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Google cloud infrastructure technology in webservers created; the code written could do ANYTHING, and sometimes I was left wondering just how much actual work they did.

Machine that is slow, yet fast enough, yet insecure enough, it has a way of making you a little more careful about the choices you make when it comes to technology. And in this sense, Amazon have a a better AI team. It makes the entire system feel much more mature and reliable. It’s always well behind Google in this area, though.

Conclusion

These are the winners when it comes to artificial intelligence, from a technical perspective. There are a number of things that I personally disagree with, and would’ve been much more careful with; mainly, the ASI line of products. First, the use of anthropomorphic AI to interface with humans.

This can be an extremely useful thing, but I don’t feel it would’ve been too useful for me. I tend to prefer being a little more impersonal with my interfaces. I’d’ve been wary about using any form of lifelike ASI for this job. The second point, was the use of surveillance and analysing the data.

I find this one a little curious, because although we know there are some tools for doing this (like SparkSpark, Machine for $10.000) it’s not something that they were very open about.

Which again, led me to wonder: were they worried about people putting these products to entirely unworkable uses?

Which is not to say they’d never use them; they probably would’ve been able to get away with it in certain situations. But that’s a world where we’ve created very safe services, and most of the time have very little human oversight. In this world, it’s easier for people to push back against these kinds of surveillance activities, and actually set them to firewalls, or whatever, if you don’t like it.

Which is pretty important, because things like identity verification and fighting terrorism are both issues that come up from time to time. But then again, many times these initiatives are orchestrated by governmental agencies, and are consequently perhaps more willing to start and enforce any kind of censorship. Oh, and just a random opinion: I really liked Torch as a web interface.

(Although, also personally, I don’t really like internet of things stuff. It’s kind of dumb.) Problem


So what are the problems in using a top down approach to building AI? There are two, really.

It’s difficult for one organisation to do it. I’d have preferred to see more of a distributed system, not as separate systems but more like layers on a cake, with AI running on the bottom, then interacting with the user interface on top. This would reduce the amount of infrastructure needed, the total data needed and the total amount of time needed to train an AI. But also, you need more data for each input. This, in turn, requires much more energy, electricity.

My laptop (13-inch MacBook Pro) draws around 550 watts when at work.


The ASI code on the ASI app on my phone (LG G3, 4.7 inch, 18:9 screen, 2300 mAh battery) is the equivalent of drawing around 515 watts of electricity. The servers at Amazon AWS are probably drawing an average of 1000 watts of electricity each. Because the machine learning requires so much energy, it actually makes a lot of sense to split the data into chunks, and save the machine learning related data as much as possible. Another issue is that there are a lot of developers out there who want to use a non-AI system for one of the applications they’re developing.

Which seems ironic because the system is always talking to the humans. And then, this ‘other’ system is the system that, ideally, you’d want to start writing code in. Another problem is that because AI algorithms and a system are optimized for one particular scenario, they can’t be used in multiple scenarios. For instance, an ASI system for a doorman will never know about pickpocketing. An ASI system for a bus driver, though, would be able to detect that.

Similarly, an AI for a journalist, will never be able to learn how to generate articles.

I wanted to use a communication system for the building so we didn’t have to always have the interface open (like Skype or Hangouts) or always have the camera out (like Facebook Messenger) while the room was empty, so I didn’t have to be there. The communication system also meant that we could have screens in the rooms that could show things like when it was a full room, which was either a meeting or a room for a different purpose.

The voice system also doesn’t handle the problem of a friend who is calling you while you’re in a meeting and you can’t hear their voice, even though you’re the only person in the room. So I needed another system to handle this. The communications system also gives us some extra control over things like the lighting. We still don’t know what will happen with Skype in 5 years, for instance.

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