When you're working on a startup the hustle is real. Walking into work this morning and seeing that somebody added us here and that it hit the front page is a great way to start the day. Thanks y'all :)<p>As a thank you here's a code to get 30,000 credits on me to test out some of the algorithms. Here's the code: HNDec2017
Competitors include:<p>* Azure Cognitive Services (REST-based)<p><a href="https://azure.microsoft.com/en-us/services/cognitive-services/directory/" rel="nofollow">https://azure.microsoft.com/en-us/services/cognitive-service...</a><p>* Amazon ML (REST-based), including Polly, Rekognition etc.<p><a href="https://aws.amazon.com/machine-learning/?nc2=h_l3_ai" rel="nofollow">https://aws.amazon.com/machine-learning/?nc2=h_l3_ai</a><p>AI-as-a-Service is a interesting space that is likely to grow over time.<p>Part of the problem with doing AI in-house is that it's really hard/expensive to get large corpuses of correctly labeled data to train your algorithms on, so what these folks are really selling is a set of trained model weights exposed as an API.
Algorithms are valuable, but in the scope of machine learning what is becoming increasingly valuable are data sets.<p>The algorithm is one very important building block but a good data set is what finally allows you to materialize a solution.
A related endeavor is James Simons' Flatiron Institute.<p>They're creating bespoke algorithms for academics in the fields of computational biology, computational astronomy, and computational quantum physics -- replacing current practice of passing down old, duct taped Fortran code from professors to grad students.<p>There was an interesting profile in the New Yorker a few days ago: <a href="https://www.newyorker.com/magazine/2017/12/18/jim-simons-the-numbers-king" rel="nofollow">https://www.newyorker.com/magazine/2017/12/18/jim-simons-the...</a>
Took a deep dive yesterday comparing a few tagging/classification algorithms to the Google Cloud Natural Language and AWS Comprehend APIs, and I have to say I'm impressed.<p>I've been searching for the right tool to try to add tagging to a large dataset of charitable data, and while I couldn't find anything off the shelf at Algorithmia, the GUI allows for easy forking and adjustment of existing algorithms. As a primarily front-end developer, never thought machine learning would be this accessible. Great work!
I never used this platform until today, wow.
This reminds me of old Mashape and early Blockspring if they had a baby (loved those). I'm very excited to dig into this more! WOW, I can tell more developers would love this if there was more outreach and love around this product!
There is an decentralized AI/ML startup building something in this domain on top of Ethereum called Synapse.AI you'll want to check out if you're interested in Web3 stuff. They do both the data, algorithms, and other functionality to help autonomous agents grow.<p>Numerai also wants to monopolize Data and ML models in a decentralized way, and I believe that is also on Ethereum.<p>There is also Enigma, Doc.ai, and a few others if you're interested in this space.