Looks really good! Maybe add in the title that it's MacOS only for now.
There's options for local and cloud-based AI - which is nice. I'd be cool to see what's the compute cost of requests!
Well, it's a great example as Dr. Earl Haas patented the first tampons in the early 30s and nobody cared. He then sold the patent to a business woman that made it successful. So effectively a man invented it but failed to market it and sell it well.
"Funding/Support: This study was partly funded by the CCSA as a subcontract for a Health Canada grant to develop guidance for Canadians on alcohol and health."
BUT
"Conflict of Interest Disclosures: Dr Stockwell reported receiving personal fees from Ontario Public Servants Employees Union for expert witness testimony and personal fees from Alko outside the submitted work. Dr Sherk reported receiving grants from Canadian Centre on Substance Use and Addiction (CCSA) during the conduct of the study. No other disclosures were reported."
Where "Alko Inc is the national alcoholic beverage retailing monopoly in Finland."
> "Of course adding poison to your body is unhealthy and will affect mortality."
This is in contradiction with the basic principle of toxicology: "dosis sola facit venenum".
A "systematic review and meta-analysis of 107 cohort studies involving more than 4.8 million participants" cannot simply be shrugged away the way you do.
In the case of municipal broadband it mostly comes from communities that suffer form poor/no investment from the big players and is the only recourse for decent internet. It usually is faster and cheaper than existing solutions. There's clearly market failure here and players abusing their local monopolies to refrain from investing / provide decent service.
The state of telco in the US is pretty shocking, even in places like NY state.
Terrier, C., 2020. Boys lag behind: How teachers’ gender biases affect student achievement. Economics of Education Review, 77, p.101981.
"Economics of Education Review is a quarterly peer-reviewed academic journal covering education economics. It was established in 1981 and is published by Elsevier."
Whilst I agree with the general sentiment, in this particular instance it has to do with the depth of network that could be trained efficiently thanks to hardware advances. LeNet was 7 layers deep, Dan's 9, VGG's 13, GoogleNet's 22, etc.
There is theory w.r.t to thick networks as well (e.g the link to Gaussian processes require infinite width).
Well except that most neural network are not deep, they have a very low number of layers but each layer can be tremendously wide.
This should have been called wide learning.
But we could imagine some learning algorithm that exploit more depth than wideness.
A more correct naming would take into account both dimensions: depth and wideness.
Note that this is hortogonal to sparsedness vs density
The depth seems to matter more than the width, at least as long as the layers are sufficiently wide. In fact, in the limit that the layer becomes infinitely wide, you just end up with a Gaussian process. In practice a width of ~100--1000 is sufficient to get behavior that is pretty close to a Gaussian process, so in general doubling the width of a layer doesn't gain you all that much compared to using those parameters for an extra layer. The real representational power seems to come from increasing depth.
Around the time the phrase "deep learning" came into vogue, the advances were indeed in training deeper networks, not wider. Later on it turned out that shallow wide networks are sufficient for many problems. (Also, it turned out the pre-training tricks that people came up with for training deep networks weren't really necessary either.)
It's also important to note that they work despite being wide, you can see that with the efficiency of pruning, and ideas such as the lottery ticket hypothesis that state that "successful" sub-networks within the wide network account for most of the performance.
In the theory literature, if you have a K-deep network, K=1 is the shallow case, K>1 is deep. Agreed naming could be better, but it's not like "deep work" or "deep thoughts" as the parent was stating.
The adjective "deep" came from deep belief networks, which are a variation on restricted boltzmann machines. RBNs have one visible and one hidden layers, DNBs have more hidden layers - hence "deep". So it's not exactly based on a distinction between "deep" and "shallow" models.
I dunno, in the resnet age, many and perhaps most networks are 20+ layers. I feel like the shallowest networks I see these days are RNNs being used for fast on device ML, which trends not to be terribly wide due to the same hardware constraints.
The dichotomy between Greenwald's complaints (censorship of his article despite contractual guarantees, Reality Winner cover-up, what editors forced Lee Fang to do, lack of reporting of Assange hearing, and "lack of editorial standards when it comes to viewpoints or reporting that flatter the beliefs of its liberal base") and the editor in chief response in the NYT [0] (he is a "grown person throwing a tantrum") is frightening.
It's not just CUDA vs ROCm, ROCm has come a long way and is pretty compelling right now. What they lack is the proper hardware acceleration (e.g tensor cores).
ROCm has come a long way but still has a long way to go. It still doesn't support the 5700 XT (or at least, not very well) --- only the Radeon Instinct and Vega are supported. You can find salty GitHub threads about this dating to the very start of when Navi was just released: https://github.com/RadeonOpenCompute/ROCm/issues/887#issueco...
And getting ROCm set up is still a buggy experience with tons of fiddling in the deep inner workings of Linux so it is nearly impossible for the average machine learning engineer to use.
It is compelling for certain bespoke projects like Europe's shiny new supercomputer, but for the vast majority of machine learning, it is totally unusable. By now in ML world the word "gpu" is synonymous with "nvidia".
Full disclosure, European here and in our team everyone is found of Linux so not representative of your average MLEngineer.
We actually had more issues with nvidia drivers messing up newcomers' machines during updates than with setting up AMD GPUs, but then again n is small (and AMD GPUs were for playing around rather than real work).
Still, a Titan Xp has CUDA support and plenty of memory, but it's better, IME, to upgrade to a model with less memory but higher cuda compute and access to tensor cores.
