DatologyAI | Member of Technical Staff | Full-time | Onsite, Redwood City, California | https://www.datologyai.com/
At DatologyAI, we build tools to improve large-scale deep-learning models by selecting the right training data. We are automating data curation at petabyte scale, helping customers to train models faster, to better performance, and enabling smaller models to achieve performance competitive with much larger models. Models are what they eat, and the data models ingest determines everything about their capabilities.
Since founding less than a year ago, we have raised $57.6M, including a $46M Series A in May 2024. Our backers include AI luminaries Geoff Hinton, Yann LeCun, Jeff Dean, and Amazon and Microsoft.
Fitbit, the leader in the fitness band market, is near a deal to acquire smartwatch maker Pebble, according to three people briefed on the deal.
The price couldn’t be learned but it is thought to be for a small amount. Pebble had been looking to sell, one of the people said. There have been signs over the past year or so that Pebble was facing financial challenges. Earlier this year it reportedly laid off about a quarter of its workforce.
THE TAKEAWAY
The expected sale of Pebble to Fitbit signals a consolidation in the wearables market.
The Pebble brand will be phased out after the deal. What Fitbit will get is Pebble's intellectual property, such as its operating system, one of the people said.
The deal signals a consolidation in the wearables space, which has been crowded with several players including Apple and Jawbone. Pebble burst onto the scene several years ago with a flashy Kickstarter campaign that drew interest from consumers. But it struggled to gain traction.
Pebble burst onto the scene several years ago with a flashy kickstarter campaign that drew interest from consumers.
Fitbit, which went public last year, has had its own issues. Its stock price which has fallen from a peak of $49 in August of last year to roughly $8 today, giving it a market cap of $1.87 billion. The company recently reported net income fell 61% to $43.5 million in the nine months to Oct. 1, despite a 39% increase in revenue to $1.6 billion. Fitbit's main product is fitness bands but it sells what some have called a smartwatch, pitting it directly against Apple's watch.
Meanwhile, Jawbone, another maker of wearable devices, has also struggled in the past year or two and had looked for a buyer, without success.
Somehow this whole startup thing often puzzles me.
I read good things about RethinkDB, bad things about MongoDB and almost nothing about ArangoDB. But Rethink closes shop, Mongo is used everywhere and Arango gets big funding.
Same with Pebble, read only good things about them, now they sell for a "small amount".
Last year there were 233,000 petitions. Only 85k petitions are actually accepted each year. Compare that to 172,500 petitions received in 2014 and 125,000 in 2013.
This means the rate of companies that use h-1b has actually slowed down by a lot. I know as a fact that most of the US tech companies that have offices in Europe, are actually transitioning to using L-1 visas and don't apply for H-1b anymore.
If you're a small startup and you want to hire someone who is not a US citizen your options are pretty limited right now.
So true! Been there myself! And have friends that are getting told by big tech companies that they don't do H1Bs anymore and prefer to do L1s and people working from their Europe offices.
I started looking at finagle, finatra and scrooge. i love all three projects. but at the same time, the documentation is really really bad.
if you have the same requirement as twitter, nice -- but if you detour even a little bit, you are going to have a shitty time (for example, for thrift, i wanted to use buffered codec instead of framed and there was not a single document explaining how to do it. i spent ~2h perusing unit tests to find a way which i don't know if it's correct or not).
No idea what Pinterest are using, but I led a team building the same thing using (mostly) commodity search kit in 2008.
Feature extraction was done with standard Java libs (proprietary algorithms though). Queries were initially performed using a vector space model, but I moved that to using an inverted index (Lucene) because in our use case the image queries were usually combined with free text and parametric search params.
The main issue we faced was scaling search with large number of query parameters, since a naive implementation created something like 300 query terms for each visual search. We did various things to optimise that, from distributing the index, to using index statistics to pick optimum words to query. I submitted some optimisation code (a modified MoreLikeThisQuery with an LRU term cache) back to Lucene, not sure what happened to it, think the JIRA issue is still open.
Caffe - deep learning feature computation and model training
OpenCV - local + other features
Zookeeper - service discovery
Cascading - batch processing jobs
We’ve built infrastructure around some of these libraries to operate at scale. For example we’ve build an incremental feature extraction pipeline that at the core uses caffe + opencv for the feature extraction (more details on how this works is in the paper).
At DatologyAI, we build tools to improve large-scale deep-learning models by selecting the right training data. We are automating data curation at petabyte scale, helping customers to train models faster, to better performance, and enabling smaller models to achieve performance competitive with much larger models. Models are what they eat, and the data models ingest determines everything about their capabilities.
Since founding less than a year ago, we have raised $57.6M, including a $46M Series A in May 2024. Our backers include AI luminaries Geoff Hinton, Yann LeCun, Jeff Dean, and Amazon and Microsoft.
We are hiring: Research Scientist: https://jobs.ashbyhq.com/DatologyAI/d5090b17-8b58-42ee-afa6-...
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ML Engineer: https://jobs.ashbyhq.com/DatologyAI/c64f9ea8-0943-476d-aabd-...
ML Infra Engineer: https://jobs.ashbyhq.com/DatologyAI/b5a42904-d7ec-45a4-96df-...
Full Stack Engineer: https://jobs.ashbyhq.com/DatologyAI/5546e184-6f86-4540-868d-...
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