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3 ways AI improves CRM

AI has the power to liberate organizations from CRM-related manual processes and improve customer engagement, sales insights, and social networking, for starters.

OpenAI debuts Python-based Triton for GPU-powered machine learning

Triton uses Python’s syntax to compile to GPU-native code, without the complexities of GPU programming.

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Design for responsible AI with Microsoft’s HAX

Microsoft Research’s Human-AI eXperience toolkit makes us think about how we use AI in our code.

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5 AI startups out to change the world

Deep learning is solving challenging problems in industries as diverse as retail, manufacturing, and agriculture. These companies are leading the way.

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How the cloud and big compute are remaking HPC

High-performance computing projects require massive quantities of compute resources. Pairing simulation and specialized hardware with the cloud powers the breakthroughs of the future.

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State of AI report finds AI is now core to business success

Appen survey of business leaders and data scientists indicates that AI initiatives are delivering business value but challenges still exist.

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5 AI startups leading MLops

From data preparation and training to model deployment and beyond, these companies offer state-of-the-art platforms for managing the entire machine learning lifecycle.

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IBM Python toolkit measures AI uncertainty

IBM’s Uncertainty Qualification 360 is an open source library of Python algorithms for quantifying, estimating, and communicating the uncertainty of machine learning models.

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3 AI startups revolutionizing NLP

Deep learning has yielded amazing advances in natural language processing. Tap into the latest innovations with Explosion, Huggingface, and John Snow Labs.

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How AI can enhance customer experience

G&J Pepsi and Zipline turn to data science and machine learning to get the right products to the right locations at the right time.

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Dataiku review: Data science fit for the enterprise

Dataiku’s end-to-end machine learning platform combines visual tools, notebooks, and code to address the needs of data scientists, data engineers, business analysts, and AI consumers.

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8 databases supporting in-database machine learning

While approaches and capabilities differ, all of these databases allow you to build machine learning models right where your data resides.

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4 key tests for your AI explainability toolkit

Enterprise-grade explainability solutions provide fundamental transparency into how machine learning models make decisions, as well as broader assessments of model quality and fairness. Is yours up to the job?

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8 ways to jump-start your machine learning

From exploratory data analysis to automated machine learning, look to these techniques to get your data science project moving — and to build better models.

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What we just learned about data science — and what’s next

The past 12 months have revealed how valuable data science can be while also exposing its limitations. Expect big advances in the year to come.

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Battling bias and other toxicities in natural language generation

Despite numerous and concerted efforts to train NLG systems to generate content without offensive elements, success is still elusive.

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Review: AWS AI and Machine Learning stacks up, and up

Amazon Web Services provides an impressively broad and deep set of machine learning and AI services, rivaling Google Cloud and Microsoft Azure.

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Review: Microsoft Azure AI and Machine Learning aims for the enterprise

Microsoft Azure combines a wide range of cognitive services and a solid platform for machine learning that supports automated ML, no-code/low-code ML, and Python-based notebooks.

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