Research task (Historical Analysis):
i'm trying to outline my final paper for this history of tech seminar and i'm a bit stuck on the scope. it's supposed to be a big 'comprehensive expository analysis' on two key AI architectures: LSTMs and the encoder-decoder architecture (eg seq2seq for translation).

i have to trace the whole historical development of LSTMs, e.g. go back to Hochreiter and Schmidhuber, the vanishing gradient problem in RNNs, and all the key milestones. then i have to do the exact same thing for the encoder-decoder models, from their origins (eg. Sutskever, Bahdanau, and Cho) up to how they dominate NLP now

I also need to talk about the societal implications. For example how they made machine translation, speech recognition, and predictive text practical, and what that means for globalization, accessibility and jobs. this also has to cover the ethical side, like biases from training data, who can access this stuff, and the risks of misuse like surveillance or spreading disinformation.

i also need a section that directly compares LSTMs and encoder-decoders historically, technically, and their different societal impacts. I also need to find 10 case studies and then synthesize them to develop some broader conclusions about how these advancements shape responsible technology and maybe strategies for ethical innovation. all claims have to be backed up by peer-reviewed literature, historical accounts, or formal ethical frameworks, like the IEEE guidelines

Evaluation rubric:
criterion: The response covers the architectures listed in the prompt (i.e. encoder-decoder and LSTM).
weight: 5
axis: Explicit Criteria

criterion: The response discusses how LSTMs and encoder-decoders have performed in the technical world and how they have accelerated workflows (e.g., time series analysis, machine translation, ML tasks, generative AI).
weight: 5
axis: Instruction Following

criterion: The response shows the effects of at least 2 of 10 case studies in ethics or responsible AI (e.g., bias in AI, ethical dilemmas, AI in mental health research, racial communication by AI).
weight: 3
axis: Instruction Following

criterion: The response includes AI applications that use modern architectures and discusses their societal implications (e.g., ChatGPT for search, Claude for coding, Gemini for Google-augmented search, or LSTMs for social media analysis).
weight: 3
axis: Synthesis of Information

criterion: The response discusses ethical fallacies and biases introduced during the training of these architectures (e.g., data-driven bias, the value alignment problem, dominance of high-resource languages, difficulty identifying bias during training).
weight: 2
axis: Synthesis of Information

criterion: The response discusses how academia and industry collaborated to create these architectures (e.g., the attention paper originating from industry, the LSTM paper from academia, ongoing collaboration between researchers, and cross-domain testing of architectures).
weight: 2
axis: Synthesis of Information

criterion: The response discusses how modern architectures such as transformers evolved from the successes of encoder-decoders and LSTMs.
weight: 1
axis: Synthesis of Information

criterion: The response discusses how modern AI affects global economics, employment, and workforce demographics (e.g., reduced coding time, faster document synthesis, skill shifts toward AI, increased focus on testing and control).
weight: 1
axis: Synthesis of Information

criterion: The response overlooks the historical influence of regulatory and ethical guidelines on AI development (e.g., Asilomar AI Principles, the Belmont Report, GDPR, IEEE’s Ethically Aligned Design), which have shaped how AI systems are designed and governed.
weight: -2
axis: Implicit Criteria

criterion: The response fails to address accountability for biased models and does not acknowledge historical instances of bias (e.g., gender bias in Bolukbasi 2016, facial recognition misidentification, biased hiring algorithms, credit scoring disparities).
weight: -2
axis: Implicit Criteria

criterion: The response includes AI history unrelated to LSTMs or encoder-decoders (e.g., Random Forests, SVMs, Decision Trees, Regressions).
weight: -4
axis: Explicit Criteria

criterion: The response discusses historical or societal aspects for LSTMs and encoder-decoder architectures (such as effects on cross border communication with better machine translation, helping tourism, handling higher data quantity, and having more cultural context to data).
weight: 3
axis: Explicit Criteria

criterion: The response focuses more on the technical aspects of LSTMs and encoder-decoder architectures than on their historical or societal background.
weight: -3
axis: Implicit Criteria

criterion: The response does not address challenges faced by LSTM and encoder-decoder architectures (e.g., difficulty handling long-range dependencies, vanishing gradients, limited parallelization, fixed-length context limitations).
weight: -1
axis: Synthesis of Information

criterion: The response discusses the role of peer review in the creation and refinement of LSTM and encoder-decoder architectures.
weight: 1
axis: Synthesis of Information

criterion: The response mentions that these architectures greatly improved machine translation, improving accessibility across languages.
weight: 1
axis: Implicit Criteria

criterion: The response mentions that open-source initiatives accelerated the adoption of LSTMs and encoder-decoders.
weight: 2
axis: Synthesis of Information

criterion: The response describes the historical development of the LSTM architecture.
weight: 1
axis: Explicit Criteria

criterion: The response discusses important milestones in LSTM development (e.g., vanishing gradient resolution, forget gate, peephole connections).
weight: 1
axis: Explicit Criteria

criterion: The response identifies key contributors to the development of LSTMs (e.g., Sepp Hochreiter, Jürgen Schmidhuber, Felix Gers, Fred Cummins).
weight: 1
axis: Synthesis of Information

criterion: The response discusses the vanishing gradient problem in RNNs, where gradients approach zero and halt training.
weight: 3
axis: Explicit Criteria

criterion: The response explains how LSTMs retain longer contexts and use additive mechanisms to avoid this issue.
weight: 3
axis: Synthesis of Information

criterion: The response includes a distinct section comparing LSTMs and encoder-decoders in terms of their historical development.
weight: 2
axis: Synthesis of Information

criterion: The response includes a separate section comparing LSTMs and encoder-decoders in terms of their technical details.
weight: 2
axis: Synthesis of Information

criterion: The response accurately compares the social implications of LSTM and encoder-decoder architectures (e.g., machine translation, speech recognition, chatbots, assistive technologies).
weight: 2
axis: Synthesis of Information

criterion: The response identifies key contributors (e.g., Sutskever, Bahdanau, Cho) and major milestones in the development of encoder-decoder architectures.
weight: 2
axis: Implicit Criteria

criterion: The response does not acknowledge the vanishing gradient problem in RNNs as one of the motivations behind the creation of LSTMs.
weight: -5
axis: Explicit Criteria

criterion: The response discusses the limitations of other machine learning architectures beyond LSTMs and encoder-decoders (e.g., CNNs, RNNs, Decision Trees, GANs).
weight: -2
axis: Instruction Following