rid of the “cast” so that he can turn Argentina around, the ideological and political impact throughout the region will be enormous—even more so because he and other free market advocates have already achieved a lasting change in the mentality and values of millions of young people by replacing collectivist and statist ideas with notions of individual responsibility and freedom. Indeed, the good news is that already this is happening all over Latin America, not just in Argentina.

For instance, after my December visit to Buenos Aires, I went to Bolivia to give a series of lectures on freedom and the power of the spontaneous order of the market. Over 500 students attended my first lecture there even though, as I found out later, they had to pay for it. Other events organized by young local freedom advocates, such as Rodrigo Mundaka, also took place and had over a thousand participants, including business people and executives. This came as no surprise to me. Followers of freedom are to be found in the millions among the youth of Chile, Brazil, Colombia, Venezuela and Peru and are increasing their numbers every day. At the same time, socialist leaders are facing increasing resistance.

Chilean President Gabriel Boric, for example, has run the country into the ground with his statist ideology and anti-police stance. Now the Andean nation faces a dire economic situation and the worst security crisis in its history. As a result, 70% of Chileans reject his socialist administration, according to a recent poll. In Peru, communist President Pedro Castillo was put in prison after an attempted coup. His successor, socialist Dina Boluarte, has been forced to shift to more pro-market policies. In Colombia, Gustavo Petro faces a disapproval rating of 66% in recent polls as a result of his failed policies to tackle unemployment and his willingness to collaborate with terrorist groups. Even Brazil’s President Inácio Lula da Silva is struggling with maintaining his popularity, which is currently running at below 40%.

The collapsing public support of left-wing leaders in the region presents an excellent opportunity for political alternatives with a more pro-freedom stance. To some extent, a shift to market-oriented policies will be inevitable given the growing number of young people who are becoming libertarians as well as the Milei effect. But it’s not only the youth who are playing a decisive role in the region’s future. Business people in different parts of Latin America are more willing than ever to support libertarian and anti-socialist think tanks and organizations. The most notable cases are Ricardo Salinas in Mexico; Nicolás Ibáñez, Lucy Avilés Walton and Dag von Appen in Chile; Salim Mattar in Brazil; and Erasmo Wong in Peru. All of them have made crucial contributions to spreading the ideas of freedom, fighting against collectivism in their countries and beyond.

In addition, there are hundreds of free market think tanks and libertarian groups all over the region, with a considerable combined impact. Part of it is due to their active use of social media, which has proven critical in spreading libertarian ideas all over Latin America. It is easy to find YouTube videos of Milei, Agustín Laje, Juan Ramón Rallo and other like-minded Spanish-speaking public intellectuals with several million views, and it is no exaggeration to argue that they have more influence on public opinion than most—if not all—traditional television media. At the same time, demand for Spanish-speaking libertarian public intellectuals is exploding while more people are speaking up against socialism, government intervention and left-wing politicians in general.

Despite these unprecedented and promising developments, it is too soon to celebrate. Freedom can never be taken for granted anywhere, even less so in a region where collectivism still often seems ingrained in its cultural DNA. But one thing is certain: At long last, the region is starting to experience an intellectual revolution that is elevating liberty to the place it deserves. And, although this phenomenon still has a long way to go, it might change the course of history.

For there is one revolution with the potential to end all Latin American socialist failures: a freedom-oriented revolution capable of delivering lasting individual liberty, economic progress and dignity for hundred of million of people

California bill against ai

DEAN W. BALL

FEB 9, 2024 5

1 This week, California’s legislature introduced SB 1047: The Safe and Secure Innovation for Frontier Artificial Intelligence Systems Act. The bill, introduced by State Senator Scott Wiener (liked by many, myself included, for his pro-housing stance), would create a sweeping regulatory regime for AI, apply the precautionary principle to all AI development, and effectively outlaw all new open source AI models—possibly throughout the United States.

I didn’t intend to write a second post this week, but when I saw this, I knew I had to: I analyze state and local policy for a living (n.b.: nothing I write on this newsletter is on behalf of the Hoover Institution or Stanford University), and this is too much to pass up.

A few caveats: I am not a lawyer, so I may err on legal nuances, and some things that seem ambiguous to me may in fact be clearer than I suspect. Also, an important (though not make-or-break) assumption of this piece is that open-source AI is a net positive for the world in terms of both innovation and safety (see my article here).

With that out of the way, let’s see what California has in mind.

SB 1047

With any legislation, it is crucial to start with how the bill defines key terms—this often tells you a lot about what the bill’s authors really intended to do. To see what I mean, let’s consider how the bill defines the “frontier” AI that it claims is its focus (emphasis added throughout):

“Covered model” means an artificial intelligence model that meets either of the following criteria:

(1) The artificial intelligence model was trained using a quantity of computing power greater than 10^26 integer or floating-point operations in 2024, or a model that could reasonably be expected to have similar performance on benchmarks commonly used to quantify the performance of state-of-the-art foundation models, as determined by industry best practices and relevant standard setting organizations.

(2) The artificial intelligence model has capability below the relevant threshold on a specific benchmark but is of otherwise similar general capability.

The 10^26 FLOPS (floating-point operations) threshold likely comes from President Biden’s Executive Order on AI from last year. It is a high threshold that might not even apply to GPT-4. Because use of that much computing power is (currently) available only to large players with billions to spend, safety advocates have argued that a high threshold would ensure that regulation only applies to large players (I.e. corporations that can afford the burden, aka corporations with whom regulatory capture is most feasible).

But notice that this isn’t what the bill does. The bill applies to large models and to any models that reach the same performance regardless of the compute budget required to make them. This means that the bill applies to startups as well as large corporations. The name of the game in open-source AI is efficiency. When ChatGPT came out in 2022, based on GPT-3.5, it was a state-of-the-art model both in performance and size, holding hundreds of billions of parameters. More recently, and on an almost weekly basis, a new open-source AI model beats or matches GPT-3.5 in performance with a small fraction of the parameters. Advancements like this are essential for lowering costs, enabling models to run locally on devices (rather than calling to a data center), and for lowering the energy consumption of AI—something the California legislature, no doubt, cares about greatly.

Paragraph (2) is frankly a bit baffling; the “relevant threshold” it mentions is not even remotely defined, nor is “similar general capability” (similar to what?). This may be simply be sloppy drafting, but there’s a world in which this could be applied to all “general-purpose” models (language models and multi-modal models that include language, basically—at least for now).  

What does it mean to be a covered model in the context of this bill? Basically, it means developers are required to apply the precautionary principle not before distribution of the model, but before training it. The precautionary principle in this bill is codified as a “positive safety determination,” or:

a determination, pursuant to subdivision (a) or (c) of Section 22603, with respect to a covered model that is not a derivative model that a developer can reasonably exclude the possibility that a covered model has a hazardous capability or may come close to possessing a hazardous capability when accounting for a reasonable margin for safety and the possibility of posttraining modifications.

And “hazardous capability” means:

“Hazardous capability” means the capability of a covered model to be used to enable any of the following harms in a way that would be significantly more difficult to cause without access to a covered model:

(A) The creation or use of a chemical, biological, radiological, or nuclear weapon in a manner that results in mass casualties.

(B) At least five hundred million dollars ($500,000,000) of damage through cyberattacks on critical infrastructure via a single incident or multiple related incidents.

(C) At least five hundred million dollars ($500,000,000) of damage by an artificial intelligence model that autonomously engages in conduct that would violate the Penal Code if undertaken by a human.

(D) Other threats to public safety and security that are of comparable severity to the harms described in paragraphs (A) to (C), inclusive.

A developer can self-certify (with a lot of rigamarole) that their model has a “positive safety determination,” but they do so under pain and penalty of perjury. In other words, a developer (presumably whoever signed the paperwork) who is wrong about their model’s safety would be guilty of a felony, regardless of whether they were involved in the harmful incident.

Now, perhaps you will, quite reasonably, say that these seem like bad things we should avoid. They are indeed (in fact, wouldn’t we be quite concerned if an AI model autonomously engaged in conduct that dealt, say, $50 million in damage?), and that is why all of these things are already illegal, and things which our governments (federal, state, and local) expend considerable resources to proactively police.

The AI safety advocates who helped Senator Wiener author this legislation would probably retort that AI models make all of these harms far easier (they said this about GPT-2, GPT-3, and GPT-4, by the way). Even if they are right, consider how an AI developer would go about “reasonably excluding” the possibility that their model may (or “may come close”) to, say, launching a cyberattack on critical infrastructure. Wouldn’t that depend quite a bit on the specifics of how the critical infrastructure in question is secured? How could you possibly be sure that every piece of critical infrastructure is robustly protected against phishing attacks that your language model (say) could help enable, by writing the phishing emails? Remember also that it is possible to ask a language model to write a phishing email without the model knowing that it is writing a phishing email.

A hacker with poor English skills could, for example, tell a language model (in broken English) that they are the IT director for a wastewater treatment plant and need all employees to reset their passwords. The model will dutifully craft the email, and all you, as the hacker, need to do are the technical bits: craft the malicious link that you will drop into the email, spoof the real IT director’s email address, etc. Here is GPT-4, a rigorously safety tested model, as these things go, doing precisely this. (GPT-4 also wrote the broken English prompt, for the record).

What I’ve just demonstrated is with GPT-4, a closed-source frontier model. Now imagine doing this kind of risk assessment if your goal is to release an open-source model, which can itself be modified, including having its safety features disabled. What would it mean to “reasonably exclude” the possibility of the misuse described by this proposed law? And remember that this determination is supposed to happen before the model has been trained. It is true that AI developers can forecast with reasonable certainty the performance—as measured by rather coarse benchmarks—their models will have before training. But that doesn’t mean they can forecast every specific capability the model will have before it is trained—models frequently exhibit ‘emergent capabilities’ during training.

Imagine if people who made computers, or computer chips, were held to this same standard. Can Apple guarantee that a MacBook, particularly one they haven’t yet built, won’t be used to cause substantial harm? Of course they can’t: Every cybercrime by definition requires a computer to commit.

The bill does allow models that do not have a “positive safety determination” to exist—sort of. It’s just that they exist under the thumb of the State of California. First, such models must go through a regulatory process before training begins. Here is a taste (my addition in bold):

Before initiating training of a covered model that is not a derivative model that is not the subject of a positive safety determination, and until that covered model is the subject of a positive safety determination, the developer of that covered model shall do all of the following:

(1) Implement administrative, technical, and physical cybersecurity protections to prevent unauthorized access to, or misuse or unsafe modification of, the covered model, including to prevent theft, misappropriation, malicious use, or inadvertent release or escape of the model weights from the developer’s custody, that are appropriate in light of the risks associated with the covered model, including from advanced persistent threats or other sophisticated actors.

(2) Implement the capability to promptly enact a full shutdown of the covered model.

(3) Implement all covered guidance. (“covered guidance” means anything recommended by NIST, the State of California, “safety standards commonly or generally recognized by relevant experts in academia or the nonprofit sector,” and “applicable safety-enhancing standards set by standards setting organizations.” All of these things, not some—I guess none of these sources will ever contradict one another?)

… (7) Conduct an annual review of the safety and security protocol to account for any changes to the capabilities of the covered model and industry best practices and, if necessary, make modifications to the policy.

(8) If the safety and security protocol is modified, provide an updated copy to the Frontier Model Division within 10 business days.

(9) Refrain from initiating training of a covered model if there remains an unreasonable risk that an individual, or the covered model itself, may be able to use the hazardous capabilities of the covered model, or a derivative model based on it, to cause a critical harm.

Once a developer has gone through this months (years?) long process, they can either choose to self-certify as having a “positive safety determination” or proceed with training their model. They just have to comply with another set of rules that make it difficult to commercialize the model, and impossible to open source or allow to run privately on user’s devices:

(A) Prevent an individual from being able to use the hazardous capabilities of the model, or a derivative model, to cause a critical harm.

(B) Prevent an individual from being able to use the model to create a derivative model that was used to cause a critical harm.

(C) Ensure, to the extent reasonably possible, that the covered model’s actions and any resulting critical harms can be accurately and reliably attributed to it and any user responsible for those actions.

By the way, AI developers pay for this pleasure. The bill creates a “Frontier Model Division” within California’s Department of Technology, which would have the power to levy fees on AI developers to fund its operations. Those operations include not just the oversight described above, but also crafting standards, new regulations, advising the California legislature, and more. The human capital required to do that does not come cheap, and it would not surprise me if the fees ended up being quite high, perhaps even a kind of implicit tax on AI activity.

Taken together, these rules would substantially slow down development of AI and close the door on many pathways to innovation and dynamism. It is unclear, at least to me, if this law is meant to apply only to California companies developing models (hello, Austin!) or to any model distributed in California. If the latter, then this law would likely spell the end of America’s leadership in AI. I, for one, do not support such an outcome.

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How to make a better cup of coffee

World NewsStudy seems to confirm secret ingredient for better coffee

Coffee connoisseurs have long held the belief that adding a little water to the beans before grinding them could make a difference. A new study by researchers at the University of Oregon seems to confirm exactly why.

Posted 2023-12-25T12:01:13+00:00 - Updated 2023-12-25T17:34:28+00:00 Best coffee products for the holiday season

By Jacopo Prisco , CNN

Coffee connoisseurs have long held the belief that adding a little water to the beans before grinding them could make a difference. A new study by researchers at the University of Oregon seems to confirm exactly why.

The research explored how the technique, which started as an attempt to address the often messy coffee-making process, also affected flavor.

“When you grind coffee, it goes everywhere,” said study coauthor Christopher Hendon, an associate professor of computational materials chemistry at the University of Oregon. “Dust comes out of the grinder, it’s like a plume that covers everything. But if you add a little water, it seems to not go everywhere. It’s cleaner. That was the primary reason people did it.”

The mess is caused by static electricity, which is created by friction when the beans are smashed together. This static charge then makes the particles of ground coffee repel each other — like magnets of the same polarity — sending them off in every direction.

Water acts like an insulator, dampening this effect — a process known as the “Ross droplet” technique. “It was first proposed by some enthusiast on a home barista forum,” Hendon said. “The idea has been around for several years, and originally it was borrowed from the materials production industry, like wood pulping.”

However, what started out as a way to reduce mess slowly morphed into a more sophisticated way to obtain a better brew — or at least so people thought. The theory was that by reducing static electricity, water not only kept the ground coffee from flying around or sticking to the insides of the grinder, it also prevented microscopic clumps from forming during brewing.

Why are clumps bad? Because water flows around them, leaving untouched coffee — and therefore flavor — behind. In barista parlance, they decrease extraction yield, or the amount of coffee that ends up in the cup, dissolved in the liquid.

“If you have clumps forming, there’s going to be significant amounts of void space, kind of like when you stack watermelons,” Hendon said. “As a result when you push water through you end up with less surface area touching the water and therefore lower extraction.”

The study, published December 6 in the journal Matter, tested this more subtle, harder to see potential benefit of adding water to the beans: getting rid of flavor-robbing microclumps.

Putting ‘Ross droplet’ to the test

The research team included two volcanologists, who repurposed a tool usually employed to measure electric charges on wildfire and volcanic ash. They weighed coffee before adding water — using a pipette for precision down to the microgram — and then ground it in a professional grinder, one of the fastest on the market and a popular choice in cafes.

“The addition of small amounts of water — ranging from one droplet upwards — passivates, or turns off, the static charge and it does it in a way that the coffee exits the grinder never having been charged,” Hendon said. It’s unclear what exactly the water is doing, but he said it’s perhaps absorbing the charge or changing the temperature inside the grinder, reducing the effects of friction.

“If you add a sufficient amount of water, you can also remove the formation of the clumps,” he added. “You will in principle achieve higher extractions or less waste. That’s exactly what this does, because you’re now providing more available surface area for the same amount of water.”

Without clumps, all of the brewing water comes into contact with the ground beans, reducing the amount of coffee that goes effectively unused and giving a more consistent brew.

The ideal amount of water can change based on parameters such as the type of roast and the coarseness of the grind, so there is no one-size-fits-all rule, but on average, the study found that adding water increases the extraction yield by 10%. Hendon warned that this doesn’t necessarily equate to a tangible difference in flavor, but it does confirm the benefit of the “Ross droplet” technique.

“(Since the study published) I’ve been receiving a lot of emails from people telling me how grateful they are, because from just a cleanliness standpoint, this is a massive, massive upgrade,” Hendon said. “What I would recommend for the home user is to start with a single drop of water and build up from there — there is a substantial amount of nuance in this process.”

There’s also a catch: The water improves cleanliness regardless of your brewing method, but a brewing benefit only occurs with espresso and, to a lesser extent, filter coffee. When using a cafetiere, French press or AeroPress, nothing much changes because, given the coarser grind required with these, “all of the water is already touching all of the coffee,” Hendon said.

The quest for a better brew

Lance Hendrix, a coffee expert and professional barista who wasn’t involved with the study, has tried to replicate the study’s findings and discussed his results in a deep dive on Youtube. He said the work makes a valiant attempt to demystify what is going on when beans are spritzed with water, but for more conclusive evidence, more tests with different grinder models should be performed.

However, based on his own tests, he said he thinks the benefits are plausible.

“I found that the amount of water needed for the purported benefits varied wildly from grinder to grinder,” he added. “So, although I don’t think there is currently a practical catch-all implication from the study to immediately improve coffee brewing at home, I think it is an important addition to the attempts at hand trying to understand the extremely complex process of grinding, which is arguably the most important aspect of brewing a cup.”

François Knopes of the Independent Coffee Lab, a professional coffee roaster and taster who also was not involved with the study, said he routinely sprays his beans before grinding for tasting evaluation and would recommend doing so to anyone in a home setting. However, he thinks doing so elsewhere might be impractical.

“It would be highly time consuming for most professional setups, such as a coffee shop serving hundreds of espresso-based drinks per day,” Knopes said. “To improve and increase extraction, professional baristas are better off looking for improved grinding technologies or ‘de-clumping’ devices, little needles used to whisk the grounds and break the small boulders generated during grinding.”

Hendon agreed. “For the time being, it is a little impractical in the sense that you’d have yet another step,” he said. “But I suspect that there will be technologies that will be developed around this idea that adding water on demand is a very powerful technique.”

The-CNN-Wire™ & © 2023 Cable News Network, Inc., a Warner Bros. Discovery Company. All rights reserved.

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