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The excitement (and drama) around AI continues to escalate. Why? Because the stakes are high. The race for competitive advantage by applying AI to new use cases is on! The launch of generative AI last year added fuel to the fire, and for good reason. Whereas the existing portfolio of AI tools had targeted the more technically minded like data scientists and engineers, new tools like ChatGPT handed the keys to the kingdom to anyone who could type a question.

Not long after graduating college, in the late 2010s, I had a data analysis job that scarred me for life. It involved reading through hundreds of documents—some of them handwritten—and meticulously, painstakingly entering data into spreadsheets. Who among us hasn’t been scarred by a similar experience? I remember thinking, as I spent mindless hours copy-pasting rows of text from one screen to another, that there had to be a better way. Mercifully, there is.

Our recently released predictions report includes a number of important considerations about the likely trajectory of cybercrime in the coming years, and the strategies and tactics that will evolve in response. Every year, the story is “Attackers are getting more sophisticated, and defenders have to keep up.” As we enter a new era of advanced AI technology, we identify some surprising wrinkles to that perennial trend.

Discussions about AI could be heard throughout the 7th annual Appian Government event, held November 29, 2023 at Capital One Hall in Tysons, Virginia. At breakfast, a customer sitting next to me mentioned how he’s looking forward to hearing how AI can help mine his historical data. “And securely,” another added. In a lunchtime conversation, yet another customer told me about the productive meeting they had with Appian on incorporating AI into their processes.

In 2023, generative AI took the spotlight, emerging as the most talked-about technology of the year. This content creating powerhouse can do everything from text, image, and video generation to answering questions through natural language queries. And its potential uses are vast. While many industries are still in the experimental phase, the insurance sector is poised to benefit significantly from the integration of artificial intelligence into its ecosystem.

This tutorial shows how to use ClearML to manage MONAI experiments. Originating from a project co-founded by NVIDIA, MONAI stands for Medical Open Network for AI. It is a domain-specific open-source PyTorch-based framework for deep learning in healthcare imaging. This blog shares how to use the ClearML handlers in conjunction with the MONAI Toolkit. To view our code example, visit our GitHub page.