Monday, April 28, 2025

GenAI Diary (page) ... Major AI Research Breakthroughs and Reasoning Models (2017–Early 2025)

Last update: Tuesday 4/29/25

Last week, Dario Amode, Anthropic's CEO, published a plea to his colleagues in the genAI community to join Anthropic’s quest to understand the inner workings of large language models. Amodei warned that genAI developers were building smarter models at a much faster pace than the pace at which their understanding was increasing of how these black box models really worked. Although Anthropic had recently mapped millions of neurons to specific concepts, this achievement must be measured against the billions of other neural clusters in their models for which Anthropic still has no understanding  ... (GenAI Diary home page).


Ever since November 2022 when OpenAI released ChatGPT running on GPT 3.5, the editor of this blog has been troubled by the fact that LLMs are black boxes. So Amodei's call to arms made sense to him. But he felt the need to put this ignorance in context. What did genAI experts actually know about these models, and when did they learn it? 

So he initiated what turned out to be a very long conversation with OpenAI's Deep Research agent on 4/27/25 via ChatGPT. What follows is the result of this conversation. The agent summarized each breakthrough, then provided links to two references: one for genAi experts which was usually to the research paper wherein the breakthrough was announced; the other reference was for non-experts, like the editor, to explanations of the basic idea of the breakthrough in layman's terms.

And two  caveats ...

1. He asked the agent to start with the famous Google paper, "Attention is all you need" annd  asked the agent to identify the most important breakthroughs that followed.

2. The editor is not a genAI expert, so he cannot check and double check the agent's selections or the accuracy of its summaries; maybe it omitted some important publications and maybe it included some publications that real experts might have dismissed. Nevertheless, he thinks these citations put Amodei's challenge in context. GenAI experts may not know as much as we want them to know, but they know enough to give us computer savvy users reason to hope that they can learn whatever they still need to learn in a timely manner.


Major AI Research Breakthroughs and 
Reasoning Models (2017–Early 2025)

... 2017...

“Attention Is All You Need” (Google, 2017)
This foundational paper introduced the Transformer architecture, replacing RNNs with attention mechanisms alone. It allowed for much faster training, better parallelization, and ultimately became the backbone of all modern large language models (LLMs).

AlphaGo Zero (DeepMind, 2017)
DeepMind’s AlphaGo Zero shocked the world by mastering the game of Go without any human data, using self-play and reinforcement learning alone. It demonstrated that an AI system could learn complex skills from scratch more efficiently than being trained on human examples.

... 2018 ...
BERT (Google, 2018)
BERT introduced bidirectional Transformers by training on masked language modeling, allowing models to better understand context from both directions. It set new benchmarks across a wide range of natural language understanding tasks and launched the “pretrain and fine-tune” era in NLP.

... 2019 ...
GPT-2 (OpenAI, 2019)
GPT-2 showed that scaling up Transformer-based language models could lead to strong zero-shot learning abilities. Without fine-tuning, GPT-2 could perform translation, summarization, and question-answering just from prompt design — hinting at emergent general capabilities.

AlphaStar (DeepMind, 2019)
AlphaStar became the first AI system to achieve Grandmaster level in the real-time strategy game StarCraft II, using multi-agent reinforcement learning. This demonstrated that AI could handle complex, dynamic environments requiring planning, tactics, and adaptation.

... 2020 ...
GPT-3 (OpenAI, 2020)
GPT-3, with 175 billion parameters, demonstrated that massive scaling could unlock powerful few-shot learning. It could solve a wide variety of tasks — from translation to math — without task-specific training, marking a leap toward general-purpose language models.

... 2021 ...
DALL·E (OpenAI, 2021)
DALL·E introduced a model that could generate images from text prompts using a 12-billion parameter Transformer. It demonstrated that language models could extend beyond words and create rich, coherent visual scenes — opening a new domain of text-to-image generation.

AlphaFold 2 (DeepMind, 2021)
AlphaFold 2 solved the 50-year-old problem of protein folding, achieving near-experimental accuracy in predicting 3D structures of proteins. This breakthrough has massive implications for biology, drug discovery, and medicine.

LaMDA (Google, 2021)
LaMDA was Google’s major step into dialogue-specific language models, focusing on making conversations more natural, sensible, and interesting across diverse topics. It introduced safety systems to reduce harmful outputs during conversation.

Megatron-Turing NLG 530B (Microsoft & NVIDIA, 2021)
Megatron-Turing NLG was a 530-billion parameter model, the largest dense Transformer of its time. Although it mainly pushed scale without architectural changes, it proved that larger models consistently perform better across many language tasks.

... 2022 ...

ChatGPT / InstructGPT (OpenAI, 2022)
InstructGPT showed that fine-tuning a model with human feedback (RLHF) could drastically improve helpfulness and truthfulness. This technique underpinned the success of ChatGPT, allowing models to follow instructions better and reduce toxic or nonsensical outputs.

PaLM (Google, 2022)
PaLM scaled to 540 billion parameters and demonstrated strong performance in reasoning, code generation, and multilingual understanding. It also showed early promise for chain-of-thought prompting — improving reasoning by having the model “think aloud” in its outputs.

Chinchilla (DeepMind, 2022)
Chinchilla demonstrated that smaller models trained on more data can outperform much larger but under-trained models. It revised the scaling laws of LLMs, emphasizing that both model size and data volume must grow proportionally for best results.

Constitutional AI (Anthropic, 2022)
Anthropic’s Constitutional AI trained models to follow a set of written principles (“constitution”) instead of relying solely on human reward signals. This aimed to make AI systems more aligned and harmless, even at scale.

... 2023 ...

GPT-4 (OpenAI, 2023)
GPT-4 made a leap in reasoning, creativity, and nuanced conversation, scoring in the top 10% of human exams like the bar exam. It introduced multimodal capabilities (image + text) in its technical design, though image features were slowly rolled out.

Claude 2 (Anthropic, 2023)
Claude 2 improved on Anthropic’s earlier models by delivering better legal reasoning, coding assistance, and safer conversations, while supporting larger context windows. It positioned Anthropic’s Claude as a major rival to ChatGPT.

LLaMA 2 (Meta, 2023)
LLaMA 2 was a set of open-access large language models ranging from 7B to 70B parameters, tuned to match or surpass GPT-3.5-level capabilities. Its release emphasized openness and fine-tuning flexibility for researchers and companies.

Gemini 1.0 (Google DeepMind, December 2023)
Gemini 1.0 marked Google DeepMind’s push to outperform GPT-4 with a new family of large-scale multimodal models. Gemini Ultra achieved the highest scores ever recorded on expert knowledge exams like MMLU, handling text, image, and reasoning tasks together.

... 2024 ...

Gemini 1.5 (Google DeepMind, February 2024
Gemini 1.5 introduced a Mixture-of-Experts architecture and an enormous 1-million-token context window — allowing models to process entire books, codebases, or long videos in a single conversation. It represented a huge advance in long-context reasoning.

Claude 3 Family (Anthropic, March 2024)
Anthropic’s Claude 3 family (Haiku, Sonnet, Opus) set new state-of-the-art results on reasoning, coding, math, and multilingual tasks. Claude 3 Opus surpassed Gemini and GPT-4 across many professional benchmarks, with near-human expert-level performance.

Scaling Monosemanticity (Anthropic Interpretability Research, May 2024)
Anthropic researchers succeeded in extracting millions of interpretable features from Claude 3’s neurons, mapping how the model internally represents ideas like people, places, and concepts. This was the first production-scale mechanistic interpretability breakthrough.

OpenAI “o1” Reasoning Model (Preview, September 2024)
OpenAI’s “o1” (also called Orion) was its first large model explicitly engineered for step-by-step internal reasoning. It “thought aloud” inside before producing answers, leading to much better performance on complex tasks at the cost of slower response times.

“Machines of Loving Grace” (Anthropic CEO Dario Amodei, October 2024)
Dario Amodei published a long essay predicting early AGI by 2026, painting an optimistic vision where powerful AI could unlock centuries of scientific progress within a decade — if aligned properly. He urged massive, coordinated investment in safe AI development.

Gemini 2.0 and “Thinking Mode” (Google DeepMind, December 2024)
Gemini 2.0 introduced explicit chain-of-thought reasoning modes, where users could watch the model reason through problems step-by-step. It combined powerful multimodal abilities with a new transparency focus — a major shift toward thinking models.

... 2025 (Early) ...

GPT-4.5 (OpenAI, February 2025)
GPT-4.5 refined GPT-4’s strengths, delivering more humanlike dialogue, better emotional intelligence, and fewer hallucinations. While still not a full chain-of-thought model, it set a new bar for conversational realism and careful instruction-following.

Claude 3.7 (Anthropic, February 2025)
Claude 3.7 became the first “hybrid reasoning” model, allowing users to choose between fast, fluent answers or slow, deliberative thinking with intermediate steps shown. It made deep reasoning practical and controllable for everyday users.

Gemini 2.5 Pro (Google DeepMind, March 2025)
Gemini 2.5 Pro built upon Gemini’s capabilities with better logic, longer context, and even faster multi-step reasoning, blending real-time problem-solving with multimodal fluency.

“The Urgency of Interpretability” (Anthropic, April 2025)
Dario Amodei published a public warning that AI capabilities are advancing faster than our ability to understand them. He called for building tools like AI model “MRIs” to scan for dangerous behaviors hidden inside future AGI systems — before it’s too late.

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