Artificial Intelligence and Machine Learning
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Optimal Auctions Through Deep Learning
We overview recent research results that show how tools from deep learning are shaping up to become a powerful tool for the automated design of near-optimal auctions.
Technical Perspective: The Quest for Optimal Multi-Item Auctions
"Optimal Auctions Through Deep Learning," by Paul Dütting et al., contributes a very interesting and forward-looking new take on the optimal multi-item mechanism computational challenge, initiating the use of deep learning for mechanism design.
Screen Recognition Makes Apps More Accessible
Apple's Screen Recognition algorithm identifies the metadata necessary to communicate the functions of any app or Web page to the visually impaired.
Joke-Cracking Chatbots Boost Learning Levels
The role of humor in training, and conversing with, chatbots.
Using AI to Drill Down in Physics
Artificial intelligence can give a boost to particle physics.
There is an emerging effort to tap artificial intelligence to write code based on short text descriptions of what the code should do.
Engineering Additional Creativity
Can artificial intelligence augment human creativity in writing?
Charles M. Geschke (1939-2021)
Charles M. Geschke helped create the modern world of computing, where beautiful typography and expressive, artistic graphics are as integral to most users' experience as numbers and text.
Droids, drones, and driverless technologies are fueling a supply chain revolution.
How can neural networks learn the rich internal representations required for difficult tasks such as recognizing objects or understanding language?
Spatial Concepts in the Conversation With a Computer
Conversing about places with a computer poses a range of challenges to current AI.
Technical Perspective: An Elegant Model for Deriving Equations
"Deriving Equations from Sensor Data Using Dimensional Function Synthesis," by Vasileios Tsoutsouras, et al., addresses the key problem of discovering relationships that hold between physical quantities from data using dimensional analysis to drastically narrow down the space of hypotheses.
Deriving Equations from Sensor Data Using Dimensional Function Synthesis
We present a new method, which we call dimensional function synthesis, for deriving functions that model the relationship between multiple signals in a physical system.
IBM, Google, and Microsoft are all developing algorithms that understand the meaning of words.
Simba: Scaling Deep-Learning Inference with Chiplet-Based Architecture
This work investigates and quantifies the costs and benefits of using multi-chip-modules with fine-grained chiplets for deep learning inference, an application domain with large compute and on-chip storage requirements.
CoCoPIE: Enabling Real-Time AI on Off-the-Shelf Mobile Devices via Compression-Compilation Co-Design
A new framework allows intelligence on mainstream end devices without special hardware.
In-Sensor Classification With Boosted Race Trees
We demonstrate the potential of a novel form of encoding, race logic, in which information is represented as the delay in the arrival of a signal.
Toward Confidential Cloud Computing
Extending hardware-enforced cryptographic protection to data while in use.
Technical Perspective: Race Logic Presents a Novel Form of Encoding
"In-Sensor Classification With Boosted Race Trees," by Georgios Tzimpragos, et al., proposes a surprising, novel, and creative approach to post-Moore's Law computing by rethinking the digital/analog boundary.
Technical Perspective: A Chiplet Prototype System for Deep Learning Inference
"Simba," by Yakun Sophia Shao, et al., presents a scalable deep learning accelerator architecture that tackles issues ranging from chip integration technology to workload partitioning and non-uniform latency effects on deep neural network performance.
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