First, let's get some terminology straight.
- AI, Artificial Intelligence, is using algorithms that mimic human intelligence
- ML, Machine Learning, is using feedback to improve (train) AI algorithms
- DL, Deep Learning, is using self-generated feedback for training
DL is a subset of ML, is a subset of AI.
For (1), I think we need to draw a distinction between using AI
to do engineering work, and using it to implement systems.
AI techniques are making inroads into Electronic Design Automation, or EDA. More about that here: https://www.electronicdesign.com/industrial-automation/article/21120058/eda-in-the-era-of-ai
Another hot topic for AI in electronics is antenna design. Lots of research and practical applications in this area.
AI systems applications on the other hand are seeing wide use for automation, signal processing, big data inference extraction and numerous other areas - way too many to list here.
For (2), AI processors are architectures optimized to run AI algorithms, commonly referred to as neural networks. Generally, they support a large number of parallel computations and may also have specific hardware for implementing core AI-specific neural-net calculations, such as non-linear thresholding (e.g., ReLU) that models how neurons process inputs.
AI processor chip examples include DSP, GPU, FPGA, and AI-specific ASICs. Each has their own trade-off in flexibility in the range of AI problem classes they can address vs. power and cost.
A server used for network development / network training wants flexibility (efficient feedback for training, fast feed-forward for inference), while an embedded application like autonomous driving can use a more restrictive architecture optimized for a specific network type and feed-forward inference only.