ΑΙhub.org
articles
Learning to explore using active neural SLAM
ML@CMU
24 Jun 2020
[latexpage]
By Devendra Singh Chaplot
Advances in machine learning, computer vision and robotics have opened up avenues of building intelligent robots which can navigate in the physical world ...
Contact tracing using anonymized mobile data
Pramod Misra
17 Jun 2020
The COVID-19 pandemic has had an unprecedented effect on society. Governments and individuals around the world have taken steps to stem the flow of the virus. Actions such as suspending travel, testin...
The usefulness of useless AI
Marija Slavkovik
09 Jun 2020
In every crisis since the fifties at least one article emerges to ask: where are the robots to save us? No robots are seen en masse collecting garbage, administering Covid-19 tests, or farming the fie...
Making decision trees accurate again: explaining what explainable AI did not
BAIR blog
08 Jun 2020
[latexpage]
By Alvin Wan, Lisa Dunlap, Daniel Ho, Jihan Yin, Scott Lee, Henry Jin, Suzanne Petryk, Sarah Adel Bargal and Joseph E. Gonzalez
The interpretability of neural networks is b...
Differentiable reasoning over text
ML@CMU
03 Jun 2020
[latexpage]
By Bhuwan Dhingra
We all rely on search engines to navigate the massive amount of online information published every day. Modern search engines not only retrieve a list of pages rele...
A summary of the keynotes at AAMAS
Lucy Smith
02 Jun 2020
A virtual edition of the International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS) conference was held on 9-13 May. Videos of the talks are now available for public viewing, and yo...
Hot papers on arXiv from the past month – May 2020
Lucy Smith
01 Jun 2020
What’s hot on arXiv? Here are the most tweeted papers that were uploaded onto arXiv during May 2020.
Results are powered by Arxiv Sanity Preserver....
Robots learning to move like animals
BAIR blog
28 May 2020
[latexpage]
By Xue Bin (Jason) Peng
Whether it’s a dog chasing after a ball, or a monkey swinging through the trees, animals can effortlessly perform an incredibly rich repertoire of agile loc...
Identifying light sources using machine learning
Lucy Smith
26 May 2020
Artistic impression of schematic experimental set-up for photon counting. The section in the light grey box corresponds to the thermal light part of the experiment and the section in the dark grey box...
Summarising the keynotes at ICLR: part two
Lucy Smith
22 May 2020
The virtual International Conference on Learning Representations (ICLR) was held on 26-30 April and included eight keynote talks. In part two of our round-up we summarise the final four presentations....
Physically realistic attacks on deep reinforcement learning
BAIR blog
21 May 2020
By Adam Gleave
Deep reinforcement learning (RL) has achieved superhuman performance in problems ranging from data center cooling to video games. RL policies may soon be widely deployed, with re...
Working towards explainable and data-efficient machine learning models via symbolic reasoning
Machine Learning Center at Georgia Tech
20 May 2020
By Yuan Yang
In recent years, we have witnessed the success of modern machine learning (ML) models. Many of them have led to unprecedented breakthroughs in a wide range of applications, such as Alp...
Shortcuts to artificial intelligence – a tale
Nello Cristianini
19 May 2020
The current paradigm of artificial intelligence emerged as the result of a series of cultural innovations, some technical and some social. Among them are seemingly small design decisions, that led to ...
Summarising the keynotes at ICLR: part one
Lucy Smith
14 May 2020
The virtual International Conference on Learning Representations (ICLR) was held on 26-30 April and included eight keynote talks, with a wide range of topics covered. In this post we summarise the fi...
Deep learning-guided surface characterization for autonomous fabrication
Jeremiah Croshaw
13 May 2020
The semiconductor industry as we know it is facing a critical roadblock that will lead to the end of Moore’s law. As transistors continue to shrink, quantum effects have a significant negative cons...
Unsupervised meta-learning: learning to learn without supervision
In unsupervised meta-learning, the agent proposes its own tasks, rather than relying on tasks proposed by a human.
By Benjamin Eysenbach (Carnegie Mellon University) and Abhishek Gupta (UC Berkeley...
Voice assistants – strategies for handling private information
COMPRISE
11 May 2020
In the latest in this series of posts, researchers from the EU-funded COMPRISE project write about privacy issues associated with voice assistants. They propose possible ways to maintain the privacy o...
Does on-policy data collection fix errors in off-policy reinforcement learning?
BAIR blog
06 May 2020
[latexpage]
By Aviral Kumar and Abhishek Gupta
Reinforcement learning has seen a great deal of success in solving complex decision making problems ranging from robotics to games to supply chai...
Hot papers on arXiv from the past month – April 2020
Lucy Smith
05 May 2020
What’s hot on arXiv? Here are the most tweeted papers that were uploaded onto arXiv during April 2020.
Results are powered by Arxiv Sanity Preserver....
Learning DAGs with continuous optimization
ML@CMU
27 Apr 2020
Can we build a bridge between the left and right hand side?
[latexpage]
By Xun Zheng, Bryon Aragam and Chen Dan
As datasets continually increase in size and complexity, our ability to uncove...
BADGR: the Berkeley autonomous driving ground robot
BAIR blog
20 Apr 2020
By Greg Kahn
Look at the images above. If I asked you to bring me a picnic blanket in the grassy field, would you be able to? Of course. If I asked you to bring over a cart full of food for a party...
Machine learning tool may help us better understand RNA viruses
Machine Learning Center at Georgia Tech
15 Apr 2020
E2Efold is an end-to-end deep learning model developed at Georgia Tech that can predict RNA secondary structures, an important task used in virus analysis, drug design, and other public health applica...
Digital health interventions: predicting individual success using machine learning
University of Basel
13 Apr 2020
Health apps could be better tailored to the individual needs of patients. A statistical technique from the field of machine learning is now making it possible to predict the success of smartphone-base...
Explaining machine learning models for natural language
Machine Learning Center at Georgia Tech
10 Apr 2020
By Sarah Wiegreffe and Yuval Pinter
Natural language processing (NLP) is the study of how computers learn to represent and make decisions about human communication in the form of written text. This...
Speeding up transformer training and inference by increasing model size
BAIR blog
09 Apr 2020
By Eric Wallace
Model Training Can Be Slow
In deep learning, using more compute (e.g., increasing model size, dataset size, or training steps) often leads to higher accuracy. This is...
Machine learning to scale up the quantum computer
Muhammad Usman and Lloyd Hollenberg
08 Apr 2020
By Dr Muhammad Usman and Professor Lloyd Hollenberg
Quantum computers are expected to offer tremendous computational power for complex problems – currently intractable even on supercomputers �...
AlphaZero learns to solve quantum problems
By Mogens Dalgaard, Felix Motzoi, and Jacob Sherson
Technologies based on quantum physics, such as the quantum computer, have the potential to revolutionize our society. However, realizing these te...
Hot papers on arXiv from the past month – March 2020
Lucy Smith
01 Apr 2020
What’s hot on arXiv? Here are the most tweeted papers that were uploaded onto arXiv during March 2020.
Results are powered by Arxiv Sanity Preserver....
Architecting a privacy-preserving dialogue system software development kit
COMPRISE
31 Mar 2020
By Gerrit Klasen
Use of dialogue systems such as Alexa, Siri, and Google Assistant raises several questions. In recent years, these virtual personal assistants have become the most popular represen...
AI scientific policies in China
Idees Magazine
30 Mar 2020
By Yi Chang and Chengqi Zhang
Artificial intelligence (AI) has entered into a new era, and its rapid development will profoundly affect the everyday life of citizens worldwide. Countries around the...
Are sixteen heads really better than one?
ML@CMU
26 Mar 2020
Hercules Slaying the Hydra
[latexpage]
By Paul Michel
Since their inception in this 2017 paper by Vaswani et al., transformer models have become a staple of NLP research. They are used in m...
Large-scale training at BAIR with Ray Tune
BAIR blog
25 Mar 2020
[latexpage]
By Richard Liaw, Eric Liang and Kristian Hartikainen
In this blog post, we share our experiences in developing two critical software libraries that many BAIR researchers ...
Can machines read our minds?
Nello Cristianini
20 Mar 2020
Many of us spend a significant portion of our day online and, in doing so, through our interactions with social media and IoT devices, leave a trail of “digital footprints” in our wake. Could this...
Combining AI and human expertise for cancer diagnosis
University of Waterloo
19 Mar 2020
Hamid Tizhoosh in his lab at the University of Waterloo. Image: University of Waterloo.
A new system combining artificial intelligence (AI) with human knowledge promises faster and more accurate ca...
Researchers use artificial intelligence to design supercompressible metamaterial
Miguel Bessa
18 Mar 2020
Researchers at TU Delft have developed a new material using Bayesian machine learning algorithms. Using the results of their computational simulations they have fabricated two designs at different len...
Working towards the next generation of voice interaction interfaces
COMPRISE
16 Mar 2020
COMPRISE (cost-effective, multilingual, privacy-driven voice-enabled services) is a European-funded Horizon 2020 project looking into the next generation of voice interaction services. The project ai...
