In November at the Canon performed a panel under the name “A Startup Panel on When Machines Start to Understand What They Learn” (Canon – Deep Learning Panel – Splash) with Dave Sullivan, Adam Gibson, Joshua Bloom, Subutai Ahmad and moderator Jordan Novet. (Sadly no one from Deepmind in UK, which probably were missed).
As we at ai-claudio missed on being there – and now online stream oder video available currently, here are some cites from: OfficeLeaseCenter:
On Wednesday, November 19th 2014, OLC attended Cognitive System of the Near Future at RocketLabs in San Francisco where a Jordan Novet moderated a panel discussion.
Jordan Novet – Staff Writer, VentureBeat also Moderator
Adam Gibson – Founder, Skymind.io
Dave Sullivan – CEO, Ersatz Labs
Subutai Ahmad – VP of Research, Numenta
Joshua Bloom – CTO, Wise.io
JN – What is deep learning and what is cognitive computing?
JB – I like to think that cognitive computing is marketing jargon.
DS – Machine learning is thinking about a program that takes an input (any kind) and you expect it to have some output. What we are trying to do is have these programs write themselves instead of by a developer.
AG – Cognitive computing is marketing. Machine learning is taking what we already know from statistics and building something with it. It can come down to big data and thinking we have this data, what can we do with it.
JB – Machine learning can help you keep up with things as the world keeps moving. Data is coming from all sorts of different sources and dimensions. It can be hard to pull the correct data out you need.
JN – You mentioned text and images, what’s the most interesting case you’ve heard about a go-to-market strategy for a new company?
DS – there are a lot of companies out there that are already doing this perhaps using Mechanical Turk to do these tasks for us where we are trying to create a program to do this. With the data collected from accelerometers, with this movement, what is the person doing?
SA – Nemo baby, is a little onesie for an infant that has a heart rate monitor, and all these other sensors connected to wifi to the cloud to an app that allows you to monitor your babies health real time, all the time.
JN – Running a company in this industry, how do you find great talent? Where do you look?
AG – You teach them! I happen to work with some schools, so I’m able to meet potential hires.
SA – We have a good internship program. We get students – they come to us actually and we work with them and train them. We don’t advertise anywhere for new hires and people just naturally come to us.
AG – There are lots of classes out there to learn this stuff. Online academies, master programs. It comes down to wanting to work on something cool and enabling people to do it.
DS – At our company, myself and our CTO are the only ones that really understand this, the rest of the folks are web developers.
JB – I get lucky because I’m a professor at Berkeley and I’m exposed to students all the time. The thing with this is you can’t always hire these people right out of school. You need your senior devs out there to help out the newcomers.
JN – I believe a lot of these good devs in deep learning are working down in Mountain View working for Google. How do you try and get these folks.
DS – We try and avoid battling with them because we can’t compete with them. We try and look for people who want to work for a battling startup.
JN – Does it make sense to keep humans in the loop..should machines replace or augment human work?
JB – I wrote a blog post about this a while ago robots where they looked real and fake and can creep people out, then you have things like Pandora that are doing machine learning. What if Google Glass told you one day you should be dating someone of the opposite sex by the data they are collecting. The machine can get to a point where it knows you better than it knows yourself.
You’ll get to a point where these services will supercharge our lives and empower us.
SA – I don’t worry about these consequences with this type of tech, I’m more worried about computer viruses and can get far worse than they are today. I read about people 3D printing DNA and open sources, so people could create some new organisms and I’m more worried about that.
AG – We’re already seeing some of it like these activity trackers such as Fitbit. We need to make these apps better to understand. If these things take off, we’ll get the data to allow people to creative better automation tools.
We’re not going to have something like Skynet viewing on us and spying on us.
JN – Google has such a talent pool of deep learning employees, and some people say they are a deep learning company. What happens if Google releases an API or created machine learning as a service.
DS – They’ll shut it down in two years after they realize they aren’t generating enough ad revenue from it.
SA – There are only a small amount of business models that work for Google. One day Google might figure something out.