As businesses are expanding and evolving, they are looking to cloud computing and artificial intelligence (AI) for solutions. Cloud computing offers numerous benefits, such as scalability, cost efficiency, and accessibility, but it also comes with specific security risks. It’s essential for organizations to be aware of these risks and take appropriate measures to mitigate them. Tech Target brought this interesting topic to our attention in their article, “AI, cloud trends shape next-gen managed services offerings.”
AI is playing a significant role in driving advancements and innovation in cloud computing. By leveraging AI-based predictive analytics, cloud providers can optimize resource allocation, enhance performance, and predict potential issues such as server failures or network bottlenecks. This helps in proactive management, reducing downtime and improving overall reliability.
The biggest challenge is that most organizations have little knowledge on how AI systems make decisions and how to interpret AI and machine learning results. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact, and its 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, the intelligence and the technology behind world-class explainable AI solutions.
