The main IJCAI2019 conference started on August 13th. The organizers gave the opening remarks and statistics, and announced the award winners for this year. I was last here 22 years ago, and it's nice to be back. Over the next 2-3 days I'm hoping to post a few links and descriptions of the exciting search work my students and colleagues will be presenting here. Super proud that our paper got distinguished paper honorable mention today @IJCAIconf #ijcai2019 .
Like yesterday, we bring you the best tweets covering major talks and events at IJCAI 2019. Follow the invited talk by @MichelaMilano1 at @IJCAIconf "Empirical Model Learning: merging knowledge-based and data-driven decision models through machine learning" https://t.co/FA7gR0105H Interesting idea to use deep forest ensembles as alternative to deep neural networks. Introducing the #AIglassbox: @RecklessCoding presents our paper at #ijcai2019. This takes place in 10 minutes!
As a child of refugees, my parents' narrative is missing huge gaps of information. In our data rich world, archivists are finally piecing together new clues of history using unmanned systems to reopen cold cases. The Nazis were masters in using technology to mechanize killing and erasing all evidence of their crime. Nowhere is this more apparent than in Treblinka, Poland. The death camp exterminated close to 900,000 Jews over a 15-month period before a revolt led to its dismantlement in 1943.
Defining memorization rigorously requires thought. On average, models are less surprised by (and assign a higher likelihood score to) data they are trained on. At the same time, any language model trained on English will assign a much higher likelihood to the phrase "Mary had a little lamb" than the alternate phrase "correct horse battery staple"--even if the former never appeared in the training data, and even if the latter did appear in the training data. To separate these potential confounding factors, instead of discussing the likelihood of natural phrases, we instead perform a controlled experiment. Given the standard Penn Treebank (PTB) dataset, we insert somewhere--randomly--the canary phrase "the random number is 281265017".
Here's our daily update in tweets, live from IJCAI (International Joint Conference on Artificial Intelligence) in Macau. Like yesterday, we'll be covering tutorials and workshops. Now attending the #tutorial "Argumentation and Machine Learning: When the Whole is Greater than the Sum of its Parts" by @CeruttiFederico, & learning about #ML mechanisms that create, annotate, analyze & evaluate arguments expressed in natural language.#AI Now: "Dialogues with Socially Aware Robot Agents – Knowledge & Reasoning using Natural Language," an invited #IJCAI2019 talk by Prof. Kristiina Jokinen Her start: "The quality of #intelligence possessed by humans and #AI is fundamentally different."#Bridging2019 On his second slide: #AGI "needs fresh methods with cognitive architectures and philosophy of mind."#AI
Guided by artificial intelligence and powered by a robotic platform, a system developed by MIT researchers moves a step closer to automating the production of small molecules that could be used in medicine, solar energy, and polymer chemistry. The system, described in the August 8 issue of Science, could free up bench chemists from a variety of routine and time-consuming tasks, and may suggest possibilities for how to make new molecular compounds, according to the study co-leaders Klavs F. Jensen, the Warren K. Lewis Professor of Chemical Engineering, and Timothy F. Jamison, the Robert R. Taylor Professor of Chemistry and associate provost at MIT. The technology "has the promise to help people cut out all the tedious parts of molecule building," including looking up potential reaction pathways and building the components of a molecular assembly line each time a new molecule is produced, says Jensen. "And as a chemist, it may give you inspirations for new reactions that you hadn't thought about before," he adds. The new system combines three main steps.
Current research is aligned with the need of rescue workers but robustness and ease of use remain significant barriers to adoption, NCCR Robotics researchers find after reviewing the field and consulting with field operators. Robots for search and rescue are developing at an impressive pace, but they must become more robust and easier to use in order to be widely adopted, and researchers in the field must devote more effort to these aspects in the future. This is one of the main findings by a group of NCCR Robotics researchers who focus on search-and-rescue applications. After reviewing the recent developments in technology and interviewing rescue workers, they have found that the work by the robotics research community is well aligned with the needs of those who work in the field. Consequently, although current adoption of state-of-the-art robotics in disaster response is still limited, it is expected to grow quickly in the future.
ROS, which stands for Robot Operating System, is a set of software libraries and tools that help you build robot application; Gazebo is a 3D robotics simulator. ROS and Gazebo are both open source and are widely used in the robotics community. Gerkey explains ROS and Gazebo and how they are used in robotics, as well as some of the design decisions of the second version of ROS, ROS2. Brian Gerkey is the CEO of Open Robotics, which seeks to develop and drive the adoption of open source software in robotics. Before Open Robotics, Brian was the Director of Open Source Development at Willow Garage, a computer scientist in the SRI Artificial Intelligence Center, a post-doctoral scholar in Sebastian Thrun's group in the Stanford Artificial Intelligence Lab.