> Building on advanced research in program synthesis (PROSE) and data cleaning, we have created a data wrangling experience (Figure 1) that drastically reduces the time that data scientists have to spend in transforming data for machine learning.
Huh?
> Figure 1: AI-powered data wrangling in the workbench learns from examples and automatically synthesizes code for data transformations using program synthesis technology.
What?
> Models can be containerized in Docker and deployed to network edge devices, allowing models to score closer to the event and in real-time. Local docker deployments can be used for debugging, while for scaled out production serving of AI, these containers can be managed with Kubernetes, using Azure Container Services.
English, Microsoft. Do you speak it?
What you're describing are tools for data engineering - sanitizing real-world datasets to be fed into models. Researchers do not have to deal with this task as much because they work with well-defined datasets to provide fair comparisons of the algorithms they develop. Industry is the opposite - the algorithms are usually formulaic and well-defined, but the data itself is not.
Check out the PROSE SDK (including an interactive playground) here. I particularly like its ability to extract JSON to something resembling a dataframe: https://microsoft.github.io/prose/documentation/extraction-j...
This will probably be more illuminating than the quoted sentence. :)
Well, thats my tongue in cheek summary of the whole thing.
>“AI entrepreneurs and researchers will climb a tree, sometimes even a tall one, and then tell you they’ve got all the workings of a space program”
That is a weird position to hold. Surely he must realize that it will work some day; or does he believe there is something more to human-like intelligence, that can never be achieved in silicon? I'm not saying we are close, or that the currently available tools are enough, but at some point in the future we will be there.
Sounds more like a cranky Luddite than anything else. "on the grounds that it wouldn’t work", lovely argument right there. The list of inventions that "wouldn't work" is vast, including trains, aircraft, personal computers, etc
All modern inventions started as feeble/underpowered/clunky inventions
Compare Atari graphics with PS4 ones
Compare the first works with Mnist digit recognition to what Snapchat filters or AlphaGo do
But that's fine since skeptics means less competition to those who actually make things work
Current-generation ML techniques have proven business value. You can talk to your phone, and it understands a decent set of common actions to be performed. It is now mass-market.
Businesses see the value of the existing ML systems, and can also see that investments in incremental improvements will pay dividends. And so, investment will continue.
Meanwhile, we're finally starting to figure out the actual architecture that allows us to think and move. [1] With continued research, we'll see progress there as well.
[1] http://slatestarcodex.com/2017/09/05/book-review-surfing-unc...
In particular, Microsoft has always been great about providing tools for no to low cost at the entry level, to get you (or more likely your company) hooked into the ecosystem. Not making a criticism, they have made some great stuff over the years (see the Visual Studio ecosystem for example).
The other angle is providing these tools, which can be complex to install/configure/manage, as a service offering via a subscription as part of the Azure platform. Recently MS has been hiring every superstar/rockstar evangelist/advocate/architect/engineer/etc to help design/build/promote/advocate for Azure that they can find (See Jessie Frazelle, @catie, and a ton of key people in the Golang world). Microsoft isn't just coming to play, they're playing to win.
In contrast this involves running a proprietary operating system, IDE, closed source, etc... Quite the contrary to anything that could be considered democratic.
Then, ML is all about volume. Open a spreadsheet with more than 10000 rows in Excel and see it squirm in pain.
This is exactly the problem -- what you're describing is not easy for anyone outside of tech to do. If you want to, say, run a simple text classification task and have thousands of labels, this is way overkill. Machine learning has the opportunity to become a common place utility for automating repetitive tasks, and the barrier to entry does not need to be learning Tensorflow, Docker, and Jupyter.
However if you go back some decades you will see that Microsoft did have smartphones, before the iPhone, just they weren't as appealing as a product.
This time around I predict it will be the same. When you look at the product (e.g: Visual Studio tools for AI) it looks very featured but not very organized... Microsoft needs to understand that more and more features doesn't mean more perceived value.
[disclaimer - microsoft]
