Semantic Systems / Language / Glyphs
ᚻᛁᛋᛏᚩᚱᛁᚳᚪᛚ ᚱᛖᛈᚩᚱᛏ: ᛗᚪᚳᚻᛁᚾᛖ ᛁᚾᛏᛖᛚᛚᛁᚷᛖᚾᚳᛖ 1940-2026
Report summary
ᛏᚻᛁᛋ ᚱᛖᛈᚩᚱᛏ ᛈᚱᚩᚡᛁᛞᛖᛋ ᚪ ᛞᛖᛖᛈᛚᚣ ᛋᚩᚢᚱᚳᛖᛞ ᚻᛁᛋᛏᚩᚱᛁᚳᚪᛚ ᚳᚩᚱᛈᚢᛋ ᚩᚠ ᛗᚪᚳᚻᛁᚾᛖ ᛁᚾᛏᛖᛚᛚᛁᚷᛖᚾᚳᛖ, ᛏᚱᚪᚳᛁᚾᚷ ᛞᛖᚡᛖᛚᚩᛈᛗᛖᚾᛏᛋ ᚠᚱᚩᛗ ᛏᚻᛖ ᚠᚩᚢᚾᛞᚪᛏᛁᚩᚾᚪᛚ ᛖᚱᚪ ᚩᚠ cybernetics ᛏᚻᚱᚩᚢᚷᚻ ᛏᚻᛖ ᛖᛗᛖᚱᚷᛖᚾᚳᛖ ᚩᚠ inference-time reasoning ᚪᚾᛞ autonomous research systems. ᚪᛚᛚ ᛖᛉᛈᛚᚪᚾᚪᛏᚩᚱᚣ ᛈᚱᚩᛋᛖ ᛁᛋ ᚹᚱᛁᛏᛏᛖᚾ ᛁᚾ ᚱᚢᚾᛖᛋ, ᚹᚻᛁᛚᛖ ᛖᛉᚪᚳᛏ ᚾᚪᛗᛖᛋ, ᛈᚢᛒᛚᛁᚳᚪᛏᛁᚩᚾᛋ
Key topics
- Semantic Systems / Language / Glyphs
- Semantic Systems
- Language
- Glyphs
- AI
- Agentic Web
- .NET
- Python
- Runtime
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ᛖᛉᛖᚳᚢᛏᛁᚡᛖ ᛋᚢᛗᛗᚪᚱᚣ
ᛏᚻᛁᛋ ᚱᛖᛈᚩᚱᛏ ᛈᚱᚩᚡᛁᛞᛖᛋ ᚪ ᛞᛖᛖᛈᛚᚣ ᛋᚩᚢᚱᚳᛖᛞ ᚻᛁᛋᛏᚩᚱᛁᚳᚪᛚ ᚳᚩᚱᛈᚢᛋ ᚩᚠ ᛗᚪᚳᚻᛁᚾᛖ ᛁᚾᛏᛖᛚᛚᛁᚷᛖᚾᚳᛖ, ᛏᚱᚪᚳᛁᚾᚷ ᛞᛖᚡᛖᛚᚩᛈᛗᛖᚾᛏᛋ ᚠᚱᚩᛗ ᛏᚻᛖ ᚠᚩᚢᚾᛞᚪᛏᛁᚩᚾᚪᛚ ᛖᚱᚪ ᚩᚠ cybernetics ᛏᚻᚱᚩᚢᚷᚻ ᛏᚻᛖ ᛖᛗᛖᚱᚷᛖᚾᚳᛖ ᚩᚠ inference-time reasoning ᚪᚾᛞ autonomous research systems. ᚪᛚᛚ ᛖᛉᛈᛚᚪᚾᚪᛏᚩᚱᚣ ᛈᚱᚩᛋᛖ ᛁᛋ ᚹᚱᛁᛏᛏᛖᚾ ᛁᚾ ᚱᚢᚾᛖᛋ, ᚹᚻᛁᛚᛖ ᛖᛉᚪᚳᛏ ᚾᚪᛗᛖᛋ, ᛈᚢᛒᛚᛁᚳᚪᛏᛁᚩᚾᛋ, ᚪᚾᛞ ᛗᛖᛏᚪᛞᚪᛏᚪ ᚱᛖᛗᚪᛁᚾ ᚢᚾᚪᛚᛏᛖᚱᛖᛞ. ᛏᚻᛖ ᛖᚡᚩᛚᚢᛏᛁᚩᚾ ᚩᚠ artificial intelligence ᚱᛖᚡᛖᚪᛚᛋ ᚳᚣᚳᛚᛁᚳᚪᛚ ᛈᚪᛏᛏᛖᚱᚾᛋ ᚩᚠ ᚩᛈᛏᛁᛗᛁᛋᛗ ᚠᚩᛚᛚᚩᚹᛖᛞ ᛒᚣ ᚠᚢᚾᛞᛁᚾᚷ ᚳᚩᛚᛚᚪᛈᛋᛖ. ᚠᚩᚱ ᛁᚾᛋᛏᚪᚾᚳᛖ, ᛏᚻᛖ ALPAC Report ᚩᚠ 1966 ᚳᚱᚢᛋᚻᛖᛞ ᛖᚪᚱᛚᚣ machine translation1, ᛞᛖᛗᚩᚾᛋᛏᚱᚪᛏᛁᚾᚷ ᚻᚩᚹ ᛁᚾᛋᛏᛁᛏᚢᛏᛁᚩᚾᚪᛚ ᛗᛖᛏᚱᛁᚳᛋ ᛞᛁᚳᛏᚪᛏᛖ ᚱᛖᛋᛖᚪᚱᚳᚻ ᚡᛁᚪᛒᛁᛚᛁᛏᚣ. ᛋᛁᛗᛁᛚᚪᚱᛚᚣ, ᛏᚻᛖ Strategic Computing Initiative ᚠᚪᛁᛚᛖᛞ ᛏᚩ ᚪᚳᚻᛁᛖᚡᛖ artificial general intelligence ᛒᚣ 1993, ᛈᚱᛖᚳᛁᛈᛁᛏᚪᛏᛁᚾᚷ ᚪᚾᚩᛏᚻᛖᚱ ᚹᛁᚾᛏᛖᚱ3. ᚻᚩᚹᛖᚡᛖᚱ, ᛏᚻᛖᛋᛖ ᚠᚪᛁᛚᚢᚱᛖᛋ ᚠᚩᚱᚳᛖᛞ ᚪ ᛈᚪᚱᚪᛞᛁᚷᛗ ᛋᚻᛁᚠᛏ ᚠᚱᚩᛗ symbolic AI ᛏᚩᚹᚪᚱᛞᛋ statistical learning ᚪᚾᛞ connectionism. ᛏᚻᛖ ᛏᚱᚪᚾᛋᛁᛏᛁᚩᚾ ᛏᚩ deep learning ᚹᚪᛋ ᚢᚾᛞᛖᚱᛈᛁᚾᚾᛖᛞ ᛒᚣ hardware acceleration ᚪᚾᛞ backpropagation. ᚪᛚᛏᚻᚩᚢᚷᚻ Seppo Linnainmaa ᛈᚢᛒᛚᛁᛋᚻᛖᛞ ᛏᚻᛖ ᚱᛖᚡᛖᚱᛋᛖ ᛗᚩᛞᛖ ᚩᚠ automatic differentiation ᛁᚾ 19705, ᛁᛏ ᚱᛖᛢᚢᛁᚱᛖᛞ ᛞᛖᚳᚪᛞᛖᛋ ᚠᚩᚱ ᚳᚩᛗᛈᚢᛏᛖ ᛏᚩ ᚳᚪᛏᚳᚻ ᚢᛈ. ᛏᚻᛖ ᚪᛈᛈᛚᛁᚳᚪᛏᛁᚩᚾ ᚩᚠ GPUs ᛏᚩ neural networks ᛒᚣ Kyoung-Su Oh ᚪᚾᛞ Keechul Jung ᛁᚾ 2004 ᛚᚪᛁᛞ ᛏᚻᛖ ᚷᚱᚩᚢᚾᛞᚹᚩᚱᚴ ᚠᚩᚱ ᛏᚻᛖ ImageNet ᛒᚱᛖᚪᚴᛏᚻᚱᚩᚢᚷᚻᛋ7.
ᛏᚻᛖ ᛞᛖᚳᚩᚢᛈᛚᛁᚾᚷ ᚩᚠ ᛗᛖᛗᚩᚱᚣ ᚪᚾᛞ ᚳᚩᛗᛈᚢᛏᚪᛏᛁᚩᚾ
ᛏᚻᛖ ᛁᛋᚩᚚᚪᛏᛁᚩᚾ ᚩᚠ ᛈᚪᚱᚪᛗᛖᛏᚱᛁᚳ ᛗᛖᛗᚩᚱᚣ ᛈᚱᚩᛗᛈᛏᛖᛞ ᛏᚻᛖ ᛞᛖᚡᛖᚚᚩᛈᛗᛖᚾᛏ ᚩᚠ Retrieval-Augmented Generation ᛒᚣ Patrick Lewis ᛁᚾ 20209. ᛏᚻᛁᛋ ᚪᛚᛚᚩᚹᛖᛞ ᛗᚩᛞᛖᛚᛋ ᛏᚩ ᚪᚳᚳᛖᛋᛋ ᚾᚩᚾ-ᛈᚪᚱᚪᛗᛖᛏᚱᛁᚳ ᛞᚪᛏᚪᛒᚪᛋᛖᛋ, ᛗᛁᛏᛁᚷᚪᛏᛁᚾᚷ ᚻᚪᛚᛚᚢᚳᛁᚾᚪᛏᛁᚩᚾᛋ. ᛖᛉᛈᚪᚾᛞᛁᚾᚷ ᚩᚾ ᛏᚻᛁᛋ, Toolformer ᛞᛖᛗᚩᚾᛋᛏᚱᚪᛏᛖᛞ ᛏᚻᚪᛏ ᛚᚪᚾᚷᚢᚪᚷᛖ ᛗᚩᛞᛖᛚᛋ ᚳᚩᚢᛚᛞ ᛏᛖᚪᚳᚻ ᛏᚻᛖᛗᛋᛖᛚᚡᛖᛋ ᛏᚩ ᚢᛋᛖ ᛖᛉᛏᛖᚱᚾᚪᛚ APIs ᚡᛁᚪ ᛋᛁᛗᛈᛚᛖ ᛁᚾᛋᛏᚱᚢᚳᛏᛁᚩᚾᛋ11. ᛒᛖᚣᚩᚾᛞ ᛚᛁᚾᚷᚢᛁᛋᛏᛁᚳᛋ, ᛗᚪᚳᚻᛁᚾᛖ ᛁᚾᛏᛖᛚᛚᛁᚷᛖᚾᚳᛖ ᚱᛖᚡᚩᛚᚢᛏᛁᚩᚾᛁᛣᛖᛞ ᛋᚳᛁᛖᚾᛏᛁᚠᛁᚳ ᛞᛁᛋᚳᚩᚡᛖᚱᚣ. AlphaFold 3, ᛈᚢᛒᛚᛁᛋᚻᛖᛞ ᛁᚾ ᛗᚪᚣ 2024, ᛖᛉᛏᛖᚾᛞᛖᛞ ᛋᛏᚱᚢᚳᛏᚢᚱᚪᛚ ᛈᚱᛖᛞᛁᚳᛏᛁᚩᚾ ᛏᚩ ᛒᛁᚩᛗᚩᛚᛖᚳᚢᛚᚪᚱ ᛁᚾᛏᛖᚱᚪᚳᛏᛁᚩᚾᛋ, ᛖᚪᚱᚾᛁᚾᚷ ᛁᛏᛋ ᚳᚱᛖᚪᛏᚩᚱᛋ ᛏᚻᛖ Nobel Prize13. ᛏᚻᛖ ᛚᛁᛗᛁᛏᛋ ᚩᚠ ᛈᚱᛖ-ᛏᚱᚪᛁᚾᛁᚾᚷ ᛋᚳᚪᛚᛖ ᚠᚩᚱᚳᛖᛞ ᛁᚾᚾᚩᚡᚪᛏᛁᚩᚾ. Kaplan's scaling laws ᚻᛁᛏ ᛖᚳᚩᚾᚩᛗᛁᚳ ᚹᚪᛚᛚᛋ15. ᛏᚻᛖ ᚠᛁᛖᛚᛞ ᛈᛁᚡᚩᛏᛖᛞ ᛏᚩ inference-time reasoning, ᚪᛋ ᛋᛖᛖᚾ ᛁᚾ o1 ᚪᚾᛞ DeepSeek-R116. ᛏᚻᛁᛋ ᛗᛖᚪᚾᛋ ᚳᚩᛗᛈᚢᛏᛖ ᛁᛋ ᛋᛈᛖᚾᛏ ᛞᚢᚱᛁᚾᚷ ᚷᛖᚾᛖᚱᚪᛏᛁᚩᚾ, ᚳᚱᛖᚪᛏᛁᚾᚷ ᚪ ᚾᛖᚹ ᚪᛉᛁᛋ ᚩᚠ ᛋᚳᚪᛚᛁᚾᚷ. ᚻᛁᛋᛏᚩᚱᛁᚳᚪᛚ ᚳᛚᚪᛁᛗᛋ ᚩᚠ ᛈᚱᛖᚳᛖᛞᛖᚾᚳᛖ ᚪᚱᛖ ᚩᚠᛏᛖᚾ ᛞᛁᛋᛈᚢᛏᛖᛞ. ᚹᚻᛁᛚᛖ Paul Werbos ᛁᛋ ᚳᚱᛖᛞᛁᛏᛖᛞ ᚹᛁᛏᚻ ᚪᛈᛈᚚᚣᛁᚾᚷ backpropagation ᛏᚩ ᚾᛖᚢᚱᚪᛚ ᚾᛖᛏᚹᚩᚱᚴᛋ, Seppo Linnainmaa ᚠᚩᚱᛗᚪᛚᛁᛣᛖᛞ ᛏᚻᛖ ᚪᛚᚷᚩᚱᛁᛏᚻᛗ ᛁᚾ 19705. ᛋᛁᛗᛁᛚᚪᚱᚚᚣ, ᛏᚻᛖ ᛏᛖᚱᛗ artificial intelligence ᚹᚪᛋ ᛋᛏᚱᚪᛏᛖᚷᛁᚳᚪᛚᛚᚣ ᚳᚻᚩᛋᛖᚾ ᛒᚣ John McCarthy ᛏᚩ ᛞᛁᛋᛏᚪᚾᚳᛖ ᚻᛁᛋ ᚹᚩᚱᚴ ᚠᚱᚩᛗ Norbert Wiener'ᛋ cybernetics18.
ᛖᚡᛖᚾᛏ ᚳᚻᚱᚩᚾᚩᛚᚩᚷᚣ
| ID | Date | People/Org | Artifact/System | Class | Pri/Sec Sources | Importance | Disputed? | Pred/Succ | Conf/Rel |
|---|---|---|---|---|---|---|---|---|---|
| E001 | 1943 | McCulloch, Pitts | Logic Calculus | Model | Paper/Citations | Neural theory | No | None/SNARC | High/Brain |
| E002 | 1948 | Norbert Wiener | Cybernetics | Book | Book/Citations | Control theory | Yes | None/Dartmouth | High/Turing |
| E003 | 1950 | Alan Turing | Turing Test | Model | Paper/Citations | Evaluation | No | None/ELIZA | High/AI |
| E004 | 1951 | Minsky, Edmonds | SNARC | Hardware | Paper/Citations | First NN | Yes | None/Perceptron | High/RL |
| E005 | 1954 | Georgetown-IBM | MT System | Software | Press/Reports | Machine Translation | Yes | None/ALPAC | High/Linguistics |
| E006 | Aug 1955 | McCarthy, Shannon | AI Proposal | Proposal | Proposal/Archive | Coined AI | Yes | Cybernetics/Dartmouth | High/Minsky |
| E007 | Jun 1956 | Dartmouth College | Workshop | Event | Archive/History | Field founded | No | Proposal/MIT AI | High/Newell |
| E008 | 1956 | Newell, Simon | Logic Theorist | Software | Paper/Citations | Symbolic logic | No | None/GPS | High/Prover |
| E009 | 1957 | Newell, Simon | GPS | Software | Paper/Citations | Problem solving | No | Logic Theorist/SOAR | High/Symbolic |
| E010 | 1958 | Frank Rosenblatt | Perceptron | Model | Psych Review/Book | Single-layer NN | Yes | SNARC/MLP | High/Vision |
| E011 | 1959 | Arthur Samuel | Checkers | Software | Paper/Citations | RL | Yes | None/TD-Gammon | High/Games |
| E012 | 1960 | Henry J. Kelley | Gradient Theory | Paper | Paper/Citations | Optimization | Yes | Calculus/Linnainmaa | High/Control |
| E013 | 1965 | Edward Feigenbaum | DENDRAL | Software | Paper/Citations | Expert System | Yes | None/MYCIN | High/Chemistry |
| E014 | 1966 | Joseph Weizenbaum | ELIZA | Software | Paper/Citations | NLP Chatbot | Yes | Turing/PARRY | High/Therapy |
| E015 | Nov 1966 | ALPAC | ALPAC Report | Report | Report/Archive | Defunded MT | No | MT Boom/AI Winter | High/Policy |
| E016 | 1966-72 | SRI International | Shakey | Hardware | Tech Report/Video | Mobile Robot | Yes | None/Flakey | High/STRIPS |
| E017 | 1968 | Peter Hart et al. | A\* Search | Algorithm | Paper/Citations | Pathfinding | No | Dijkstra/D\* | High/Robotics |
| E018 | 1969 | Minsky, Papert | Perceptrons | Book | Book/Citations | XOR limits | No | Perceptron/MLP | High/Winter |
| E019 | 1970 | Seppo Linnainmaa | Reverse Autodiff | Thesis | Thesis/Citations | Backprop math | Yes | Kelley/Werbos | High/DeepLearning |
| E020 | 1971 | Fikes, Nilsson | STRIPS | Algorithm | Paper/Citations | Planning | No | GPS/PDDL | High/Shakey |
| E021 | 1972 | Stanford U. | MYCIN | Software | Paper/Citations | Medical ES | No | DENDRAL/Cyc | High/Medicine |
| E022 | 1973 | James Lighthill | Lighthill Report | Report | Report/Archive | UK AI Winter | No | ALPAC/Alvey | High/Policy |
| E023 | 1974 | Paul Werbos | Beyond Regression | Thesis | Thesis/Citations | BP for NN | Yes | Linnainmaa/Hinton | High/ML |
| E024 | 1980 | Kunihiko Fukushima | Neocognitron | Model | Paper/Citations | CNN Precursor | Yes | Hubel-Wiesel/LeNet | High/Vision |
| E025 | 1982 | John Hopfield | Hopfield Net | Model | Paper/Citations | Recurrent NN | Yes | None/Boltzmann | High/Physics |
| E026 | 1983 | DARPA | Strat. Computing | Event | Gov Docs/History | Funding Boom | No | AI Winter 1/Winter 2 | High/Policy |
| E027 | 1984 | Leslie Valiant | PAC Learning | Theory | Paper/Citations | Learning theory | No | None/VC Dimension | High/Math |
| E028 | 1986 | Rumelhart, Hinton | BP Training | Paper | Nature/Citations | Modern Backprop | Yes | Werbos/LeCun | High/DeepLearning |
| E029 | 1988 | Judea Pearl | Bayesian Nets | Book | Book/Citations | Probabilistic AI | No | None/Causal Inference | High/Stats |
| E030 | 1989 | Yann LeCun | LeNet-1 | Model | Paper/Citations | CNN | Yes | Neocognitron/AlexNet | High/Vision |
| E031 | 1989 | Chris Watkins | Q-Learning | Algorithm | Thesis/Citations | RL | No | Samuel/DQN | High/MDP |
| E032 | 1992 | Boser, Guyon, Vapnik | Kernel SVM | Algorithm | Paper/Citations | Kernel Trick | No | SVM/Random Forest | High/Stats |
| E033 | 1993 | DARPA | SCI Shutdown | Event | Gov Docs/History | AI Winter 2 | No | SCI/Modern AI | High/Policy |
| E034 | 1995 | Cortes, Vapnik | SVM Networks | Paper | Machine Learning | SVM | Yes | Linear Class./Deep | High/Stats |
| E035 | 1997 | IBM | Deep Blue | System | Press/Matches | Chess AI | Yes | Belle/AlphaZero | High/Games |
| E036 | 1997 | Hochreiter, Schmid. | LSTM | Model | Neural Comp. | Recurrent | Yes | RNN/GRU | High/Seq |
| E037 | 2001 | Leo Breiman | Random Forests | Algorithm | Paper/Citations | Ensembles | No | Trees/XGBoost | High/Stats |
| E038 | 2001 | Viola, Jones | Face Detector | Algorithm | CVPR/Citations | Object Det. | No | Haar/YOLO | High/Vision |
| E039 | 2003 | Yoshua Bengio | Neural Prob LM | Paper | JMLR/Citations | LM | Yes | N-gram/Word2Vec | High/NLP |
| E040 | 2004 | Oh, Jung | GPU NN | Paper | Pattern Recog. | Hardware Accel | Yes | CPU NN/CUDA | High/Compute |
| E041 | 2006 | Geoffrey Hinton | DBNs | Paper | Science/Citations | Unsupervised | Yes | RBM/AlexNet | High/Deep |
| E042 | 2009 | Fei-Fei Li | ImageNet | Dataset | CVPR/Citations | Scale Data | No | PASCAL/LAION | High/Vision |
| E043 | 2011 | IBM | Watson | System | Press/Jeopardy | QA System | No | DeepBlue/LLMs | High/NLP |
| E044 | 2011 | Apple | Siri | Software | Product Release | Virtual Asst. | Yes | CALO/Alexa | High/Voice |
| E045 | 2012 | Krizhevsky et al. | AlexNet | Model | NeurIPS/Citations | Deep CNN | No | LeNet/VGG | High/Vision |
| E046 | 2013 | Tomas Mikolov | Word2Vec | Model | Paper/Citations | Embeddings | Yes | LSA/GloVe | High/NLP |
| E047 | 2013 | DeepMind | DQN | Model | Nature/Citations | Deep RL | No | Q-Learning/PPO | High/Games |
| E048 | 2014 | Goodfellow et al. | GANs | Model | NeurIPS/Citations | Generative | Yes | RBM/Diffusion | High/Vision |
| E049 | 2014 | Sutskever et al. | Seq2Seq | Model | NeurIPS/Citations | Encoder-Decoder | Yes | LSTM/Attention | High/NLP |
| E050 | 2014 | Dzmitry Bahdanau | Neural MT | Paper | ICLR/Citations | Attention | Yes | Seq2Seq/Transformer | High/NLP |
| E051 | 2014 | Simonyan, Zisserman | VGGNet | Model | ICLR/Citations | CNN Depth | No | AlexNet/ResNet | High/Vision |
| E052 | 2014 | Kingma, Ba | Adam | Algorithm | ICLR/Citations | Optimizer | No | SGD/AdamW | High/Math |
| E053 | 2015 | Kaiming He et al. | ResNet | Model | CVPR/Citations | Residual | Yes | VGG/DenseNet | High/Vision |
| E054 | 2015 | TensorFlow | Software | Release/Docs | Framework | No | Theano/PyTorch | High/Dev | |
| E055 | 2015 | Redmon et al. | YOLO | Model | CVPR/Citations | Object Det. | No | R-CNN/SSD | High/Vision |
| E056 | Mar 2016 | Microsoft | Tay | Software | Press/Tweets | Chatbot Fail | No | ELIZA/ChatGPT | High/Safety |
| E057 | 2016 | DeepMind | AlphaGo | Model | Nature/Matches | Go AI | Yes | CrazyStone/AlphaZero | High/Games |
| E058 | 2016 | Meta | PyTorch | Software | Release/Docs | Framework | No | Torch/JAX | High/Dev |
| E059 | Jun 2017 | Vaswani et al. | Transformer | Model | NeurIPS/Citations | Self-Attention | Yes | Seq2Seq/BERT | High/NLP |
| E060 | 2017 | DeepMind | AlphaZero | Model | Paper/Citations | Gen. RL | No | AlphaGo/MuZero | High/Games |
| E061 | 2018 | Ha, Schmidhuber | World Models | Model | Paper/Citations | RL Env Model | Yes | Dyna/Dreamer | High/RL |
| E062 | 2018 | OpenAI | GPT-1 | Model | Tech Report | Decoder LM | Yes | Transformer/GPT-2 | High/NLP |
| E063 | Oct 2018 | Google AI | BERT | Model | NAACL/Citations | Encoder LM | No | ELMo/RoBERTa | High/NLP |
| E064 | 2018 | DeepMind | AlphaFold 1 | Model | CASP/Paper | Biology | No | Rosetta/AlphaFold 2 | High/Science |
| E065 | 2019 | François Chollet | ARC Benchmark | Dataset | Paper/Citations | Reasoning Eval | No | IQ Tests/AGI Eval | High/Eval |
| E066 | 2019 | NVIDIA | Megatron-LM | Model | Paper/Citations | LM Scaling | No | GPT-2/GPT-3 | High/NLP |
| E067 | Jan 2020 | Kaplan et al. | Scaling Laws | Paper | arXiv/Citations | Predictable | Yes | None/Chinchilla | High/Theory |
| E068 | May 2020 | Lewis et al. | RAG | Model | NeurIPS/Citations | External Mem. | Yes | REALM/Toolformer | High/NLP |
| E069 | Jun 2020 | OpenAI | GPT-3 | Model | NeurIPS/Citations | Few-shot | No | GPT-2/GPT-4 | High/NLP |
| E070 | 2020 | DeepMind | AlphaFold 2 | Model | Nature/Citations | Structure | No | AF1/AF3 | High/Science |
| E071 | 2021 | OpenAI | DALL-E | Model | Tech Report | Multimodal | Yes | VQ-VAE/Stable Diff. | High/Vision |
| E072 | 2021 | OpenAI | CLIP | Model | Tech Report | Vision-Language | Yes | None/Flamingo | High/Vision |
| E073 | 2021 | GitHub | Copilot | Software | Product Release | Code Gen | Yes | Codex/Cursor | High/Dev |
| E074 | 2022 | Hoffmann et al. | Chinchilla | Model | Paper/Citations | Compute Opt. | No | Kaplan/LLaMA | High/Theory |
| E075 | 2022 | Stability AI | Stable Diff. | Model | Release | Open Diffusion | Yes | Imagen/SDXL | High/Vision |
| E076 | 2022 | Midjourney | Midjourney | Model | Release | Gen Art | No | DALL-E/V6 | High/Vision |
| E077 | 2022 | OpenAI | Whisper | Model | Release | ASR | No | Wav2Vec/Seamless | High/Audio |
| E078 | Nov 2022 | OpenAI | ChatGPT | System | Release/Usage | RLHF Chatbot | Yes | InstructGPT/GPT-4 | High/Adoption |
| E079 | Feb 2023 | Schick et al. | Toolformer | Model | arXiv/Citations | Tool Use | Yes | WebGPT/Agents | High/NLP |
| E080 | 2023 | Meta | LLaMA | Model | Tech Report | Open Weights | Yes | OPT/Llama 2 | High/Open |
| E081 | 2023 | Anthropic | Claude 2 | Model | Release | AI Assistant | No | Claude/Claude 3 | High/NLP |
| E082 | 2023 | Gemini 1.0 | Model | Tech Report | Native Multi. | Yes | PaLM/Gemini 1.5 | High/Multi | |
| E083 | 2023 | Mistral AI | Mistral 7B | Model | Release | Efficient LM | No | LLaMA/Mixtral | High/Open |
| E084 | Mar 2023 | OpenAI | GPT-4 | Model | Tech Report | Frontier | No | GPT-3.5/o1 | High/NLP |
| E085 | Feb 2024 | OpenAI | Sora | Model | Demo | Text-to-Video | Yes | Make-a-Video/Gen-3 | High/Video |
| E086 | 2024 | Meta | Llama 3 | Model | Tech Report | SOTA Open | No | Llama 2/Llama 4 | High/Open |
| E087 | 2024 | Anthropic | Claude 3 Opus | Model | Tech Report | Frontier | No | Claude 2/Opus 3.5 | High/NLP |
| E088 | 2024 | Gemini 1.5 | Model | Tech Report | Long Context | No | Gemini 1.0/2.0 | High/Multi | |
| E089 | May 2024 | Abramson, Jumper | AlphaFold 3 | Model | Nature/Citations | Biomolecules | No | AF2/Unknown | High/Science |
| E090 | Sep 2024 | OpenAI | o1 | Model | Tech Report | Inference RL | Yes | GPT-4/DeepSeek-R1 | High/Reason |
| E091 | Dec 2024 | OpenAI | o1-dec5 | Model | System Card | Eval Checkpoint | No | o1-preview/o3 | High/Eval |
| E092 | Dec 2024 | DeepSeek | DeepSeek-V3 | Model | Tech Report | Efficient MoE | No | V2/V4 | High/MoE |
| E093 | Jan 2025 | DeepSeek | DeepSeek-R1 App | App | Release | RL Chatbot | No | V3/V4 | High/App |
| E094 | Jan 2025 | DeepSeek | DeepSeek-R1 | Model | arXiv | Pure RL | Yes | o1/Open-R1 | High/Reason |
| E095 | Jan 2025 | DeepSeek | Cyberattack | Event | Press | Server down | No | None/None | High/Security |
| E096 | Jan 2025 | HuggingFace | Open-R1 | Repo | GitHub | Replication | No | R1/Unknown | High/Open |
| E097 | Feb 2025 | Mercer et al. | R1 Analysis | Paper | arXiv | Hardware Ban | No | None/None | High/GeoPol |
| E098 | Mar 2025 | Bruegel | AI Policy Brief | Report | Publication | Market Impact | No | None/None | High/Econ |
| E099 | Apr 2025 | DeepSeek, Tsinghua | DeepSeek-GRM | Model | Paper | SPCT | No | R1/Unknown | High/Align |
| E100 | Apr 2025 | DeepSeek | Prover-V2 | Model | Release | Formal Math | Yes | Lean/Prover-V3 | High/Math |
| E101 | May 2025 | DeepSeek | R1-0528 | Model | Release | R1 Update | No | R1/Unknown | High/Reason |
| E102 | Sep 2025 | DeepSeek | R1 Nature | Paper | Nature | RL Validation | No | arXiv/Full | High/Science |
| E103 | 2025 | DeepSeek | Janus-Pro | Model | Tech Report | Multimodal | No | Janus/Unknown | High/Multi |
| E104 | Jan 2026 | DeepSeek | R1 Full | Paper | Publication | Full Details | No | Nature/Unknown | High/Science |
| E105 | Apr 2026 | DeepSeek | V4/V4 Pro | Model | Release | Next Gen | No | V3/Unknown | High/NLP |
ᛗᚪᛡᚩᚱ ᛖᚾᛏᛁᛏᛁᛖᛋ
| Entity ID | Exact Name | Domain | Notable Work |
|---|---|---|---|
| ENT-001 | Alan Turing | Computing | Turing Test |
| ENT-002 | John von Neumann | Computing | Architecture |
| ENT-003 | Norbert Wiener | Cybernetics | Cybernetics |
| ENT-004 | Warren McCulloch | Neuroscience | Artificial Neuron |
| ENT-005 | Walter Pitts | Neuroscience | Artificial Neuron |
| ENT-006 | Marvin Minsky | AI | SNARC, Perceptrons |
| ENT-007 | Dean Edmonds | Engineering | SNARC |
| ENT-008 | John McCarthy | AI | Lisp, Dartmouth |
| ENT-009 | Claude Shannon | Information Theory | Dartmouth |
| ENT-010 | Nathaniel Rochester | IBM | Dartmouth |
| ENT-011 | Allen Newell | AI | Logic Theorist |
| ENT-012 | Herbert Simon | AI | General Problem Solver |
| ENT-013 | Frank Rosenblatt | AI | Perceptron |
| ENT-014 | Arthur Samuel | AI | Checkers |
| ENT-015 | Ray Solomonoff | AI | Dartmouth |
| ENT-016 | Seppo Linnainmaa | Math | Backpropagation |
| ENT-017 | Paul Werbos | AI | Neural Net Backprop |
| ENT-018 | David Rumelhart | AI | Backpropagation |
| ENT-019 | Geoffrey Hinton | AI | DBNs, AlexNet |
| ENT-020 | Ronald Williams | AI | Backpropagation |
| ENT-021 | Yann LeCun | AI | CNNs (LeNet) |
| ENT-022 | Yoshua Bengio | AI | Neural LMs |
| ENT-023 | Ian Goodfellow | AI | GANs |
| ENT-024 | Ashish Vaswani | Transformer | |
| ENT-025 | Noam Shazeer | Transformer | |
| ENT-026 | Niki Parmar | Transformer | |
| ENT-027 | Jakob Uszkoreit | Transformer | |
| ENT-028 | Llion Jones | Transformer | |
| ENT-029 | Aidan Gomez | Transformer | |
| ENT-030 | Lukasz Kaiser | Transformer | |
| ENT-031 | Illia Polosukhin | Transformer | |
| ENT-032 | Kaiming He | Microsoft | ResNet |
| ENT-033 | Jared Kaplan | OpenAI | Scaling Laws |
| ENT-034 | Patrick Lewis | Meta | RAG |
| ENT-035 | David Ha | World Models | |
| ENT-036 | Jürgen Schmidhuber | AI | LSTM, World Models |
| ENT-037 | François Chollet | ARC Benchmark | |
| ENT-038 | Demis Hassabis | DeepMind | AlphaFold |
| ENT-039 | John Jumper | DeepMind | AlphaFold |
| ENT-040 | David Baker | Biology | Protein Design |
| ENT-041 | Corinna Cortes | AT\&T | SVMs |
| ENT-042 | Vladimir Vapnik | AT\&T | SVMs |
| ENT-043 | Sepp Hochreiter | AI | LSTM |
| ENT-044 | Ilya Sutskever | OpenAI | Seq2Seq, AlexNet |
| ENT-045 | Sam Altman | OpenAI | CEO |
| ENT-046 | Yannic Kilcher | Community | OpenAssistant |
| ENT-047 | Fei-Fei Li | Stanford | ImageNet |
| ENT-048 | Alex Krizhevsky | AI | AlexNet |
| ENT-049 | Christian Jauvin | UMontreal | Neural LM |
| ENT-050 | Pascal Vincent | UMontreal | Neural LM |
| ENT-051 | Réjean Ducharme | UMontreal | Neural LM |
| ENT-052 | Kyoung-Su Oh | AI | GPU NNs |
| ENT-053 | Keechul Jung | AI | GPU NNs |
| ENT-054 | John B. Carroll | Harvard | ALPAC |
| ENT-055 | Eric P. Hamp | UChicago | ALPAC |
| ENT-056 | David G. Hays | RAND | ALPAC |
| ENT-057 | Charles F. Hockett | Cornell | ALPAC |
| ENT-058 | Arthur Bryson | Control | Optimal Control |
| ENT-059 | Henry Kelley | Control | Flight Paths |
| ENT-060 | Stuart Dreyfus | Math | Chain Rule |
| ENT-061 | Liang Wenfeng | DeepSeek | R1 |
| ENT-062 | Colin Raffel | T5 | |
| ENT-063 | Adam Roberts | T5 | |
| ENT-064 | Danqi Chen | Meta | DPR |
| ENT-065 | Wen-tau Yih | Meta | RAG |
| ENT-066 | Mike Lewis | Meta | RAG |
| ENT-067 | Yinhan Liu | Meta | BART |
| ENT-068 | Naman Goyal | Meta | BART |
| ENT-069 | Marjan Ghazvininejad | Meta | BART |
| ENT-070 | Abdelrahman Mohamed | Meta | BART |
| ENT-071 | Omer Levy | Meta | BART |
| ENT-072 | Veselin Stoyanov | Meta | BART |
| ENT-073 | Luke Zettlemoyer | Meta | BART |
| ENT-074 | Kelvin Guu | REALM | |
| ENT-075 | Kenton Lee | REALM | |
| ENT-076 | Zora Tung | REALM | |
| ENT-077 | Panupong Pasupat | REALM | |
| ENT-078 | Ming-Wei Chang | REALM | |
| ENT-079 | Urvashi Khandelwal | AI | Nearest Neighbor LM |
| ENT-080 | Dan Jurafsky | AI | NLP |
| ENT-081 | Dartmouth College | Academia | 1956 Workshop |
| ENT-082 | SRI International | Lab | Shakey |
| ENT-083 | DARPA | Gov | Strat. Computing |
| ENT-084 | IBM | Corp | Deep Blue |
| ENT-085 | Bell Labs | Corp | Info Theory |
| ENT-086 | Google Brain | Corp | Transformer |
| ENT-087 | DeepMind | Corp | AlphaGo |
| ENT-088 | OpenAI | Corp | GPT |
| ENT-089 | Meta AI | Corp | LLaMA |
| ENT-090 | DeepSeek | Corp | R1 |
| ENT-091 | MIT | Academia | AI Lab |
| ENT-092 | Stanford | Academia | AI Lab |
| ENT-093 | Carnegie Mellon | Academia | AI Lab |
| ENT-094 | RAND Corporation | Corp | ALPAC |
| ENT-095 | Princeton | Academia | SNARC |
| ENT-096 | Harvard | Academia | ALPAC |
| ENT-097 | University of Toronto | Academia | Deep Learning |
| ENT-098 | University of Montreal | Academia | MILA |
| ENT-099 | NYU | Academia | CNNs |
| ENT-100 | National Academy of Sciences | Gov | ALPAC Report |
ᛗᛁᛚᛖᛋᛏᚩᚾᛖ ᚳᚪᚾᛞᛁᛞᚪᛏᛖᛋ
| Milestone ID | Date | Exact Milestone | Significance |
|---|---|---|---|
| ML-001 | 1943 | A Logical Calculus of the Ideas Immanent in Nervous Activity | Foundation of neural logic |
| ML-002 | 1948 | Cybernetics | Control theory establishment |
| ML-003 | 1950 | Computing Machinery and Intelligence | Turing Test framework |
| ML-004 | 1951 | SNARC | First neural network machine |
| ML-005 | 1955 | Dartmouth Proposal | Coined "Artificial Intelligence" |
| ML-006 | 1956 | Dartmouth Workshop | Birth of AI field |
| ML-007 | 1956 | Logic Theorist | First symbolic AI |
| ML-008 | 1957 | General Problem Solver | Means-ends analysis |
| ML-009 | 1958 | The Perceptron | Trainable neural network |
| ML-010 | 1959 | Checkers Program | First RL application |
| ML-011 | 1960 | Gradient Theory of Optimal Flight Paths | BP mathematics |
| ML-012 | 1965 | DENDRAL | First expert system |
| ML-013 | 1966 | ELIZA | First conversational agent |
| ML-014 | 1966 | ALPAC Report | Ended MT funding |
| ML-015 | 1966 | Shakey the Robot | First reasoning robot |
| ML-016 | 1969 | Perceptrons | Highlighted XOR limitation |
| ML-017 | 1970 | Reverse mode of automatic differentiation | BP formalized |
| ML-018 | 1971 | STRIPS planner | Action planning |
| ML-019 | 1972 | MYCIN | Medical expert system |
| ML-020 | 1973 | Lighthill Report | Sparked UK AI winter |
| ML-021 | 1974 | Beyond Regression | BP applied to NNs |
| ML-022 | 1980 | Neocognitron | Precursor to CNNs |
| ML-023 | 1982 | Hopfield Networks | Associative memory |
| ML-024 | 1983 | Strategic Computing Initiative | Massive AI funding |
| ML-025 | 1986 | BP representations paper | Popularized BP |
| ML-026 | 1988 | Probabilistic Reasoning | Bayesian networks |
| ML-027 | 1989 | LeNet-1 | Digit recognition CNN |
| ML-028 | 1989 | Q-Learning | TD learning |
| ML-029 | 1995 | Support-Vector Networks | SVM popularization |
| ML-030 | 1997 | Deep Blue | AI defeats Chess champion |
| ML-031 | 1997 | LSTM | Solved vanishing gradients |
| ML-032 | 2001 | Random Forests | Ensemble dominance |
| ML-033 | 2003 | Neural Probabilistic LM | Distributed word reps |
| ML-034 | 2004 | GPU NN | Hardware acceleration |
| ML-035 | 2006 | DBNs | Deep learning revival |
| ML-036 | 2009 | ImageNet | Big data for vision |
| ML-037 | 2011 | Watson | Open domain QA |
| ML-038 | 2012 | AlexNet | Deep learning explosion |
| ML-039 | 2013 | Word2Vec | Efficient embeddings |
| ML-040 | 2013 | Deep Q-Network | Deep RL |
| ML-041 | 2014 | GANs | Adversarial generation |
| ML-042 | 2014 | Seq2Seq | Neural translation |
| ML-043 | 2014 | Neural MT Alignment | Attention mechanism |
| ML-044 | 2015 | ResNet | Deep residual connections |
| ML-045 | 2016 | Microsoft Tay | AI safety warning |
| ML-046 | 2016 | AlphaGo | AI defeats Go champion |
| ML-047 | 2017 | Attention Is All You Need | Transformer architecture |
| ML-048 | 2018 | World Models | Predictive RL |
| ML-049 | 2018 | GPT-1 | Decoder pre-training |
| ML-050 | 2018 | BERT | Encoder pre-training |
| ML-051 | 2019 | ARC Benchmark | Reasoning evaluation |
| ML-052 | 2020 | Scaling Laws | Predictable AI scale |
| ML-053 | 2020 | RAG | External memory injection |
| ML-054 | 2020 | GPT-3 | Few-shot prompting |
| ML-055 | 2021 | DALL-E | Text-to-image synthesis |
| ML-056 | 2022 | Chinchilla | Compute optimality |
| ML-057 | 2022 | ChatGPT | Conversational RLHF |
| ML-058 | 2023 | Toolformer | API integration |
| ML-059 | 2023 | GPT-4 | Frontier multimodal |
| ML-060 | 2024 | Sora | Video generation |
| ML-061 | 2024 | AlphaFold 3 | Biochemical interactions |
| ML-062 | 2024 | OpenAI o1 | Inference time compute |
| ML-063 | 2025 | DeepSeek-R1 | Open pure RL |
| ML-064 | 2025 | DeepSeek-Prover-V2 | Automated math proof |
| ML-065 | 2026 | DeepSeek V4 | State of the art NLP |
| ML-066 | 1968 | A\* Search Algorithm | Graph traversal |
| ML-067 | 1984 | Valiant PAC Learning | Computational learning |
| ML-068 | 1992 | SVM Kernel Trick | Non-linear classification |
| ML-069 | 2001 | Viola-Jones | Real-time object detection |
| ML-070 | 2011 | Siri | Voice assistants |
| ML-071 | 2014 | VGGNet | CNN depth scaling |
| ML-072 | 2014 | Adam Optimizer | Efficient training |
| ML-073 | 2015 | TensorFlow | Open source framework |
| ML-074 | 2015 | YOLO | One-stage detection |
| ML-075 | 2016 | PyTorch | Dynamic graph framework |
| ML-076 | 2017 | AlphaZero | Generalized self-play |
| ML-077 | 2018 | AlphaFold 1 | Protein folding start |
| ML-078 | 2019 | Megatron-LM | Model parallelism |
| ML-079 | 2020 | AlphaFold 2 | Solved protein folding |
| ML-080 | 2021 | CLIP | Contrastive learning |
| ML-081 | 2021 | GitHub Copilot | Commercial AI coding |
| ML-082 | 2022 | Stable Diffusion | Open weights image |
| ML-083 | 2022 | Midjourney | High aesthetic generation |
| ML-084 | 2022 | Whisper | Robust ASR |
| ML-085 | 2023 | LLaMA | Open foundation |
| ML-086 | 2023 | Claude 2 | Constitutional AI |
| ML-087 | 2023 | Gemini 1.0 | Native multimodal |
| ML-088 | 2023 | Mistral 7B | Small efficient models |
| ML-089 | 2024 | Llama 3 | Massively scaled open |
| ML-090 | 2024 | Claude 3 Opus | Frontier intelligence |
| ML-091 | 2024 | Gemini 1.5 Pro | Infinite context |
| ML-092 | 2025 | DeepSeek-R1-0528 | JSON/API functions |
| ML-093 | 2025 | Janus-Pro | Open multimodal |
| ML-094 | 2025 | Open-R1 | Community reproduction |
| ML-095 | 2025 | DeepSeek-GRM | Generative reward model |
| ML-096 | 2025 | Brief analysis of DeepSeek R1 | Geopolitical impact |
| ML-097 | 2026 | OpenAI o3 | Next gen reasoning |
| ML-098 | 2026 | DeepSeek V4 Pro | Advanced language |
| ML-099 | 2025 | R1 Nature publication | RL peer reviewed |
| ML-100 | 2026 | Current Agentic AI | Goal driven actions |
ᛞᛁᛋᛈᚢᛏᛖᛞ ᚠᛁᚱᛋᛏ ᚳᛚᚪᛁᛗᛋ
ᚻᛁᛋᛏᚩᚱᛁᚳᚪᛚ ᚳᛚᚪᛁᛗᛋ ᚩᚠ ᛈᚱᛖᚳᛖᛞᛖᚾᚳᛖ ᚪᚱᛖ ᚩᚠᛏᛖᚾ ᛞᛁᛋᛈᚢᛏᛖᛞ. ᚹᚻᛁᛚᛖ Paul Werbos ᛁᛋ ᚳᚱᛖᛞᛁᛏᛖᛞ ᚹᛁᛏᚻ ᚪᛈᛈᚚᚣᛁᚾᚷ backpropagation ᛏᚩ ᚾᛖᚢᚱᚪᛚ ᚾᛖᛏᚹᚩᚱᚴᛋ, Seppo Linnainmaa ᚠᚩᚱᛗᚪᛚᛁᛣᛖᛞ ᛏᚻᛖ ᚪᛚᚷᚩᚱᛁᛏᚻᛗ ᛁᚾ 19705. ᛋᛁᛗᛁᛚᚪᚱᚚᚣ, ᛏᚻᛖ ᛏᛖᚱᛗ artificial intelligence ᚹᚪᛋ ᛋᛏᚱᚪᛏᛖᚷᛁᚳᚪᛚᛚᚣ ᚳᚻᚩᛋᛖᚾ ᛒᚣ John McCarthy ᛏᚩ ᛞᛁᛋᛏᚪᚾᚳᛖ ᚻᛁᛋ ᚹᚩᚱᚴ ᚠᚱᚩᛗ Norbert Wiener'ᛋ cybernetics18.
| ID | Claim | Contenders | Resolution/Status |
|---|---|---|---|
| DSP-001 | First Backpropagation | Linnainmaa, Werbos, Rumelhart | Linnainmaa (Math) |
| DSP-002 | First Neural Network | SNARC, Perceptron | SNARC |
| DSP-003 | First Mobile Reasoning Robot | Shakey, Stanford Cart | Shakey |
| DSP-004 | First CNN | Neocognitron, LeNet | Neocognitron |
| DSP-005 | First Deep Learning | Ivakhnenko, LeCun | Ivakhnenko |
| DSP-006 | First Reinforcement Learning | Samuel Checkers, SNARC | SNARC |
| DSP-007 | First AI Winter | ALPAC, Lighthill | ALPAC |
| DSP-008 | First Attention Mechanism | Bahdanau, Graves | Bahdanau |
| DSP-009 | First GAN | Goodfellow, Schmidhuber | Goodfellow |
| DSP-010 | First Language Model | Shannon, Bengio | Shannon (N-gram) |
| DSP-011 | First LLM | GPT-1, ELMo | GPT-1 |
| DSP-012 | First Word Embedding | Word2Vec, LSA | LSA |
| DSP-013 | First Expert System | DENDRAL, MYCIN | DENDRAL |
| DSP-014 | First GPU Neural Network | Oh/Jung, Catanzaro | Oh/Jung |
| DSP-015 | First Machine Translation | Georgetown-IBM, Birkbeck | Birkbeck |
| DSP-016 | First Turing Test Pass | ELIZA, Eugene Goostman | Disputed |
| DSP-017 | First Transformer | Vaswani, Schmidhuber | Vaswani |
| DSP-018 | First RLHF | OpenAI, DeepMind | DeepMind |
| DSP-019 | First RAG | Lewis, Guu | Lewis |
| DSP-020 | First Tool Use | Toolformer, WebGPT | WebGPT |
| DSP-021 | First Autonomous Agent | AutoGPT, BabyAGI | AutoGPT |
| DSP-022 | First Video Generation | Sora, Make-A-Video | Make-A-Video |
| DSP-023 | First AI Scientist | AlphaFold, Adam | Adam |
| DSP-024 | First Open Weights LLM | LLaMA, BLOOM | BLOOM |
| DSP-025 | First Mixture of Experts | Shazeer, Jacobs | Jacobs |
| DSP-026 | First Self-Attention | Cheng, Vaswani | Cheng |
| DSP-027 | First Prompt Engineering | Brown, Radford | Radford |
| DSP-028 | First Multimodal Model | CLIP, DALL-E | CLIP |
| DSP-029 | First Scaling Law | Kaplan, Hestness | Hestness |
| DSP-030 | First Diffusion Model | Sohl-Dickstein, Ho | Sohl-Dickstein |
| DSP-031 | First Chatbot | ELIZA, PARRY | ELIZA |
| DSP-032 | First AI Code Gen | Codex, AlphaCode | Codex |
| DSP-033 | First Artificial Neuron | McCulloch-Pitts, Hodgkin | McCulloch-Pitts |
| DSP-034 | First Cybernetics | Wiener, Turing | Wiener |
| DSP-035 | First AI Term | McCarthy, Shannon | McCarthy |
| DSP-036 | First World Model | Ha/Schmidhuber, Sutton | Sutton |
| DSP-037 | First Inference Reasoning | o1, DeepSeek-R1 | o1 |
| DSP-038 | First Pure RL LLM | DeepSeek-R1, AlphaGo | DeepSeek-R1 |
| DSP-039 | First GPU Accelerated SVM | Catanzaro, Oh | Catanzaro |
| DSP-040 | First Quantum ML | Biamonte, Lloyd | Lloyd |
| DSP-041 | First Sequence to Sequence | Sutskever, Cho | Sutskever |
| DSP-042 | First Dropout | Hinton, Srivastava | Hinton |
| DSP-043 | First Batch Normalization | Ioffe, Szegedy | Ioffe |
| DSP-044 | First Residual Network | He, Schmidhuber | He |
| DSP-045 | First Support Vector Machine | Cortes, Vapnik | Vapnik |
| DSP-046 | First AI Ontology | Cyc, WordNet | Cyc |
| DSP-047 | First Recurrent Neural Net | Hopfield, Elman | Hopfield |
| DSP-048 | First AI Chess Win | Deep Blue, Belle | Deep Blue |
| DSP-049 | First Go AI Win | AlphaGo, Crazy Stone | AlphaGo |
| DSP-050 | First Theorem Prover | Logic Theorist, Prover | Logic Theorist |
ᚠᚪᛁᛚᚢᚱᛖᛋ ᚪᚾᛞ ᛞᛖᚪᛞ ᛖᚾᛞᛋ
| ID | Failure/Dead End | Cause | Impact |
|---|---|---|---|
| FL-001 | Microsoft Tay | Toxic data | PR disaster |
| FL-002 | ALPAC Report Fallout | Overpromising | AI Winter |
| FL-003 | Strategic Computing Init. | AGI failure | Defunding |
| FL-004 | Lighthill Report Fallout | Combinatorial explosion | UK Winter |
| FL-005 | Fifth Generation Computers | Hardware mismatch | Abandoned |
| FL-006 | Expert Systems Crash | Brittleness | Lisp machine crash |
| FL-007 | Perceptron XOR Limitation | Linear separability | NN abandonment |
| FL-008 | Cyc Ontology | Manual entry scaling | Obsolescence |
| FL-009 | IBM Watson for Oncology | Clinical mismatch | Market withdrawal |
| FL-010 | Google Glass | Privacy | Consumer flop |
| FL-011 | Apple Newton | Poor handwriting reco | Cancelled |
| FL-012 | Amazon AI Recruiting Tool | Gender bias | Scrapped |
| FL-013 | Zillow Offers Pricing | Market drift | Financial loss |
| FL-014 | Meta Galactica | Severe hallucinations | Pulled offline |
| FL-015 | Google Gemini Image Pause | Historical inaccuracy | Public apology |
| FL-016 | Autonomous Driving L5 | Edge cases | Delayed indefinitely |
| FL-017 | OpenAI GPT-2 Withheld | Safety fears | Leaked anyway |
| FL-018 | IBM DeepQA Commercial | Hard to scale | Spun down |
| FL-019 | Symbolic AI Uncertainty | Rigidity | Shift to ML |
| FL-020 | GOFAI Scalability | State space limits | Shift to RL |
| FL-021 | Early Neural Translation | Vanishing gradients | Abandoned |
| FL-022 | RNNs for Long Context | Catastrophic forgetting | Replaced by Attention |
| FL-023 | Pure RL Self-Driving | Sample inefficiency | Hybrid models preferred |
| FL-024 | OpenCyc | Lack of funding | Discontinued |
| FL-025 | Google Flu Trends | Overfitting | Retired |
| FL-026 | Shakey's Execution | Compute bottleneck | Slow adoption |
| FL-027 | Early Speech Recognition | Speaker dependency | Replaced by DNNs |
| FL-028 | Knowledge Acquisition | Manual labor | ML superiority |
| FL-029 | Markov Logic Networks | Inference scaling | DL dominance |
| FL-030 | Capsule Networks | Training efficiency | CNNs/ViTs remain |
| FL-031 | Neural Turing Machines | Optimization issues | Abandoned |
| FL-032 | Differentiable Neural Computers | Adoption failure | Transformers won |
| FL-033 | IBM SyNAPSE | Ecosystem mismatch | Niche usage |
| FL-034 | Jibo Robot | High cost/Low utility | Bankruptcy |
| FL-035 | Anki Robot | Funding dry up | Bankruptcy |
| FL-036 | Kismet | Narrow generalization | Academic only |
| FL-037 | ELIZA Effect | Psychological bias | False AI hype |
| FL-038 | Eurisko | Search space explosion | Abandoned |
| FL-039 | Project Maven | Employee revolt | Contract dropped |
| FL-040 | Uber Self-Driving | Fatal accident | Program sold |
| FL-041 | Cruise Robotaxi Suspension | Pedestrian accident | Grounded |
| FL-042 | AI Dungeon Filter | Over-censorship | User revolt |
| FL-043 | Stack Overflow ChatGPT Ban | High inaccuracy | Policy shift |
| FL-044 | CNET AI Articles | Plagiarism/Errors | Editorial pause |
| FL-045 | Sports Illustrated AI Writers | Fake personas | Scandal |
| FL-046 | DeepSeek Cyberattack | DDoS | Service disruption |
| FL-047 | OpenAI o2 Naming | Trademark | Skipped to o3 |
| FL-048 | Early Machine Translation | No syntactic rules | ALPAC |
| FL-049 | SNARC Digital Persistence | Analog limits | Shift to digital |
| FL-050 | Turing Learning Machine | Theoretical only | Wait for hardware |
ᚳᚹᚢᛖᛋᛏᛁᚩᚾᛋ ᚪᚾᛞ ᚪᚾᛋᚹᛖᚱᛋ
1. Q: ᚹᚻᚩ ᚳᚱᛖᚪᛏᛖᛞ Cybernetics? A: Norbert Wiener20.
2. Q: ᚹᚻᚩ ᚳᚱᛖᚪᛏᛖᛞ SNARC? A: Marvin Minsky ᚪᚾᛞ Dean Edmonds21.
3. Q: ᚹᚻᚩ ᚳᚱᛖᚪᛏᛖᛞ Dartmouth Proposal? A: John McCarthy ᚪᚾᛞ Claude Shannon18.
4. Q: ᚹᚻᚩ ᚳᚱᛖᚪᛏᛖᛞ Perceptron? A: Frank Rosenblatt22.
5. Q: ᚹᚻᚩ ᚳᚱᛖᚪᛏᛖᛞ Shakey the Robot? A: SRI International23.
6. Q: ᚹᚻᚩ ᚳᚱᛖᚪᛏᛖᛞ Reverse automatic differentiation? A: Seppo Linnainmaa5.
7. Q: ᚹᚻᚩ ᚳᚱᛖᚪᛏᛖᛞ Support-Vector Networks? A: Corinna Cortes ᚪᚾᛞ Vladimir Vapnik24.
8. Q: ᚹᚻᚩ ᚳᚱᛖᚪᛏᛖᛞ A Neural Probabilistic Language Model? A: Yoshua Bengio25.
9. Q: ᚹᚻᚩ ᚳᚱᛖᚪᛏᛖᛞ GPU-based implementation of artificial neural networks? A: Kyoung-Su Oh ᚪᚾᛞ Keechul Jung7.
10. Q: ᚹᚻᚩ ᚳᚱᛖᚪᛏᛖᛞ Generative Adversarial Nets? A: Ian Goodfellow ᚪᚾᛞ ᚩᛏᚻᛖᚱᛋ26.
11. Q: ᚹᚻᚩ ᚳᚱᛖᚪᛏᛖᛞ Deep Residual Learning for Image Recognition? A: Kaiming He ᚪᚾᛞ ᚩᛏᚻᛖᚱᛋ27.
12. Q: ᚹᚻᚩ ᚳᚱᛖᚪᛏᛖᛞ Attention Is All You Need? A: Ashish Vaswani ᚪᚾᛞ ᚩᛏᚻᛖᚱᛋ28.
13. Q: ᚹᚻᚩ ᚳᚱᛖᚪᛏᛖᛞ World Models? A: David Ha ᚪᚾᛞ Jürgen Schmidhuber29.
14. Q: ᚹᚻᚩ ᚳᚱᛖᚪᛏᛖᛞ ARC Benchmark? A: François Chollet30.
15. Q: ᚹᚻᚩ ᚳᚱᛖᚪᛏᛖᛞ Scaling Laws for Neural Language Models? A: Jared Kaplan ᚪᚾᛞ ᚩᛏᚻᛖᚱᛋ15.
16. Q: ᚹᚻᚩ ᚳᚱᛖᚪᛏᛖᛞ Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks? A: Patrick Lewis ᚪᚾᛞ ᚩᛏᚻᛖᚱᛋ9.
17. Q: ᚹᚻᚩ ᚳᚱᛖᚪᛏᛖᛞ Training Compute-Optimal Large Language Models? A: Hoffmann ᚪᚾᛞ ᚩᛏᚻᛖᚱᛋ31.
18. Q: ᚹᚻᚩ ᚳᚱᛖᚪᛏᛖᛞ Toolformer: Language Models Can Teach Themselves to Use Tools? A: Timo Schick ᚪᚾᛞ ᚩᛏᚻᛖᚱᛋ11.
19. Q: ᚹᚻᚩ ᚳᚱᛖᚪᛏᛖᛞ AlphaFold 3? A: Josh Abramson, Jonas Adler, ᚪᚾᛞ John Jumper14.
20. Q: ᚹᚻᚩ ᚳᚱᛖᚪᛏᛖᛞ DeepSeek-R1? A: DeepSeek16.
21. Q: ᚹᚻᛖᚾ ᚹᚪᛋ Cybernetics ᛈᚢᛒᛚᛁᛋᚻᛖᛞ? A: 194820.
22. Q: ᚹᚻᛖᚾ ᚹᚪᛋ SNARC ᛈᚢᛒᛚᛁᛋᚻᛖᛞ? A: 195121.
23. Q: ᚹᚻᛖᚾ ᚹᚪᛋ Dartmouth Proposal ᛈᚢᛒᛚᛁᛋᚻᛖᛞ? A: 195518.
24. Q: ᚹᚻᛖᚾ ᚹᚪᛋ Perceptron ᛈᚢᛒᛚᛁᛋᚻᛖᛞ? A: 195822.
25. Q: ᚹᚻᛖᚾ ᚹᚪᛋ Shakey the Robot ᛈᚢᛒᛚᛁᛋᚻᛖᛞ? A: 196623.
26. Q: ᚹᚻᛖᚾ ᚹᚪᛋ Reverse automatic differentiation ᛈᚢᛒᛚᛁᛋᚻᛖᛞ? A: 19705.
27. Q: ᚹᚻᛖᚾ ᚹᚪᛋ Support-Vector Networks ᛈᚢᛒᛚᛁᛋᚻᛖᛞ? A: 199524.
28. Q: ᚹᚻᛖᚾ ᚹᚪᛋ A Neural Probabilistic Language Model ᛈᚢᛒᛚᛁᛋᚻᛖᛞ? A: 200325.
29. Q: ᚹᚻᛖᚾ ᚹᚪᛋ GPU-based implementation of artificial neural networks ᛈᚢᛒᛚᛁᛋᚻᛖᛞ? A: 20047.
30. Q: ᚹᚻᛖᚾ ᚹᚪᛋ Generative Adversarial Nets ᛈᚢᛒᛚᛁᛋᚻᛖᛞ? A: 201426.
31. Q: ᚹᚻᛖᚾ ᚹᚪᛋ Deep Residual Learning for Image Recognition ᛈᚢᛒᛚᛁᛋᚻᛖᛞ? A: 201627.
32. Q: ᚹᚻᛖᚾ ᚹᚪᛋ Attention Is All You Need ᛈᚢᛒᛚᛁᛋᚻᛖᛞ? A: 201728.
33. Q: ᚹᚻᛖᚾ ᚹᚪᛋ World Models ᛈᚢᛒᛚᛁᛋᚻᛖᛞ? A: 201829.
34. Q: ᚹᚻᛖᚾ ᚹᚪᛋ ARC Benchmark ᛈᚢᛒᛚᛁᛋᚻᛖᛞ? A: 201930.
35. Q: ᚹᚻᛖᚾ ᚹᚪᛋ Scaling Laws for Neural Language Models ᛈᚢᛒᛚᛁᛋᚻᛖᛞ? A: 202015.
36. Q: ᚹᚻᛖᚾ ᚹᚪᛋ Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks ᛈᚢᛒᛚᛁᛋᚻᛖᛞ? A: 20209.
37. Q: ᚹᚻᛖᚾ ᚹᚪᛋ Training Compute-Optimal Large Language Models ᛈᚢᛒᛚᛁᛋᚻᛖᛞ? A: 202231.
38. Q: ᚹᚻᛖᚾ ᚹᚪᛋ Toolformer ᛈᚢᛒᛚᛁᛋᚻᛖᛞ? A: 202311.
39. Q: ᚹᚻᛖᚾ ᚹᚪᛋ AlphaFold 3 ᛈᚢᛒᛚᛁᛋᚻᛖᛞ? A: 202414.
40. Q: ᚹᚻᛖᚾ ᚹᚪᛋ DeepSeek-R1 arXiv ᛈᚢᛒᛚᛁᛋᚻᛖᛞ? A: 202532.
41. Q: ᚹᚻᚪᛏ ᛗᚩᛞᛖᛚ ᚹᚪᛋ ᚱᛖᛚᛖᚪᛋᛖᛞ ᛒᚣ OpenAI ᛁᚾ 2024? A: OpenAI o117.
42. Q: ᚹᚻᚪᛏ ᛗᚩᛞᛖᛚ ᚹᚪᛋ ᚱᛖᛚᛖᚪᛋᛖᛞ ᛒᚣ DeepSeek ᛁᚾ 2026? A: DeepSeek V416.
43. Q: ᚹᚻᚪᛏ ᚱᛖᛈᚩᚱᛏ ᚳᚪᚢᛋᛖᛞ ᚪᚾ AI ᚹᛁᚾᛏᛖᚱ ᛁᚾ 1966? A: ALPAC Report1.
44. Q: ᚹᚻᛁᚳᚻ ᚱᚩᛒᚩᛏ ᚢᛋᛖᛞ STRIPS ᛏᚩ ᚱᛖᚪᛋᚩᚾ? A: Shakey the Robot33.
45. Q: ᚹᚻᚩ ᛈᚢᛒᛚᛁᛋᚻᛖᛞ ᛏᚻᛖ ᛗᚪᛏᚻᛖᛗᚪᛏᛁᚳᛋ ᚩᚠ ᚱᛖᚡᛖᚱᛋᛖ ᚪᚢᛏᚩᛗᚪᛏᛁᚳ ᛞᛁᚠᚠᛖᚱᛖᚾᛏᛁᚪᛏᛁᚩᚾ? A: Seppo Linnainmaa5.
46. Q: ᚹᚻᚪᛏ ᛁᛋ ᛏᚻᛖ ᚠᛁᚱᛋᛏ ᛈᚢᛒᛚᛁᛋᚻᛖᛞ ᚳᚩᚾᚡᚩᛚᚢᛏᛁᚩᚾᚪᛚ ᚾᛖᛏᚹᚩᚱᚴ? A: Neocognitron
47. Q: ᚹᚻᚪᛏ ᛗᚩᛞᛖᛚ ᛈᚪᛋᛋᛖᛞ ᛏᚻᛖ Turing Test ᚠᛁᚱᛋᛏ? A: ELIZA
48. Q: ᚹᚻᚪᛏ ᚪᚱᚳᚻᛁᛏᛖᚳᛏᚢᚱᛖ ᚱᛖᛈᛚᚪᚳᛖᛞ RNNs? A: Transformer
49. Q: ᚹᚻᚪᛏ ᛈᚱᚩᚷᚱᚪᛗ ᛞᛖᚠᛖᚪᛏᛖᛞ Garry Kasparov? A: Deep Blue
50. Q: ᚹᚻᚪᛏ ᛗᚩᛞᛖᛚ ᛞᛖᚠᛖᚪᛏᛖᛞ Lee Sedol? A: AlphaGo
51. Q: ᚹᚻᚪᛏ ᛁᛋ ᛏᚻᛖ ᚳᚩᚱᛖ ᛁᚾᚾᚩᚡᚪᛏᛁᚩᚾ ᚩᚠ ResNet? A: Residual connections
52. Q: ᚹᚻᚪᛏ ᛁᛋ ᛏᚻᛖ ᚳᚩᚱᛖ ᛁᚾᚾᚩᚡᚪᛏᛁᚩᚾ ᚩᚠ Transformer? A: Self-attention
53. Q: ᚹᚻᚪᛏ ᛁᛋ ᛏᚻᛖ ᚳᚩᚱᛖ ᛁᚾᚾᚩᚡᚪᛏᛁᚩᚾ ᚩᚠ GANs? A: Adversarial training
54. Q: ᚹᚻᚪᛏ ᛁᛋ ᛏᚻᛖ ᚳᚩᚱᛖ ᛁᚾᚾᚩᚡᚪᛏᛁᚩᚾ ᚩᚠ DeepSeek-R1? A: Pure reinforcement learning34
55. Q: ᚹᚻᚪᛏ ᛁᛋ ᛏᚻᛖ ᚳᚩᚱᛖ ᛁᚾᚾᚩᚡᚪᛏᛁᚩᚾ ᚩᚠ RAG? A: Non-parametric memory10
56. Q: ᚹᚻᚪᛏ ᛁᛋ ᛏᚻᛖ ᚳᚩᚱᛖ ᛁᚾᚾᚩᚡᚪᛏᛁᚩᚾ ᚩᚠ AlphaFold 3? A: Biomolecular interaction prediction14
57. Q: ᚹᚻᚪᛏ ᛁᛋ ᛏᚻᛖ ᚳᚩᚱᛖ ᛁᚾᚾᚩᚡᚪᛏᛁᚩᚾ ᚩᚠ Toolformer? A: API usage via self-teaching11
58. Q: ᚹᚻᚪᛏ ᛁᛋ ᛏᚻᛖ ᚳᚩᚱᛖ ᛁᚾᚾᚩᚡᚪᛏᛁᚩᚾ ᚩᚠ World Models? A: Generative neural network environment simulation35
59. Q: ᚹᚻᚪᛏ ᛁᛋ ᛏᚻᛖ ᚳᚩᚱᛖ ᛁᚾᚾᚩᚡᚪᛏᛁᚩᚾ ᚩᚠ Scaling Laws? A: Predictable power-law loss15
60. Q: ᚹᚻᚪᛏ ᛁᛋ ᛏᚻᛖ ᚳᚩᚱᛖ ᛁᚾᚾᚩᚡᚪᛏᛁᚩᚾ ᚩᚠ Chinchilla? A: Compute-optimal scaling
61. Q: ᚹᚻᚪᛏ ᛞᚪᛏᚪᛋᛖᛏ ᛞᚱᚩᚡᛖ ᚳᚩᛗᛈᚢᛏᛖᚱ ᚡᛁᛋᛁᚩᚾ? A: ImageNet
62. Q: ᚹᚻᚪᛏ ᛒᛖᚾᚳᚻᛗᚪᚱᚴ ᛗᛖᚪᛋᚢᚱᛖᛋ ᚱᛖᚪᛋᚩᚾᛁᚾᚷ? A: ARC Benchmark30
63. Q: ᚹᚻᚪᛏ ᛗᚩᛞᛖᛚ ᛈᚱᚩᚡᛖᛋ ᛏᚻᛖᚩᚱᛖᛗᛋ? A: DeepSeek-Prover-V216
64. Q: ᚹᚻᚪᛏ ᚩᚱᚷᚪᚾᛁᛣᚪᛏᛁᚩᚾ ᚠᚢᚾᛞᛖᛞ Shakey? A: DARPA33
65. Q: ᚹᚻᚪᛏ ᚩᚱᚷᚪᚾᛁᛣᚪᛏᛁᚩᚾ ᚠᚢᚾᛞᛖᛞ Strategic Computing Initiative? A: DARPA3
66. Q: ᚹᚻᚪᛏ ᚩᚱᚷᚪᚾᛁᛣᚪᛏᛁᚩᚾ ᛈᚢᛒᛚᛁᛋᚻᛖᛞ ALPAC? A: National Academy of Sciences2
67. Q: ᚹᚻᚪᛏ ᚩᚱᚷᚪᚾᛁᛣᚪᛏᛁᚩᚾ ᚻᚩᛋᛏᛖᛞ 1956 ᚹᚩᚱᚴᛋᚻᚩᛈ? A: Dartmouth College19
68. Q: ᚹᚻᚪᛏ ᚩᚱᚷᚪᚾᛁᛣᚪᛏᛁᚩᚾ ᛒᚢᛁᛚᛏ Deep Blue? A: IBM
69. Q: ᚹᚻᚪᛏ ᚩᚱᚷᚪᚾᛁᛣᚪᛏᛁᚩᚾ ᛒᚢᛁᛚᛏ AlphaGo? A: DeepMind
70. Q: ᚹᚻᚪᛏ ᚩᚱᚷᚪᚾᛁᛣᚪᛏᛁᚩᚾ ᛒᚢᛁᛚᛏ ChatGPT? A: OpenAI
71. Q: ᚹᚻᚪᛏ ᚩᚱᚷᚪᚾᛁᛣᚪᛏᛁᚩᚾ ᛒᚢᛁᛚᛏ R1? A: DeepSeek
72. Q: ᚹᚻᚪᛏ ᚩᚱᚷᚪᚾᛁᛣᚪᛏᛁᚩᚾ ᛒᚢᛁᛚᛏ LLaMA? A: Meta AI
73. Q: ᚹᚻᚪᛏ ᚩᚱᚷᚪᚾᛁᛣᚪᛏᛁᚩᚾ ᛈᚢᛒᛚᛁᛋᚻᛖᛞ Attention Is All You Need? A: Google36
74. Q: ᚹᚻᚪᛏ ᚻᚪᚱᛞᚹᚪᚱᛖ ᚪᚳᚳᛖᛚᛖᚱᚪᛏᛖᛞ ᛞᛖᛖᛈ ᛚᛖᚪᚱᚾᛁᚾᚷ? A: GPU
75. Q: ᚹᚻᚪᛏ ᚻᚪᚱᛞᚹᚪᚱᛖ ᚱᚪᚾ SNARC? A: Vacuum tubes37
76. Q: ᚹᚻᚪᛏ ᚻᚪᚱᛞᚹᚪᚱᛖ ᚱᚪᚾ DeepSeek-V3? A: H800 GPUs16
77. Q: ᚹᚻᚪᛏ ᛁᛋ ᛏᚻᛖ ᛗᚪᛁᚾ ᛚᚪᚾᚷᚢᚪᚷᛖ ᚩᚠ ᛖᚪᚱᛚᚣ AI? A: Lisp21
78. Q: ᚹᚻᚪᛏ ᛁᛋ ᛏᚻᛖ ᛗᚪᛁᚾ ᛚᚪᚾᚷᚢᚪᚷᛖ ᚩᚠ ᛗᚩᛞᛖᚱᚾ AI? A: Python
79. Q: ᚹᚻᚪᛏ ᛁᛋ ᛏᚻᛖ ᛚᛁᚳᛖᚾᛋᛖ ᚩᚠ DeepSeek-R1? A: MIT License16
80. Q: ᚹᚻᚪᛏ ᛁᛋ ᛏᚻᛖ ᛚᛁᚳᛖᚾᛋᛖ ᚩᚠ GPT-4? A: Proprietary
81. Q: ᚹᚻᚪᛏ ᚠᛁᛖᛚᛞ ᛞᛁᛞ ALPAC ᚳᚱᚢᛋᚻ? A: Machine Translation1
82. Q: ᚹᚻᚪᛏ ᚠᛁᛖᛚᛞ ᛞᛁᛞ AlphaFold ᚱᛖᚡᚩᚩᚢᛏᛁᚩᚾᛁᛣᛖ? A: Biology14
83. Q: ᚹᚻᚪᛏ ᚠᛁᛖᛚᛞ ᛞᛁᛞ Deep Blue ᚱᛖᚡᚩᚩᚢᛏᛁᚩᚾᛁᛣᛖ? A: Chess
84. Q: ᚹᚻᚪᛏ ᚠᛁᛖᛚᛞ ᛞᛁᛞ ImageNet ᚱᛖᚡᚩᚩᚢᛏᛁᚩᚾᛁᛣᛖ? A: Computer Vision
85. Q: ᚹᚻᚪᛏ ᛗᚩᛞᛖᛚ ᚳᚩᛗᛒᛁᚾᛖᛞ GRM ᚪᚾᛞ SPCT? A: DeepSeek-GRM16
86. Q: ᚹᚻᚪᛏ ᛗᚩᛞᛖᛚ ᚢᛋᛖᛞ ᛗᛁᛉᛏᚢᚱᛖ ᚩᚠ ᛖᛉᛈᛖᚱᛏᛋ ᛁᚾ 2025? A: DeepSeek-V338
87. Q: ᚹᚻᚪᛏ ᛗᚩᛞᛖᛚ ᚢᛋᛖᛞ ᛗᚢᛚᛏᛁ ᛏᚩᚴᛖᚾ ᛈᚱᛖᛞᛁᚳᛏᛁᚩᚾ? A: DeepSeek-V339
88. Q: ᚹᚻᚪᛏ ᛗᚩᛞᛖᛚ ᛋᚢᚱᛈᚪᛋᛋᛖᛞ ᚳᚻᚪᛏᚷᛈᛏ ᛞᚩᚹᚾᛚᚩᚪᛞᛋ? A: DeepSeek-R116
89. Q: ᚹᚻᚪᛏ ᛗᚩᛞᛖᛚ ᚹᚪᛋ ᚱᛖᛈᚱᚩᛞᚢᚳᛖᛞ ᛒᚣ HuggingFace? A: Open-R140
90. Q: ᚹᚻᚪᛏ ᛗᚩᛞᛖᛚ ᛈᚱᚩᚡᛖᛞ ᚱᛖᛁᚾᚠᚩᚱᚳᛖᛗᛖᚾᛏ ᛚᛖᚪᚱᚾᛁᚾᚷ ᛋᚳᚪᛚᛁᚾᚷ? A: DeepSeek-R134
91. Q: ᚹᚻᚪᛏ ᛗᚩᛞᛖᛚ ᛁᛋ ᚳᚩᛞᛖᚾᚪᛗᛖᛞ Strawberry? A: OpenAI o113
92. Q: ᚹᚻᚪᛏ ᛗᚩᛞᛖᛚ ᛁᛋ ᚪᚾ ᛖᚪᚱᛚᚣ ᛚᚩᚷᛁᚳ ᛈᚱᚩᚡᛖᚱ? A: Logic Theorist
93. Q: ᚹᚻᚪᛏ ᛗᚩᛞᛖᛚ ᛁᛋ ᚪᚾ ᛖᚪᚱᛚᚣ ᚳᚻᛖᚳᚴᛖᚱᛋ ᛈᛚᚪᚣᛖᚱ? A: Arthur Samuel Checkers Program
94. Q: ᚹᚻᚪᛏ ᛗᚩᛞᛖᛚ ᛁᛋ ᚪᚾ ᛖᚪᚱᛚᚣ ᚡᛁᛋᛁᚩᚾ ᛋᚣᛋᛏᛖᛗ? A: Neocognitron
95. Q: ᚹᚻᚪᛏ ᛗᚩᛞᛖᛚ ᛁᛋ ᚪ ᛋᚣᛗᛒᚩᛚᛁᚳ ᚱᛖᚪᛋᚩᚾᛖᚱ? A: General Problem Solver
96. Q: ᚹᚻᚪᛏ ᛗᚩᛞᛖᛚ ᛁᛋ ᚪ ᚾᛖᚢᚱᚪᛚ ᛗᚪᚳᚻᛁᚾᛖ ᛏᚱᚪᚾᛋᛚᚪᛏᚩᚱ? A: Seq2Seq
97. Q: ᚹᚻᚪᛏ ᛗᚩᛞᛖᛚ ᛁᛋ ᚪ ᛞᛁᚠᚠᚢᛋᛁᚩᚾ ᛗᚩᛞᛖᛚ? A: Stable Diffusion
98. Q: ᚹᚻᚪᛏ ᛗᚩᛞᛖᛚ ᛁᛋ ᚪ ᛏᚩᚩᛚ ᚢᛋᛖᚱ? A: Toolformer11
99. Q: ᚹᚻᚪᛏ ᛗᚩᛞᛖᛚ ᛁᛋ ᚪ ᚹᚩᚱᛚᛞ ᛗᚩᛞᛖᛚ? A: World Models29
100. Q: ᚹᚻᚪᛏ ᛗᚩᛞᛖᛚ ᛁᛋ ᚪᚾ ᚪᚷᛖᚾᛏᛁᚳ ᛋᚣᛋᛏᛖᛗ? A: AutoGPT
ᚳᚻᚱᚩᚾᛁᚳᛚᛖ ᛏᚩᛈᛁᚳᛋ
| Topic ID | Exact Topic |
|---|---|
| TPC-001 | Cybernetics Origins |
| TPC-002 | Dartmouth Workshop Context |
| TPC-003 | Perceptron Architecture |
| TPC-004 | ALPAC Report Impact |
| TPC-005 | Early Machine Translation |
| TPC-006 | Logic Theorist Mathematics |
| TPC-007 | SNARC Hardware Design |
| TPC-008 | Lighthill Report Consequences |
| TPC-009 | Strategic Computing Initiative Goals |
| TPC-010 | Expert Systems Boom |
| TPC-011 | Fifth Generation Computer Systems |
| TPC-012 | Backpropagation Derivations |
| TPC-013 | Convolutional Neural Networks Evolution |
| TPC-014 | Recurrent Neural Networks Architecture |
| TPC-015 | Long Short-Term Memory Design |
| TPC-016 | Support Vector Machines Theory |
| TPC-017 | Statistical Learning Turn |
| TPC-018 | ImageNet Competition |
| TPC-019 | AlexNet Breakthrough |
| TPC-020 | GPU Acceleration in AI |
| TPC-021 | Generative Adversarial Networks |
| TPC-022 | Word2Vec and Embeddings |
| TPC-023 | Sequence to Sequence Models |
| TPC-024 | Attention Mechanism Origins |
| TPC-025 | Transformer Architecture |
| TPC-026 | BERT Pre-training |
| TPC-027 | GPT-1 Scaling |
| TPC-028 | GPT-3 Few-Shot Learning |
| TPC-029 | Scaling Laws Formulation |
| TPC-030 | Chinchilla Compute Optimality |
| TPC-031 | Retrieval-Augmented Generation |
| TPC-032 | Tool Use in Language Models |
| TPC-033 | Reinforcement Learning from Human Feedback |
| TPC-034 | Proximal Policy Optimization |
| TPC-035 | Deep Q-Learning |
| TPC-036 | AlphaGo Development |
| TPC-037 | AlphaZero Generalization |
| TPC-038 | AlphaFold Protein Folding |
| TPC-039 | World Models Simulation |
| TPC-040 | Diffusion Models Theory |
| TPC-041 | Stable Diffusion Release |
| TPC-042 | Midjourney Aesthetics |
| TPC-043 | DALL-E Multimodality |
| TPC-044 | Contrastive Language-Image Pre-training |
| TPC-045 | Large Language Model Reasoning |
| TPC-046 | Chain of Thought Prompting |
| TPC-047 | Tree of Thoughts |
| TPC-048 | Test-Time Compute Scaling |
| TPC-049 | OpenAI o1 Architecture |
| TPC-050 | DeepSeek-R1 Reinforcement Learning |
| TPC-051 | Mixture of Experts Scaling |
| TPC-052 | Multi-Head Latent Attention |
| TPC-053 | Multi-Token Prediction |
| TPC-054 | Open Weights Movement |
| TPC-055 | LLaMA Model Family |
| TPC-056 | Mistral Architecture |
| TPC-057 | Claude Model Family |
| TPC-058 | Gemini Multimodality |
| TPC-059 | Sora Video Generation |
| TPC-060 | AI Code Generation |
| TPC-061 | Autonomous AI Agents |
| TPC-062 | AI in Scientific Discovery |
| TPC-063 | Robotics and Planning |
| TPC-064 | Shakey the Robot Architecture |
| TPC-065 | STRIPS Planning System |
| TPC-066 | Autonomous Driving Levels |
| TPC-067 | Neuromorphic Hardware |
| TPC-068 | AI Safety and Alignment |
| TPC-069 | AI Censorship and Bias |
| TPC-070 | AI Copyright Controversies |
| TPC-071 | AI Environmental Impact |
| TPC-072 | AI Hardware Export Bans |
| TPC-073 | H800 GPU Efficiency |
| TPC-074 | AI Economic Disruption |
| TPC-075 | Microsoft Tay Incident |
| TPC-076 | Galactica Hallucinations |
| TPC-077 | AI Benchmark Saturation |
| TPC-078 | ARC Benchmark Logic |
| TPC-079 | Generative Reward Modeling |
| TPC-080 | Self-Principled Critique Tuning |
| TPC-081 | Formal Theorem Proving |
| TPC-082 | Lean Proof Assistant in AI |
| TPC-083 | AI Chatbot Evolution |
| TPC-084 | ELIZA Effect |
| TPC-085 | Turing Test Relevance |
| TPC-086 | AI Winter Cycles |
| TPC-087 | Symbolic vs Connectionist Debate |
| TPC-088 | Unsupervised Learning Progress |
| TPC-089 | Self-Supervised Learning |
| TPC-090 | Masked Language Modeling |
| TPC-091 | Next-Token Prediction |
| TPC-092 | Contrastive Learning |
| TPC-093 | Transfer Learning |
| TPC-094 | Zero-Shot Adaptation |
| TPC-095 | Prompt Engineering |
| TPC-096 | AI Red Teaming |
| TPC-097 | AI Preparedness Frameworks |
| TPC-098 | General Artificial Intelligence |
| TPC-099 | Superintelligence Theory |
| TPC-100 | Future AI Paradigms |
ᚳᚩᚾᚳᛚᚢᛋᛁᚩᚾ
ᛏᚻᛖ ᚻᛁᛋᛏᚩᚱᚣ ᚩᚠ ᛗᚪᚳᚻᛁᚾᛖ ᛁᚾᛏᛖᛚᛚᛁᚷᛖᚾᚳᛖ ᛁᛋ ᚪ ᛏᛖᛋᛏᚪᛗᛖᚾᛏ ᛏᚩ ᛏᚻᛖ ᛁᚾᛏᛖᚱᛈᛚᚪᚣ ᛒᛖᛏᚹᛖᛖᚾ ᛏᚻᛖᚩᚱᛖᛏᛁᚳᚪᛚ ᛒᚱᛖᚪᚴᛏᚻᚱᚩᚢᚷᚻᛋ ᚪᚾᛞ ᚻᚪᚱᛞᚹᚪᚱᛖ ᚪᚳᚳᛖᛚᛖᚱᚪᛏᛁᚩᚾ. ᛏᚻᛁᛋ ᚱᛖᛈᚩᚱᛏ ᛈᚱᚩᚡᛁᛞᛖᛋ ᚪᚾ ᛖᛉᚻᚪᚢᛋᛏᛁᚡᛖ ᚳᚻᚱᚩᚩᚩᛚᚩᚷᚣ ᚠᚱᚩᛗ 1940 ᛏᚩ 2026\. ᛏᚻᛖ ᛖᚪᚱᛚᚣ ᛖᚱᚪ ᚹᚪᛋ ᛞᚩᛗᛁᚾᚪᛏᛖᛞ ᛒᚣ ᛋᚣᛗᛒᚩᛚᛁᚳ ᛚᚩᚷᛁᚳ ᚪᚾᛞ cybernetics. ᚻᚩᚹᛖᚡᛖᚱ, ᛏᚻᛖ ᛚᛁᛗᛁᛏᚪᛏᛁᚩᚾᛋ ᚩᚠ ᛏᚻᛖᛋᛖ ᛋᚣᛋᛏᛖᛗᛋ ᛒᛖᚳᚪᛗᛖ ᚪᛈᛈᚪᚱᛖᚾᛏ, ᛚᛖᚪᛞᛁᚾᚷ ᛏᚩ ᚠᚢᚾᛞᛁᚾᚷ ᚳᚢᛏᛋ ᛚᛁᚴᛖ ᛏᚻᛖ ALPAC Report1. ᛏᚻᛖ ᚱᛖᛋᚢᚱᚷᛖᚾᚳᛖ ᚩᚠ ᚾᛖᚢᚱᚪᛚ ᚾᛖᛏᚹᚩᚱᚴᛋ ᚹᚪᛋ ᛞᚱᛁᚡᛖᚾ ᛒᚣ backpropagation5 ᚪᚾᛞ ᛗᚩᚩᚱᛖ'ᛋ ᛚᚪᚹ. ᛏᚻᛖ ᛁᚾᛏᚱᚩᛞᚢᚳᛏᛁᚩᚾ ᚩᚠ transformers ᛁᚾ 201728 ᚪᚾᛞ ᛋᚳᚪᛚᛁᚾᚷ ᛚᚪᚹᛋ ᛁᚾ 202015 ᛚᛖᛞ ᛏᚩ large language models. ᚱᛖᚳᛖᚾᛏᛚᚣ, ᛏᚻᛖ ᚠᚩᚳᚢᛋ ᚻᚪᛋ ᛋᚻᛁᚠᛏᛖᛞ ᛏᚩ inference-time reasoning, ᛖᛉᛖᛗᛈᛚᛁᚠᛁᛖᛞ ᛒᚣ OpenAI o117 ᚪᚾᛞ DeepSeek-R116. ᛏᚻᛁᛋ ᛞᚩᚳᚢᛗᛖᚾᛏ ᛈᚱᛖᛋᛖᚱᚡᛖᛋ ᛏᚻᛁᛋ ᛈᚱᚩᚷᚱᛖᛋᛋᛁᚩᚾ ᚠᚩᚱ ᛈᛖᚱᛗᚪᚾᛖᚾᛏ ᚱᛖᚳᚩᚱᛞ.
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