I wonder how this would apply with vision models? I tried with a few example of single images and they appear to do well. I did a few toy examples and they seem to do pretty well (Claude + Gemini) with spotting differences. An example image: https://www.pinterest.com/pin/127578601938412480/
They seem to struggle more when you flip the image around (finding fewer differences, and potentially halluciating)
I was with Amazon but wasn't part of Alexa. I was working closely with the Alexa team however.
I remember vividly the challenge of building centralized infra for ML at Amazon: we had to align with our organization's "success metrics" and while our central team got ping ponged around, and our goals had to constantly change. This was exhausting when you're trying to build infra to support scientists across multiple organizations and while your VP is saying the team isn't doing enough for his organization.
Sadly our team got disbanded eventually since Amazon just can't justify funding a team to build infra for their ML.
> Amazon just can't justify funding a team to build infra for their ML.
Sounds like they didn't plan it out correctly. It should have been done in phases, one team at a time, starting with the Alexa team, or the smallest team with the smallest amount of effort as a test bed, while keeping the other teams informed in case they have suggestions or feedback for when their turn comes along.
Isn't this a challenge for any big tech companies. Success metrics tend to be attached to a particular product and yet... central infra is a necessary foundational block for everyone, but it is without a specific success metrics.
We were working with folks from other central ML infra teams in other companies. Amazon is the one that doesn't have any funding for central ML infra (they have the central infra for software). Having interacted with many companies after leaving Amazon, they are just really bad in terms of investing in central ML.
Amazon did AWS, so they can do central infra. Possibly the massive success of AWS makes them expect the same level of self funding for other infra projects even if those would be better served as internal only with internal funding.
I was at Amazon but wasn't part of Alexa. I remember taking a look at their code. It was an endless spaghetti of if statements. The top one started like this
if(tenant == "spotify") { ...
and everything else was downhill from there.
The rest of the description on how Amazon operates is quite accurate. Impossible for anyone to do anything meaningful anymore.
For me the big problem was how hard Hoverboard was to use - they built nicer tooling around it eventually but getting onboarded took weeks to months, getting GPUs usable enough to do LLM training would be nigh-impossible, an _extremely rigorous_ dedication to customer security meant transferring any data into and out of it for analysis was a major pain....
I remember being in the office when GPT2 dropped and thinking the entire Alexa Engine/skill routing codebase became outmoded overnight. That didn't really happen, but now that MCP servers are so easy to build I'm surprised Alexa doesn't just use tool-calling (unless it does in Alexa+?)
I don't think this decision can force the Department of State to issue new visas for Havard students unfortunately. At least existing students *might* be alright...
I've been a long term Alexa user and kinda hard to get out of the ecosystem, but it's gone downhill fast in the last few years in terms of the UX.
* It forgets about my configured routine ("good night" -> "turn off all lights", now it would randomly reply "good night" and does nothing).
* It keeps trying to push down other services/products on top with long winded follow up question. There's no way to turn this off
* Keep asking if it's answering from the correct room
* Its ability to understand my command has gone downhill - and I'm just ordering it to turn the lights on and off in certain rooms. Now it keeps saying "cannot find bedroom" for example even though there's clearly a group called "Bedroom" set up in Alexa
European country really gave me bad taste when it comes to visa experience. Gave me the absolute minimum despite having a good paying job - and even after marrying a EU citizen I still had a hard time.
This is news because this woman happens to be from a developed country and not a developing countries.
When I was at a big tech, the interpretation by their lawyers was that by running AGPL, we will have to open source everything in the network to users. The problem is in the definition of "Modification" - Per [AGPL](https://www.gnu.org/licenses/agpl-3.0.en.html#:~:text=To%20%...):
> "The "Corresponding Source" for a work in object code form means all the source code needed to generate, install, and (for an executable work) run the object code and to modify the work, including scripts to control those activities."
This can be interpreted as even modifying any configuration to allow the software to run on your own infrastructure. Obviously this is a very aggressive interpretation but the lawyers didn't want us to test this phrase in court so all AGPL software had a blanket ban.
You can come on L1B and then try to get into the H1B lottery (will be tough since you don't have a US degree - I assume). I decided to go with L1B since H1B was impossible to get back then and the timeline worked in my favor.
I came to the US via L1B but went straight into green card. Unfortunately EB2 ROW is backlogged at the moment so you might be looking at 2-3 years. And if your company does lay off, they'd have to pause the PERM process for 6 months, which will add further delays to your timeline.
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