The progress of artificial intelligence (AI) models over the past few years has happened faster than anyone expected. Maybe too fast? This interesting information came to us from New Scientist in their article, “How AI chatbots in search engines will completely change the internet.”
One of the primary inhibitors to data science and ultimately AI has to do with data storage and retrieval. Only recently have data warehouses evolved to store massive amounts of data in a way that’s useful, not cost-prohibitive, and not requisite of an army of technology personnel to maintain. We’ve had data warehouses for decades, but they’ve been complicated and costly.
The AI market has been on a swift growth path for several years – so much so that the industry is expected to reach $42.4 billion this year. The momentum has continued noticeably with the debut of powerful, new, AI-powered tools and services across industries.
The real problem lies in understanding. Most organizations have little knowledge on how AI systems make decisions or how to grasp and leverage the results. Explainable AI is used to describe an AI model, its expected impact, and it potential biases. Why is this important? Because explainability becomes critical when the results can have an impact on data security or safety.
Melody K. Smith
| Data Harmony is an award-winning semantic suite that leverages explainable AI. |
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Sponsored by Access Innovations, changing search to found.
