Apart from the debugging and auditing of the models, data scientists need to look out for meeting data privacy standards in the context of explainability. With increasing business applications such as autonomous driving and medical diagnosis, there is a growing demand from the stakeholders to know what is at stake. Machine learning algorithms are infamous for their black-box nature. Stage: Innovation Trigger & Peak of inflated expectations resp. For good or for worse, these networks are here to stay for a while now. Generative networks are all fun and games until they start interfering with matters of national interest. Malicious online players can now generate disinformation in the form of images and videos that can fool many. The proliferation of generative networks has had many unwanted results. In the Gartner Hype Cycle, Generative AI featured in the ‘innovation trigger’ segment of the graph. These networks are considered to be one of the critical turning points in the history of ML. All this thanks to Generative Adversarial Networks (GANs). 2| Generative AIĪI can now paint auction-worthy art, generate songs and even create faces of people who never existed.
The safe, secure and real-time output is the theme of the modern world. Whereas, Google’s Coral toolkit can be leveraged to bring machine learning to edge.
As we approach an era of IoT, 5G and portable medical devices, it is crucial to facilitate developers to develop and deploy edge applications quickly.įor example, with the NVIDIA Jetson AGX Xavier developer kit, as shown above, one can easily create and deploy end-to-end AI robotics applications for manufacturing, retail, smart cities, and more. Thanks to the efforts of top companies to place supercomputers in the pockets, Edge computing systems have garnered significant attention.
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