Soon there will be Lemonade style app driven per hospitalization insurance on top of normal health insurance where for elective procedures you will be able to protect yourself from surprises like balance billing etc.
Translation for those not proficient in Academese: Serverless computing a novel paradigm enabled by a large cloud provider, does not matches our past research, thus we argue why it's not the right thing.
This is classic "if theory does not matches practice lets change the practice" approach, which is far too common in academic systems research.
I trust AWS with knowing what its customers truly want (in terms of performance and cost) and what it can provide,Since AWS has real financial stakes in its success.
A decade ago the same researchers would have mourned emergence of cloud computing as a wrong thing and instead asked for P2P computing since that's what they had spent the decade before doing research on.
Scientists/Academics treat prestige as primary currency, and most would view lack of access to information just as a sign of not being in prestigious enough institution.
You are being naive if you think academia is filled with do-gooders it’s just a race but of another kind.
Unless there is a direct impact of doing something more openly e.g. accessible code base that thousands of researchers can use to publish and cite your work quickly or significant risk of getting scooped. There is very little to motivate any change.
Also if you think journals/review system is bad, just get a glimpse of “grant review” system by NSF/NIH or “tenure committees” etc. They make the worst stack ranking performance review etc. seem light hearted fun.
I think you are overegenralizing applicability of Neural Architecture Search etc. and cherry picking individual examples. There is an enormous gap between what gets published in academia with what’s actually useful.
E.g. Compute wars have only intensified with TPUs and FPGA. sure for training you might be okay with few 1080ti but good luck building any reliable, cheap and low latency service that uses DNNs. Similarly big data for academia is few terabytes but real Big data is Petabytes of street level imagery, Videos/Audio etc.
"When you end up with a bunch of papers showing that genetic algorithms are competitive with your methods, this does not mean that we’ve made an advance in genetic algorithms. It is far more likely that this means that your method is a lousy implementation of random search."
I am working on a project to build an ML and CV enabled database for images and videos. It supports visual search/NN as core primitive and developed for scalability using Kubernetes.
Hwang lets you perform "efficient random access" across the video by building a GOP/Segment aware index. This comes in handy in a lot of applications where you want to access particular frame or set of frames but do not wish to decode and store all frames. Most tools such as ffmpeg (command line application not library) are optimized for sequential decode use case.
Scanner is a great tool I have been following it's development very closely over the last year. Great to see it reach 1.0 . I highly recommend checking out Hwang [1] (a sparse video decoder) which is bundled and used in Scanner.
Scanner is one of the first few tools to leverage Docker/Kubernetes by demonstrating ability to ship complex heterogenous architecture in a reliable/reproducible manner.
I think tf.data is amazing, its far far better than the previous queue and string_input_producer style approach.
More than documentation, I would argue that TF especially tf.data lacks a tracing tool that would let a user quickly debug how data is being transformed and if there are any obvious ways to speed up. E.g. image_load -> cast -> resize vs image_load -> resize -> cast had different behavior and lead to hard to identify bugs. For tf.data prefetch which ends up being key to improving speed yet its is not documented, the only way I actually found out about it was by reading your TF.Data presentation.
A large scale visual data analytics platform, think SQL/MapReduce/Full-text search but for images and videos using Deep Learning. Now writing few papers on/using it to finish and get my PhD.
This is ridiculous, NNP has not even reached the market and he is already betting the farm on it. The reason Titan V does not yet significantly outperform previous generations is because those Tensor Cores have not been properly utilized. E.g. Current stable version of TF 1.4 does not uses CUDA 9.0 .
This isn't about dubious tactic at all, if the government thinks its dubious it could simply direct NSF/NIH to disallow amount from being used to pay tuition and lower overhead allowed. You don't even need any legislation for that.
In fact PhD's are merely a collateral damage in ending tution tax exemption which affects all students (and represents a much larger chunk when you account for number of undergrads). It's just that in other cases "per-student" effects are much lower, since either the student has no income or the lack of exemption mere dissuades parents from filling jointly.
We live in an age of libraries like cudnn which are vendor provided/optimized, for 99.9% of ML applications you won't need to write custom CUDA code. Further as I mentioned earlier with Intermediate Representation languages like TVM you are guaranteed to get the best underlying constructs across different computing architectures.
Julia might still be good enough MATLAB replacement for Computational Simulation style tasks, but its clearly not suited for Machine learning.
Julia is a good language for computational simulation experiments but in a world that's heading towards heterogeneous compute enabled by GPUs, TPUs and intermediate representation languages/frameworks like ONNX, TVM [1] and Tensor Flow its difficult to take claim of "Language for Modern ML" seriously.
If you disagree please show me how many ML researchers/labs/companies use Julia over Python/C++. Its cool to claim "Modern", "Deep", "ML", but I don't see any evidence.
The ML community did take the Theory approach trying to prove bounds, SLT/SRM, PAC, etc. and that was an excercise in futility. While I don't deny that there is value to looking under the hood but for a long while the community abandoned any empirical results that didn't fit their paradigm. Between rigorously validating their methods and writing yet another 4 page long proof. A lot of researchers would prefer latter, effectively locking out empirical approaches from most dissemination venues and eventually funding.