Category: robotics/AI – Page 1,558
Automatic debugging of software
Circa 2016
Computer programs often contain defects, or bugs, that need to be found and repaired. This manual “debugging” usually requires valuable time and resources. To help developers debug more efficiently, automated debugging solutions have been proposed. One approach goes through information available in bug reports. Another goes through information collected by running a set of test cases. Until now, explains David Lo from Singapore Management University’s (SMU) School of Information Systems, there has been a “missing link” that prevents these information gathering threads from being combined.
Dr Lo, together with colleagues from SMU, has developed an automated debugging approach called Adaptive Multimodal Bug Localisation (AML). AML gleans debugging hints from both bug reports and test cases, and then performs a statistical analysis to pinpoint program elements that are likely to contain bugs.
“While most past studies only demonstrate the applicability of similar solutions for small programs and ‘artificial bugs’ [bugs that are intentionally inserted into a program for testing purposes], our approach can automate the debugging process for many real bugs that impact large programs,” Dr Lo explains. AML has been successfully evaluated on programs with more than 300,000 lines of code. By automatically identifying buggy code, developers can save time and redirect their debugging effort to designing new software features for clients.
Artificial General Intelligence Is Not as Imminent as You Might Think
A close look reveals that the newest systems, including DeepMind’s much-hyped Gato, are still stymied by the same old problems.
AI translates maths problems into code to make them easier to solve
An artificial intelligence can translate maths problems written in plain English to formal code, making them easier for computers to solve in a crucial step towards building a machine capable of discovering new maths.
Axon halts plans to make a drone equipped with a Taser
Axon has paused work on a project to build drones equipped with its Tasers. A majority of its artificial intelligence ethics board quit after the plan was announced last week.
Nine of the 12 members said in a resignation letter that, just a few weeks ago, the board voted 8–4 to recommend that Axon shouldn’t move forward with a pilot study for a Taser-equipped drone concept. “In that limited conception, the Taser-equipped drone was to be used only in situations in which it might avoid a police officer using a firearm, thereby potentially saving a life,” the nine board members wrote. They noted Axon might decline to follow that recommendation and were working on a report regarding measures the company should have in place were it to move forward.
The nine individuals said they were blindsided by an announcement from the company last Thursday — nine days after 19 elementary school students and two teachers were killed in a mass shooting in Uvalde, Texas — about starting development of such a drone. It had an aim of “incapacitating an active shooter in less than 60 seconds.” Axon said it “asked the board to re-engage and consider issuing further guidance and feedback on this capability.”
Ionic Liquid-Based Reservoir Computers: Efficient and Flexible Edge Computing
Researchers from Japan design a tunable physical reservoir device based on dielectric relaxation at an electrode-ionic liquid interface.
In the near future, more and more artificial intelligence processing will need to take place on the edge — close to the user and where the data is collected rather than on a distant computer server. This will require high-speed data processing with low power consumption. Physical reservoir computing is an attractive platform for this purpose, and a new breakthrough from scientists in Japan just made this much more flexible and practical.
Physical reservoir computing (PRC), which relies on the transient response of physical systems, is an attractive machine learning framework that can perform high-speed processing of time-series signals at low power. However, PRC systems have low tunability, limiting the signals it can process. Now, researchers from Japan present ionic liquids as an easily tunable physical reservoir device that can be optimized to process signals over a broad range of timescales by simply changing their viscosity.