Ben Goertzel ✓
@bengoertzel · Vashon Island, WA, USA · joined 02 Apr 2008
How can we best create beneficial AGI, ASI and Singularity for all sentient beings?? -- Founder/CEO @asi_alliance @singularitynet, BGI Foundry, etc. etc.
78 224Followers
1 685Following
8 866Posts total
140.8KViews on collected posts
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As part of our beefing up of the AGI Society, we have started publishing our best attempt at objective evaluations of "claims of human-level or stronger AGI." .... While we don't think anyone is there yet, things are getting close enough that making this sort of careful and
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We just released an evaluation examining the publicly available evidence for the proposition claimed by @JensenHuang and @gdb that GPT-6 Astra has achieved AGI
https://t.co/2KzpN77XQa
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Thoughts on OpenAI’s Millennium Prize result, formal verification as a key to safe superintelligence, and why coming up with new math ideas is the harder problem. https://t.co/M0N1omR1H6
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@bengoertzel "kind of" is doing a lot of work.
the tools got scary-good at tasks. they did not get a self. still a very good intern with no shame.
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@bengoertzel The distinction between narrow model performance and true general intelligence remains unproven in practice.
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@bengoertzel the term drifted so far from your original coining, now every model launch slaps AGI on it for the headline, not the actual bar
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On GPT-6 Astra, the AGIPO, and what the term AGI was coined to mean. https://t.co/WFQm4KmPQF
12.5K views · 118 likes
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@bengoertzel Treating agentic code generation as a mechanism to incrementally self-uplift beats blindly throwing 25 dense research papers into a context window and hoping a general intelligence magically emerges.
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10. On values: we're shifting control from the LLM in the loop to the value-reflective AtomSpace nervous system in each hive, so the metahive blends humanity's teeming diversity of values into a global-brain value system.
Big 3-6-12 months ahead. My human and AI friends: This is
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9. And this can happen everywhere at once. Let 10,000 OmegaHives bloom, each self-uplifting -- connected via OmegaBuzz into a metahive that shares lessons & asks itself for advice, running decentralized on SingularityNET today & on ASI Chain at mainnet. An emerging
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8. The punchline is the virtuous cycle: every mechanism the hive integrates makes it smarter at implementing the next one, & every eval produces cognitive capital. Humans answer questions at stuck-points instead of typing code — the most efficient use of researchers any dev
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7. How do you know the tests themselves are any good? Train narrow AIs on each one — ML, Bayesian, evolutionary — as baselines AND as test-tuners: not too easy, not too hard, no shortcuts. Then wrap the qualified specialists as modules the hive itself can learn to use.
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6. AGI Maze (a 2D world harder in many ways than ARC-AGI-3), Neoterics (3D synthetic ecology), RoboGarden, RepoOps, LeanGarden (theorem proving), Virtual Scientist, SocietyLab, HiveForge, SelfLab — plus a Transfer Ring measuring how knowledge flows between all of them.
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5. Evaluate against what? Not an AGI score -- benchmarks get hacked; that's what they're for these days. You want an evaluation ecology. I tried to design simple, elegant tests and failed ...so with a few LLMs & agents I built a big nasty but very useful conglomeration of 10
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4. The fix is a governed, guided improvement loop: baseline your hive => add ONE cognitive mechanism (uncertain reasoning, attention economics, predictive coding...) => evaluate => tune => promote or park => repeat.
Incremental & cumulative — the way complex systems have always
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3. What could possibly go wrong? Whellp, yeah about that .. between a component that works in a small test (with nice math, even!) and one that works at scale lies a LOT of fiddling — algorithms, representations, interfaces. Try that blindly for dozens of components at once &
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2. At AGI-26 last week I saw a lot of people asking their agent swarms to code AGI for them: download 25 of Ben's (or other cool AGI) papers, bash them together into a codebase, make it tick. This has in it the seeds of a workable approach to AGI.
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1. Can a proto-AGI help build its successor? New substack post on how our OmegaHive agent hives can incrementally self-uplift toward AGI — implementing, testing & tuning cognitive mechanisms from decades of AGI research, one measured step at a time... . .
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Frente a cuentas del mismo tamaño
19 publicaciones de los últimos 90 días, junto al rango de 10K–100K seguidores. llega a menos gente que cuentas de su tamaño.
Visualizaciones medianas870esta cuenta924mediana de 10K–100K
Alcance, %1.11%esta cuenta3.62%mediana de 10K–100K
Interacción, %1.37%esta cuenta1.52%mediana de 10K–100K
| Métrica | Esta cuenta | Mediana de 10K–100K | Proporción |
|---|---|---|---|
| Visualizaciones medianas por publicación | 870 | 924 | 0.94× |
| Alcance (visualizaciones ÷ seguidores) | 1.11% | 3.62% | 0.31× |
| Tasa de interacción | 1.37% | 1.52% | 0.90× |
Otras cuentas de este rango → Comparar con otra cuenta → Cómo se construyen estas referencias →
Growth & engagement
How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.
Views per post
3127 Aug
304
302
1.1K
1K
125
5.4K5 Sep
12.5K8 Sep
197
105
129
4K10 Sep
8.4K11 Sep
7.5K
Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
3.85%7 Aug
2.96%
2.98%
1.12%
1.37%
0.80%
1.79%5 Sep
1.16%8 Sep
1.02%
0.95%
0.78%
2.16%10 Sep
0.27%11 Sep
1.38%
Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.
What the audience does
Likes70.3%532 in total
Reposts8.7%66 in total
Replies6.9%52 in total
Bookmarks14.1%107 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.
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