Stanford AI Lab @StanfordAILab Twitter profile

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Stanford AI Lab

@StanfordAILab

3 years

We support our @StanfordAILab alum @timnitGebru , her critical research on fairness & ethics in AI, and her tireless work in organizing for a more ethical & inclusive AI community. It’s urgent that voices like hers be heard and that we prioritize work on inclusion & ethics in AI.

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Stanford AI Lab

@StanfordAILab

1 year

We are proud to offer many of our best AI courses free on YouTube. Want to study with a cohort and course assistants? You can do that in the AI Professional Program via @StanfordOnline . New this year: Deep Multi-Task and Meta Learning with @chelseabfinn

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Stanford AI Lab

@StanfordAILab

4 months

Congratulations to @StanfordAILab Director @chrmanning , awarded the 2024 IEEE John von Neumann Medal, one of @IEEEAwards ’s top awards “for outstanding achievements in computer-related science and technology”, for his advances in #NLProc .

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Stanford AI Lab

@StanfordAILab

3 years

The Stanford Artificial Intelligence community is sustained and built in a major way by our Asian-American and Asian members. ‘Them’ is ‘us’. Love not hate. 💔

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Stanford AI Lab

@StanfordAILab

3 years

Wouldn't it be great if AI could do debug our programs for us? Check out our latest blog post on using ML to repair programs from error messages, with a promising approach that leverages program-feedback graphs and self-supervised learning.

Learning to Fix Programs from Error Messages

Learning to fix programs from error messages: a new approach leveraging program-feedback graphs and self-supervised learning.

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

5 years

The @Stanford AI Lab is excited to share that Chris Manning— @chrmanning has become the Director of SAIL. And many thanks to Fei-Fei Li— @drfeifei for doing so much to grow SAIL & its community. She is now Co-Director of the new Human-Centered AI Initiative

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Stanford AI Lab

@StanfordAILab

3 years

Adapting to your data distribution changing after you train your model remains one of the big challenges of Machine Learning. Check out our latest blog post on work addressing this challenge -

Adapting on the Fly to Test Time Distribution Shift

We meta-learn models that can use unlabeled test data to adapt to group distribution shift.

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

2 years

Curious about why self-training with unlabeled data can magically improve a classifier’s performance? Check out the theoretical explanation in the following blog post: from @ColinWei11 , @jhaochenz , and @tengyuma

Understanding Deep Learning Algorithms that Leverage Unlabeled Data, Part 1: Self-training

Theoretical analysis of self-training algorithms for leveraging unlabeled data.

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

3 years

The Stanford AI Lab supports and deeply appreciates the talented Iranian members of our community. We strive for equitable treatment of people from any nation of origin in both admissions and the operation of the lab. Discrimination has no place in our community. 💚

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Stanford AI Lab

@StanfordAILab

6 months

Progress in Foundation Models needs not just GPUs but great ideas! Here are some of our impactful ones from 2023: • Flash Attention—and FA 2—much faster transformers by careful rethinking of GPU data paths • Stanford Alpaca—How to build small, good instruction-tuned LLMs 1/2

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Stanford AI Lab

@StanfordAILab

2 years

At least we’re only beaten by a non-American university 🤣

Science Is Strategic

@scienceisstrat1

2 years

Most observers dramatically UNDERESTIMATE how good the best Chinese universities have become, especially in STEM education. 4/25

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Stanford AI Lab

@StanfordAILab

5 years

An exciting announcement: the Stanford AI Lab now has an official blog! Follow the link to check it out, read the inaugural posts, and subscribe:

All Posts

The Stanford AI Lab (SAIL) Blog is a place for SAIL students, faculty, and researchers to share our work with the general public.

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

6 months

Artificial intelligence isn’t just language models! We’re hard at work on robotic systems that understand and work in the physical 3D world. Recent highlights include: • @leto__jean et al. 𝗧𝗶𝗱𝘆𝗕𝗼𝘁: How can household robots learn your preferences from just a few examples?

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Stanford AI Lab

@StanfordAILab

2 months

Silicon Valley is pricing academics out of AI “ @drfeifei Li is at the forefront of a growing chorus who argue the sky-high cost of working with AI models is boxing researchers out of the field, compromising independent study of the burgeoning technology”

Silicon Valley is pricing academics out of AI research

A growing chorus of academics say the sky-high cost of working with AI models is boxing researchers out of the field, compromising independent study of the technology.

www.washingtonpost.com

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Stanford AI Lab

@StanfordAILab

3 years

AI courses at @Stanford : “It’s all student demand-driven, which just reflects the huge breakthroughs that have been made in AI recently and the huge enthusiasm among students to learn this,” said Christopher Manning Ph.D. ’94 @chrmanning , director of SAIL.

The rise of AI in Stanford's CS curricula

During the 2010s, AI-related courses experienced large growth, both in terms of the number of courses and the number of students enrolling in these courses.

stanforddaily.com

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Stanford AI Lab

@StanfordAILab

3 years

Congratulations to @StanfordAILab faculty Dorsa Sadigh on receiving an MIT Tech Review TR-35 award for her work on teaching robots to be better collaborators with people

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Stanford AI Lab

@StanfordAILab

3 years

Now on the SAIL blog: @hazyresearch and @krandiash write a retrospective on the Hazy lab's journey in data-centric AI, and a new effort to build a community resource for data-centric AI on GitHub (). Check it out!

Our Journey towards Data-Centric AI: A Retrospective

A retrospective narrative from the Hazy research lab on our work in data-centric AI, and current efforts on engaging the broader machine learning community.

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

4 years

AI postdocs available! The Stanford AI Lab is trying to help in the current #COVID19 pandemic. Some of that is via research but another need is jobs for great young people. We’re opening positions for 2 years of innovative research with Stanford AI Faculty

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Stanford AI Lab

@StanfordAILab

3 years

Wouldn't it be great if AI could reason with commonsense knowledge? Check out our latest blog post on a new question answering model, QA-GNN, that jointly reasons with language models and knowledge graphs, by @michiyasunaga !

Reasoning with Language Models and Knowledge Graphs for Question Answering

Reasoning with Language Models and Knowledge Graphs for Question Answering.

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

3 years

Congrats to Ron Dror, @raphaeljlt , Stephan Eismann, and Masha Karelian at @DrorLab and coauthors at @RDasLab for the front page of @ScienceMagazine Geometric deep learning of RNA structure Perspective Podcast

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Stanford AI Lab

@StanfordAILab

4 years

AI postdocs available! Stanford AI Lab is delighted to offer postdocs to some exciting young AI researchers in these difficult times. Positions for 2 years working with SAIL faculty. If you’ve procrastinated, this is the week to get your application in!

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Stanford AI Lab

@StanfordAILab

5 years

Read our newest blog post! On using weak supervision, or high-level noisy sources of labels, to efficiently label training data. Courtesy of @paroma_varma , @ajratner , @bradenjhancock , and others at Professor Chris Ré's @HazyResearch lab.

Weak Supervision: A New Programming Paradigm for Machine Learning

On using weak supervision, or high-level noisy sources of labels, to efficiently label training data.

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

4 years

What if we can teach robots to do new task just by showing them one demonstration? In our newest blog post, @deanh_tw and @danfei_xu show us three approaches that leverage compositionality to solve long-horizon one-shot imitation learning problems.

Leveraging Compositionality for One-Shot Imitation Learning

We introduce a class of algorithms that solve long-horizon one-shot imitation learning by leveraging compositional priors.

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

4 years

How contextual are contextualized word representations? In our new SAIL blog post, @ethayarajh discusses how ELMo, BERT, and GPT-2 contextualize words — how they’re alike, and how they’re different.

BERT, ELMo, & GPT-2: How Contextual are Contextualized Word Representations?

Geometric properties of contextualized word representations and what that says about how contextualized they are.

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

3 years

Language is a powerful mechanism for people to communicate goals, beliefs and concepts. Can we use language to train machine learning models? Read our new blog post on Learning from Language Explanations:

Learning from Language Explanations

Language is a crucial way for humans to teach other humans. Can we use language to teach machines?

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

3 years

ICML 2021 is being hosted virtually this week. Check out all the great work from the Stanford AI Lab being presented there in our latest blog post!

Stanford AI Lab Papers and Talks at ICML 2021

All the great work from the Stanford AI Lab accepted at ICML 2021, all in one place!

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

3 years

A major long-term goal of computational neuroscience is to identify the brain's learning algorithms. Can we use artificial neural networks to guide this discovery? Check out this blog post by @aran_nayebi about recent work accepted at #NeurIPS2020 :

A Model-Based Approach Towards Identifying the Brain's Learning Algorithms

A major long-term goal of computational neuroscience is to identify the brain's learning algorithms. Can we use artificial neural networks to guide this discovery?

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

2 years

A big—if still virtual—welcome to the new @Stanford Computer Science Ph.D. Admits! We hope to see all AI enthusiasts at Stanford in the Autumn!

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Stanford AI Lab

@StanfordAILab

2 years

Congratulations to our new-ish faculty Jiajun Wu and our recent grad Pranav Rajpurkar on being named to the Forbes 30 under 30 in Science list!

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Stanford AI Lab

@StanfordAILab

2 years

Large language models can learn in-context even with random labels. This post from @sangmichaelxie and @sewon__min explains this with a Bayesian inference framework for in-context learning, connecting and .

How does in-context learning work? A framework for understanding the differences from traditional...

A Bayesian inference framework for in-context learning in large language models.

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

5 years

The Stanford AI Lab has joined twitter—eventually! Some of our 2018 news: We welcome star new faculty member @tengyuma ; @ermonste won the 2018 IJCAI Computers and Thought Award; and Oussama Khatib and Nils Nilsson were elected to the National Academy of Engineering ( @theNAEng ).

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Stanford AI Lab

@StanfordAILab

4 years

ICLR 2020 is being hosted virtually right now -- we’re excited to share all the work from SAIL that’s being presented with this blog post in which you’ll find links to papers, videos and blogs posts for all our papers:

SAIL at ICLR 2020: Accepted Papers and Videos

All the great work from the Stanford AI Lab accepted at ICLR 2020, in one place.

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

4 months

LLMs have achieved remarkable success in code generation, but even 99% accuracy in small-scale generation can accumulate errors when scaling up. Curious about how to address the issue? Check out Clover: from @chuyue99 , @ying11231 , @oded , @ClarkBarrett7 .

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Stanford AI Lab

@StanfordAILab

3 years

Great article about @Stanford @ai4allorg – @StanfordAILab ’s program to broaden who is included in the study of AI – came out yesterday in the @StanfordDaily :

Stanford’s AI4ALL program mentors new generation of diverse leaders in artificial intelligence

Stanford’s AI4ALL program introduces the theory and application of AI to students, many from underrepresented backgrounds in AI, across the world.

stanforddaily.com

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Stanford AI Lab

@StanfordAILab

5 years

Stanford CS and SAIL PhD students and our very own @drfeifei representing at #GHC19 . Come say hi at Stanford Booth 116.

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Stanford AI Lab

@StanfordAILab

3 years

SAIL Postdoc #3 : Ruohan Gao ( @RuohanGao1 ) obtained his Ph.D. from The University of Texas at Austin and won its best doctoral dissertation award for 2021. Ruohan is currently working on teaching machines to see, hear, touch, and interact with the world through multisensory data.

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Stanford AI Lab

@StanfordAILab

5 years

Ever wondered just how we could ever really trust our notoriously unexplainable neural nets with anything substantial? Check out our latest blog post! Courtesy of @StanfordHAI Director of Research Steve Eglash.

Progress Toward Safe and Reliable AI

An overview of research at SAIL related to new techniques that allow us to look inside the black box of neural networks, to how it is possible to find and remove bias, and to how safety in autonomous...

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

4 years

How can a robot solve complex sequential problems? In our newest blog post, @KuanFang introduces CAVIN, an algorithm that hierarchically generate plans in learned latent spaces.

Sequential Problem Solving by Hierarchical Planning in Latent Spaces

We propose a hierarchical planning algorithm in learned latent spaces. Our method uses deep generative models to prioritize promising actions for sampling-based planning.

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

2 years

Berkeley students miss their time at the @StanfordAILab ! 🤣🤣🤣

Kevin Zakka

@kevin_zakka

2 years

Caption: “super excited kid wants to play with all the toys 🤖” (was in the area and nostalgic so snuck into Gates unannounced)

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Stanford AI Lab

@StanfordAILab

3 years

GPT-3 is a powerful in-context learner, but what does that mean? Learn about some of the quirks behind this little-understood phenomenon at our latest SAIL blog post by @frieda_rong :

Extrapolating to Unnatural Language Processing with GPT-3's In-context Learning: The Good, the Bad,...

In this post, we examine in-context learning in GPT-3 through a series of experiments dissecting GPT-3's ability to extrapolate to less natural inputs.

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

5 years

Check out our latest SAIL Blog Post! Courtesy of @qi2peng2 and @danqi_chen from @stanfordnlp , an overview of two new datasets meant to enable more conversational, explainable, and capable question answering systems.

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Stanford AI Lab

@StanfordAILab

2 years

SAIL is delighted to welcome two new Asst Profs this year! @Diyi_Yang works on Computational Social Science & socially aware #NLProc @sanmikoyejo works on trustworthy machine learning, including federated ML & metric elicitation

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Stanford AI Lab

@StanfordAILab

3 years

Congratulations to @YSongStanford , @StefanoErmon , and colleagues (at Google, some ex-Stanford!) on their ICLR 2021 Outstanding Paper Award for Score-Based Generative Modeling through Stochastic Differential Equations

Announcing ICLR 2021 Outstanding Paper Awards

By ICLR 2021 Program Co-Chairs Ivan Titov (U. Edinburgh / U. Amsterdam), Naila Murray (Facebook AI Research), and Alice Oh (KAIST), and…

iclr-conf.medium.com

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Stanford AI Lab

@StanfordAILab

2 years

Where do the rewards for robotic reinforcement learning come from? In this blog post we explore how using crowdsourced language annotations and videos of humans, we can learn reward functions and enable them to generalize more broadly.

Reward Isn't Free: Supervising Robot Learning with Language and Video from the Web

Where do the rewards for robotic reinforcement learning come from? In this blog post we study how using crowdsourced language annotations and videos of humans, we can learn reward functions in a...

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

2 years

Hop, hop! In new work published at #ACL2022 , @michiyasunaga @jure and @percyliang demonstrate how explicitly modeling document relations during LM pretraining significantly improves multi-hop reasoning across documents. Check it out!

LinkBERT: Improving Language Model Training with Document Link

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

1 year

Two @StanfordHAI blog posts this week cover the recent work of @_eric_mitchell_ , @chelseabfinn , @chrmanning , and colleagues: DetectGPT—A new method for detecting large language model output How can we cheaply edit and update LLMs?

Human Writer or AI? Scholars Build a Detection Tool

DetectGPT can determine with up to 95% accuracy whether a large language model wrote that essay or social media post.

hai.stanford.edu

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Stanford AI Lab

@StanfordAILab

5 years

Take a look at our latest blog post by @abigail_e_see , on how we can make chatbots that are good at conversing with people!

What makes a good conversation?

The Stanford AI Lab (SAIL) Blog is a place for SAIL students, faculty, and researchers to share our work with the general public.

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

2 years

#NeurIPS2021 is happening this week and we are super excited! Check out all the amazing talks and papers from the Stanford AI Lab that will be presented in our new blog post.

Stanford AI Lab Papers and Talks at NeurIPS 2021

All the great work from the Stanford AI Lab accepted at NeurIPS 2021 in one place!

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

2 years

Congratulations to @StanfordAILab faculty Ken Salisbury on receiving an @ieeeras Pioneer Award and @chelseabfinn and @DorsaSadigh on receiving the Early Academic Career Awards in Robotics and Automation.

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Stanford AI Lab

@StanfordAILab

11 months

Our video with @StanfordVideo , AI at Stanford 1962–2022 last night won Gold at the 52nd Northern California Area Emmy Awards 2023 ( @EmmySFTV ) in the Science and Technology Category, following on from earlier Gold and Silver Telly Awards. Full video (13m):

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Stanford AI Lab

@StanfordAILab

4 years

New SAIL blog post out! @MinaLee__ writes about a simple way to extend the capabilities of language models to fill in the blanks:

How to Fill in the Blanks with Language Models

Simple way to extend the capabilities of language models to fill in the blanks.

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

3 years

Check out our post on WILDS, a benchmark of in-the-wild distribution shifts! Distribution shifts are ubiquitous, but real-world shifts are underrepresented in the literature. WILDS presents 10 datasets with real-world shifts to help close this gap!

WILDS: A Benchmark of in-the-Wild Distribution Shifts

A curated benchmark of 10 datasets with real-world distribution shifts.

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

3 years

@mike_ng0 @RadioCacaNFT @USMverse @BinanceChain No. Don’t even know what RACA is. (And probably no Google AI partnership either…)

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Stanford AI Lab

@StanfordAILab

3 years

Popular @StanfordAILab ML teacher @AndrewYNg tells @_KarenHao lessons learned doing AI in companies: “Forget about building an AI-first business—start with a mission”. Focusing on technology is great for a research lab but almost never for a business.

Andrew Ng: Forget about building an AI-first business. Start with a mission.

An AI pioneer reflects on how companies can use machine learning to transform their operations and solve critical problems.

www.technologyreview.com

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Stanford AI Lab

@StanfordAILab

5 years

“There’s nothing artificial about AI. It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”— @drfeifei in Wired.

Fei-Fei Li's Quest to Make Machines Better for Humanity

Artificial intelligence has a problem: The biases of its creators are getting hard-coded into its future. Fei-Fei Li has a plan to fix that—by rebooting the field she helped invent.

www.wired.com

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Stanford AI Lab

@StanfordAILab

5 years

Now live, a new blog post by @ebiyik94 ! "Have you ever felt you are losing too much time in traffic? In this post, we present a mathematical model of traffic congestion and show that autonomous cars have the potential of significantly reducing it!"

Altruistic Autonomy: Beating Congestion on Shared Roads

The Stanford AI Lab (SAIL) Blog is a place for SAIL students, faculty, and researchers to share our work with the general public.

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

4 years

The International Conference on Robotics and Automation (ICRA) 2020 is being hosted virtually from May 31 – Jun 4. We’re excited to share all the work from SAIL and Stanford that’s being presented in our new blog post:

SAIL and Stanford Robotics at ICRA 2020

All the great work from the Stanford AI Lab accepted at ICRA 2020, all in one place.

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

3 years

What are Knowledge Graphs, and how are they useful in search, data integration and AI? Check out our latest post on the SAIL blog!

An Introduction to Knowledge Graphs

What are Knowledge Graphs and how are they useful in search, data integration and AI?

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

3 years

Sometimes, machine learning models focus on irrelevant patterns in data; yet, removing those patterns can hurt performance, and this issue can disproportionately affect certain groups. Why is that? Read our latest post to find out more! -

Removing Spurious Features can Hurt Accuracy and Affect Groups Disproportionately

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

4 years

While humans can seamlessly combine our sensory inputs, can we teach robots to do the same? @michellearning writes about how we can use self-supervision to learn a representation that combines vision and touch.

Making Sense of Vision and Touch: Multimodal Representations for Contact-Rich Tasks

We learn a multimodal representation that combines vision and touch through self-supervision.

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

3 years

Large language models are inefficient, opaque, and static—but *retrieval* can help. New post highlights how retrieval-based systems ColBERT-QA and Baleen advance the state of the art for answering open-domain questions and verifying complex claims.

Building Scalable, Explainable, and Adaptive NLP Models with Retrieval

By tapping into knowledge stored explicitly in text corpora, retrieval helps tackle the inefficiency, opaqueness, and static nature of large language models.

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

5 years

Our latest blog post is out! Check it out for a recording and summary of highlights from the event "AI and the Future of Work" featuring Dr. Kai Fu-Lee @kaifulee , professor @Susan_Athey , and professor Erik Brynjolfsson @erikbryn .

AI and the Future of Work

Professors Susan Athey, Erik Brynjolfsson, and Dr. Kai-Fu Lee discuss the race for international leadership in AI as well as the ways AI will shape the future of work.

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

2 months

We do a lot of cutting edge research at the Stanford AI Lab, but really our main job is training people. Here is a list of the great SAIL Graduates of 2024, who are variously looking for academic and industry jobs. Compiled by @judyhshen and @johnhewtt .

Stanford AI Lab Graduates 2024

A list of our excellent students graduating roughly in summer 2024.

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

2 years

Want something better than k-means? Great work from @mo_tiwari & co on BanditPAM, a SOTA k-medoids algorithm. `pip3 install banditpam` and you're good to go! Check out the blog post:

BanditPAM: Almost Linear-Time k-medoids Clustering via Multi-Armed Bandits

We present an almost-linear time algorithm for the k-medoids problem.

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

6 months

ChatGPT is 11 months old today! It’s hard to still grasp that it’s been less than a year. 😲 Interest in Artificial Intelligence has always been much higher than for Data Science, and increased in the 2020s, but it exploded in the wake of the release of ChatGPT & other GenAI.

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Stanford AI Lab

@StanfordAILab

5 years

Did you know chatbots can be used in self-paced learning to significantly improve student recall and retention over flashcards? Check out an exciting new post on this on our blog! By @brycebowl13 @sherrysruan , with leadership from @AIforHI @landay

Towards an Educational Revolution Through Chatbots

QuizBot is an AI-powered chatbot to help college students review questions through natural-language conversations. Our experimental results suggest that educational chatbot systems may have benefic...

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

8 months

Doug Lenat (1950–2023) passed away yesterday. Doug got a Stanford CS PhD in 1976 for his work at SAIL on AM (Automated Mathematician) and was Stanford faculty 1977–1982. Thereafter, Doug worked until the end on Cyc, his vision of building computers with common sense knowledge.

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Stanford AI Lab

@StanfordAILab

4 years

How can we enable autonomous vehicles to naturally navigate social scenarios, such as merging into traffic? @iamborisi tackles this question in our latest blog post, diving into recent trajectory forecasting methods for autonomous driving.

Back to the Future: Planning-Aware Trajectory Forecasting for Autonomous Driving

Recent advances in deep generative modeling have brought forth a paradigm shift in trajectory forecasting. In this blog post, we provide an overview of existing work, highlight new opportunities, and...

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

8 months

Congratulations to the @StanfordAILab Chirpy Cardinal team, led by Ryan Chi and mentored by @chrmanning , which has won first place for Scientific Invention and Innovation in the Alexa Prize Science SocialBot Grand Challenge 5! Full team details are here:

Amazon Science

@AmazonScience

8 months

Announcing the winners of the #AlexaPrize SocialBot Grand Challenge 5: Team GauchoChat of @UCSB for the overall competition, and team Chirpy Cardinal of @Stanford for the scientific innovation category. Congrats, all! 🎉

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Stanford AI Lab

@StanfordAILab

3 years

Hot off the press: an interactive dashboard showing the predicted effects of COVID-19 restrictions that won KDD Best Paper in the Applied Data Science Track! A must-read post. Congrats to @serinachang5 et al on their awesome work.

Supporting COVID-19 policy response with large-scale mobility-based modeling

A novel computational tool for policymakers to assess the impacts of thousands of different mobility measures on predicted COVID-19 infections, helping them to navigate difficult tradeoffs between...

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

3 years

New blog post on AI for education: how can meta-learning help teachers give feedback to student work at scale? Spoiler: model was deployed to 16,000 student solutions in a large online course for code education. NYT Coverage:

Meta-Learning Student Feedback to 16,000 Solutions

A meta-learning system for automatic feedback to student code education with applications to university level courses and a large scale online learning platform.

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

3 years

How can we design collaborative AI-agents that can adapt to the conventions you prefer? We explore the use of modular policies for transferring and learning new conventions!

Conventions in Multi-Agent Collaboration

Learning convention-aware agents for adaptive multi-agent collaboration

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

5 years

Check out our latest blog post by @RickardGabriels ! Topology is a combinatorial property that is tricky to utilize in gradient based methods, and so underexploited in Machine Learning - TopologyLayer might change that.

A Topology Layer for Machine Learning

Topology is a combinatorial property that is tricky to utilize in gradient based methods, but it is also a useful and underexploited feature of data. We present an easy-to-use TopologyLayer that...

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

4 years

The International Conference on Machine Learning (ICML) 2020 is being hosted virtually this week. We’re excited to share all the work from SAIL that’s being presented, and you’ll find links to papers, videos and more in our latest blog post:

Stanford AI Lab Papers and Talks at ICML 2020

All the great work from the Stanford AI Lab accepted at ICML 2020, all in one place.

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

1 year

A full house at @StanfordEng for today’s @StanfordAILab workshop on Generative AI and Foundation Models organized by Chris Ré @HazyResearch . (Want to be at next year’s workshop? Get in touch!)

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Stanford AI Lab

@StanfordAILab

5 years

The NLP community has made great progress on open-domain question answering, but our systems still struggle to answer complex questions using lots of text. Read our latest blog post, by @qi2peng2 , about enabling multi-step reasoning in these systems!

Answering Complex Open-domain Questions at Scale

The Stanford AI Lab (SAIL) Blog is a place for SAIL students, faculty, and researchers to share our work with the general public.

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

6 months

Get a look at the whole trajectory of artificial intelligence research at Stanford in our AI at Stanford 1962-2022 video. Released in 2023, it received an Emmy Award! (Northern California area, Science and Technology category)

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Stanford AI Lab

@StanfordAILab

3 years

How heavy is an elephant? In our latest blog post, @xikun_zhang_ explains that a weakness in current language models is that they don't capture scales well, and proposes a new model, NumBERT, that does. Length of reading time? 2 minutes to 2 years.

Do Language Models Know How Heavy an Elephant Is?

The Stanford AI Lab (SAIL) Blog is a place for SAIL students, faculty, and researchers to share our work with the general public.

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

3 years

EMNLP 2020 is being hosted virtually this week, and we're looking forward to seeing everyone there! We’re excited to share all the work from SAIL that’s being presented, and you’ll find links to papers, videos and more in our latest blog post:

Stanford AI Lab Papers and Talks at EMNLP 2020

All the great work from the Stanford AI Lab accepted at EMNLP 2020, all in one place.

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

3 years

Congratulations to SAIL PhD student Anna Goldie on receiving an MIT Tech Review TR-35 award for her work on AI layout of computer chips with deep reinforcement learning

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Stanford AI Lab

@StanfordAILab

5 years

Reinforcement Learning has been making great progress, but is still hard to apply to settings where collecting a lot of experience is difficult -- such as robotics. Check out our latest blog post on a method for speeding things, with teachers!

AC-Teach: A Bayesian Actor-Critic Method for Policy Learning with an Ensemble of Suboptimal Teachers

Presenting AC-Teach, a unifying approach to leverage advice from an ensemble of sub-optimal teachers in order to accelerate the learning process of actor-critic reinforcement learning agents.

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

3 years

Congratulations to ⁦ @chelseabfinn for her ONR Young Investigator Award for 2021 on ⁩Flexible Vision-Based Robotic Manipulation via Meta-Learning and Deep Reinforcement Learning!

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Stanford AI Lab

@StanfordAILab

1 year

What did people at the @StanfordAILab do before the rise of modern deep learning? Find out in our new video on AI at Stanford 1962–2022.

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Stanford AI Lab

@StanfordAILab

2 years

What's one key to help take the next leap in AI? In this blog post, @HazyResearch , @tri_dao , @realDanFu , and @krandiash explain why their answer is *longer sequences*, and highlight recent work in this direction (FlashAttention, S4). Check it out!

Can Longer Sequences Help Take the Next Leap in AI?

Why we think modeling longer sequences is exciting, and highlights of our recent work advancing this task.

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

5 years

It has been a great quarter for AI Salon discussion events here at SAIL, so we would like to thank all guests who made them possible - @beenwrekt , Emma Brunskill @AIforHI , Sarah Thornton, @MarcoPavoneSU , @lexfridman , @timnitGebru , @mmitchell_ai , @svlevine , and Mykel Kochenderfer.

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Stanford AI Lab

@StanfordAILab

5 years

Read the latest SAIL blog post, out now! SAIL PhDs Shushman Choudhury, Michelle Lee ( @michellearning ), and @andrey_kurenkov review answers to "what are best practices AI researchers can follow to avoid unintended consequences of their research?"

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Stanford AI Lab

@StanfordAILab

5 years

Excited to host an #AI Salon discussion on "AI and the Future of Work" with the amazing participants @kaifulee , @erikbryn , and @Susan_Athey here at @Stanford ! And, big thanks for a great job moderating to SAIL PhD student and AI Salon organizer @michellearning !

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Stanford AI Lab

@StanfordAILab

1 year

Causal abstraction provides a powerful set of tools for accurate, human-interpretable explanations of AI models. Check out our latest from Atticus Geiger, @ZhengxuanZenWu , @KarelDoostrlnck , @ElisaKreiss , Noah Goodman, Thomas Icard, and @ChrisGPotts :

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Stanford AI Lab

@StanfordAILab

6 months

The @StanfordAILab & @StanfordOnline pioneered the AI Professional Program to offer up-to-date, deep, technical #AI content—the material we teach at @Stanford —to everyone, either as free YouTube playlists or in a cohort-based class with TAs for a fee. 👇

Artificial Intelligence Professional Program | Stanford Online

Artificial intelligence is transforming our world and helping organizations of all sizes grow, serve customers better, and make smarter decisions. The Artificial Intelligence Professional Program...

online.stanford.edu

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Stanford AI Lab

@StanfordAILab

3 years

#ACL2021NLP is happening this week and we couldn't be more excited! Check out all the great work from the Stanford AI Lab being presented in our latest blog post.

Stanford AI Lab Papers at ACL-IJCNLP 2021

All the great work from the Stanford AI Lab accepted at ACL-IJCNLP 2021, all in one place!

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

3 years

Congratulations to @DorsaSadigh , @chelseabfinn , and students, Annie, Dylan, and Ryan, on winning the Best Paper Award at the 4th Conference on Robot Learning (CoRL) for: Learning Latent Representations to Influence Multi-Agent Interaction.

Conference on Robot Learning (CoRL 2020) Best Paper Awards Announced | Synced

CoRL 2020 kicked off yesterday and runs virtually through November 18. This year saw 475 papers submitted, a 20 percent rise over 2019.

syncedreview.com

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Stanford AI Lab

@StanfordAILab

3 years

SAIL Postdoc #2 : Esin Durmus ( @esindurmusnlp ) is working on building more faithful text generation systems and improving metrics for evaluation of generation systems. She is also exploring social applications of #NLProc such as detection of racism, colorblindness on social media.

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Stanford AI Lab

@StanfordAILab

3 years

Congratulations to former @StanfordAILab postdoc Ginger Smith on receiving an MIT Tech Review TR-35 award for her work on federated learning preserving fairness and privacy

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Stanford AI Lab

@StanfordAILab

3 years

Looking for a good read for the holidays? Check out our new blog post by @fereshte_khani , with a simple example explaining when machine learning models are incentivized to discriminate and why they devalue a good candidate from a lower-performing group.

Blue People v. City of Ney

We show that when ML models use noisy features, they're incentivized to devalue a good score when the candidate is from a lower-performing group.

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

3 years

Do neural networks trained with SGD contain hidden geometries? By combining symmetry and differential equations, @KuninDaniel and @Hidenori8Tanaka uncover broken conservation laws, which they verify empirically! Find out more in our latest post -

Neural Mechanics: Symmetry and Broken Conservation Laws In Deep Learning Dynamics

Language is a crucial way for humans to teach other humans. Can we use language to teach machines?

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

5 years

How can we combine data from different sources of human feedback to better learn their reward functions? Read about in our new blog post from Andy Palan, where inverse reinforcement learning meets preference-based learning

Learning Reward Functions by Integrating Human Demonstrations and Preferences

The Stanford AI Lab (SAIL) Blog is a place for SAIL students, faculty, and researchers to share our work with the general public.

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

4 years

If you would like to keep up with Stanford #AI Lab researchers and affiliates, we now have a Twitter list for you! Subscribe to follow our MS, PhD, post docs, PIs, and labs:

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Stanford AI Lab

@StanfordAILab

6 months

• DPO—Direct Preference Optimization: A better way to instruction-tune models than RLHF • H3—Language Modeling with State Space Models and (almost) no attention Refs: • • • •

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Stanford AI Lab

@StanfordAILab

3 years

Interested in automatically fixing your code, or in breaking other people's code realistically? In our latest post, @michiyasunaga describes BIFI, an unsupervised approach that learns how to do both at the same time to repair code better. Check it out!

Break-It-Fix-It: Unsupervised Learning for Fixing Source Code Errors

Break-It-Fix-It: Unsupervised Learning for Fixing Source Code Errors.

ai.stanford.edu

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Stanford AI Lab

@StanfordAILab

4 years

Check out the latest blog post on our blog! Does adding a theorem to any paper increase its chance of acceptance? On algorithms and a python package for measuring the importance of ngram features on an outcome, while controlling for confounds.

Text Feature Selection for Causal Inference

Algorithms and a python package for measuring the importance of ngram features on an outcome, while controlling for confounds.

ai.stanford.edu

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