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Machine learning vs data science: What’s the difference?
By Debabrata Deb last updated
In-depth Both machine learning and data science are fields that extract insights from data, but the methods vary significantly
Four reasons to be excited about the future of AI – and three reasons to worry
By Keumars Afifi-Sabet last updated
In-depth Smaller models and liquid networks are some reasons to be excited about AI, but leaders will have to keep track of AI risks and emissions
The pros and cons of chatbots for customer service
By Lisa Sparks published
In-depth As IT leaders evaluate the risks and rewards of chatbots in customer service, user experience will have to be balanced against potential productivity gains
How TUI rapidly scaled its generative AI integration through Amazon Bedrock
By George Fitzmaurice published
Case study The travel industry giant has been hard at work gearing itself up to utilize generative AI over the last few years
What is generative AI?
By John Loeppky last updated
In-depth Generative AI has powerful potential and comes with inherent risks – all leaders should know how it works and what it can offer business
Concerns raised over lack of open source representation on Homeland Security AI safety board
By Solomon Klappholz published
News The Department of Homeland Security has set up an advisory board on AI safety and security to provide guidance on elevated cyber threats facing critical infrastructure
The language of innovation
By ITPro published
whitepaper Why and how to gain your customers’ trust with your AI and digital transformation strategy
Just how open are the leading open source AI models?
By George Fitzmaurice published
Analysis Open source AI needs a rethink, as big-name AI models increasingly fall short of the traditional definition
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