dplarson

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].

[0] https://www.microsoft.com/en-us/worklab/work-trend-index/bre...

[1] https://www.microsoft.com/en-us/worklab/work-trend-index/202...

I found this presentation helpful for an intro to H3's design/motivation: "Engineering Sub-City Geos for a Hyper-Local Marketplace with Uber": https://youtu.be/wDuKeUkNLkQ?si=-9JmxZQJ2LZo6Kh4

> Geothermal energy source found in Presidio County, Texas

Contrary to the clickbait title, the article included the key point in the first bullet point.

For folks that don't recognize the author (Ian Stapleton Cordasco), he's one of the core maintainers for python-requests (sigmavirus24 on github).

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."

Tweet with other links: https://twitter.com/mmbronstein/status/1404380398856060932

Paper (PDF): https://arxiv.org/pdf/2104.13478.pdf

Blog post: https://towardsdatascience.com/geometric-foundations-of-deep...

This Twitter thread [1] points to research on the subject of reducing police violence, which I found fairly informative.

[1] https://twitter.com/samswey/status/1180655701271732224?s=19

Since the article didn't link to the paper:

- "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks"

- https://arxiv.org/abs/1803.03635

Additional context from Ali Rahimi and Ben Recht: http://www.argmin.net/2017/12/11/alchemy-addendum/

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

This is great to hear! Also, the video linked in the article [1] provided a nice overview of the work that will be supported by the grant.

[1] https://www.youtube.com/watch?v=fowHwlpGb34

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.

Thanks for the info and context!

I'm having trouble finding the source of the figure shown, but I did find a page with similar information ("Bitcoin Energy Consumption Index"): https://digiconomist.net/bitcoin-energy-consumption

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’

I found the Github page to be a better source in this case: https://imatge-upc.github.io/detection-2016-nipsws/

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]: http://www.beepnetworks.com/img/datasheet.pdf

[1]: http://www.digi.com/products/xbee-rf-solutions/rf-modules/xb...

Neat, they use a Teensy microcontroller with an accelerometer breakout board from Adafruit for the hardware.

A very interesting and informative post, as to be expected from someone like bunnie.

It's cool that they provided bindings for Torch, but also interesting. Torch is obviously very widespread/popular as a deep learning framework, but I got the impression that Baidu's Silicon Valley AI Lab (SVAIL) ran mostly a custom C/C++ codebase.

Most likely the Torch bindings were added to help spur a wider variety of researchers to use their CTC implementation (i.e. it doesn't mean they've switch to Torch internally). But still interesting to see.

Another great piece of content from Julia Evans :)

They named their method "YOLO"…

Edit: to add something more "helpful" to this comment, their paper links to a YouTube channel [1] that shows demos of their method, which I think is great.

[1] https://goo.gl/bEs6Cj

It's mentioned in the article, but here's a direct link to the paper on arXiv: http://arxiv.org/abs/1512.03385

And direct link to the PDF: http://arxiv.org/pdf/1512.03385v1.pdf

Related discussion (on Baidu's Deep Speech 2 results) from 2 days ago: https://news.ycombinator.com/item?id=10707538

During the GPU Technology Conference (GTC) 2015, Andrew Ng showed a live demo of Deep Speech (1?) [0] (demo starts ~41 minute mark). There are other videos showing Deep Speech, but I found this one the most useful/interesting (of the ones I've seen).

[0] http://www.ustream.tv/recorded/60113824

No worries. Also, I found the article very interesting and informative. Thanks!

I think the author revised the figure(s) between the time of the parent comment (by gcr) and your comment. At least, the A * B = C figure's filename seems to imply a revision [1].

EDIT: yep, the figures were revised. Compare the corrected version [1] vs the original [2].

[1] https://petewarden.files.wordpress.com/2015/04/gemm_correcte...

[2] https://petewarden.files.wordpress.com/2015/04/gemm.png

EDIT 2: I completely missed that the author put a notice (about having revised the figures) at the bottom of the post.