- Securing endpoints is an increasingly complex undertaking as workforces grow more disparate and user devices become more diverse. It’s time to rethink your endpoint security strategies and technology to keep up with unprecedented change. Tune into the Evolution of Endpoint Security summit to hear leading experts discuss advanced and emerging endpoint security technology as well as helpful best practices for managing the chaotic endpoint environment many companies will contend within 2023.
- A well-designed data architecture enables effective data management, fosters a modern data-driven work culture, and delivers analytics to help organizations make smarter business decisions. This is a straightforward concept, but there are several considerations and steps for creating a successful data architecture. Attend the three-day Data Architecture Best Practices summit for expert insights and resources to help you build and manage a data architecture that leverages data intelligence to solve the business problems of today and the future.
Show Your Work with XAI
Recent generative AI developments have raised several questions as the products created around these new developments moved into the consumer space. AI chatbots were confidently wrong about certain topics. Critics pointed out that generative image AI used training models based on other artists’ work. None of this is new for those of us who have been watching these stories develop. What is new is the push to make these models more transparent about how they generate the responses they provide.
XAI, or Explainable Artificial Intelligence, increases AI model transparency so users can understand the logic behind its answers. For something like Bing’s new AI-enhanced search engine, it’s pretty easy to understand where the answer to our query comes from. It’s less easy to understand why the model chose to give you one result over another. Users have not typically been able to see the inner workings of generative AI models and had to trust the product. With XAI, there is a focus on developing models that clearly explain their decisions. The increased transparency of those decisions allows easier detection of AI training biases.
There is a significant amount of distrust towards tech companies from the general public. From expanding monopoly power of tech giants to the lack of data privacy protections to the misuse of that private data, people have the right to be concerned. With user-friendly consumer AI, people are now fearful of the elimination of whole job sectors. While transparency won’t solve most of these concerns, it can boost public sentiment for the time being.
Contact The DSC Team if you are interested in contributing.
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