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Sneha Nanavati

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Interactive embodied agents for Human-AI collaboration

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What data should you label to get the most value for your money?

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A benchmark for image-based food recognition

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Behavioral Representation Learning from Animal Poses.

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ASCII-rendered single-player dungeon crawl game

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5 Puzzles 21 Days. Can you solve it all?

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Measure sample efficiency and generalization in reinforcement learning using procedurally generated environments

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5 Puzzles 21 Days. Can you solve it all?

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Self-driving RL on DeepRacer cars - From simulation to real world

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3D Seismic Image Interpretation by Machine Learning

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5 Puzzles 21 Days. Can you solve it all?

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5 Puzzles 21 Days. Can you solve it all?

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5 Puzzles 21 Days. Can you solve it all?

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Multi-Agent Reinforcement Learning on Trains

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A benchmark for image-based food recognition

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Sample-efficient reinforcement learning in Minecraft

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5 Puzzles, 3 Weeks. Can you solve them all? 😉

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Multi-agent RL in game environment. Train your Derklings, creatures with a neural network brain, to fight for you!

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Predicting smell of molecular compounds

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5 Problems 21 Days. Can you solve it all?

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5 Puzzles 21 Days. Can you solve it all?

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5 Puzzles, 3 Weeks | Can you solve them all?

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Grouping/Sorting players into their respective teams

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5 Problems 15 Days. Can you solve it all?

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5 PROBLEMS 3 WEEKS. CAN YOU SOLVE THEM ALL?

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Remove Smoke from Image

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Classify Rotation of F1 Cars

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Can you classify Research Papers into different categories ?

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Can you dock a spacecraft to ISS ?

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Multi-Agent Reinforcement Learning on Trains

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Multi-Class Object Detection on Road Scene Images

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Detect Mask From Faces

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Identify Words from silent video inputs.

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NeurIPS 2022: MineRL BASALT Competition

👋 Welcome to NeurIPS 2022: MineRL BASALT

20 days ago

The MineRL Benchmark for Agents that Solve Almost-Lifelike Tasks competition aims to promote research in learning from human feedback to enable agents that can accomplish tasks without crisp, easily-defined reward functions. Our sponsors have generously provided :moneybag:20,000 USD​:moneybag: in prize money to support this research, with an additional 100,000 USD for especially surprising results (see “Prizes”)!

This is the second iteration of this competition. You can find the page for the BASALT 2021 competition here. Major changes this year include:

  • :hammer_and_pick: New MineRL simulator version with human-level observation and action-spaces. This change means, for example, that crafting requires opening the inventory UI and using the mouse to craft items.
  • :brain: Pretrained models trained on different Minecraft tasks, which you are free to use in your solutions as you see fit (e.g., fine-tune for a specific task, use for a specific part of behaviour)
  • :trophy: Prizes to encourage exploring learning from human-feedback, even if the solution does not reach the top performance.
  • :gem: An “intro” track, in which the task is the original, non-restrictive MineRL competition task “Obtain diamond shovel”, to ease entry to the competition. The MineRL Benchmark for Agents that Solve Almost-Lifelike Tasks (MineRL BASALT) competition aims to promote research in learning from human feedback to enable agents that can accomplish tasks without crisp, easily-defined reward functions. Our sponsors have generously provided :moneybag:20,000 USD​:moneybag: in prize money to support this research, with an additional 100,000 USD for especially surprising results (see “Prizes”)!

:dollar: Prizes

There are three categories of prizes:

  1. Winners
  2. 1st place: $7,000 USD
  3. 2nd place: $4,000 USD
  4. 3rd place: $3,000 USD
  5. Blue Sky award: $100,000 USD
  6. Research prizes: $5,000 USD
  7. Community support: $1,000 USD

Winners. As described in the Evaluation section, we will evaluate submissions using human feedback to determine how well agents complete each of the four tasks. The three teams that score highest on this evaluation will receive prizes of $7,000, $4,000, and $3,000.

Blue Sky award. This award of $100,000 will be given to submissions that achieve a very high level of performance: human-level performance on at least 3 of the 4 tasks. (Human-level performance is achieved if the human evaluators prefer agent-generated trajectories to human demonstrations at least 50% of the time.) If multiple submissions achieve this milestone, the award will be split equally across all of them.

Research prizes. We have reserved $5,000 of the prize pool to be given out at the organizers’ discretion to submissions that we think made a particularly interesting or valuable research contribution. We might give prizes to:

  1. Submissions that present novel negative results (e.g. a submission that shows that having humans correct the AIs behavior doesn’t help)
  2. Submissions that get particularly good results given their approach (e.g. best submission based on behavior cloning, or best submission based on learning from preferences)
  3. Approaches that create interesting agent behavior beyond “solves the task” (e.g. most human-like agent)
  4. New, interesting knowledge about learning from human feedback (e.g. an empirically validated scaling law that predicts how much human data is required for a given level of performance, or guidelines on how to decide which types of human feedback to use at any given point in fine-tuning)

If you wish to be considered for a research prize, please include some details on interesting research-relevant results in the README for your submission. We expect to award around 2-10 research prizes in total.

Community support. We will award $1,000 of the prize pool at the organizers’ discretion to people who provide community support, for example by answering other participant’s questions, or creating and sharing useful tools.


:notebook: Learn about the Community Contribution Prize
:people_holding_hands: Looking for teammates
:speech_balloon: Join the discord channel to meet other challenge participants like you.

🏆 Community Contribution Prize [MineRL BASALT Competition]

20 days ago

:wave: Hello Participants,

Community Contribution Prizes are a way to celebrate the creativity of the challenge participants. AIcrowd is a platform for you to share your creative problem-solving approach with the community and get rewarded for it. :gift:

:sparkles: For this challenge, we have two prizes: Research Prizes and Community Support Prize.

Research prizes

We have reserved $5,000 of the prize pool to be given out at the organizers’ discretion to submissions that we think made a particularly interesting or valuable research contribution. We might give prizes to:

  1. Submissions that present novel negative results (e.g. a submission that shows that having humans correct the AIs behavior doesn’t help)
  2. Submissions that get particularly good results given their approach (e.g. best submission based on behavior cloning, or best submission based on learning from preferences)
  3. Approaches that create interesting agent behavior beyond “solves the task” (e.g. most human-like agent)
  4. New, interesting knowledge about learning from human feedback (e.g. an empirically validated scaling law that predicts how much human data is required for a given level of performance, or guidelines on how to decide which types of human feedback to use at any given point in fine-tuning)

If you wish to be considered for a research prize, please include some details on interesting research-relevant results in the README for your submission. We expect to award around 2-10 research prizes in total.

Community support.

We will award $1,000 of the prize pool at the organizers’ discretion to people who provide community support, for example by answering other participant’s questions, or creating and sharing useful tools.

Your contributions can be for anything that adds value towards solving the problem - data analysis, exploration, article or video on your approach - all supported by a working piece of code snippet. The prizes typically go to individuals or teams who are extremely active in the community, share resources - or even answer questions - that benefit the whole community greatly!

All the best! :sparkles:

:speech_balloon: Please feel free to contact us if you have any queries, or just reply to this post! Join the discord channel to meet other challenge participants like you.

Team AIcrowd

👥 Looking for teammates? Reply here!

21 days ago

Competing is more fun with a team! Introduce yourself here and find others who are looking to team up! :sparkles: Some questions to help you find your teammate :point_down:

  1. What’s your background?
  2. What are your strengths and skills?
  3. What are you hoping to achieve for this challenge?

Join the discord channel to meet other challenge participants like you. Have a great time!

NeurIPS 2022: CityLearn Challenge

👥 Looking for teammates? Reply here!

About 1 month ago

Competing is more fun with a team! Introduce yourself here and find others who are looking to team up! :sparkles: Some questions to help you find your teammate :point_down:

  1. What’s your background?
  2. What are your strengths and skills?
  3. What are you hoping to achieve for this challenge?

Join the discord channel to meet other challenge participants like you. Have a great time!

👋 Welcome to NeurIPS 2022: CityLearn Challenge

About 1 month ago

:deciduous_tree: Hello Participant,

Welcome to NeurIPS 2022: CityLearn Challenge. In this challenge, participants have to coordinate the energy consumed by each of the buildings within a simulated micro-grid.

The CityLearn Challenge 2022 provides an avenue to address energy problems by leveraging CityLearn, an OpenAI Gym environment for the implementation of AI agents for demand response. In this challenge, participants must coordinate the energy consumed by each of the buildings within a simulated micro-grid.

The challenge utilizes 1 year of operational electricity demand and generation time-series data from 20 single-family buildings that were studied for Grid integration of zero net energy communities.

This challenge is part of the NeurIPS 2022 Competition Track.


:trophy: Challenge Prizes

:medal_military: Leaderboard Prize
This challenge has a total prize pool of USD 15,000 for the third phase of the challenge.

:1st_place_medal: 1st on the leaderboard USD 8,000
:2nd_place_medal:2nd on the leaderboard USD 5,000
:3rd_place_medal:3rd on the leaderboard USD 2,000

:scientist: NeurIPS 2022 Conference

This challenge is part of the NeurIPS 2022 Competition Track.

The Annual Conference on Neural Information Processing Systems fosters the exchange of research on neural information processing systems. It is a coveted interdisciplinary conference that brings together top researchers from various domains.

The top 3 participants will present at the NeurIPS competition track workshop virtually. They will also get a chance to author a joint research paper for NeurIPS 2022 Competition Workshop.

:1st_place_medal: Community Contribution Prize

SIGEnergy is sponsoring three travel grants to ACM BuildSys or ACM e-Energy.


The CityLearn Challenge is an opportunity for researchers from multi-disciplinary fields to investigate the potential of artificial intelligence and distributed control systems to tackle multiple problems within the energy domain.

:notebook: Learn about the Community Contribution Prize
:people_holding_hands: Looking for teammates
:speech_balloon: Join the discord channel to meet other challenge participants like you.

🏆 Community Contribution Prize [City Learn Challenge]

About 1 month ago

:wave: Hello Participants,

Community Contribution Prizes are a way to celebrate the creativity of the challenge participants. AIcrowd is a platform for you to share your creative problem-solving approach with the community and get rewarded for it. :gift:

Your contributions can be for anything that adds value towards solving the problem - data analysis, exploration, article or video on your approach - all supported by a working piece of code snippet. The prizes typically go to individuals or teams who are extremely active in the community, share resources - or even answer questions - that benefit the whole community greatly!

:sparkles: Be as creative as you can be, and make sure to dazzle the community!

:alarm_clock: Deadline: 31st October, 2022

:trophy: Community Contribution Prize

The ACM Special Interest Group on Energy Systems and Informatics (SIGEnergy) sponsors three travel grants for up to USD 1000 each to attend ACM BuildSys or ACM e-Energy in 2023.


:balance_scale: TERMS

  1. You must post your explainers through the ‘Notebooks’ section on the challenge page.
  2. The prizes will be awarded as per the discretion of the organizers, and the popularity of the post in the community (based on the number of likes :heart: ) - so share your post widely to spread the word!
  3. You can make multiple submissions, but you’re only eligible for the prize once.
  4. Your work needs to be published under a license and on a platform that allows other participants to use it. For example, code should be provided under an open-source license, and articles should be readable freely.

All the best! :heart:
Please feel free to reach out to us if you have any queries, or just reply to this post! Join the discord channel to meet other challenge participants like you.

Team AIcrowd

Multi Agent Behavior Challenge 2022

Travel Grant for CVPR Workshop

3 months ago

We are excited to offer a limited number of travel grants to students and postdocs to attend CVPR and Multi-Agent Behavior workshop. Each award will cover at least $500 towards travel and meeting attendance costs (larger grants may be available to those travelling longer distances.)

You will get a chance to meet a panel of experts from various disciplines and discuss the key goals of multi-agent behavior research. CVPR is the premier annual computer vision & pattern recognition event regarded as one of the most important conferences in its field.

:airplane: How To Apply For Travel Grant

Apply for travel grants by submitting an extended abstract or paper to Round 2, and we will review travel grant applications submitted between now and May 27th on a rolling basis.

:alarm_clock: Timeline

Round 2 Submission deadline: May 27th, midnight AoE
Submit at: Conference Management Toolkit - Login

Travel grants with submissions reviewed on a rolling basis.
Final Decisions: June 3rd
Camera-ready Deadline: June 10th

:books: Read More Details on the CVPR Workshop & Travel Grant


Just a quick reminder, we’ve extended Round-2 deadline by one and a half months after receiving your feedback that the current timeline was too short to work with video data.

[Name], now with the new deadline of 3rd July, 2022, we look forward to seeing your unique submission.

:writing_hand: Don’t miss the chance to present your work at CVPR workshop by participating in Round-2.

Make Your Submission For Round-2 :green_book:

[Round-1 Winners] Here Are The Winners For Round-1

4 months ago

:wave:t3: Hello AIcrowd,

As Round-1 of Multi-Agent Behavior Challenge 2022 comes to an end, let’s shine a spotlight on the winners of the Mouse Triplet Task and Fruit Fly Task.

Round-1 saw 224 participants making 700+ submissions. We thank you all for your participation. Here are the winners of Round-1 of Multi-Agent Behavior Challenge 2022. :clap:

:honeybee: Fruit Fly Winners

:trophy: Leaderboard Ranks

:1st_place_medal: Zac Partridge
:2nd_place_medal: Team DIG111
:3rd_place_medal: Param Uttarwar

:mouse: Mouse Triplet Winners

:trophy: Leaderboard Ranks

:1st_place_medal: Param Uttarwar
:2nd_place_medal: Team Jerry Mouse
:3rd_place_medal: Zac Partridge


:notebook: Winning Solutions

@edhayes1 from :2nd_place_medal: Team Jerry Mouse shared his solution with the participants. Click here to learn from his solution that won second place for :mouse: Mouse Triplet Task.

@Zac the :1st_place_medal: first-place winner for :honeybee: Fruit Fly Task shares the casual overview of his approach over here:

"I used a Bert style encoder, treating handcrafted features as the tokens and performing masked language modelling. Initially, every frame of key points is converted into a large number of handcrafted features representing angles, distances and velocities between body parts within an animal as well as features from each animal to all others. For the flies, I had 2222 features and 456 for the mice (but would use more in the future, especially for the mice) and these features are all normalised.

I’ll refer to the neural network as having a head, body and tail where the head and tail act on the single frame level and the body acts on the sequence level. The head is a two-layer fully connected network that reduces the input features down to the target dimension size (256 or 128). The body does the “language modelling” part - input is 512 partially masked tokens (masked before the head) and output is a sequence of the same shape (batch_size, seq_length, 128 or 256) but now hopefully includes higher-level sequence features. I used huggingface’s perceiver model for this - Perceiver.

The tail is a single linear layer and can be thought of in two parts, original unmasked features reconstruction and predicting any known labels so for example that would be of size 458 for the mice. The loss function I was using was mean square error for reconstructing the features and cross-entropy loss for the (non-nan) labels where the mean square error loss was weighted approximately 10 times more." Stayed tuned for a more in-depth breakdown of his solution.


:hourglass_flowing_sand: What Next? Participate In Round-2

In the new round, you’ll be given two sets of raw overhead videos and tracking data. As in Round 1, we ask you to submit a frame-by-frame representation of the dataset. We hope this video dataset will inspire you to try new ideas and see how much incorporation of information from video improves your ability to represent animal behaviors! Explore the sub-tasks to know more :point_down:

:mouse: Mouse Triplet Video Data
:ant: Ant & Beetle Video Data

The end goal remains the same, create a representation that captures behavior and generalizes well in any downstream task.

:trophy: $6000 USD cash prize pool for the two subtasks
:spiral_calendar: Round-2 runs till May 20th, 2022, 23:59 UTC
:medal_sports: Claim up to $400 AWS credits in Round-2
:writing_hand: Submit Your Solution to CVPR Workshop 2022

Lip Reading

[Suggestion Box 📥 ] What AI puzzle do you want to solve?

3 months ago

Hello you :wave:

At AIcrowd, we have a very creative team working on new AI puzzles for you to solve. They set the problem, brew the dataset and create solution notebooks.

What kind of puzzles do you want to solve next?

Suggest puzzle ideas or new topics we should explore. Comment on the types of puzzles you’ll like to see. Want to collaborate with us to create a unique AI puzzle? Drop an email at blitz@aicrowd.com.

AIcrowd Blitz is for you. So help us make it better by creating problems you want to solve!

Happy Blitzing
Team AIcrowd

[Feedback Corner 🗣] We Want To Hear Your Thoughts

3 months ago

Hello you,

We have built Blitz through extensive user interviews and in-depth surveys of existing participants. We want AIcrowd Blitz to be a comprehensive AI companion. Blitz is a place where you can learn, solve and show your AI skillset.

We want to make Blitz better for you. We want to hear your unfiltered, honest thoughts on Blitz. You can also answer these questions (either through comments below or by email at blitz@aicrowd.com).

  1. What is one thing you love about Blitz?
  2. What is one thing you immediately wish to change about Blitz?
  3. How would you rate your Blitz experience so far? (Out of 5, where 5 is excellent and 1 is poor)
  • This can be the user experience, quality of puzzles and solutions and overall ease of subscription and access to Blitz.
  1. How would you rate Blitz AI puzzles? (Out of 5, where 5 is excellent and 1 is poor)
  • This can be about the puzzle’s difficulty, the quality of the dataset or the puzzle content.
  1. How would you rate Blitz AI solutions? (Out of 5, where 5 is excellent and 1 is poor)
  • This can be based on ease of understanding, explanation quality, and implementation ease.
  1. How easy was it to deploy your first ML App? (Out of 5 where 5 is very easy and 1 is very hard)
  • This can be based on the ease of implementation and sharing of the app.
  1. Would you recommend Blitz to a friend? If not, why?
  2. What is one item we should improve?

We have tried to incorporate features that will accelerate your AI learning. We have also polished & perfected the things you love about Blitz.

Like you, we love learning AI & solving new problems to build our skills. So we want to hear your thoughts on AIcrowd Blitz. Please share your honest feedback to help us provide the best AI platform to you. We are always available on @AIcrowdHQ on Twitter and blitz@aicrowd.com.

Happy Blitzing
Team AIcrowd

[New Launch] AIcrowd Blitz Is Now Live

3 months ago

:wave: Hello you,

For the last 1.5 years, we have been developing unique AI puzzles and sharing them with you every month. The next phase of the evolution of AI Blitz is here!

AIcrowd Blitz is a comprehensive AI Learning companion with unique AI puzzles, expert solutions, your own AI community & most excitingly, an impressive ML portfolio.

AI Puzzles You Love + Easy to understand Expert Solutions

Blitz boasts a library of hand-crafted AI puzzles designed around home-brewed datasets on real-world problems. Signup and get these unique AI puzzles delivered straight to your inbox.

Learn As You Go

Unlock solutions and resources created by experts for beginners. Learn different approaches to solve AI puzzles, understand the theory and gain hands-on experience.

Build & Share Your ML Portfolio
Create AI Solutions For The World To See

Don’t just sharpen your skills; show them to the world! Through Blitz ML Apps, make your solutions accessible to all. Host live ML Apps for anyone to use in real-time. With Blitz, you can easily build a stellar ML Portfolio.

Meet Your AI Tribe

We know learning is hard, so we never want you to feel alone in your AI journey. Through Blitz, meet like-minded AI enthusiasts that will inspire & help you. Get exclusive access to live problem-solving events and 24*7 community support.

Like you, we love learning AI & solving new problems to build our skills. So we want to hear your thoughts on AIcrowd Blitz. Please share your honest feedback to help us provide the best AI platform to you.

Happy Blitzing
Team AIcrowd

Data Purchasing Challenge 2022

[Announcement] Leaderboard Winners

4 months ago

As the Round-2 of Data Purchasing Challenge 2022 comes to an end, let’s shine a spotlight on the winners.

This challenge saw 300+ participants making 2200+ submissions. We thank you all for your participation. Here are the winners of Data Purchasing Challenge 2022. :clap:

:trophy: Leaderboard Winners

:1st_place_medal: xiaozhou_wang USD 6,000
:2nd_place_medal: sergey_zlobin USD 4,500
:3rd_place_medal:ArtemVoronov USD 3,000

Community Contribution prize winners are being finalised and will be announced soon, stay tuned.

snehananavati has not provided any information yet.