Articles
One major roadblock continues to prevent enterprises from realizing AI’s full potential — the fear and distrust of biased AI.
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With enterprises looking for more and more data to enable cutting-edge technology, many are turning to synthetic data to make up for shortfalls.
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Machine learning research is progressing at a startling pace, with downstream implications for marketers and businesses alike.
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Getting enterprise search right is hard, but it's starting to reap the benefits from advances in machine learning and AI.
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Marketers have been talking about personalization for years, so why haven’t all brands perfected it?
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Human-machine collaboration is where we are today, and it isn't about technology. This is about a mind shift in how businesses approach how we work.
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Businesses are turning to AI for a competitive edge. However, AI is expensive, requires considerable investment and ROI is not guaranteed.
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As AI is gaining traction in the enterprise so too is AutoML, or automated machine learning. Here's is how it's adding value to the process.
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Understanding how overfit and underfit work in data modeling can help marketers work better with technical teams when selecting data for ML models.
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Machine learning-based systems bring the potential for business growth, but also carry some challenges and risks. Being aware of the latter is the first step.
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Use the VDOCR model to build your ML implementation plan: Vision, Data, Organizational alignment, Change management and Revalidation.
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Imagine if your digital marketing tools had the capacity to predict the future. What would you do with that crystal ball?
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Designed well, conversational AI delivers seamless customer experiences. Designed poorly, conversational AI can result in frustrated customers.
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Most organizations are still at the point where machine learning as a technology remains in the realm of research and exploration.
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