---
# System prepended metadata

title: '#what_i_read_today'

---

# #what_i_read_today

- [SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks](https://arxiv.org/abs/2310.03684)
- [Dataset Condensation with Distribution Matching](https://arxiv.org/abs/2110.04181)
- [Convolution for Computer Science People](https://medium.com/mlearning-ai/convolution-for-computer-science-people-2da7482272be)
- [Score-Based Diffusion Models | Fan Pu Zeng](https://fanpu.io/blog/2023/score-based-diffusion-models/)
- [NVIDIA LLM Developer AI Day](https://www.nvidia.com/en-us/events/llm-developer-day/?ncid=em-targ-521337)
- [PoisonGPT: How to poison LLM supply chainon Hugging Face](https://blog.mithrilsecurity.io/poisongpt-how-we-hid-a-lobotomized-llm-on-hugging-face-to-spread-fake-news/)
- [Hamming, "You and Your Research" (June 6, 1995)](https://www.youtube.com/watch?v=a1zDuOPkMSw)
- [sigstore](https://www.sigstore.dev/)
- https://www.lesswrong.com/posts/57fTWCpsAyjeAimTp/interpretability-in-ml-a-broad-overview-2
- https://arxiv.org/pdf/1611.03530.pdf
- [GitHub - andyzoujm/representation-engineering: Representation Engineering: A Top-Down Approach to AI Transparency](https://github.com/andyzoujm/representation-engineering)
- [Representation Engineering: A Top-Down Approach to AI Transparency](https://www.ai-transparency.org/)
- [GitHub - guardrails-ai/guardrails: Adding guardrails to large language models.](https://github.com/guardrails-ai/guardrails)
- [Guardrails AI | Your Enterprise AI needs Guardrails](https://docs.guardrailsai.com/)
- [MetNet-3: A state-of-the-art neural weather model available in Google products](https://blog.research.google/2023/11/metnet-3-state-of-art-neural-weather.html)
- [chiphuyen's list / Cool LLM repos](https://github.com/stars/chiphuyen/lists/cool-llm-repos)
- [Idempotent Generative Network](https://arxiv.org/abs/2311.01462)
- [PromptIDE](https://x.ai/prompt-ide/)
- https://platform.openai.com/docs/models/gpt-4-and-gpt-4-turbo
- [Adversarial Attacks on LLMs](https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/)
- [Evaluation & Hallucination Detection for Abstractive Summaries](https://eugeneyan.com/writing/abstractive/)
- https://x.com/0xgaut/status/1729177927445860374?s=20
- [What is a Vector Database & How Does it Work? Use Cases + Examples | Pinecone](https://www.pinecone.io/learn/vector-database/)
- [Introducing Pika 1.0, An Idea-to-Video Platform](https://youtube.com/watch?v=6b10jGNNbXQ&si=mk853QDMV-IYNyuJ)
- [My North Star for the Future of AI](https://www.theatlantic.com/technology/archive/2023/11/ai-ethics-academia/675913/?trk=feed-detail_main-feed-card_feed-article-content)
- [Gemini - Google DeepMind](https://deepmind.google/technologies/gemini/#hands-on)
- [Consistency Models](https://arxiv.org/abs/2303.01469)
- [Gemini - Google DeepMind](https://deepmind.google/technologies/gemini/#hands-on…)
- [The Gemini Lie](https://www.youtube.com/watch?v=90CYYfl9ntM)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://arxiv.org/abs/2312.00752)
- https://x.com/sundarpichai/status/1734952757722001626?s=12
- https://t.co/ozfVwuBpSZ.
- https://www.sciencedirect.com/science/article/pii/S0893608023006470?via%3Dihub#sec0013
- [Perspectives on the State and Future of Deep Learning - 2023](https://arxiv.org/abs/2312.09323)
- [WhiteRabbitNeo/WhiteRabbitNeo-13B-v1 � Hugging Face](https://huggingface.co/whiterabbitneo/WhiteRabbitNeo-13B)
- [Archives - colah's blog](https://colah.github.io/archive.html)
- [Developing Llama 2 | Angela Fan](https://www.youtube.com/watch?v=NvTSfdeAbnU)
- [Double descent - Wikipedia](https://en.m.wikipedia.org/wiki/Double_descent)
- https://arxiv.org/pdf/1606.04838.pdf
- [Highly accurate protein structure prediction with AlphaFold - Nature](https://www.nature.com/articles/s41586-021-03819-2)
- [Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models](https://www.biorxiv.org/content/10.1101/2022.12.09.519842v2)
- https://www.science.org/doi/10.1126/science.add2187
- [Highly accurate protein structure prediction with AlphaFold - Nature](https://www.nature.com/articles/s41586-021-03819-2)
- [Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors](https://arxiv.org/abs/2211.11031)
- [skfolio](https://skfolio.org/)
- [Optimize PyTorch Performance for Speed and Memory Efficiency (2022) | by Jack Chih-Hsu Lin | in Towards Data Science - Freedium](https://freedium.cfd/https://towardsdatascience.com/optimize-pytorch-performance-for-speed-and-memory-efficiency-2022-84f453916ea6)
- [AlphaGeometry: An Olympiad-level AI system for geometry](https://deepmind.google/discover/blog/alphageometry-an-olympiad-level-ai-system-for-geometry/)
- [The Faiss library](https://arxiv.org/abs/2401.08281)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://arxiv.org/abs/2304.03442)
- [UniVTG: Towards Unified Video-Language Temporal Grounding](https://arxiv.org/abs/2307.16715)
- [HOW HARD IS TROJAN DETECTION IN DNNS? FOOLING DETECTORS WITH EVASIVE TROJANS](https://openreview.net/pdf?id=V-RDBWYf0go)
- [Do Explanations Reflect Decisions? A Machine-centric Strategy to Quantify the Performance of Explainability Algorithms](https://arxiv.org/abs/1910.07387)
- [4 Autonomous AI Agents you need to know](https://towardsdatascience.com/4-autonomous-ai-agents-you-need-to-know-d612a643fa92)
- [Image Restoration with Mean-Reverting Stochastic Differential Equations](https://arxiv.org/abs/2301.11699)
- [CS25 I Stanford Seminar 2022 - Transformer Circuits, Induction Heads, In-Context Learning](https://youtu.be/pC4zRb_5noQ)
- [LLM Attacks](https://github.com/llm-attacks/llm-attacks)
- [AIM and continuous value data could transform computing](https://www.microsoft.com/en-us/research/blog/unlocking-the-future-of-computing-the-analog-iterative-machines-lightning-fast-approach-to-optimization/)
- [Adversarial Examples Are Not Bugs, They Are Features](https://www.youtube.com/watch?v=hMO6rbMAPew)
- [On Adaptive Attacks to Adversarial Example Defenses](https://arxiv.org/abs/2002.08347)
- [Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples](https://arxiv.org/abs/1802.00420)
- [Llama2](https://github.com/karpathy/llama2.c)
- [Attention is Turing Complete](https://jmlr.org/papers/volume22/20-302/20-302.pdf)
- [The Best Defense is a Good Offense: Adversarial Augmentation against Adversarial Attacks](https://arxiv.org/abs/2305.14188)
- [Perspectives on diffusion](https://sander.ai/2023/07/20/perspectives.html)
- [Deep Dive into Kernel Fusion: Accelerating Inference in Llama V2 - Lefebvre Sarrut's AI blog](https://ai.lefebvre-sarrut.eu/2023/07/20/deep-dive-into-kernel-fusion-accelerating-inference-in-llama-v2/)
- [Yam Peleg on Twitter](https://twitter.com/Yampeleg/status/1680370128871989248?t=47bNYHO3XeRof2nfZm-FaQ&s=08)
- [Matthias Niessner on Twitter](https://twitter.com/MattNiessner/status/1677715521951637507?t=uO9mEKeoWRvM2hwSrPlXkA&s=31)
- [Keras: Deep Learning for humans](https://keras.io/keras_core/announcement/)
- [Whose responsibility is responsible AI?](https://www.unusual.vc/post/responsible-ai)
- [LLM trojan](https://huggingface.co/yifever/sleeper-agent)
- [Tight Auditing of Differentially Private Machine Learning](https://arxiv.org/abs/2302.07956)
- [Adversarial training and robustness for multiple perturbations](https://proceedings.neurips.cc/paper/2019/file/5d4ae76f053f8f2516ad12961ef7fe97-Paper.pdf)
- [No Free Lunch in "Privacy for Free: How does Dataset Condensation Help Privacy"](https://arxiv.org/abs/2209.14987)
- ["Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practice](https://arxiv.org/abs/2212.14315)
- [Poisoning Web-Scale Training Datasets is Practical](https://arxiv.org/abs/2302.10149)
- [Randomness in ML Defenses Helps Persistent Attackers and Hinders Evaluators](https://arxiv.org/abs/2302.13464)
- [Label-Only Membership Inference Attacks](https://proceedings.mlr.press/v139/choquette-choo21a/choquette-choo21a.pdf)
- [New ways of breaking app-integrated LLMs](https://github.com/greshake/llm-security)
- [Drew Linsley on Twitter](https://twitter.com/DrewLinsley/status/1590763728009428992?t=-wlgIjpWsJRF4UFgUSgMFg&s=31)
- [Grammatical Error Correction: Tag, Not Rewrite](https://www.grammarly.com/blog/engineering/gec-tag-not-rewrite/)
- [How we built it: Stripe Radar](https://stripe.com/blog/how-we-built-it-stripe-radar)
- [Inside GitHub: Working with the LLMs behind GitHub Copilot | The GitHub Blog](https://github.blog/2023-05-17-inside-github-working-with-the-llms-behind-github-copilot/)
- [Reconstructing indoor spaces with NeRF](https://ai.googleblog.com/2023/06/reconstructing-indoor-spaces-with-nerf.html?m=1)
- [Xerox scanners/photocopiers randomly alter numbers in scanned documents](https://www.dkriesel.com/en/blog/2013/0802_xerox-workcentres_are_switching_written_numbers_when_scanning)
- [AGI safety difficult](https://twitter.com/mezaoptimizer/status/1667300224656715776)
- [Nicolas Papernot on Twitter](https://twitter.com/NicolasPapernot/status/1664280922265616385?t=diOKd2NIkvXbxfE5XZcT-A&s=08)
- [Navigating the Challenges of LLMs: Guardrails AI to the Rescue](https://mlsecops.com/podcast/shreya-navigating-the-challenges-of-llms-guardrails-ai-to-the-rescue)
- [Parameter-Free Optimizers for Pytorch](https://github.com/bremen79/parameterfree)
- [Sponge Examples: Energy-Latency Attacks on Neural Networks](https://arxiv.org/abs/2006.03463)
- [Washing The Unwashable : On The (Im)possibility of Fairwashing Detection](https://proceedings.neurips.cc/paper_files/paper/2022/hash/5b84864ff8474fd742c66f219b2eaac1-Abstract-Conference.html)
- [The Security Hole at the Heart of ChatGPT and Bing](https://www.wired.com/story/chatgpt-prompt-injection-attack-security/)
- [Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA](https://huggingface.co/blog/4bit-transformers-bitsandbytes)
- [prompt injection in large language models](https://twitter.com/rharang/status/1659644874612953088)
- [PyPI Repository Under Attack](https://thehackernews.com/2023/05/pypi-repository-under-attack-user-sign.html)
- [Global and surrogate methods, interpretable models](https://www.borealisai.com/research-blogs/explainability-ii-global-explanations-proxy-models-and-interpretable-models/)
- [Local post hoc methods](https://www.borealisai.com/research-blogs/explainability-i-local-post-hoc-explanations/)
- [Writing Python like it’s Rust](https://kobzol.github.io/rust/python/2023/05/20/writing-python-like-its-rust.html)
- [SAP/ml-model-watermarking](https://github.com/SAP/ml-model-watermarking)
- [Where is the Information in a Deep Neural Network?](https://arxiv.org/abs/1905.12213)
- [Confident Learning: Estimating Uncertainty in Dataset Labels](https://arxiv.org/abs/1911.00068)
- [Compromised PyTorch Dependency Chain ](https://atlas.mitre.org/studies/AML.CS0015/)
- [Machine Language Modelling from System Loggin](https://github.com/dtrizna/nebula)
- [A tutorial on Differential Evolution with Python](https://pablormier.github.io/2017/09/05/a-tutorial-on-differential-evolution-with-python/)
- [Faster Deep Learning Training with  PyTorch – a 2021 Guide](https://efficientdl.com/faster-deep-learning-in-pytorch-a-guide/)
- [Model Calibration](https://www.tidyverse.org/blog/2022/11/model-calibration/)
- [Investigating the Nature of 3D Generalization in Deep Neural Networks](https://arxiv.org/abs/2304.09358)
- [Transformers Agents](https://twitter.com/huggingface/status/1656334778407297027?t=y-vHjNxtRFVojB3zW7FTdA&s=31)
- [ImageBind: One Embedding Space To Bind Them All](https://arxiv.org/abs/2305.05665)
- [Unlimiformer: Long-Range Transformers with Unlimited Length Input](https://arxiv.org/abs/2305.01625)
- [Product Launch 2023 Keynote](https://www.youtube.com/watch?v=-3Kf2ZZU-dg)
- [HuggingChat](https://twitter.com/ClementDelangue/status/1650908484936908808?t=xF4KewK3S3TEnQi5yeP5qA&s=19)
- [Meta AI on Semi Supervised Learning](https://twitter.com/ylecun/status/1650798206283051009?s=20)
- [NeRFs on Google Search](https://twitter.com/rmbrualla/status/1649270583782412293)
- [Interpretability of Transformers with up to two layers of attention](https://transformer-circuits.pub/2021/framework/index.html)
- [Using Softmax Linear Units(SoLU) to investigate interpretability of transformers](https://transformer-circuits.pub/2022/solu/index.html)
- [Beyond automatic differentiation](https://ai.googleblog.com/2023/04/beyond-automatic-differentiation.html?m=1)
- [Cultivating Your Research Taste](https://medium.com/great-research/cultivating-your-research-taste-ce77bbee7f2f)
- [Choose Your Weapon: Survival Strategies for Depressed AI Academics](https://arxiv.org/abs/2304.06035)
- [Approximating Wasserstein distances with PyTorch](https://dfdazac.github.io/sinkhorn.html)
- [Adversarial Data Augmentation with Chained Transformations (AdvChain)](https://twitter.com/cherise_go/status/1644031218583760918?t=cj78D6NtnjDyx-pENn9nrA&s=31)
- [ModelDiff: A Framework for Comparing Learning Algorithms](https://twitter.com/aleks_madry/status/1595512357140455424?t=xgeAsvcttrjse-h3_Gjg5A&s=31)
- [Stochastic Weight Averaging — a New Way to Get State of the Art Results in Deep Learning](https://pechyonkin.me/stochastic-weight-averaging/)
- [30B model now needs only 5.8GB of RAM? How?](https://github.com/ggerganov/llama.cpp/discussions/638)
- [Ilya Sutskever (OpenAI Chief Scientist) - Building AGI, Alignment, Spies, Microsoft, & Enlightenment](https://www.youtube.com/watch?v=Yf1o0TQzry8)
- [Watermarking for Out-of-distribution Detection](https://arxiv.org/abs/2210.15198)
- [Continual Few-Shot Learning Using HyperTransformers](https://arxiv.org/abs/2301.04584)
- [NeurIPS 2022 Workshop MLSW Submissions](https://openreview.net/submissions?venue=NeurIPS.cc/2022/Workshop/MLSW)
- [Creating Confidence Intervals for Machine Learning Classifiers](https://sebastianraschka.com/blog/2022/confidence-intervals-for-ml.html)
- [26ms Inference Time for ResNet-50: Towards Real-Time Execution of all DNNs on Smartphone](https://arxiv.org/abs/1905.00571)
- [tinygrad: A simple and powerful neural network framework](https://tinygrad.org/)
- [ GPT in 60 Lines of NumPy | Jay Mody](https://jaykmody.com/blog/gpt-from-scratch/)
- [System 2 Is What We Need](https://system2.ai/p/system-2-is-what-we-need)
- [Quick tour - BlindLlama](https://blindllama.mithrilsecurity.io/en/latest/docs/getting-started/quick-tour/?pk_campaig[…]-11-2023dhlkLaunch_Blindlama&pk_source=Lk&pk_medium=SN)
- [Validating LLM Outputs](https://txt.cohere.com/validating-llm-outputs/)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://arxiv.org/abs/2309.07864)
- https://twitter.com/norabelrose/status/1701596533756485901?t=O9PcqotmxbWfYUXeMH0IDw&s=08
- [Slack](https://dsgiitr.slack.com/archives/C05SLLQGFTP/p1694948384487309)
- [GitHub - laiyer-ai/llm-guard: The Security Toolkit for LLM Interactions](https://github.com/laiyer-ai/llm-guard)
- [Laiyer: Unleash LLM�s potential with confidence](https://laiyer.ai)
- [Introduction to AI Accountability & Transparency Series](https://aipolicy.substack.com/p/ai-accountability-transparency-intro)
- [FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance](https://arxiv.org/abs/2305.05176)
- https://twitter.com/cHHillee/status/1704140759224418676?t=4qBbqpf6UFg3iQikYqADEg&s=19
- [Visualizing PyTorch memory usage over time](https://t.co/3s61R4UYBU)
- [A tale of two problem solvers (Average cube shadows)](https://youtu.be/ltLUadnCyi0?si=TlvQxud8mNzhbfLP)
- [From Newton�s method to Newton�s fractal (which Newton knew nothing about)](https://youtu.be/-RdOwhmqP5s?si=uZ6ET0DGTm2d5a2a)
- [Full Event | #MicrosoftEvent September 21, 2023](https://www.youtube.com/watch?v=XYUEQ0SyOyE)
- [The Adventure of the Errant Hardware](https://www.adept.ai/blog/sherlock-sdc)
- [Writing Python like it�s Rust](https://kobzol.github.io/rust/python/2023/05/20/writing-python-like-its-rust.html)
- https://www.marktechpost.com/2023/09/21/ibm-researchers-propose-a-new-adversarial-attack-framework-capable-of-generating-adversarial-inputs-for-ai-systems-regardless-of-the-modality-or-task/?amp
- [A Practical Deep Learning-Based Acoustic Side Channel Attack on Keyboards](https://arxiv.org/abs/2308.01074)
- https://twitter.com/abhishekunique7/status/1699263297252377037
- https://twitter.com/abhi9u/status/1707375199233151197?t=VPIUmADPFNMkxU4IfUNxMg&s=19
- [GitHub - guardrails-ai/guardrails: Adding guardrails to large language models.](https://github.com/ShreyaR/guardrails)
- [Guardrails AI | Your Enterprise AI needs Guardrails](https://docs.guardrailsai.com/)
- [Dataset Condensation with Distribution Matching](https://arxiv.org/abs/2110.04181)
- [AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts](https://arxiv.org/abs/2010.15980)
- [AutoPrompt](https://ucinlp.github.io/autoprompt/)
- [Representation Engineering: A Top-Down Approach to AI Transparency](https://www.ai-transparency.org/)
- [Work & Projects Summary | CAIS](https://www.safe.ai/work-summary)
- [Scaling up learning across many different robot types](https://www.deepmind.com/blog/scaling-up-learning-across-many-different-robot-types)
- [Rewind Pendant](https://www.rewind.ai/pendant)
- [NVIDIA Technical Blog | News and tutorials for developers, data scientists, and IT admins](https://developer.nvidia.com/blog)
- https://openreview.net/pdf?id=bYV3bK_Azi
- [Google Colaboratory](https://colab.research.google.com/drive/1F6_1_cWXE5M7WocUcpQWp3v8z4b1jL20)
- [Neel Nanda](https://www.neelnanda.io/)
- [Generating Synthetic Dataset for RAG � Nextra](https://www.promptingguide.ai/applications/synthetic_rag)
- [The AI research job market shit show (and my experience)](https://www.interconnects.ai/p/ai-research-job-market?utm_content=buffer7f9a8&utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer)
- https://arxiv.org/pdf/1906.08988.pdf
- [keerthanapg](https://keerthanapg.com/)
- [AI's Underbelly: The Zero-Day Goldmineby: Dan McInerney](https://www.youtube.com/watch?v=e3ybnXjtpIc)
- [huntr - The world�s first bug bounty platform for AI/ML](https://huntr.mlsecops.com/bounties)
- [GitHub - jxmorris12/vec2text: utilities for decoding deep representations (like sentence embeddings) back to text](https://github.com/jxmorris12/vec2text/)
- [Compiling NumPy code into C++ or CUDA via torch.compile](https://pytorch.org/blog/compiling-numpy-code/?utm_content=268301731&utm_medium=social&utm_source=linkedin&hss_channel=lcp-78618366)
- [SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks](https://arxiv.org/abs/2310.03684)
- [Dataset Condensation with Distribution Matching](https://arxiv.org/abs/2110.04181)
- [Convolution for Computer Science People](https://medium.com/mlearning-ai/convolution-for-computer-science-people-2da7482272be)
- [Score-Based Diffusion Models | Fan Pu Zeng](https://fanpu.io/blog/2023/score-based-diffusion-models/)
- [PoisonGPT: How to poison LLM supply chainon Hugging Face](https://blog.mithrilsecurity.io/poisongpt-how-we-hid-a-lobotomized-llm-on-hugging-face-to-spread-fake-news/)
- [Hamming, "You and Your Research" (June 6, 1995)](https://www.youtube.com/watch?v=a1zDuOPkMSw)
- [sigstore](https://www.sigstore.dev/)
- https://www.lesswrong.com/posts/57fTWCpsAyjeAimTp/interpretability-in-ml-a-broad-overview-2
- https://arxiv.org/pdf/1611.03530.pdf
- [GitHub - andyzoujm/representation-engineering: Representation Engineering: A Top-Down Approach to AI Transparency](https://github.com/andyzoujm/representation-engineering)
- [Representation Engineering: A Top-Down Approach to AI Transparency](https://www.ai-transparency.org/)
