Microsoft has a blog with more details [0] but the Axios article does a good job of summarizing the main points. The full report is also available to read [1].
If it uses a hash of an image, does that mean an edited image (eg cropped or resized) wouldn't be detected? Or is there a way to extend a single hash to multiple image variants? I imagine the answer is no, but I would also like to be optimistic and think even detecting the original version would stop a lot of NCII sharing (ie it's not a perfect solution, but still helpful).
Quite an interesting review, with lots of useful learnings for practitioners (in my opinion). I also liked the use of the term "graphical literacy" in the abstract, which is something I've seen cause challenges first hand (though I never thought to use that term):
"Effectively designed data visualizations allow viewers to use their powerful visual systems to understand patterns in data across science, education, health, and public policy. But ineffectively designed visualizations can cause confusion, misunderstanding, or even distrust—especially among viewers with low graphical literacy."
In case it's helpful, here's the first paragraph from the post:
"The original KiCad domain name (kicad-pcb.org) was recently sold to an unnamed third party that is not affiliated with the KiCad Project or members of the KiCad Development Team. This sale was unexpected and may pose a risk to KiCad users. The new owners may simply post advertisements or (worst-case scenario) they may host malicious versions of the KiCad software for download."
I wish the subtitle could have been included in the HN posting: "Scientists scramble to harvest ice cores as glaciers melt"
The next paragraph provides more context: "Ice provides historical records about climate and shows the impact humanity has had. But many glaciers are now melting, prompting renewed urgency among scientists."
For numerical optimization, a couple good textbooks are:
- "Practical Optimization" by P. E. Gill, W. Murray and M. H. Wright: a little old (1982), but provides a solid foundation
- "Convex Optimization" by S. Boyd and L. Vandenberghe: the standard for learning convex optimization (also available as a free PDF from the author's website)
- "Convex Analysis and Monotone Operator Theory in Hilbert Spaces" by H. H. Bauschke and P. L. Combettes: covers a more specialized area of numerical optimization, but the notation is beautiful (IMO) and it acts as a useful reference for recent research on, e.g., operator splitting methods
The topics covered are fairly broad and overall it seems like a nice collection of notebooks for teaching. Also, I agree with the choice to use Anaconda to install the dependencies. In my experience teaching similar type workshops (to engineering undergrad and grad students), Anaconda provides a good balance of simplicity and coverage, particularly with audiences of varying backgrounds.
This paper appears to be from 1998 [0]. No judgment on its quality; I'm just trying to provide a reference for other readers of the post.
[0]: A.C.C. Coolen, in ‘Concepts for Neural Networks - A Survey’ (Springer 1998; eds. L.J. Landau and J.G. Taylor), 13-70
‘A Beginner’s Guide to the Mathematics of Neural Networks’
They have a datasheet [0] for an "Environmental Monitoring Sensor" which lists a line-of-sight range of 50 miles and 1–3 miles in urban settings. For comparison, Digi's XBee PRO ZigBee wireless modules have a line-of-sight range of 2 miles [1]. I'm a little bit skeptical about the 50 mile range claim (seems too good to be true), but this is an interesting product nonetheless.
[0] https://www.microsoft.com/en-us/worklab/work-trend-index/bre...
[1] https://www.microsoft.com/en-us/worklab/work-trend-index/202...