diff --git a/conductor/tracks/video_analysis_platonic_intelligence_kumar_20260621/artifacts/ocr.md b/conductor/tracks/video_analysis_platonic_intelligence_kumar_20260621/artifacts/ocr.md new file mode 100644 index 00000000..c0b8e31f --- /dev/null +++ b/conductor/tracks/video_analysis_platonic_intelligence_kumar_20260621/artifacts/ocr.md @@ -0,0 +1,932 @@ +# OCR Results + +## frame_00001.jpg + +``` +The World is not Random +``` + +## frame_00002.jpg + +``` +Towards a Platonic Intelligence with +Unified Factored Representations +sae +Good Adaptability +Unified Factored +Representation +Open—Ended +Search +Identical +output +behavior +Inputs +Solves Task +Function Space +4 +Poor Adaptability +Fractured Entangled +Representation +Conventional +SGD +Akarsh Kumar +MIT CSAIL +November 4, 2025 +1 +``` + +## frame_00004.jpg + +``` +The World has Structure +Real World +``` + +## frame_00008.jpg + +``` +(no text extracted) +``` + +## frame_00016.jpg + +``` +Intelligent Agents must capture this Structure +``` + +## frame_00017.jpg + +``` +Capturing Structure with AI +``` + +## frame_00019.jpg + +``` +Capturing Structure with AI +• What about everything else? +• Example: lighting invariance +• How do you capture lighting invariance? +Architecture +Lighting +Invariance +6 +``` + +## frame_00020.jpg + +``` +Capturing Structure with AI +• What about everything else? +• Example: lighting invariance +• How do you capture lighting invariance? +• We don't know +• Solution: train on lots of data with SGD +Architecture +Lighting +Invariance +``` + +## frame_00021.jpg + +``` +Does this Work? +ChatGPT +``` + +## frame_00022.jpg + +``` +Hypothesis: Fractured Entangled Representations +8 +``` + +## frame_00023.jpg + +``` +Hypothesis: Fractured Entangled Representations +• Conventional SGD training finds +neural representations which are +fractured and entangled +output +behavior +Inputs +Solves Task * +Function S ace +Fractured Entangled +Representation +Conventional +SGD +8 +``` + +## frame_00024.jpg + +``` +Hypothesis: Fractured Entangled Representations +• Conventional SGD training finds +neural representations which are +fractured and entangled +• Doesn't capture the underlying +regularities of the world +• Position: Open-Ended Search +may be the solution to learn +unified and factored neural +representations +Unified Factored +Representation +Open—Ended +Search +Identical +output +behavior +Inputs +Solves Task +Fractured Entangled +Representation +Conventional +SGD +Function S ace +8 +``` + +## frame_00025.jpg + +``` +Hypothesis: Fractured Entangled Representations +• Conventional SGD training finds +neural representations which are +fractured and entangled +• Doesn't capture the underlying +regularities of the world +• Position: Open-Ended Search +may be the solution to learn +unified and factored neural +representations +• Internal representation affects +generalization, creativity, and +continual learning +Good Adaptability +Unified Factored +Representation +Open—Ended +Search +Identical +output +behavior +Inputs +Solves Task * +Function S ace +Poor Adaptability +Fractured Entangled +Representation +Conventional +SGD +8 +``` + +## frame_00026.jpg + +``` +Compositional Pattern Producing Network +(CPPN) +``` + +## frame_00027.jpg + +``` +Compositional Pattern Producing Network +(CPPN) +Toy domain to study neural representations: implicitly represent an image +• Inspired by biological developmental process +``` + +## frame_00028.jpg + +``` +CPPNs are an Analogy +``` + +## frame_00029.jpg + +``` +Picbreeder! +restart TilJtate +SEjve +11 +``` + +## frame_00030.jpg + +``` +Picbreeder! +• Evolve the underlying CPPNs +restart +11 +``` + +## frame_00031.jpg + +``` +Picbreeder! +• Online website for humans to breed images to +their desire +• Evolve the underlying CPPNs +restart +11 +``` + +## frame_00032.jpg + +``` +Picbreeder! +z. zzz +• Online website for humans to breed images to +their desire +• Evolve the underlying CPPNs +uzzzz +zzzzz +restart +11 +``` + +## frame_00033.jpg + +``` +Picbreeder! +• Online website for humans to breed images to +their desire +• Evolve the underlying CPPNs +restart +11 +``` + +## frame_00034.jpg + +``` +Picbreeder! +• Online website for humans to breed images to +their desire +• Evolve the underlying CPPNs +• No end goal, do whatever you want! +oosæ +restart +11 +``` + +## frame_00035.jpg + +``` +What Do You Expect to Find? +``` + +## frame_00036.jpg + +``` +What People Actually Found! +13 +``` + +## frame_00037.jpg + +``` +Why Greatness Cannot be Planned +• Many insights on the nature of search +• Deception +• Serendipity +• Open-Endedness +• Case studies: +• Natural Evolution +• Scientific Innovation +Kenneth 0. Stanley • Joel Lehman +Why Greatness +Cannot Be Planned +The Myth of the Objective +@Springer +14 +``` + +## frame_00038.jpg + +``` +Picbreeder has Intriguing Properties +Open-Ended +``` + +## frame_00039.jpg + +``` +Picbreeder has Intriguing Properties +Open-Ended +oäaua e +ana +16 +``` + +## frame_00040.jpg + +``` +Picbreeder has Intriguing Properties +Serendipitous Exaptation +• When a trait evolved for one function but gets repurposed for another function +Warmh +Gliding +17 +``` + +## frame_00041.jpg + +``` +Picbreeder has Intriguing Properties +Serendipitous Exaptation +Stepping stone to the Teapot +Stepping stone to the Skull +Stepping stone to Jupiter +Stepping stone to the Butterfly +Stepping stone to the Penguin +Stepping stone to the Lamp +18 +``` + +## frame_00042.jpg + +``` +Picbreeder has Intriguing Properties +Emergence of Evolvability +• Natural evolution has developed adaptable genotypes: +• Canalization +• Regularity +Modularity +• Symmetry +• Certain axes of variation become more likely while +others become impossible +synurwtry +synvnetry +human +19 +``` + +## frame_00043.jpg + +``` +Picbreeder has Intriguing Properties +Emergence of Evolvability +Most Picbreeder images +feature forms of canalization +Mutating single genes of these images +holistically affects distinct aspects of the image +mutation in +Nut.uon +Mutation in +ObJect Oni' Spatnght Only +These images have a structurally organized +(i.e. modular and hierarchical) genome +Global Lighting +genes +Shadow Only +Spotlight Only +Some Picbreeder images +show oor canalization +Mutating single genes of these images +affects none or many parts of the image +»ut.uøn Mutation in Nuuuon in +And +These images have little structural +organization in their genome +genes +Object Only +genes +genes +Shadow And +Object genes +No Effect +Global +Distortion +genes +Dolphin Eye +And Body +genes +These images have fit decendants +These images have unfit decendants +20 +``` + +## frame_00045.jpg + +``` +Learning the Picbreeder Skull with SGD +22 +``` + +## frame_00046.jpg + +``` +Learning the Picbreeder Skull with SGD +Picbreeder Skull +22 +``` + +## frame_00047.jpg + +``` +Learning the Picbreeder Skull with SGD +• Let's train a conventional network to recreate the skull +• Perfect reconstruction! +Picbreeder Skull +SGD Trainin +SGD Skull +22 +``` + +## frame_00048.jpg + +``` +Layerization +23 +``` + +## frame_00049.jpg + +``` +Layerization +• Convert everything to a universal architecture space: MLP +sin(0) +do +cos(O) +bias +dx +lio +itie +-Afia +lin +ReLO • +tanh +23 +``` + +## frame_00050.jpg + +``` +Layerization +• Convert everything to a universal architecture space: MLP +• Existence proof of Picbreeder solution MLP weight space +e +DLI +Neuron +24 +``` + +## frame_00051.jpg + +``` +Picbreeder Skull +Unified Factored Representation +e +Neuron +ssgg +anon +25 +``` + +## frame_00052.jpg + +``` +SGD Skull +Fractured Entangled Representation +Zigle +aaaa +Neuron +26 +``` + +## frame_00053.jpg + +``` +Picbreeder Skull +Unified Factored Representation +Controls Mouth Opening +Controls Eye Winking +Controls Eye Width +Controls Jaw Width +Sweeping Weight Value +SGD Skull +Fractured Entangled Representation +Sweeping Weight Value +27 +``` + +## frame_00054.jpg + +``` +Picbreeder Butterfly +Unified Factored Representation +Neuron +ooaa +28 +``` + +## frame_00055.jpg + +``` +SGD Butterfly +Fractured Entangled Representation +Neuron +29 +``` + +## frame_00056.jpg + +``` +Picbreeder Butterfly +Unified Factored Representation +Controls Wing Area +Controls Color +Converts Butterfly to Fly +Controls Vertical Shape +Sweeping Weight Value +SGD Butterfly +Fractured Entangled Representation +Aw= —1 +Sweeping Weight Value +30 +``` + +## frame_00057.jpg + +``` +Picbreeder Apple +Unified Factored Representation +Neuron +nannæg•• +31 +``` + +## frame_00058.jpg + +``` +SGD Apple +Fractured Entangled Representation +ooose•n +oec +Cloe +D •BRA +x yd 1 +Neuron +32 +``` + +## frame_00059.jpg + +``` +Picbreeder Apple +Unified Factored Representation +Controls Stem Angle +ooooo +Controls Apple Size +Cleans Background +ooooo +Removes Stem +AYI/ = +1 +Sweeping Weight Value +SGD Apple +Fractured Entangled Representation +ecoe +Controls Apple Size += Cleans Background +ooooe +Aw = +1 +Sweeping Weight Value +33 +``` + +## frame_00060.jpg + +``` +How does this Apply to LLMs? +34 +``` + +## frame_00061.jpg + +``` +FER In LLMs +Evidence in GPT-3 +Example 1: +Me: I have 3 pencils, 2 pens, and 4 erasers. How many things do I +have? +GPT-3: You have 9 things. [always correct] +Example 2: +Me: I have 3 chickens, 2 ducks, and 4 geese. How many things do I +have? +GPT-3: You have 10 animals total. [always incorrect] +35 +``` + +## frame_00062.jpg + +``` +GSM-Symbolic: Understanding the Limitations of +Mathematical Reasoning in Large Language Models +Iman Mirzadeht Keivan Alizadeh Hooman Shahrokhi* +Samy Bengio Mehrdad Farajtabart +Oncel Tuzel +Apple +Abstract +Recent advancements in Large Language Models (LLMs) have sparked interest in their formal +reasoning capabilities, particularly in mathematics. The GSM8K benchmark is widely used +to assess the mathematical reasoning of models on grade-school-level questions. While the +performance of LLMs on GSM8K has significantly improved in recent years, it remains unclear +whether their mathematical reasoning capabilities have genuinely advanced, raising questions +about the reliability of the reported metrics. To address these concerns, we conduct a large- +scale study on several state-of-the-art open and closed models. To overcome the limitations of +existing evaluations, we introduce GSM-Symbolic, an improved benchmark created from symbolic +templates that allow for the generation of a diverse set of questions. GSM-SymboIic enables +more controllable evaluations, providing key insights and more reliable metrics for measuring the +reasoning capabilities of models.Our findings reveal that LLMs exhibit noticeable variance when +responding to different instantiations of the same question. Specifically, the performance of all +models declines when only the numerical values in the question are altered in the GSM-Symbolic +benchmark. Furthermore, we investigate the fragility of mathematical reasoning in these models +and demonstrate that their performance significantly deteriorates as the number of clauses in +a question increases. We hypothesize that this decline is due to the fact that current LLMs +are not capable of genuine logical reasoning; instead, they attempt to replicate the reasoning +steps observed in their training data. When we add a single clause that appears relevant to the +question, we observe significant performance drops (up to 65%) across all state-of-the-art models, +even though the added clause does not contribute to the reasoning chain needed to reach the +final answer. Overall, our work provides a more nuanced understanding of LLMs' capabilities +and limitations in mathematical reasoning. +36 +``` + +## frame_00063.jpg + +``` +Reasoning or Reciting? Exploring the Capabilities and Limitations of +Language Models Through Counterfactual Tasks +Zhaofeng Wue Linlu Qiue Alexis Rosse EkinAkyürekC Boyuan Chene +Bailin Wange Najoung Kima Jacob Andrease Yoon Kim +e MIT O Boston University +zfw@csail.mit.edu +G PT-4 +Performance +Default +Counterfactual +Spatial +COO Of +Arithmetic +Drawing +Draw a +Code Exec. +pytim +L"bo". "nb"l +"bo"l +Chord Fingering +play C +a guitar +Code Gen. +Sort list by the +second c +in +Note in Melody +The 4th note of +Twinkle Twinkle +in C major +in Ai +Basic Syntax +Find the main +subject and verb +think 'MS +(they. think) +(they, 'hink) +IS the move +TX are Y, Y are Z. +Are X Z? +SET Game +Rule: +or +•hap.. +Figure 1: GPT-4's performance on the default version of various tasks (blue) and counterfactual counterparts +The shown results use O-shot chain-of-thought prompting (54; GPT-4 consistently +orange +and substantially underperforms on counterfactual variants compared to default task instantiations. +37 +``` + +## frame_00068.jpg + +``` +On the Biology of a Large Language Model +We investigate the internal mechanisms used by Claude 3.5 Haiku — Anthropic's lightweight +production model — in a variety of contexts, using our circuit tracing methodology. +add —57 +95 +sum = _5 +add _9 +Inputs near 30 make this +early feature fire +-30 +calc : +36 +calc : +Hover to see +visualizations! +Example +low precision +features +_6 +36 +36+59= +sum —92 +-40 + -50 +5- +-59 +sum = +-36 + -60 +59 +59 +Exam mod O +eat ures +_9 +Ull +Sum Features +The model has finally computed information +about the sum: its value mod 10, mod 100, +and its approximate magnitude. +Lookup Table Features +The model has stored information about +particular pairs of input properties. They take +input from the original addends (via attention) +and the Add Function features. Operand plots +ssibly with repetition (modular) or +are points. +smearing (tw•preciston) +Add Function Features +The model separately determines the ones +digit Of the number to be added and its +approximate magnitude. Operand plots show +vertical or horizontal stripes, +Input Features +The model has features specific to the ones +digit and Io the approximate magnitude, at +various scales. +Most computation +takes place on the +•zu token +38 +``` + +## frame_00069.jpg + +``` +Scaling helps... but in what way? +Scaling Laws for Neural Language Models +2024 +10—9 +L = (Cmin/2.3 • +10-7 +10-5 +10-3 +10—1 +Compute +PF-days, non-embedding +4.2 +3.9 +3.6 +3.3 +3.0 +2.7 +101 +L = (0/5.4 +108 +Dataset Size +tokens +Kaplan et al. (2020) +5.6 +4.8 +3.2 +2.4 +109 +105 +L = (N18.8 • +107 +Parameters +non-embedding +109 +39 +``` + +## frame_00070.jpg + +``` +Scaling helps... but in what way? +Platonic Representation Hypothesis +The Platonic Representation Hypothesis +Neural networks, trained with different objectives +on different data and modalities, are converging to a +shared statistical model of reality in their representa- +tion spaces. +z +x +Img +A red sphere next to +ft ext +Huh et al. (2020) +40 +``` + +## frame_00071.jpg + +``` +Scaling helps... but in what way? +41 +``` + +## frame_00072.jpg + +``` +What could be better? +42 +``` + +## frame_00073.jpg + +``` +What could be better? +• Complexification (ex: morphogenesis, etc.) +• Builds regularities on top of other regularities (bottom up) +• Emergence +• Adaptability +• Pressures the learned regularities to be robust to environmental changes +• Representation must capture axes of variation which "carve nature at its joints" +• Serendipity (order matters for learning!) +• Much higher chance of finding a useful learning curriculum +• What learning paradigm captures all of these? Open-Endedness! +Function Space +FER +olution Space +of Skull +UFR +: Open—Ended +Search +Conventional +SGD +42 +``` + +## frame_00074.jpg + +``` +Is this a Platonic Intelligence? +Space of Forms +Real World +Intelligent Agents +Unified Factored +Representation +43 +``` + +## frame_00075.jpg + +``` +Is this a Platonic Intelligence? +Aspirational Ideal +Unified Factored +Representation +Instantiation +Fractured Entangled +Representation +44 +``` + +## frame_00076.jpg + +``` +Collaborators +Jeff Clune +UBC +Vector Institute +Joel Lehman +University of Oxford +Kenneth Stanley +Lila Sciences +UBC +45 +``` + +## frame_00077.jpg + +``` +Thank You! +46 +``` + +## frame_00081.jpg + +``` +Thanh You! +``` + +## frame_00082.jpg + +``` +(no text extracted) +``` diff --git a/conductor/tracks/video_analysis_platonic_intelligence_kumar_20260621/artifacts/phase2.log b/conductor/tracks/video_analysis_platonic_intelligence_kumar_20260621/artifacts/phase2.log new file mode 100644 index 00000000..61426cd1 --- /dev/null +++ b/conductor/tracks/video_analysis_platonic_intelligence_kumar_20260621/artifacts/phase2.log @@ -0,0 +1,2 @@ +Phase 2 Keyframes for C:\projects\manual_slop\conductor\tracks\video_analysis_platonic_intelligence_kumar_20260621\artifacts\video.mp4 + OK: kept 62 frames diff --git a/conductor/tracks/video_analysis_platonic_intelligence_kumar_20260621/artifacts/phase3.log b/conductor/tracks/video_analysis_platonic_intelligence_kumar_20260621/artifacts/phase3.log new file mode 100644 index 00000000..2bc73535 --- /dev/null +++ b/conductor/tracks/video_analysis_platonic_intelligence_kumar_20260621/artifacts/phase3.log @@ -0,0 +1,2 @@ +Phase 3 OCR for C:\projects\manual_slop\conductor\tracks\video_analysis_platonic_intelligence_kumar_20260621\artifacts\frames (winsdk) + OK: OCR'd 62 frames in 3.7s