Some ideas:
1. Buy O'reilly (and other tech) books as they come out. This will have a lag, but essentially somebody did this research & summarization work, and wrote it up for you in chapters. Note that you don't have to read everything in a book. Also, $50 is a great investment if it saves you 10s of hours of time.
2. Talks on Youtube at conferences by industry leaders, like Yann LeCun, or maintainers of popular libraries, etc. Also, YT videos on the topic that are upvoted/linked.
3. If you're interested in hardcore research, look for review articles on arxiv.
4. Look at tutorials/examples in the documentation/repo of popular ML/AI libraries, like Pytorch.
5. Try to cover your blindspots. One way or another, you'll know how new AI is applied to SWE and related fields. But how is AI applied to perpendicular fields, like designing buildings, composing music, or balancing a budget? Trying to cover these areas will be tougher, because it will be more noisy, as most commenters will be non-experts compared to you. To get a feel for this, do something that feels unnatural, like watch TED talks that seem bullshity, read HBR articles intended for MBAs, and check out what Palantir is doing.