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Conversational AI use cases for enterprises

IBM Journey to AI blog

Beyond the simplistic chat bubble of conversational AI lies a complex blend of technologies, with natural language processing (NLP) taking center stage. This sophisticated foundation propels conversational AI from a futuristic concept to a practical solution. billion by 2030.

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The most important AI trends in 2024

IBM Journey to AI blog

The incoming generation of interdisciplinary models, comprising proprietary models like OpenAI’s GPT-4V or Google’s Gemini, as well as open source models like LLaVa, Adept or Qwen-VL, can move freely between natural language processing (NLP) and computer vision tasks.

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How AI saves money and improves banking complaint handling

Snorkel AI

AI is accelerating complaint resolution for banks AI can help banks automate many of the tasks involved in complaint handling, such as: Identifying, categorizing, and prioritizing complaints. Natural language processing to extract key information quickly. Assigning complaints to staff.

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How AI saves money and improves banking complaint handling

Snorkel AI

AI is accelerating complaint resolution for banks AI can help banks automate many of the tasks involved in complaint handling, such as: Identifying, categorizing, and prioritizing complaints. Natural language processing to extract key information quickly. Assigning complaints to staff.

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How AI saves money and improves banking complaint handling

Snorkel AI

AI is accelerating complaint resolution for banks AI can help banks automate many of the tasks involved in complaint handling, such as: Identifying, categorizing, and prioritizing complaints. Natural language processing to extract key information quickly. Assigning complaints to staff.

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NeurIPS 2023: Key Takeaways From Invited Talks

Topbots

Presenters from various spheres of AI research shared their latest achievements, offering a window into cutting-edge AI developments. In this article, we delve into these talks, extracting and discussing the key takeaways and learnings, which are essential for understanding the current and future landscapes of AI innovation.