Good (but scary) overview of state of AI in the og article, some points:

• The transformation of work by AI is already underway, not a distant future event. The NBER model quantifies this as a potential 366% productivity boost coupled with a 23% employment reduction, with half the job displacement happening within five years. This underscores the urgency of being intentional about how AI reshapes work environments.

• The future of AI in the workplace favors augmentation over replacement. Most workers prefer AI to automate repetitive tasks and to act as partners or coaches rather than substitutes. Enterprise AI strategies should therefore focus on complementing human skills, freeing teams to concentrate on creative and interpersonal value-driving activities.

• AI adoption and impact are highly role-specific. While some jobs like logistics management face high automation risks, others like hands-on mechanics remain largely unaffected. Similarly, AI is democratizing expertise by leveling the playing field for freelancers, which challenges traditional premium skill valuations. Effective AI strategies require granular, nuanced understanding of these variations to maximize benefits and mitigate harms.

How Base 3 Computing Beats Binary

Metadata

• Author: Stephen Ornes
• Category: rss
• URL: https://www.quantamagazine.org/how-base-3-computing-beats-binary-20240809/

Highlights

Three, as Schoolhouse Rock! told children of the 1970s, is a magic number. Three little pigs; three beds, bowls and bears for Goldilocks; three Star Wars trilogies. You need at least three legs for a stool to stand on its own, and at least three points to define a triangle.

If a three-state system is so efficient, you might imagine that a four-state or five-state system would be even more so. But the more digits you require, the more space you’ll need. It turns out that ternary is the most economical of all possible integer bases for representing big numbers.

Surprisingly, if you allow a base to be any real number, and not just an integer, then the most efficient computational base is the irrational number e.

Despite its natural advantages, base 3 computing never took off, even though many mathematicians marveled at its efficiency. In 1840, an English printer, inventor, banker and self-taught mathematician named Thomas Fowler invented a ternary computing machine to calculate weighted values of taxes and interest. “After that, very little was done for years,” said Bertrand Cambou, an applied physicist at Northern Arizona University.

Why didn’t ternary computing catch on? The primary reason was convention. Even though Soviet scientists were building ternary devices, the rest of the world focused on developing hardware and software based on switching circuits — the foundation of binary computing. Binary was easier to implement.

Highlights

The first scaling crisis happened in 1996, when Linus wrote that he was "buried alive in emails". It was addressed by adopting a more modular architecture, with the introduction of loadable kernel modules, and the creation of the maintainers role, who support the contributors in ensuring that they implement the high standards of quality needed to merge their contributions.

The second scaling crisis lasted from 1998 to 2002, and was finally addressed by the adoption of BitKeeper, later replaced by Git. This distributed the job of merging contributions across the network of maintainers and contributors.

In both cases, technology was used to reduce the amount of dependencies between teams, help contributors keep a high level of autonomy, and make it easy to merge all those contributions back into the main repository, Bernhard said.

Technology can help reduce the need to communicate between teams whenever they have a dependency on another team to get their work done. Typical organizational dependencies, such as when a team relies on another team’s data, can be replaced by self-service APIs using the right technologies and architecture, Bernhard mentioned. This can be extended to more complicated dependencies, such as infrastructure provisioning, as AWS pioneered when they invented EC2, offering self-service APIs to spin up virtual servers, he added.

Another type of dependency is dealing with the challenge of merging contributions made to a similar document, whether it’s an illustration, a text, or source code, Bernhard mentioned. This has been transformed in the last 15 years by real-time collaboration software such as Google Docs and distributed versioning systems such as Git, he said.

Anyone trying to scale an agile organization should study lean thinking to benefit from decades of experience on how to lead large organizations while staying true to the spirit of the agile manifesto, he concluded.

Resurfaced in my feed. Obvious in retrospect.

Many microbes and cells are in deep sleep, waiting for the right moment to activate.

Harsh conditions like lack of food or cold weather can appear out of nowhere. In these dire straits, rather than keel over and die, many organisms have mastered the art of dormancy. They slow down their activity and metabolism. Then, w

Sitting around in a dormant state is actually the norm for the majority of life on Earth: By some estimates, 60% of all microbial cells are hibernating at any given time. Even in organisms whose entire bodies do not go dormant, like most mammals, some cellular populations within them rest and wait for the best time to activate.

“Life is mainly about being asleep.”

Because dormancy can be triggered by a variety of conditions, including starvation and drought, the scientists pursue this research with a practical goal in mind: “We can probably use this knowledge in order to engineer organisms that can tolerate warmer climates,” Melnikov said, “and therefore withstand climate change.”

Balon is notably absent from Escherichia coli and Staphylococcus aureus, the two most commonly studied bacteria and the most widely used models for cellular dormancy. By focusing on just a few lab organisms, scientists had missed a widespread hibernation tactic, Helena-Bueno said. “I tried to look into an under-studied corner of nature and happened to find something.”

“Most microbes are starving,” said Ashley Shade, a microbiologist at the University of Lyon who was not involved in the new study. “They’re existing in a state of want. They’re not doubling. They’re not living their best life.”

“This is not something that’s unique to bacteria or archaea,” Lennon said. “Every organism in the tree of life has a way of achieving this strategy. They can pause their metabolism.”

“Before the invention of hibernation, the only way to live was to keep growing without interruptions,” Melnikov said. “Putting life on pause is a luxury.”

It’s also a type of population-level insurance. Some cells pursue dormancy by detecting environmental changes and responding accordingly. However, many bacteria use a stochastic strategy. “In randomly fluctuating environments, if you don’t go into dormancy sometimes, there’s a chance that the whole population will go extinct” through random encounters with disaster, Lennon said. In even the healthiest, happiest, fastest-growing cultures of E. coli, between 5% and 10% of the cells will nevertheless be dormant. They are the designated survivors who will live should something happen to their more active, vulnerable cousins.

More fundamentally, Melnikov and Helena-Bueno hope that the discovery of Balon and its ubiquity will help people reframe what is important in life. We all frequently go dormant, and many of us quite enjoy it. “We spend one-third of our life asleep, but we don’t talk about it at all,” Melnikov said. Instead of complaining about what we’re missing when we’re asleep, maybe we can experience it as a process that connects us to all life on Earth, including microbes sleeping deep in the Arctic permafrost.

Highlights

We may be close to rediscovering thousands of texts that had been lost for millennia. Their contents may reshape how we understand the Ancient World.

We don’t have original copies of anything, not of the Iliad, or the Aeneid, or Herodotus, or the Bible. Instead of originals, we find ourselves dealing with copies. These were first written on scrolls but later in books – the Romans called books codexes – starting in the first century AD. Did I say copies? That’s actually not correct either. We don’t have first copies of anything. What we do have is copies of copies, most of which date hundreds of years after the original was penned. Even many of our copies are not complete copies.

To most fully acclimate the reader to how tenuous this process is, this essay will focus on three different texts. The first will be a very well-known work that was never lost. Nevertheless, almost no one read it in earnest until the nineteenth century. I will then focus on a text that was lost to history, but that we were able to recover from the annals of time. Such examples are fortuitous. Our third example will be a text that we know existed, but of which we have no copies, and consider what important ramifications its discovery could hold. Finally, we’ll turn our attention again to the Villa of the Papyri and the gold mine of texts discovered there that new technologies are currently making available to classicists.

However, many of the scrolls from the Villa of the Papyri remain not only unread, but also unopened. This is because the eruption of Vesuvius left the scrolls carbonized, making it nearly impossible to open them. Despite this obstacle, Dr. Brent Seales pioneered a new technology in 2015 that allowed him and his team to read a scroll without opening it. The technique, using X-ray tomography and computer vision, is known as virtual unwrapping, and it was first used on one of the famous Dead Sea Scrolls, specifically the En-Gedi scroll, the earliest known copy of the Book of Leviticus (likely 210–390 CE). The X-rays allow scholars to create a virtual copy of the text that can then be read like any other ancient document by those with the proper language and paleography skills. Using Dr. Seales’s technique, scholars have been able to upload many of the texts online. A group of donors led by Nat Friedman and Daniel Gross have offered cash prizes to teams of classicists who can decipher the writings. The race to read the virtually unwrapped scrolls is known as the Vesuvius Challenge.

Highlights

When seawater gets cold, it gets viscous. This fact could explain how single-celled ocean creatures became multicellular when the planet was frozen during “Snowball Earth,” according to experiments.

A series of papers from the lab of Carl Simpson proposes an answer linked to a fundamental physical fact: As seawater gets colder, it gets more viscous, and therefore more difficult for very small organisms to navigate. Imagine swimming through honey rather than water. If microscopic organisms struggled to get enough food to survive under these conditions, as Simpson’s modeling work has implied, they would be placed under pressure to change — perhaps by developing ways to hang on to each other, form larger groups, and move through the water with greater force. Maybe some of these changes contributed to the beginning of multicellular animal life.

The experiment comes with a few caveats, and the paper has yet to be peer-reviewed; Simpson posted a preprint on biorxiv.org earlier this year. But it suggests that if Snowball Earth did act as a trigger for the evolution of complex life, it might be due to the physics of cold water.

It is difficult to precisely date when animals arose, but an estimate from molecular clocks — which use mutation rates to estimate the passage of time — suggests that the last common ancestor of multicellular animals emerged during the era known as the Sturtian Snowball Earth, sometime between 717 million and 660 million years ago. Large, unmistakably multicellular animals appear in the fossil record tens of millions of years after the Earth melted following another, shorter Snowball Earth period around 635 million years ago.

The paradox — a planet seemingly hostile to life giving evolution a major push — continued to perplex Simpson throughout his schooling and into his professional life. In 2018, as an assistant professor, he had an insight: As seawater gets colder, it grows thicker. It’s basic physics — the density and viscosity of water molecules rises as the temperature drops. Under the conditions of Snowball Earth, the ocean would have been twice or even four times as viscous as it was before the planet froze over.

As large creatures, we don’t think much about the thickness of the fluids around us. It’s not a part of our daily lived experience, and we are so big that viscosity doesn’t impinge on us very much. The ability to move easily — relatively speaking — is something we take for granted. From the time Simpson first realized that such limits on movement could be a monumental obstacle to microscopic life, he hasn’t been able to stop thinking about it. Viscosity may have mattered quite a lot in the origins of complex life, whenever that was.

“Putting this into our repertoire of thinking about why these things evolved — that is the value of the entire thing,” he said. “It doesn’t matter if it was Snowball Earth. It doesn’t matter if it happened before or after. Just the idea that it can happen, and happen quickly.”

Highlights

Amanda Randles wants to copy your body. If the computer scientist had her way, she’d have enough data — and processing power — to effectively clone you on her computer, run the clock forward, and see what your coronary arteries or red blood cells might do in a week. Fully personalized medical simulations, or “digital twins,” are still beyond our abilities, but Randles has pioneered computer models of blood flow over long durations that are already helping doctors noninvasively diagnose and treat diseases.

Her latest system takes 3D images of a patient’s blood vessels, then simulates and forecasts their expected fluid dynamics. Doctors who use the system can not only measure the usual stuff, like pulse and blood pressure, but also spy on the blood’s behavior inside the vessel. This lets them observe swirls in the bloodstream called vortices and the stresses felt by vessel walls — both of which are linked to heart disease. A decade ago, Randles’ team could simulate blood flow for only about 30 heartbeats, but today they can foresee over 700,000 heartbeats (about a week’s worth). And because their models are interactive, doctors can also predict what will happen if they take measures such as prescribing medicine or implanting a stent.

It’s a lot of data. We’re running simulations with up to 580 million red blood cells. There’s interactions with the fluid and red blood cells, the cells with each other, the cells with the walls — you’re trying to capture all of that. For each model, one time point might be half a terabyte, and there are millions of time steps in each heartbeat. It’s really computationally intense.

Interesting findings

cross-posted from: https://group.lt/post/1926151

Don't read if you don't want to ruin your day.

The One About The Web Developer Job Market

Metadata

• Author: Baldur Bjarnason
• Category: rss
• URL: https://www.baldurbjarnason.com/2024/the-one-about-the-web-developer-job-market/

Highlights

Many organisations are also resorting to employee-hostile strategies to increase employee churn, such as forced Return-To-Office policies.

Finding a non-bullshit job is likely only going to get harder.

• Finding effective documentation, information, and training is likely to get harder, especially in specialised topics where LLMs are even less effective than normal.

as soon as you start to try to predict the second or third order consequences things very quickly get remarkably difficult.

In short, futurists are largely con artists.

you need to do something Note: ah.. the famous "you need to do something" This is not a one-off event but has turned into a stock market driven movement towards reducing the overall headcount of the tech industry.

What this means is that when the bubble ends, as all bubbles must, the job market is likely to collapse even further.

The stock market loves job cuts

Activist investors see it as an opportunity to lower developer compensation

Management believes they can replace most of these employees with LLM-based automation

Discovering whether it’s true or not is actually quite complicated as it, counter-intuitively, doesn’t depend on the degree of LLM functionality but instead depends entirely on what organisations, managers, and executives are using software projects for.

Since, even in the best case scenario of the most optimistic prediction of LLM power, you’re still going to need to structure a plan for the code and review it, the time spent on code won’t drop to zero. But if you believe in the best-case prediction, a 20-40% improvement in long term productivity sounds reasonable, if a bit conservative.

The alternate world-view, one that I think is much more common among modern management, is that the purpose of software development is churn.

None of these require that the software be free or even low on defects. The project doesn’t need to be accessible or even functional for a majority of the users. It just needs to look good when managers, buyers, and sales people poke at it.

The alternate world-view, which I think I can demonstrate is dominant in web development at least, is that software quality does not matter. Production not productivity is what counts. Up until now, the only way to get production and churn has been to focus on short-term developer experience, often at the expense of the long term health of the project, but the innovation of LLMs is that now you can get more churn, more production, with fewer developers.

This means that it doesn’t matter who is correct or not in their estimate of how well these tools work.

You aren’t going to notice the issue as an end-user. From your perspective the system is working perfectly.

Experienced developers will edit out the issues without thinking about it, focusing on the time-saving benefit of generating the rest. Inexperienced developers won’t notice the issues and think they’ve just saved a lot of time, not realising they’ve left a ticking time bomb in the code base.

The training data favours specific languages such as Python and JavaScript.

• The same tool that enhances their productivity by 20-30% might also be outright harmful to a junior developer’s productivity, once you take the inevitable and eventual corrections and fixes into account.

From the job market perspective, all that improved and safe LLM-based coding tools would mean is more job losses.

Because manager world-views are more important than LLM innovation.

If the technology is what’s promised, the churn world-view managers will just get more production, more churn, with even fewer developers. The job market for developers will decline.

they will still use the tech to increase production, with fewer developers, because software quality and software project success isn’t what they’re looking for in software development. The job market for developers will decline.

The more progress you see in the automation, the fewer of us they’ll need. It isn’t a question of the nature of the improvement, but of the attitudes of management.

Even if that weren’t true, technical innovations in programming generally don’t improve project or business outcomes.

The odds of a project’s success are dictated by user research, design, process, and strategy, not the individual technological innovations in programming. Rapid-Application-Development tools, for example, didn’t shift outcomes in meaningful ways.

What matters is whether the final product works and improves the business it was made for. Business value isn’t solely a function of code defects. Technical improvements that address code defects are necessary, but not sufficient.

tech industry management is firmly convinced that less is more when it comes to employing either.

Whether you’re a bear or a bull on LLMs, we as developers are going to get screwed either way, especially if we’re web developers.

Most web projects shipped by businesses today are broken, but businesses rarely seem to care.

Most websites perform so badly that they don’t even finish loading on low-end devices, even when business outcomes directly correlate with website performance, such as in ecommerce or ad-supported web media.

The current state of web development is as if most Windows apps released every year simply failed to launch on 20-40% of all supported Windows installs.

If being plausible is all that matters, then that’s the literal, genuine, core strength of an LLM.

This is a problem for the job market because if all that matters to these organisations is being seen plausibly chasing cutting-edge technology – that the actual business outcomes don’t matter – then the magic of LLMs mean that you don’t actually need that many developers to do that for much, much less money.

Web media is a major employer, both directly and indirectly, of web developers. If a big part of the web media industry is collapsing, then that’s an entire sector that isn’t hiring any of the developers laid off by Google, Microsoft, or the rest. And the people they aren’t hiring will still be on the job market competing with everybody else who wouldn’t have even applied to work in web media.

The scale of LLM-enabled spam production outstrips the ability of Bing or Google to counter it.

But it gets even worse as every major search engine provider on the market is all-in on replacing regular keyword search with chatbots and LLM-generated summaries that don’t drive any traffic at all to their sources.

It’s reasonable to expect that the job market is unlikely to ever fully bounce back, due to the collapse of web media alone.

Experience in Node or React is not a reliable signifier of an ability to work on successful Node or React projects because most Node or React projects aren’t even close to being successful from a business perspective. Lack of experience in Node or React – such as a background in other frameworks or vanilla JS – conversely isn’t a reliable signifier that the developer won’t be a successful hire.

Lower pay, combined with the information asymmetry about employer dysfunction, would then lead to more capable workers leaving the sector, either to run their own businesses – a generally dysfunctional web development sector is likely to have open market opportunities – or leave the industry altogether. This would exacerbate the job market’s dysfunctions even further, deepening the cycle.

The first thing to note is that, historically, whenever management adopts an adversarial attitude towards labour, the only recourse labour has is to unionise.

As employees, we have nothing to lose from unionising. That’s the first consequence of management deciding that labour is disposable.

Diversifying your skills has always been a good idea for a software developer. Learning a new language gives you insight into the craft of programming that is applicable beyond that language specifically.

But the market for developer training in general has collapsed.

Some of it is down to the job market. Why invest in training if tech cos aren’t hiring you anyway? Why invest in training your staff if you’re planning on replacing them with LLM tools anyway?

After all, as Amy Hoy wrote in 2016:

Running a biz is a lot less risky than having a job, because 1000 customers is a lot less fragile than 1 employer.

The tech industry has “innovated” itself into a crisis, but because the executives aren’t the ones out looking for jobs, they see the innovations as a success.

The rest of us might disagree, but our opinions don’t count for much.

But what we can’t do is pretend things are fine.

Because they are not.

Thoughts?

Good before going to bed on Friday evening :)

Some things I have noted:

Until this day, I say I've done three things: insert levels of indirection, trade off space and time, and three, try to get my clients to tell me what they really want.

If you're doing something with ERP systems, well, first of all, I apologize and feel bad for you in life, but that's a whole other conversation.

Let's think somebody ultimately is paying for this thing to be built. Somebody somewhere has a vision on what they want it to be. There are humans who will eventually be using it. It needs to meet those business or mission needs. It has to start with that.

what bothers me is when people make implicit assumptions and don't make them explicit.

"The decisions that you make upfront are the ones that," and this is paraphrasing, "the ones that are too expensive and you cannot change later."

But I'm talking about when you make a decision, write it down, make an architectural decision record. It can be itty bitty, itty bitty. But just Tracy made this decision today. Context, we don't have a license for that and it's going to take eight months to get the new license or acquisition or whatever else.

The other thing that struck me about your example about the people doing the two front ends, and I'm going to use this to loop back into the conversation about developers and architects, is that they don't understand, or appear not to understand, that in the global perspective, by picking two different UI designs, you've made the programmer hiring decision harder.

I've had a lot of contentious conversations with folks who say, "I'm a solution architect." Well, what technologies do you dabble with? Well, I haven’t touched code in about 20 years.

It's, if you're going to be an architect and have that mindset, you need to be able to go from the boiler room to the boardroom. You need to be able to communicate, but it also means that in order to be trusted, you have to bring your chops to the table.

I think lack of taxonomy is probably one of the killers in any organization. You and I don't agree on what that word means. And with that comes so much nuance and with that comes muscle memory and process issues. That's something that just drives me crazy on a daily basis.

So I am a real junkie when it comes to people talking about how processes don't work. Well, let's have an hour conversation. Let's map out how it's actually working. I'm not talking about Lean Six Sigma values. I'm talking about, let's find the waste.

One of the things that I would have people to take away is the need to constantly be considering how you can decouple or loosely couple things, because that aids in the longevity. If you think about even electronics and things that you bought in your house, the big integrated front of your dishwasher, I now have to replace the entire dishwasher.

Because back in my day, full stack meant you are actually worth your salt.

My way is not always the right way. Don't let anybody hear that, but it's true.