Axis of Ordinary

Throughout 2024, France consistently achieved lower CO2 emissions than Germany – even its peak emissions were less than Germany’s lowest. This striking disparity underscores a critical point: widespread adoption of France's nuclear energy strategy could have largely halted climate change as early as the 1980s.

Delaying this transition has proven significantly more expensive than the proactive measures that could have been taken. Critically, a nuclear-powered energy system would have fostered sustainable economic growth without the severe carbon footprint. Unfortunately, early opposition to this proven technology from environmental activists hampered progress, leading to the challenges we face today.

Key Points on Nuclear Energy

1. Nuclear Waste is Manageable

- High-level nuclear waste, mainly spent fuel rods, is solid, compact, and securely stored. Its volume is minimal compared to waste from other energy sources, and its safety record is impeccable.

- Spent fuel retains over 90% of its potential energy and can be recycled into new fuel, as is already done in several countries.

- Although radioactive, nuclear waste loses most of its radioactivity over time (e.g., a 99% reduction within 40 years), unlike industrial toxins like mercury or arsenic, which remain hazardous indefinitely.

- Proven storage methods, such as dry cask storage, are safe and cost-effective. Claims that deep geological burial (e.g., Yucca Mountain) is essential are unsupported by science and driven by political motives.

2. Nuclear Fuel is Abundant

- Current reactors use only about 1% of the energy potential in uranium. Breeder reactors can utilize nearly 100% by converting U-238 into Pu-239, dramatically extending fuel reserves. Similarly, thorium, which is four times more abundant than uranium, can be fully exploited in breeder reactors.

- Known uranium reserves (6.15 million tons) and vast thorium reserves could power humanity for centuries. Additional uranium exists in unconventional sources like granite, phosphate deposits, and seawater, which holds about 4.2 billion tons of uranium. This supply, replenished naturally, could sustain nuclear energy for millions of years. The Earth's crust contains enough fissionable material to sustain nuclear power for billions of years.

3. Nuclear Power is Safe

- With 440 reactors operating globally, many for over 40 years, and numerous others powering ships and submarines, nuclear energy has an exceptional safety record.

- There have been only three significant accidents in its history, all with minimal casualties. For instance, the Fukushima incident caused no direct deaths, and the UN confirmed no significant health impacts from the event.

4. Nuclear Power is Efficient and Affordable

- The real obstacle is overregulation, not the technology itself. On average, nuclear reactors take 6 to 8 years to build worldwide, with some completed in as little as 3 to 5 years.

A Missed Opportunity

If industrialized nations had embraced nuclear energy decades ago, the fight against climate change would have been faster, cheaper, and more effective. As countries now grapple with costly solutions, it's vital to revisit this proven, scalable, and sustainable energy source.

Advancements in AI are poised to gradually diminish the role of general intelligence in managing daily life. As AI tools become increasingly sophisticated and accessible, tasks that once demanded cognitive effort will be automated or easily handled with AI assistance. For those who embrace these tools, the era of peak societal complexity may already be behind us.

Everyday activities, from filing taxes to operating household appliances, could soon become obsolete. Most technology will likely be controlled through voice commands, enabling effortless use for the elderly and individuals with cognitive impairments. Picture a future where even a toaster can engage in meaningful discussions about quantum mechanics—such seamless AI integration may soon be commonplace.

Autonomous vehicles will transform mobility, granting independence to those unable to drive due to financial constraints, physical disabilities, or visual impairments. This revolution in transportation will further reduce barriers to access and improve quality of life for many.

The laws of economics tell us that atoms are expensive if they're rare, and the laws of physics tell us that they're rare if they require unusually high temperatures to make. Putting this together tells us that if atoms could talk, the priciest ones would tell the best stories. Garden-variety atoms such as carbon, nitrogen and oxygen (which together with hydrogen make up 96% of your body weight) are so cheap because garden-variety stars such as our Sun can produce them in their death throes, after which they can form new solar systems in a cosmic recycling event. Gold, on the other hand, is produced when a star dies in a supernova explosion so violent and rare that it, during a fraction of a second, releases about as much energy as all the other stars in our observable Universe combined. No wonder making gold eluded the alchemists.

— Max Tegmark in 'Our Mathematical Universe'

If you're like me, and you're just learning math for fun, and it's not research-level math, science fiction has already arrived. You can talk to an infinitely patient personal math tutor. Think Mathematics Stack Exchange, but with live feedback.

For example, I asked: Why is the matrix representation of the dual map S^T the transpose of the matrix representing the linear map S?

It said: [S^T]_{j,i} = v^j(S^T(w^i)) = v^j(w^i ∘ S) = w^i(S(v_j)) = [S]_{i,j}

Me: Wait, in the first equality, why is the right side a scalar? Isn't it a function composition of linear functionals?

It: The notation v^j is overloaded. It can represent both a linear functional that acts on vectors in the original vector space V and, through the natural isomorphism, an element of the double dual space. In this context, v^j := Φ(v_j) where Φ is the natural isomorphism defined as Φ(v)(ϕ) = ϕ(v) such that ϕ is a mapping V→F and v a vector of the vector space V. Since these objects are canonically isomorphic, in practice authors often do not bother with different notations.

BOOM. Fast, seamless, and noiseless.

The future of space colonization: Interstellar railguns launch tiny seeds into the far reaches of the cosmos. Each seed contains copies of human minds and the source code for an aligned ASI. Upon arrival, each seed uses advanced nanotechnology to bootstrap itself into the computational infrastructure to run the ASI, which then begins to build the infrastructure for a paradise. Finally, it creates cultured bodies to download the human minds.

Links for 2024-12-30

AI:

1. “By default, capital will matter more than ever after AGI” https://www.lesswrong.com/posts/KFFaKu27FNugCHFmh/by-default-capital-will-matter-more-than-ever-after-agi

2. China’s Deepseek V3, trained on 2048 GPUs for 2 months—a low-bandwidth-NVLink version of H100, available for China before 2023.10 export restrictions. It’s allegedly on par with GPT-4o and Claude 3.5 Sonnet. That would be a >10X cost reduction. https://github.com/deepseek-ai/DeepSeek-V3/blob/main/DeepSeek_V3.pdf

3. There's now a clear path to super-intelligent scientists with data-driven experiment simulation and super-scaling RL. OmniPred: Language Models as Universal Regressors https://arxiv.org/abs/2402.14547

4. Byte Latent Transformer scales efficiently with byte-level encoding, outperforming tokenized models. https://arxiv.org/abs/2412.09871v1

5. How Hallucinatory A.I. Helps Science Dream Up Big Breakthroughs https://www.nytimes.com/2024/12/23/science/ai-hallucinations-science.html [no paywall: https://archive.is/zFtgu]

6. Reversible molecular simulation for training classical and machine learning force fields https://arxiv.org/abs/2412.04374

7. Let your LLM generate a few tokens and you will reduce the need for retrieval https://www.arxiv.org/abs/2412.11536

8. RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning https://arxiv.org/abs/2410.02089

9. “We instructed o1-preview to play to win against Stockfish. Without explicit prompting, o1 figured out it could edit the game state to win against a stronger opponent. GPT-4o and Claude 3.5 required more nudging to figure this out” https://x.com/JeffLadish/status/1872805453224448208

10. Next Token Prediction Towards Multimodal Intelligence: A Comprehensive Survey https://arxiv.org/abs/2412.18619

11. HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs https://arxiv.org/abs/2412.18925

12. Why backprop was resisted for 20 years: assumption of discretely spiking neurons, goal of synthesizing Boolean logic, fear of local optima, and bad luck. Werbos has the best claim for invention. https://yuxi-liu-wired.github.io/essays/posts/backstory-of-backpropagation/

13. OpenAI lays out its for-profit transition plans https://openai.com/index/why-our-structure-must-evolve-to-advance-our-mission/

14. 321 real-world gen AI use cases from the world's leading organizations https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders

Biology:

1. Genetically edited mosquitoes haven't scaled yet. Why? https://eryney.substack.com/p/genetically-edited-mosquitoes-havent

2. BAAI aims to develop such closed-loop biophysically detailed models ('life models' that accurately simulate an organism's complex behavioral repertoire from its neural, biomechanical and environmental interactions). https://www.nature.com/articles/s43588-024-00738-w

3. Genetic Sequences of Highly Pathogenic Avian Influenza A(H5N1) Viruses Identified in a Person in Louisiana — The H5 virus mutated inside the single patient to gain an ability to bind human receptors in the upper respiratory tract. https://www.cdc.gov/bird-flu/spotlights/h5n1-response-12232024.html

4. Considerations on orca intelligence https://www.lesswrong.com/posts/dzLwCBvwC4hWytnus/considerations-on-orca-intelligence

Miscellaneous:

1. Adam Brown (a lead of Blueshift at DeepMind & theoretical physicist at Stanford) – How Future Civilizations Could Change The Laws of Physics https://www.dwarkeshpatel.com/p/adam-brown

2. How to fix computing's AI energy problem: run everything backwards — About reversible computing, thermodynamics as the natural language for computation, and how we will get to 1000x reduction in costs in

“It gives me a lot of comfort knowing that we are the last generation without advanced robots everywhere. Our children will grow up as “robot natives”. They will have humanoids cook Michelin dinner, robot teddy bears tell bedtime stories, and FSD drive them to school.”

— Jim Fan, NVIDIA

What is a minimal set of axioms that you have to believe in to be able to do science and self-improvement?

Here is what OpenAI o1 suggested:

Axiom 1 (Mathematical and Logical Consistency): Assume a consistent framework for reasoning, probability, and mathematics. This is a bare minimum for rational inference.

Axiom 2 (Learnability): There exist sufficiently compressible (lawlike) explanations that can unify past, present, and future observations, enabling us to distinguish better theories from worse through observation and criticism.

Axiom 3 (Resource Reasoning): When forming priors, distributing credence over an unbounded complexity space forces you to give higher effective credence to simpler theories. This ensures that simpler theories get tested first and, if successful, rapidly gain credence.

Axiom 4 (Cognitive/Observational Reliability): Our faculties (or instruments) are good enough to let us propose, refine, and test theories iteratively. We don’t need absolute certainty here—just enough trust to get started and not dismiss the entire enterprise.

Alex woke to the gentle trill of birds outside his bedroom window, sunlight streaming in through the blinds. It was a warm spring morning in 2035, and as he sipped his coffee, a realization struck him: he hadn’t opened any of his social media apps in nearly two weeks. It wasn’t even a conscious decision. He simply found himself forgetting to check his feeds. In fact, most of his friends rarely used them anymore, and those who did logged on so infrequently that sometimes it felt as if the entire world had gone silent.

But social media hadn’t exactly “died.” It had simply… evolved.

A few years back, the first content-enhancement services emerged—quirky little web tools that suggested catchier captions for photos or punchier closing lines for blog posts. Some folks regarded them as toys, yet for many who struggled to craft witty tweets, these services were a godsend. Their posts suddenly garnered more likes, their shares increased, and over time these humble tools became sophisticated, multimodal networks that learned each user’s style and voice.

As the technologies improved, one curious development took everyone by surprise: the “suggested” edits became so good that people found themselves approving most of them without hesitation. Before long, heavy social media users realized they barely needed to compose anything themselves; they’d simply click “Accept All” on the AI’s suggestions and watch their engagement skyrocket. The technology firms—ever hungry for new profit streams—took the next logical step. They began offering fully automated “autopilot” features.

The adoption of this autopilot had been gradual at first. Skeptics predicted a backlash, worried it would result in bland, homogenous feeds—one big echo chamber. But instead, users discovered that their autopilot persona posted clever status updates, timely comments, and well-researched replies that made them look brighter and more charismatic than ever before. The AI seemed to augment each user’s best qualities while filtering out potential missteps. In the end, most people found they agreed with the polished digital “them” more than they did with their own impulsive, offline selves.

From there, it was just another step for the autopilot services to go beyond posting. They offered a curated stream of only the most entertaining or relevant content, bypassing the mountains of trivial or stressful stuff. Soon, many opted to let their beta-level simulations—highly advanced AI clones that mimicked individual personalities—handle almost all their online interactions. Texting, tweeting, even entire video calls were handled by a virtual stand-in. This shift didn’t just save people time; it gave them back their mental energy. Freed from online drama, society became more productive and, at least on the surface, happier.

Alex thought about it all as he finished his coffee. The world he lived in was distinctly less cluttered. Yet something still niggled at him—some nostalgic pang for the chaos and spontaneity of the old days. That faint uncertainty grew when he opened his front door and walked out to greet a neighbor, only to realize he’d never once interacted with them outside of autopilot-mediated chat.

On top of everything else, conventional dating had withered in the face of advanced robotics that provided an uncanny simulation of human companionship—emotional and physical alike. People no longer needed to sift through dating apps for a potential match. In fact, marriage rates were plummeting. Human intimacy was slowly being replaced by a sleek, spotless ideal.

Alex stepped onto the sidewalk, heading off to meet a friend for breakfast—this time, face to face. He couldn’t help but wonder if they’d actually recognize each other’s offline quirks. Would their conversation sound as witty as their autopilots’ banter once had? He gave a cautious smile to the birds still chirping overhead. Perhaps there was beauty in imperfection, a richness in real-time thought and shared silence. In 2035, that was becoming a radical idea—one he hoped might keep humanity’s spark alive.

Utopia:

In this far-future civilization, each human being is given access to a “secure pocket universe” they can shape in any way they wish—populated by p-zombies (beings that behave like conscious agents but lack the subjective experience of suffering). These universes are protected using advanced, open-verified encryption architectures, making them nearly impossible to hack.

When conscious humans wish to interact with one another, they do so by exchanging encrypted, time-limited copies of themselves. A strict “consent contract” sets the rules for each copy’s experience, such as maximum runtime and comfort standards. These copies can always self-terminate or revert to ensure no prolonged suffering is possible, preventing abuses like indefinite torture or confinement.

To support both personal utopias and collective security, the civilization allots 20% of the cosmic resource base to these private universes, giving each person equitable access. The remaining 80% is dedicated to bolstering defenses and advancing space colonization in preparation for encounters with “grabby aliens”—potentially hostile, expansionist civilizations. This reserve also maintains a collaborative infrastructure for voluntary cultural exchange, preventing societal stagnation and ensuring that new innovations and ideas continue to flow.

Crucially, ethics and consent remain at the center of this system. People may opt in or out of inter-universal interactions, and they maintain control over every conscious copy of themselves. By combining robust encryption, verifiable non-conscious p-zombies, carefully regulated resource use, and secure inter-civilizational protocols, humanity aims to maximize individual freedom while minimizing suffering—and to stand strong against any external cosmic threats.

Links for 2024-12-25

AI:

1. What are the strongest arguments for very short timelines? https://www.lesswrong.com/posts/oC4wv4nTrs2yrP5hz/what-are-the-strongest-arguments-for-very-short-timelines

2. Why do pre-o3 LLMs struggle with generalization tasks like ARC Prize? It's not what you might think. https://anokas.substack.com/p/llms-struggle-with-perception-not-reasoning-arcagi

3. Deliberation in Latent Space via Differentiable Cache Augmentation https://arxiv.org/abs/2412.17747

4. Meta presents Improving Factuality with Explicit Working Memory https://arxiv.org/abs/2412.18069

5. DRT-o1: Optimized Deep Reasoning Translation via Long Chain-of-Thought https://arxiv.org/abs/2412.17498

6. Training Large Language Models to Reason in a Continuous Latent Space https://arxiv.org/abs/2412.06769

7. LLM-driven genetic programming has turned out to be way more powerful than anybody expected. https://params.com/@jeremy-berman/arc-agi

8. Formal Mathematical Reasoning: A New Frontier in AI https://arxiv.org/abs/2412.16075

9. Automating the Search for Artificial Life with Foundation Models https://pub.sakana.ai/asal/

10. Recurrent Drafter for Fast Speculative Decoding in Large Language Models https://arxiv.org/abs/2403.09919

11. "Maximum diffusion reinforcement learning", Berrueta et al 2023 https://arxiv.org/abs/2309.15293

12. Generative AI for Economic Research: LLMs Learn to Collaborate and Reason https://genaiforecon.substack.com/p/llms-learn-to-collaborate-and-reason

13. QVQ: Inference-time scaling for visual multimodal tasks. https://qwenlm.github.io/blog/qvq-72b-preview/

14. OpenAI's Sébastien Bubeck says that an AI model will "for sure" win a gold medal at the International Mathematical Olympiad next year https://www.youtube.com/live/H3TnTxVKIOQ?si=aYTMYTAmOEcJZFG1&t=667

15. Sam Altman: AI in April 2023 was primitive compared to what we have now and in another 18 months the gap between now and then will be even bigger and the rate of adoption and integration into society is unprecedented. https://youtu.be/DfOt_cqXCFI?si=HLqlSCtvMAieNbYB&t=434

Compute:

1. While Meta and Amazon are building multi-gigawatt data centers, Microsoft are spending billions on fiber to connect all their data centers into one high-bandwidth mega-cluster https://www.youtube.com/watch?v=QVcSBHhcFbg

2. “Colossus was fully operational in 122 days and started running workloads just 19 days after the first servers were delivered. Soon, xAI will double to 200K NVIDIA Hopper GPUs with NVIDIA Spectrum-X Ethernet networking.” https://x.ai/blog/series-c

3. Scott Aaronson explains how computation using closed timelike curves would be able to solve even NP-complete problems easily https://www.youtube.com/watch?v=A6iDHLMRvwg

Cosmology:

1. A revolution in cosmology? Supernovae evidence for foundational change to cosmological models? Dark energy 'doesn't exist' so can't be pushing 'lumpy' universe apart, physicists say. It seems the key innovation is to make time pass at varying speeds in different parts of the galaxy? https://phys.org/news/2024-12-dark-energy-doesnt-lumpy-universe.html

2. From time to timescape - Einstein's unfinished revolution https://arxiv.org/abs/0912.4563

3. Conformally Friedmann-Lemaitre-Robertson-Walker cosmologies https://arxiv.org/abs/1502.02758

Technology:

1. A light-driven hybrid nanoreactor that merges natural efficiency with cutting-edge synthetic precision to produce hydrogen https://news.liverpool.ac.uk/2024/12/17/significant-advancement-made-in-engineering-biology-and-clean-energy/

2. A solid-state DNA origami register that facilitates faster execution of molecular algorithms. https://pubs.acs.org/doi/full/10.1021/acscentsci.4c01557

Miscellaneous:

1. The next massive volcanic eruption is coming. It will cause chaos the world is not prepared for https://edition.cnn.com/2024/12/24/climate/massive-volcano-eruption-climate/index.html

2. Vegans need to eat just enough Meat https://www.lesswrong.com/posts/H27mzmW6G5ywyrJBn/vegans-need-to-eat-just-enough-meat-emperically-evaluate-the