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Top AI Trends of 2026 You Need to Watch
What will 2026 bring for AI users, AI experts, and the AI-curious? Look out for common AI trends in 2026: more and better ways to work with AI, lots more use of agents and multi-agent systems, big efficiency gains, and of course, robots. AI technology will continue moving from the broad to the specific, so now is the time to think about how you can get started, if you haven’t already.
Read now5 ways AI has impacted the healthcare industry
The healthcare industry lags behind others in AI adoption at scale, so there’s plenty of room for innovation. But AI has already made an impact in the areas of patient diagnoses, drug discovery research, predictive equipment maintenance, reduced call wait times, and more. See how healthcare companies have already made strides with AI and started to tackle the problems of workforce shortages, operational inefficiencies, and fragmented IT systems.
Read now6 Ways to Use AI at Work
AI at work can bring relief to workers and take on tasks like workflow automation, data analysis and reporting, and IT department ticketing.
Read nowWhat multimodal AI is and how it works
Multimodal AI is emerging as part of the fast-moving world of AI technology, bringing together multiple types and modalities of data to build a model that’s context-aware. Multimodal AI uses sophisticated neural networks, transformer models, and specialized frameworks to pull in text, image, video, and other data to generate rich outputs.
Read nowA Guide to AI Models: What They Are and How They Work
An accurate, high-performing AI model is a valuable business asset, but it requires good data, fine-tuning, updating, and an understanding of how it’s performing over time. Learn more about what an AI model is, how algorithms and LLMs work, what types of models are available, and how to build, train, and fine-tune an AI model that can improve business outcomes.
Read nowWhat is a multi-agent system?
Multi-agent systems bring together the abilities of generative AI agents to solve real problems. These multi-agent systems work exponentially faster than what a single AI agent can do, bringing together their speed and deep learning into a unified environment and assuring communication and collaboration. Agents can each tackle part of a bigger problem or workflow to automate IT ticketing, ensure smooth transportation in trucking or shipping, and conduct operations for massive sensor networks.
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