Show notes

I’m pleased to release the first episode today. Here are the show notes:

  • 0:00 Intro
  • 1:30 \m/
  • 1:46 Self-introduction (Ray)
  • 5:29 The Learn AI Guide
  • 6:00 Explaining the format of the show
  • 9:29 Why are we doing this show now?
  • 14:48 Plans for interviews
  • 14:48 Preview of next show

And here is a full transcript of the show:

Constructed Intellect Intro

You are listening to the Constructed Intellect podcast, where we cover the business and technology of artificial intelligence. I’m your host, Ray Grieselhuber, and today we’re going to be discussing the creation of this and what we have in store for you going forward. You can follow along with the show notes, which you can find for every episode at Today’s episode is episode 1. (If this is your first time listening), Thanks for joining. The Constructed Intellect podcast is going to be produced pretty much every week. Come back often, tell your friends, and be sure to subscribe via iTunes or Stitcher. We also post full-transcripts of each show, along with the show, on our website. You can subscribe to subscribe to our RSS feed to get regular updates there as well. We’re on Twitter at @conintellect and Facebook at All links are in the show notes. If you’re looking for daily news on AI and machine learning, we have an email newsletter that goes out almost every day, summarizing the latest news and trends you’re most going to want to be aware of, and you can subscribe right on our website. Alright, let’s get into the show.

This is the first show of Constructed Intellect, so today I want to talk about why I’m doing it and what I have in store for you all.


First, a little bit about me. Again, my name is Ray Grieselhuber. I’m based in San Francisco, and for the last five years I’ve been running a SaaS company, where we provide analytics and insights to online marketers. I’m an engineer by training and most of my experience is around building enterprise cloud-based applications, with a particular focus on big data, analytics and, more recently, machine learning and artificial intelligence. In 2010, I went through the summer class of the Y Combinator startup accelerator program and made of lot of great friends as a result of that experience. In addition to running my company, GinzaMetrics, I also advise and invest from time to time in other startups and naturally focus on companies with a data analytics or machine learning focus. Most recently, I’ve also become involved with a new company I’m also very excited about, called Functionize, which is the first cloud-based, browser-driven testing automation platform that actually works.

I decided to start this podcast rather quickly, just a few weeks ago. I was having a conversation with a good friend of mine about how we are at a turning point in the industry now where there is just enough underlying maturity in infrastructure and open source software to enable a new wave of AI-focused startups to get off the ground, but still early enough that the whole industry still feels wide open. In a way, it feels like the early days of internet 1.0. I plan to talk in great detail about how this all came together over the last couple of years and where I think things are headed.

I got into AI in much the same way that I think many others have and will. I’m not an expert by any measure but I’m always learning and as I learn, I realized that it would probably be useful to others if I organized things along the way and shared them here. So, here’s how I got into AI: gradually and a little bit unexpectedly. And I think that’s how a lot of people are going to get into it over the next year. We’ve hit a point now where the combination of mindset around cloud technology, the type of insights that can be delivered from machine learning, and open source software are all going to combine to enable AI hackers.

Small teams of guys in a bedroom building platforms and products that can learn. And here is the biggest paradigm shift that business people and engineers alike are going to need to have: Instead of coding business logic, you will build agents designed to learn your business logic. Let me repeat that. All of the application design and development to date in the software industry has been around engineers coding business logic. What has changed is, going forward, we will be focused on building agents, or bots, or whatever you want to call them, that can learn your business logic into whatever domain they are set into.

And ideally they will learn new things that you never would have figured out on your own.

So, one of my major goals with this podcast and the site is to enable more and more AI hackers and the businesses that will grow up around them.

The Learn AI Guide

One of the first things I’ve done so far, even though it is a small scale thing, has been to release a guide for learning AI. I spend a lot of time curating links, courses, books, and so forth in my business and also as I prepare for this show. I want to provide a single place that people new to AI can go and begin to learn right away. I will continue to add to it as I find more and am always happy to receive suggestions from you out there. I’ve already started to receive some great recommendations.

The Format of the Show

So, I want to talk for a few minutes about the format of the show and how I plan to run things going forward. Obviously we’ll continue to get better at producing this show. As we continue to create more episodes, we are going to learn about what creates a good show, how to work with our guests that we interview and so forth. You can expect this show to keep getting better and better. In terms of overall format, it’s going to depend a little bit on the episode in question. In general, I want to cover any news that may stand out at the beginning of the show. As I mentioned earlier in the show, we have an email newsletter that we’re sending out now. And that’s going to be your day to day kind of top things that you want to be aware of. But there are also going to be stories that have a larger impact than the type of stuff we cover in the newsletter, so I will be sure to highlight those stories in the beginning of each show.

In addition to that, what I think is going to be particularly valuable about the type of commentary we’re going to provide on the news is just that we’re bringing our perspective. Everybody has a unique perspective and I’ve seen some things along the way and connected some dots that I think could be helpful to other people. So part of what I’ll be doing during this show is to provide some of that commentary.

We may be getting some listener questions as we continue to grow, obviously we don’t have any today, being our first show. Going forward, I expect that we’ll receive some great feedback from you out there. Don’t be shy. You can ping us on Twitter at any time, you can comment on our Facebook page, you can send me an email. So we’ll be focusing some time on feedback that we get from listeners and readers.

One of the areas I’m most excited about is interviews. I know a lot of great people here in Silicon Valley and San Francisco that are focused on building really interesting solutions to problems that they see and, increasingly, these new startups, platforms, and tools are centered around a machine intelligence approach. Again, going back to what I was talking about earlier, instead of building everything yourself as an engineer, build the agents that can learn and build it for you. Not that that’s any easier, of course. In many cases, it’s harder, but the expected payoff from this approach is that you’ll get better results and systems that scale better to new types of problems. So, we’ve got some cool interviews that we’re starting to plan here and I think this is going to be one of the most exciting parts of our show going forward.

Also, I think that we’re going to see a lot of new books come out this year. I’ve already been made aware of a couple that are in the printing process right now, the publishing process…do we still say printing? Anyway, I guess we do still print some books. As those new books come out, I’ll take some time to review them.

In addition to the show and the blog, I’ve mentioned the newsletter. We’ve also got some other things planned, events, meetups and so further that we’re going to be trying to arrange here. I think that one of the most interesting things about the whole topic of AI is the people who are coming together to build it. And I think it’s good for us to get to know each other. So, I’m going to be doing my best to make sure we have the chance to do that.

Also, because I’ve spent enough time in various software industries, I’ve got a pretty good feel for the context for a lot of this, I’m going to be putting together some more high level, analytical reports about where I think things are going. More on that when I’ve got that put together too.

Why are we doing this show now?

The question really today, is, why are we doing this show now? And how can we address the really big problems like the heat death of the universe

It’s a really interesting time, actually. This is one of the most important questions, not only just about the show but why people, in general, if you’re in tech at all, why should you start to be paying attention to AI, if you haven’t so far. I think now we are at a point in the industry, where we have hit such a level of maturity around cloud-based technology and infrastructure, people have gotten their heads wrapped around the ideas in data science and machine learning as well, and, more an more, what people were hesitant to call AI a year or two ago, I’m hearing it all the time.

For anyone familiar with the history of the industry, you’re going to be very familiar with the AI winters, which were these periods that occurred, a couple of times actually, where there was a lot of hype and excitement about AI, and the technology maturity just wasn’t there. Excitement dried up very quickly. VCs started to shun anybody that associated themselves with AI from an investment standpoint, at least. But I think we’re finally starting to see the thaw of that happen here. We have been for some time.

Google, in many ways, was kind of the beginning of this. Their secret weapon, arsenal, I should say, has really been machine learn, from very early in their life as a company. They realized, very quickly, with their search engine, that there was no way they were going to be able to keep up with the spammers, demands on search quality, accuracy, and performance without help from a machine learning-based approach. They had to create systems that could learn from the data they were collecting, in order to create a better quality platform. And that mentality has been extended into pretty much every area of their product management philosophy, for the dozens if not hundreds of products that they manage.

So I think in many ways, Google has been very much responsible for this. But, you know, they’ve been around for over 17 years now, so it’s not only them anymore but it certainly has taken some time. And we’re starting to see some of the other big tech giants invest in this too: Amazon, Facebook, Twitter, IBM, and others. Amazon, I don’t think you can overstate the massive impact that the cloud technology infrastructure movement has had on this industry. Companies like Amazon have now brought within the reach of those AI hackers that I mentioned earlier the power of massive data centers, on demand, with the ability to scale up and down. This lets people create and run new algorithms, machine learning algorithms, that were simply not feasible and accessible.

I think this is a large part of why we’re starting to see a resurgence in neural net technology as well. I first encountered neural nets back in 2006 when I was working on another startup and some of the early guys there had come from the financial industry, and they had used a pretty sophisticated set of neural networks in order to build up a financial analysis package that helped determine credit scoring and risk scoring and so forth. And I remember hearing about that and thinking it was really cool. I was just kind of your basic bitch software engineer at the time and thinking it was really cool. I had actually studied some data mining concepts in college, and had learned a little bit about decision trees and classification, etc. So it was cool to see some of that actually being used out in the industry. But the number one comment that I had from those people at the time was, well, there are just so many limitations around the hardware and the performance of neural networks that it’s really hard to build any massively scalable system. That was always the bottleneck.

And I think that with the evolution of cloud-based technology, and all of the lessons that we’ve learned, not just within large sanctuaries like Google, but out in the general public, with open source software certainly helping that along the way, we’ve learned a lot about parallel processing, distributed algorithms, and so forth. There are still some major challenges. Not all of these algorithms are easily distributable. But I think this is going to continue to evolve. And, as the mindset continues to evolve among programmers, we’re all going to get smarter about building these platforms. This is why, at least these are some of the reasons why, that I think we saw a massive resurgence in interest in AI and machine intelligence over 2015.

I’m going to be talking about this more in my next show, but I think that 2016 is going to be a really interesting year. To say the least.

So, that’s why we’re doing the show now. We’re at a point of critical mass, you can call it an inflection point, you can call it whatever you want. It’s certainly going to be interesting.

Plans for interviews

I also want to talk a little bit more about interviews. I mentioned it a few minutes ago, but I want to focus a little bit more on helping people understand, because if you’re a listener of the show or checking out some of our content, and you’re an expert in some of the things we’re talking about, let me know. I’ve got some people, some guests that I’m lining up for interviews on the show, but I think that one of the more interesting opportunities we have with a show like this is to bring more people into the light, so to speak. Get them more aware of what’s going on. Because we are still such a small, tightly knit industry that pretty much all the players who are contributing something of value deserve some recognition. We can all benefit from that. We’ve got some really interesting interviews planned, with a mixed focus on my side.

We’re going to be talking to some of the true thought leaders, investors who are focused on AI and machine learning, this new group of startup founders that I call AI hackers, and also I want to bring in some people who are probably not technical, at least in their day to day jobs, but they are also contributing to this industry. That will include business people, biz dev people, journalists, academics, researchers, policy-makers, and more. I think there are a lot of really interesting cultural ramifications of what’s going to happen here in the next few years and all of us need to keep the big picture in mind.

So, if you’re doing something interesting in AI, regardless of how long you’ve been in the industry, drop me a note. I may want to talk to you and I may want to interview you on the show. I can’t guarantee that I will respond right away but I will definitely read your message and be sure to respond if I’d like to do something with you.

Finally, as we grow, and this is me just kind of going off the cuff here at the moment, about what I think we could do with this show, but I’m serious when I say it, I think we could potentially break this show into different segments, and potentially bring in some additional hosts as well. I see, already, a potential for different segments focused on areas that I already mentioned. We could do a hacker-focused show, I think we could do an investor-focused show, I think we could do something focused more on the hustlers and the corporates. So, we’ll see where it goes.

That’s basically what I want to cover in the show today. We’re going to keep it brief. I’ll give you a little preview of what we’re going to do in the next show.

Preview of next show

The next show is 2015 in review, and I’ve got some predictions for 2016 and I haven’t yet decided if I’m going to do them in the next show, or in a separate show all by itself. We’ll see how that plays out. But, at a minimum, we’re going to cover what have been the most important developments in my opinion, over the last year. I’ll give you a bit of a preview where I think we’re going with some of this stuff.

So, that’s the show for today. Hopefully, this was enjoyable for you. Feel free to send me any feedback, I’d love to hear from you: comments, questions, and so forth. Hope to see you again.