yahyaheee

That’s not the same as on the spot coding challenges in a whiteboard though

This is amazing!

With highly skilled workers they are likely to just leave. I would prefer to help the causes that are close to me

Couldn’t the employees decide what they wanted to do with their money?

Not sure why it still stakes hours to days to upload a video from my iPhone.

Guess the “we’re cool like apple” play didn’t work out

Except the compilation time of course

This is an amazing accomplishment, I could see the ideas here being applied to more data structures

It's such a ridiculous position they are taking

So due to how Go handles memory, it needs to do something called "trampoline", over to the C stack. This causes Go->C calls to have a substantial overhead 70-200ns/op. Most other languages that value is closer to 1ns. This means that every call to a GPU suffers from this latency.

This isn't too much of a problem if your doing all supervised learning with batch operations because the speedup of a GPU over a bigger operation outweighs the FFI latency.

However, it's a problem that doesn't appear to have a solution due to Go's memory management choices, and will hamper it ever being used for accelerated computing problems. This is one of the reasons Rust moved to using ownership rules.

You can read a bit more at https://dave.cheney.net/2016/01/18/cgo-is-not-go

I feel where they are trying to go with this, I wrote https://github.com/aunum/gold in Go because of all the nice parts of Go.

This solves some of the pain points but is still fatally flawed as any other Go ML tool in that it can’t accelerate due to the Go<->C FFI.

Until that issue is resolved Go simply won’t be broadly accepted in data science.

This is particularly impactful since macOS won’t support CUDA drivers anymore.

Works great! ... till it doesn’t

Not to mention acceleration is an issue with the go/c ffi

Exactly, and he went on to offer more ways by which they could get women into tech based on that study. But I guess that makes you a misogynist

Yea and he was essentially complaining about google brainwashing

Power is currency

Google very much does not own Kubernetes. They have vested interest in a number of SIGs but the SIGs have been good about preventing the chairs from being Google dominated.

Redhat, Microsoft and vmware are major contributors to the product. Few of the founders work for Google anymore. Google probably has the largest influence but it is very much a community.

This sounds a lot like toxic work culture, but unfortunately there’s a lot of that in tech

This is an insane amount of funding when you look at the projected revenue for machine learning over the next couple years.

Maybe those projections are wrong, and I certainly love all the energy in this space, but it feels like a lot of investors are going to get burned.

Yea this is the thing, I’m terrible at white boarding but I have an amazing github that can show what I’ve done. An amazing github presence should far exceed any whiteboarding

I think we can all agree that C++ is a dragon that needs to be slain here. Swift could potentially get close to that for most of the needs, but I still wouldn't bet data scientists would write swift.

Yep this is the key, its not really the data science end its the engineering piece.

Can you get things done in python/c++ sure, but the two language problem is a well known issue, and python has a number of problems. People certainly want a better option, and google investing as much as they did validates that notion.

Eh wrote it for a couple years my tldr; multiple dispatch is odd, types are too shallow, jit is really slow to boot, the tooling is poor.

Kotlin is an interesting option, especially now that it compiles to the LLVM. One of the issues I see is the mind space these languages occupy. Kotlin is deeply linked to the Andriod community which oddly feels like baggage.

Julia has done a great job at marketing itself as the language built for modern numerical computing by numerical computing people. They have effectively recruited a lot of the scientific community to build libraries for it. I think the language is flawed is some deep ways, but there is a lot to learn in how they positioned themselves.

I like Nim quite a bit, I would put it and Julia as the best contenders at the moment.

Yeah its a very closed ecosystem, that is heavily Apple centric. The Apple war with NVidia is ridiculous, I guess they are just betting on TPUs or AMD to make up the difference. It is also strange that its so tied to Tensorflow rather than being just a better numeric language. Julia is definitely a better option, but I still feel we can do better than that.

Yea I don't hate the language I sort of see it as a better Java. I just don't think its right for the ML/Data science community.

I would disagree, first Go has been tremendously successful and yes will have generics soon. The ML community really needs a better language than Python, the current alternatives (Julia, Nim, R) are alright but seem to miss the mark in this arena. I see few data scientists excited about Swift, its too heavy handed and deeply embedded in the Apple iOS community.

People are searching for a better language in this space and it's something that often needs a corporate backing. Google is aware of this problem and hired Chris Latner to fix it, its just a bit of unfortunate oversight, I guess we'll keep using Python for now.