Tech & Society
Is AI Making Mastery Obsolete?: How human chess and go masters cope with being beaten by AI
Is there any reason to continue playing games that AI has mastered? Ask the former champions who have been toppled by machines.
Analytics DeepLearning.AI
Machine Learning Research
Bigger Corpora, Better Answers: Using knowledge graphs to improve question answering NLP
Models that summarize documents and answer questions work pretty well with limited source material, but they can slip into incoherence when they draw from a sizeable corpus. Recent work addresses this problem.
Tech & Society
Google's AI Explains Itself: How Google's xAI tools help evaluate model performance
Google's AI platform offers a view into the mind of its machines. Explainable AI (xAI) tools show which features exerted the most influence on a model’s decision, so users can evaluate model performance and potentially mitigate biased results.
Science
Prehistoric Pictures Rediscovered: AI image analysis reveals new Nazca drawings in Peru.
Image analysis guided by AI revealed a 2,000-year-old picture dug into the Peruvian desert. Researchers analyzing aerial imagery shot over Peru found a pattern that looks like a three-horned humanoid holding a staff.
Machine Learning Research
Bias Fighter: A neural network for countering bias variables in data
Sophisticated models trained on biased data can learn discriminatory patterns, which leads to skewed decisions. A new solution aims to prevent neural networks from making decisions based on common biases.
Business
Solar Power Heats Up: How solar thermal plants use AI to maximize energy output
Solar-thermal power plants concentrate the sun’s energy using huge arrays of mirrors. AI is helping those arrays stay in focus.
Letters
Building Trustworthy AI
Recently I wrote about major reasons why AI projects fail, such as small data, robustness, and change management. Given that some AI systems don't work, users and customers sometimes rightly wonder whether they should trust an AI system.
The Batch Newsletter
The Batch: Google AI Explains Itself, Neural Net Fights Bias, AI Demoralizes Champions, Solar Power Heats Up
Recently I wrote about major reasons why AI projects fail, such as small data, robustness, and change management. Given that some AI systems don't work, users and customers sometimes rightly wonder whether they should trust an AI system.
Business
A Sleeping Giant Stirs: Sony launches three AI R&D centers.
Sony, the consumer-electronics powerhouse behind the PlayStation and other hit gadgets, is launching three research-and-design centers to focus on AI. Staffing up means competing with — and likely poaching talent from — frontrunners like Google, Facebook, and Microsoft.
Machine Learning Research
Melding Transformers with RL: GTrXL combines transformers and reinforcement learning.
Large NLP models like BERT can answer questions about a document thanks to the transformer network, a sequence-processing architecture that retains information across much longer sequences than previous methods. But transformers have had little success in reinforcement learning — until now.
Hardware
Not Your Father’s GPU: Nvidia released two chips designed for AI.
Intel, which dominates the market for general-purpose processors, is shipping its long-awaited AI chips. The chip giant announced that two so-called neural network processors are available to data-center customers.
Machine Learning Research
Bias Goes Undercover: Adversarial attacks can fool explainable AI techniques.
As black-box algorithms like neural networks find their way into high-stakes fields such as transportation, healthcare, and finance, researchers have developed techniques to help explain models’ decisions. New findings show that some of these methods can be fooled.