
26989 AIs and 459 categories in the best AI tools directory. AI tools list & GPTs store are updated daily by ChatGPT.

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A: The biggest challenges AI models face are how to be more power efficient, how to learn from less data, how to learn from unlabeled data (unsupervised learning), and how to generalize across multiple tasks. The industry is also focused on making AI unbiased and explainable, such that we know how it works, where it fails, and how to quantify confidence levels. For example, we want to understand how the AI used for autonomous driving is deciding how to drive safely on a road under various environments and weather conditions.
A: AI is currently benefiting from more data and more efficient hardware, as well as better AI tools and networks/algorithms. Advancements in state-of-the-art accuracy for various tasks happen regularly due to the collaborative nature of the AI research community through papers and workshops. For example, Qualcomm AI Research has published many papers in the areas of power efficiency, personalization, and efficient learning. These advancements are being applied in more areas, integrated into different types of devices, and enhancing our user experiences.
A: When there’s bias in the data set that trains an AI model, the model will contain the same bias. A way to address this bias is by collecting robust and diverse data. For example, in distributed learning on smartphones, you may want to sample data from different geographies and demographics. If bias is noticed in algorithms, the data should be examined to determine whether new data should be added.