Comment by AndrewKemendo | Hacker News Reader

Comment by AndrewKemendo | Hacker News Reader

Just as electricity transformed many industries roughly 100 years ago, AI will also now change nearly every major industry

This is an underrated point, and something I don't think most people outside of the high level Machine Learning/AI world take seriously.

It's also one of the biggest challenges for the industry going forward because of natural monopolies. I say that because if AI is electricity then data is the coal/oil that drives it.

The big technology players have a massive advantage in their ability to build and deploy tools that collect the data, and then bring it back to be turned into "electricity." If we aren't careful they will be the only groups who can make progress and show actual real world ML driven capabilities - making the barriers to entry even higher.

If you just look at the computer vision space, to do really good Machine Vision you need a LOT of novel image data and the primary platforms creating image content are largely owned by the top 5 players in the form of collection (smartphones) and warehousing (cloud servers).

I'm not sure if there are solutions here that make it possible for a lot of companies to do really well - everyone will just be bought up or out competed by the bigs once the big ones notice a threat on the horizon.

> AI is electricity then data is the coal/oil that drives it. The big technology players have a massive advantage in their ability to build and deploy tools that collect the data, and then bring it back to be turned into "electricity."

Data is important now, but when we have solved vision, speech, text and robotics to a decent degree, data won't matter as much. The great thing about AI is that we can cheaply copy already trained models or already labeled datasets. There aren't so many datasets needed to solve the most interesting and financially profitable few problems. Of course, there will always be fringe projects where more data is needed, but the main applications will be in the commons. You can copy an AI model if you can talk to it (use it to produce sample outputs). Any model could be copied in a dataset and transferred into another model. The great thing about machine learning is that it learns directly from data, so it's cheap to copy by tracing the inputs and outputs of other public AIs, just as current AIs are taught by tracing the inputs and outputs of people (supervision).

The distinction should be made between training data and [actionable] data.

In the former case we are taking data, labeling it, then using it to build our nets and models. You are correct to an extent that it's a usable model once trained and that data is less important.

However, equally if not more important is the data that is being put into the net to come out as a result/action. Arguably this data comes through the same pipe as training data - and the pipes are similarly limited. So its ALWAYS important because you can't take an action or classify or otherwise without it.

When you add in the reinforcement mechanism, or later unsupervised techniques then those data mechanisms blur between training and action data so the point is moot. It's not a one run process in the long run, it's iterative and always evolving based on the user.

If you don't have data to "put into the net" then you don't have a problem that you want to solve.

The key point is that it's about data friction. If you already have uploaded all of your photos to google cloud, then any new tool or capability google comes up with using a CNN, will be immediately applicable without you having to do anything.

Was about to say the same thing. The incumbents are probably even at a disadvantage because as a young upstart you can always get in on the action by building a more efficient and simpler model and then querying the incumbents to bootstrap yourself.

you can always get in on the action by building a more efficient and simpler model

Except the big 5 have hoovered up a good portion of the ML talent from the universities and are themselves leading the pack in iterative improvement on ML capabilities.

If you are trying to bootstrap a ML company with the trickle of raw data that the big 5 puts out, you're not going to ever get to their size.

This is not about being able to create a 300k/yr company off the back of some table scraps. It's about major companies having too much influence over one market.

Interesting, this is the same argument made by social theorist Evengy Morozov: https://www.theguardian.com/commentisfree/2016/dec/04/data-p...

so did Kevin Kelly, in "The Inevitable". He stretches the electrical metaphor for about a chapter.

"The AI on the horizon looks more like Amazon Web Services — ... This common utility will serve you as much IQ as you want but no more than you need. You’ll simply plug into the grid and get AI as if it was electricity. It will enliven objects, much as electricity did more than a century past."

It's not just AI. Here's Jeff Bezos talking about the internet as electrification

https://www.ted.com/talks/jeff_bezos_on_the_next_web_innovat...

seems a rather common metaphor for a sweeping wave of technological change.

For an alternate view to the "data is oil" theme, see http://bokonads.com/data-is-fallout-not-oil/

Maybe open source crowdsourced data? e.g. If various people need thousands of examples of people's handwriting, someone makes an open-source repository and hopefully lots of people upload for the good of everyone.

So the big trick is to convince humanity that we all need to be the teachers of AI as an intentional practice. AGI will be our offspring so why shouldn't we all have a hand it teaching it and seeing the fruits of our labor?

It's not clear that it will change every industry; it'll definitely lead to great job losses in certain fields with the oncoming ADAS boom.

I can't think of other areas where it'll have such a big impact. Robotics (other than Automated driving) has decades more left to fulfill its promise.

Another huge market of AI is medical. e.g. algorithms are already doing a better job than human experts in detecting cancer.

Reply on news.ycombinator.com