At Instagram, we have the world's largest deployment of the Django web framework, which is written entirely in Python. We began using Python early on because of its simplicity, but we've had to do many hacks over the years to keep it simple as we've scaled. Last year we tried dismissing the Python garbage collection (GC) mechanism (which reclaims memory by collecting and freeing unused data), and gained 10% more capacity. However, as our engineering team and number of features have continued to grow, so has memory usage. Eventually, we started losing the gains we had achieved by disabling GC.
Because many processing facilities can't quickly identify the chemicals in this household waste, the items are often simply lumped together and incinerated – which is expensive. Their start-up, Smarter Sorting, has installed a barcode scanning system at four waste disposal sites in the US used by the public – in Austin, Texas; Salt Lake City, Utah; Portland, Oregon; and Mesa County, Colorado. "The machine goes'beep' and at that point the screen simply tells the worker, 'this is where you should place this item'," says Chris Ripley, who co-founded Smarter Sorting together with Charlie Vallely. Also testing the technology is Hope Petrie, hazardous materials manager at Mesa County Hazardous Waste Collection Facility, although she isn't yet using it to alter the way large numbers of items are processed.
The Knowledge-based approach allowed the system to be implemented as three separate modules: inference engine, knowledge base, and user interface. Initially required to run under MS-DOS on a PC AT equivalent with 640K of RAM, a second release to run under Windows 3.1 reused the inference engine and knowledge base, requiring only a revised user interface. Enhancements made to the inference engine and the knowledge base were immediately available to both environments.
Software as a service platform Jodone's latest design makes sorting recyclables from trash faster, more efficient and ultimately, more profitable. Made for use with industry standard robots from multiple suppliers, the interface turns the acts of recognizing and categorizing recyclables into a game. As waste travels along a conveyer belt, workers swipe a touch screen to classify items as recyclable. As environmental standards rise and governments get tougher on industries and their waste management processes, Jodone sees gamification as key to both cost saving and improved working conditions.
Béjar, Ramón (Universitat de Lleida) | Fernández, César (Universitat de Lleida) | Mateu, Carles (Universitat de Lleida) | Manyà, Felip (IIIA-CSIC) | Sole-Mauri, Francina (RosRoca Envirotec) | Vidal, David (RosRoca Envirotec)
One of the most challenging problems on modern urban planning and one of the goals to be solved for smart city design is that of urban waste disposal. Given urban population growth, and that the amount of waste generated by each of us citizens is also growing, the total amount of waste to be collected and treated is growing dramatically (EPA 2011), becoming one sensitive issue for local governments. A modern technique for waste collection that is steadily being adopted is automated vacuum waste collection. This technology uses air suction on a closed network of underground pipes to move waste from the collection points to the processing station, reducing greenhouse gas emissions as well as inconveniences to citizens (odors, noise, . . . ) and allowing better waste reuse and recycling. This technique is open to optimize energy consumption because moving huge amounts of waste by air impulsion requires a lot of electric power. The described problem challenge here is, precisely, that of organizing and scheduling waste collection to minimize the amount of energy per ton of collected waste in such a system via the use of Artificial Intelligence techniques. This kind of problems are an inviting opportunity to showcase the possibilities that AI for Computational Sustainability offers.
Full text not available. Volume 1: Expert Problem Solving, Natural Language Understanding and Intelligent Computer Coaches, Representation and Learning Volume II: Understanding Vision: Representing and Computing Visual Information; Visual Detection of Light Sources; Representing and Analyzing Surface Orientation; Registering Real Images Using Synthetic Images; Analyzing Curved Surfaces Using Reflectance Map Techniques; Analysis of Scenes from a Moving Viewpoint; Manipulation and Productivity Technology: Force Feedback in Precise Assembly Tasks; A Language for Automatic Mechanical Assembly; Kinematics, Statics, and Dynamics of Two-Dimensional Manipulators; Understanding Manipulator Control by Synthesizing Human Handwriting; Computer Design and Symbol Manipulation: The LISP Machine; Shallow Binding in LISP 1.5; Optimizing Allocation and Garbage Collection of Spaces; Compiler Optimization Based on Viewing LAMBDA as RENAME Plus GOTO; Control Structure as Patterns of Passing Messages.Cambridge, Mass.: MIT Press