Mo Patel (@mopatel) header
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Leadership: AI | Security | Distributed System. Experiencing the human condition on Jiu Jitsu mats.

Austin, TX
Joined December 2008
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If there is one constant in management as you get older it is that the companies at peak interest (by any measure) are the ones where no matter what they are doing it is a best practice and that is the source of endless fawning. Later in a corporate arc, these same companies will do the inverse and if successful that becomes the "pivot" playbook of the day. And when not doing well whatever they were doing becomes the third rail of management. If what you're doing works then it works. It isn't always best or the "right" answer. Context is everything. Also blindly pivoting to the other thing almost never works. You can tell from the reorg or announcement mail since it leads with the implementation and not the problem being solved (eg first, here's our new "functional" org chart, or first, here's our new "pizza team" org chart.)
Anthropic's leadership team on how they built a culture with no internal politics: In this conversation, Anthropic's co-founders and executives explain why the company works the way it does. It comes down to trust, low ego, and a single shared mission. President @DanielaAmodei explains the foundation: "I think Anthropic is really low politics... it's because of low ego. And I do think our interview process and just the type of people who work here... there's almost a like allergic reaction to politics." That low-politics environment produces something rare: genuine trust between teams. Co-founder @NotTomBrown describes the level of alignment inside the company: "Everybody was just like, 'Yes, obviously we're going to do this.'" He adds: "I do think that the trust thing is a special thing. That's extremely rare." Where does that culture come from? Co-founder Sam McCandlish points to leadership: "I credit Daniela with keeping the bar high. I credit the fact that we keep the reason the culture scaled." @jackclarkSF who leads policy, notes something simple but easy to overlook: "I think people say how nice people are here, which is actually a wildly important thing." CEO @DarioAmodei ties it all together with the principle that keeps every department pointed in the same direction: "Unity is so important. The idea that the product team, the research team, the trust and safety team, the go to market team, the policy team, the safety folks, they're all trying to contribute to kind of the same goal, the same mission of the company... I think it's dysfunctional when different parts of the company think they're trying to accomplish different things, think the company is about different things or think that other parts of the company are trying to undermine what they're doing." He continues: "The most important thing we've managed to preserve is... this idea that it's not, you know, there's some parts of the company causing damage and other parts of the company trying to repair it. But that there are different parts of the company doing different functions and they all function under a single theory of change."
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3) Harnesses make a huge difference in cost-performance. The very simple Pi harness (@badlogicgames) got the same success rate as harnesses from the LLM vendors with Opus and GPT 5.5, but at 2x less cost! Seems to be mainly due to smaller inputs to the LLM.
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I have Asthma, allergies, dyslexia, ADD, anxiety, and Depression. But I will tell you that what you have does not define what you can become. Why Not You!
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new post 📈 Interest in #PyTorch among researchers is growing rapidly oreilly.com/ideas/one-simple…
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Mo Patel retweeted
.@Mapillary does Seamless Scene Segmentation and 3D fusion across 600m+ images A [post + code release] with tips for: - dealing with asymmetric graphs, variable-length images and unbalanced batches - in-place batchnorm - distributed training pytorch.org/blog/mapillary-r…
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We’re open sourcing PyTorch-BigGraph, a tool that makes it much faster and easier to produce graph embeddings for extremely large graphs. Quickly produce high-quality embeddings without specialized computing resources like GPUs or huge amounts of memory. ai.facebook.com/blog/open-so…
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Mo Patel retweeted
Our community has a defeatist narrative even with a growing list of promptly forgotten David v Goliath stories. Isolate what matters in the long term from what gets the spotlight at this moment. You may find "irrational" courage a skill worth cultivating. smerity.com/articles/2018/li…
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Mo Patel retweeted
MaskRCNN-Benchmark: - A fast, modular reference of {Mask,Faster}RCNN - by @fvsmassa (PyTorch), optimized by Nvidia - reusable components, pre-trained models - optimized inference, live demo Hope to see mmdetection and other great projects reuse the code! github.com/facebookresearch/…
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Scaling up ML yields interesting/terrifying regression testing / reproducibility challenges. According to Yangqing Jia, FB recently found that changing the number of threads used to perform matmuls results in accumulating non-determinism in a RNN & impacts performance.
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Mo Patel retweeted
{mmdetection, mmcv} by Multimedia Lab @ CUHK - a modular, object detection and segmentation framework - fast state-of-the-art models like Mask RCNN, RetinaNet, etc. - powered the winning entry of COCO Detection 2018 challenge. github.com/open-mmlab/mmdete… mmcv.readthedocs.io/en/lates…
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Why you need to improve your training data, and how to do it: petewarden.com/2018/05/28/wh…
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new post: How to build analytic products in an age when data privacy has become critical #StrataData #TheAIconf goo.gl/hGRGcM
Mo Patel retweeted
Dear PyTorch users, We're sharing the roadmap for PyTorch 1.0: production ready PyTorch. Coming later this summer. pytorch.org/2018/05/02/road-…
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The first iteration of DAWNBench is done! We saw a 477x improvement in ImageNet training time and a 23x improvement in cost! dawn.cs.stanford.edu/2018/04…
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Building tools for the AI applications of tomorrow 🌟 I collected some thoughts on future AI systems and wrote a new post with @mikeloukides ✍️ See you at #TheAIconf NYC (trainings start tomorrow) goo.gl/GJRvyz
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1/ Model interpretability is a red herring. Humans can't even do it. e.g. why did you: - Decide to eat that sandwich with your left hand? - Put one foot in front of the other? You might say AI engineers should know how the system works, even if system *itself* does not...
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PyTorch now available on @pypi $ pip install torch thanks to all the devs who worked on Warehouse -- the new PyPI infrastructure
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1/ People (mostly people working with Computer Vision) say that CV is ahead of other ML application domains by at least 6 months - 1 year. I would like to explore why this is, if this is something to be concerned about, and what it might take to catch up.
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