In the News
This is why AI has yet to reshape most businesses
For many companies, deploying AI is slower and more expensive than it might seem. Applies throughout industries, from to dating sites to retail, insurance, or telcos.
Nvidia's got a cunning plan to keep powering the AI revolution
Nvidia’s artificial intelligence journey started with cats. Now it's heading to the kitchen
Partner with Neon to Develop New Conversational AI Apps
Take your apps and devices to the next level. Neon’s time-tested, white-label product delivers easy-to-install code with endless possibilities. Our Polylingual AI offers real-time translation, transcription, natural language understanding, home automation and more. Let's build the future together.
14 NLP Research Breakthroughs You Can Apply To Your Business
BERT, sequence classification with human attention, SWAG, Meta-learning, Multi-task learning etc.
The technology behind OpenAI’s fiction-writing, fake-news-spewing AI, explained
The language model can write like a human, but it doesn’t have a clue what it’s saying.
"Productionizing machine learning/AI/data science is a challenge. Not only are the outputs of machine-learning algorithms often compiled artifacts that need to be incorporated into existing production services, the languages and techniques used to develop these models are usually very different than those used in building the actual service. In this post, I want to explore how the degrees of freedom in versioning machine learning systems poses a unique challenge. I'll identify four key axes on which machine learning systems have a notion of version, along with some brief recommendations for how to simplify this a bit."
Foundations Built for a General Theory of Neural Networks
Neural networks can be as unpredictable as they are powerful. Now mathematicians are beginning to reveal how a neural network’s form will influence its function.
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