The rise of Big Data I want to take you back to 2012 . In those early years, our focus was big data, and everyone else was looking at the cloud. We were pioneering, but in a world that was still beginning to understand the value of data.
Transform Enterprise Operations with Google’s Latest AI Stack: Smarter Agents, Multimodal Interfaces, Real Business Impact The annual Google Cloud Next event took place in Las Vegas in April, where Google presented its latest innovations for Google Cloud Platform (GCP).
The hidden dangers of AI: key risks every business should know
Exec summary By now, we all witnessed the vast possibilities that ChatGPT and other large language models offer. They accelerated the growing potential of AI to an unseen speed and are delivering value as we speak.
Strategy, values and regulation as guidance It is critical for decision makers to gain a solid functional understanding of AI.
Large language models (LLMs) are taking the world by storm. An avalanche of use cases is being unlocked each day, and there are no signs of slowing. Various industries -also those previously thought to be “safe” from automation, see their core business change at neck-breaking speed.
Introduction The blog post discusses the performance of Automated Machine Learning (AutoML) tools that aim to automate the tasks of applying machine learning to real-world problems without expert knowledge. The blog argues that the tools available today do not deliver upon these promises and uses two different approaches to answer the question "What can you expect from Automated Machine Learning?" The first approach is zooming in on the technical aspects of AutoML, and the second approach is zooming out to show the bigger picture. The technical aspects show that there is no consensus on the exact components that should be automated, and different AutoML tools offer a wide variety of features. The bigger picture shows that AutoML is just a single step in a bigger problem-solving story. Without expertise, humans in this story risk making mistakes at the interfaces. Therefore, AutoML users need at least some expert knowledge to work with AutoML effectively.
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AI has great potential to accelerate efforts to protect our environment, such as reducing emissions or making more efficient use of scarce resources. Let’s dive into some examples of use cases - with no ambition to be exhaustive - where AI can help tackle important challenges.
AI has great potential to accelerate efforts to protect our environment, such as reducing emissions or making more efficient use of scarce resources. Let’s dive into some examples of use cases - with no ambition to be exhaustive - where AI can help tackle important challenges.
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