I used Clarifai at a hackathon a couple months ago. It was certainly impressive, but I didn't really see the difference between it and Google Cloud Services AI whatever offerings, other than Google seemed to have a better handle on deployment and production scaling.
I guess I'm wondering how companies like Clarifai and the couple other similar "AI API cloud service" companies intend to take on behemoths like Google?
At the time, Clarifai was the "best" one (I caveat by quoting because this was a for a small corpus, with subjective results, not a real train-test cycle). I re-ran the results about a month ago (linked to from the post), and found that Google and others have continued to invest and improve.
Do you play the tablas? My wife and I studied sitar but our instrument was destroyed by the movers in our latest relocation to Shenzhen :( The tabla teacher where we studied was able to play a very complex taal while chewing betel nut and rolling his eyes back in their sockets, immediately switch to a pitch, bend and time-perfect rendition of 'pink panther' melody, then switch back to a very complex taal without skipping a beat. Brilliant to see.
I am not a deep learning practitioner, but would be curious to know from experts how their custom model feature might work; and from any of their users on how well it actually does.
Tablas: haha, great description of your teacher. I do play, with enthusiasm, but poorly. For those in Seattle, there is an amazing teacher who teaches up on Cap Hill .
E.g., recall the awesome project for lego sorting by jacquesm ? He built his own model using Keras and Tensorflow, but you may be able to achieve similar results by using Clarifai's feature to train your own models with no understanding of deep learning. This is great if your goal is to build a thing, like a lego sorter, but not so much if you want to learn how to build a state of the art image classifier.
If you're interested in learning about computer vision or deep learning, I recommend searching this site to find threads that cover that extensively. Good luck!
IBM, Apple, Amazon, Facebook, Google will all buy these types of companies until they have what they need in house. Other bigger FXXX companies may buy a few as well if customer pilots go well.
Publishing research papers is a great way to get acquired but it's not the best use of your time if you're a small startup that's trying to make money. They still do research but it makes it into the product instead of getting published.
Mark was the key member of the VOC project, and it would have been impossible without his selfless contributions.
Mark Everingham passed away in 2012, bringing the VOC project to an end.
In contrast to ImageNet, only the validation and test data were manually labeled. Impressively, on such data, an accuracy of ~95% (top5) is achieved by the winning solution this year.
Nexar just released Nexet, a large dataset of 55K road images (5K of which are labeled). I think it's an interesting one as the images were captured by mobile phones (setup as dashcams), so you get "real world" data. Of-course there's an accompanying competition coming up: https://www.getnexar.com/challenge-2/
What I found was that WordNet has crap coverage, eg. large numbers of real adjectives were unlisted. In my ancient Chinese translation work (hobby), I have basically concluded that Wiktionary is the best thing going, and also easiest to contribute corrections and background data to. So I thought "why not adapt English Wiktionary data to produce an English adjective list?". This proved far more complicated than a quick hack, since the only way the data seemed to be supplied was as a huge XML file mixing Wiki and XML markup. In the end I downloaded a Wiktionary-derived dataset known as dbnary and parsed wordlists out of that, resulting in a satisfying list of 103,125 English adjectives. Total time invested, under 2 hours.
However, the prime use case of WN is as an verified inventory of machine readable senses, providing structured information about the semantic attributes of the word by providing the semantic links to other words/senses. Wictionary will tell you that a word is an adjective, but it doesn't tell a machine what does that adjective mean, how it relates to other adjectives.
E.g., the wiktionary entry on 'corgi' has a few sentences that mention that it's a type of dog, but it's not structured enough for this information to be used, and if you just extract it yourself without manual verification then it won't have high accuracy; WN, on the other hand, has an explicit structured link (hypernym) between corgi and dog. (http://wordnetweb.princeton.edu/perl/webwn?o2=&o0=1&o8=1&o1=...)
I think machine learning through medicine will save more lives than self driving cars.
I'm not saying it's easy on any front. It is possible, and it would be hugely beneficial to humans to have such a dataset. Raising awareness about the problem may be one step towards solving it.
Any effort to create additional medical datasets would probably help enhance machine learning diagnostics.
The data that you want to be perfect is the test set, which is smaller than training sets.
... from my fortune clone @ http://github.com/globalcitizen/taoup
> Matthew Zeiler built Clarifai based off his 2013 ImageNet win, and is now backed by $40 million in VC funding.
On a side note, this looks like a fun company to build: hacking on ML, exposing it via APIs, developers as customers, paid for on a resource basis like a VPS style per-use payment plan.
It's interesting how many of these AI startups are raising huge rounds. The words AI seems to draw them in. At least this one has an obvious business model.
Turned out he was an investor with some VC firm. I did a little mention of the place I worked at the time (which isn't an AI company, but wanted to be when it was young), but didn't have a business card to give him at he time.
I was actually kinda surprised an investor was attending a talk and saying, quite so uncritically, "hmmm, who do the smart people at talks say I should invest with?"
You can make assessment even if you are not an expert, but I would think you need more efforts to backup your claim a little bit to convince people and avoid downvoting.
The reason imagenet is popular is because it has allowed google to claim that they can beat human performance on image recognition by artifically lowering human performance on image recognition.