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Learning Words with Pictures

Natural language processing (NLP) machines have made great progress by learning to recognize complex statistical patterns in sentences and paragraphs. Work with modern deep learning models such as the transformer has shown that sufficiently large networks (hundreds of millions parameters) can do a good job processing language (e.g., translation), without having any information about what … Continue reading “Learning Words with Pictures”

Navigating the Cost Terrain with Minibatches

Training a Machine Learning system requires a journey through the cost terrain, where each location in the terrain represents particular values for all ML system parameters, and the height of the terrain is the cost, a mathematical value that reflects how well the ML system is performing for that parameter set (smaller cost means better … Continue reading “Navigating the Cost Terrain with Minibatches”

Learning Without a Teacher

Machine learning applications generally rely on supervised learning, learning from training samples that have been labeled by a human ‘teacher’. Unsupervised learning learns what it can from unlabeled training samples. What can be learned this way are basic structural characteristics of the training data, and this information can be a useful aid to supervised learning. In my latest … Continue reading “Learning Without a Teacher”

The Road to Human-Level Natural Language Processing

Language is a hallmark of human intelligence, and Natural Language Processing (NLP) has long been a goal of Artificial Intelligence. The ability of early computers to process rules and look up definitions made machine translation seem right around the corner. However language proved to be more complicated than rules and definitions. The observation that humans … Continue reading “The Road to Human-Level Natural Language Processing”

Encoding Human and Machine Knowledge for Machine Learning

iMerit is a remarkable company of over 4000 people that specializes in annotating the data needed to train machine learning systems. I am writing a series of blogs for them on various aspects of machine learning. In my latest blog I explain how ML systems embody both human intelligence and a form of machine ‘intelligence’. Just as … Continue reading “Encoding Human and Machine Knowledge for Machine Learning”

The Three Edge Case Culprits: Bias, Variance, and Unpredictability

iMerit is a remarkable company of over 4000 people that specializes in annotating the data needed to train machine learning systems. I am writing a series of blogs for them on various aspects of machine learning. In my latest blog I explain how ML systems can be fooled by being either too ‘simple‘, too ‘inexperienced‘, or faced … Continue reading “The Three Edge Case Culprits: Bias, Variance, and Unpredictability”

How Does Mislabeled Training Data Affect ML System Performance?

iMerit is a remarkable company of over 4000 people that specializes in annotating the data needed to train machine learning systems. I am writing a series of blogs for them on various aspects of machine learning. In my latest blog I explain how inaccuracies in training data labels (‘label noise’) affect ML system performance. It turns … Continue reading “How Does Mislabeled Training Data Affect ML System Performance?”

Inside an AI

Artificial intelligence gets its name from the fact that AIs perform tasks associated with human intelligence, such as recognizing faces or understanding language or playing chess. For these tasks, we can measure AI performance and compare it to human performance, using a single ‘yardstick’, such as accuracy or word error rate or games won. But … Continue reading “Inside an AI”

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