IUI Example – Creative Inspiration, from Google

If you read any of “the year ahead” predictions for 2019, or even ones from the last few years, one thing you’ll undoubtedly come across is that Robots or AI will eventually take everyone’s job… maybe not today, or tomorrow, but eventually. I personally don’t buy this line of thinking, and think that Marc Andreessen had it ‘mostly’ right in a post he wrote way back in 2014: “This is probably a good time to say that I don’t believe that robots will eat all the jobs…”

One of the most interesting topics in modern times is the “robots eat all the jobs” thesis. It boils down to this: Computers can increasingly substitute for human labor, thus displacing jobs and creating unemployment. Your job, and every job, goes to a machine.

This sort of thinking is textbook Luddism, relying on a “lump-of-labor” fallacy – the idea that there is a fixed amount of work to be done…The counterargument to a finite supply of work comes from economist Milton Friedman — Human wants and needs are infinite, which means there is always more to do. I would argue that 200 years of recent history confirms Friedman’s point of view.

Marc Andreessen

Marc then goes on to rip apart the Robots take all the jobseventuality” with a number of really compelling arguments and thought experiments.

The post is incredible and I’m probably not smart enough to do it justice, but let me try to offer a quick summary of his arguments and then I’ll provide my take… and circle back to the creative inspiration title of this post!

The overly simplistic version of the points in his very long post are:

  1. For the Luddites to be right today (that robots / AI will take all the jobs) one has to believe that there won’t be any new wants or needs (which runs counter to human nature) and that people / humans won’t contribute to those things being created
  2. While it’s true that automation / technology displaces work (and that is something that must be addressed), the flip side is that the same automation / technology increases standards of living. The way that I’ve always thought about this is thinking about all the safety features in cars – blind-spot monitoring systems were only available in high end vehicles a few years ago and now most cars have them, this is a life-saving feature… not just a cheaper big screen TV. Technology enables both of these…
  3. He offers three suggestions to help people who are hurt by technological change: first, focus on increasing access to education. Second, let markets work. Third, create & sustain a vigorous social safety net… with those three things in place, humans will do what they’ve always done: create things to address and/or create new wants and needs.

Now, I love all three of those, and it would probably be enough to stop there… but Marc goes MUCH further…

  • The flip side of robots eating jobs is that this same technological advancement also democratizes manufacturing – it puts the power of production in everyone’s hands! I love this thinking!
  • Costs go down / things get cheaper… robots producing things means lower costs, which lead to falling prices, which stretches people’s purchasing power and raises people’s standard of living.

He wraps up the long post by restating his “this is a good time to state that I don’t think robots will eat all the jobs…” and offers the following…  which I’ll take one by one…

First, robots and AI are not nearly as powerful and sophisticated as I think people are starting to fear. Really. With my venture capital and technologist hat on I wish they were, but they’re not. There are enormous gaps between what we want them to do, and what they can do.

Marc Andreessen

I think Marc is still right on the first one. I’m a goofy optimist. Always have been, always will be. There is a line that someone used on me once that I’ll never forget: “don’t mistake a clear vision for short distance” and while technically true given the subject we were discussing, one never knows where the next breakthrough is going to come from! Again, he was right in 2014, but probably less so every day.

One other point I’d like to make here is that they don’t have to be as powerful & sophisticated as people talk about in the general press. What I’m focused on are the specific examples of real progress. I think we are a long way from the “Singularity” (great video here) but trying to pinpoint it’s arrival isn’t really super exciting to me, not practically anyway.

The questions to really be asking are: “which jobs”, “when”, and “how”. My thought is that it won’t be some zero-sum game.

Second, even when robots and AI are far more powerful, there will still be many things that people can do that robots and AI can’t. For example: creativity, innovation, exploration, art, science, entertainment, and caring for others. We have no idea how to make machines do these.

Okay, this is one of the main reasons I wanted to write about this, and as someone who works in a creative field, Creativity is something that I’m incredibly interested in… so I was a bit floored when I started digging into Magenta…

A primary goal of the Magenta project is to demonstrate that machine learning can be used to enable and enhance the creative potential of all people.

There is a lot to Magenta, so I’ll focus on two parts that really caught my attention.

First, is the Sketch-RNN – given a source sketch, it will auto generate additional sketches. Pretty rudimentary, but two things: first, it is a rudimentary starting image and second, imagine what this could do over time?

Second is Music Transformer – given a starting sequence, it will generate music with long term coherence to the original sample provided.

It doesn’t take much imagination to come up with ideas on where were going to start seeing uses for this type of innovation. If you’ve ever played with Garageband on your iOS devices (or the full blown Logic Studio on a Mac, or similar DAW software), you can start to think that we’re seeing a further advancement in the democratization for the creation of art & entertainment.

As designers, the sketching stuff should be of particular interest. One of the most important parts of my design process is the Diverge / Emerge / Converge diamond. I can see a future where tools like this will help us quickly explore more divergent ideas.

You should really go check out the samples on that page, they are truly incredible.

And check out some of the other demo apps.

I thought about ending here, but there are two more points to cover, so back to Marc…

Third, when automation is abundant and cheap, human experiences become rare and valuable. It flows from our nature as human beings. We see it all around us. The price of recorded music goes to zero, and the live music touring business explodes. The price of run-of-the-mill drip coffee drops, and the market for handmade gourmet coffee grows. You see this effect throughout luxury goods markets — handmade high-end clothes.

Marc Andreessen

I love this line of thinking. Imagine a future where we see labels in clothes that read “Made by Humans” like the “Made in the USA” labels we see today?

Finally, his last point…

Fourth, just as most of us today have jobs that weren’t even invented 100 years ago, the same will be true 100 years from now.

Marc Andreessen

That final point is so true. Marc actually ends his post stating he is ‘way long’ on human creativity, as am I…

What do you think? Will technology take our creative jobs away? Change them? Thoughts?


What’s Next in Tech – Macro Drivers

A few years ago I had the good fortune of getting to build and help lead an Advanced Technology Lab for a large consumer goods company. During that time I created something called the ‘Future Opportunity Framework’. The goal of the framework was to give the company the ability to “peak around corners” when it came to the future of technology… to create a practical toolkit to drive prioritized investments and action.

There were four main sections of content:

  1. Macro Drivers – these were the big factors that were fundamental in helping to understand where things were headed. I’m going to share a handful of them in this post.
  2. Models of Change – more of a sidebar, actually. Just an outline of the 6 major types of change (e.g. Linear, Asymptotic, Cyclic, etc.).
  3. Technology Insights – this was the bulk of the content, a little more than 2 dozen insights that could be used as input into the process for ideation
  4. Framework Process – a simple process of What, So What, Now What that could be used to leverage the content above to generate hypotheses, identify opportunities, and determine next next steps.

I’ll be sharing the models of change and some of the Technology Insights in future posts, but wanted to get some of the macro drivers out in order to reference them later. None of these should really be much of a surprise, these were some of the big things that were going on in the world that helped explain some of the changes we were seeing.

Here are 6 of the original 9 macro drivers from early 2015…  again, these should all be fairly obvious…

Increasing Number of Software Libraries & Open Source Projects

Everything is now an API. From accepting payments (Stripe) to incorporating Machine Intelligence (Mahout, DeepLearning4J, etc), developers can find software libraries & open source projects for just about anything. This isn’t just the surface stuff either, we’re seeing an explosion in the availability of software infrastructure & management code availability as well. A great example is when Netflix open sourced their Simian Army, an API to improve availability & reliability of the Netflix Service. Netflix is not alone in this, Leading tech companies are all donating leading edge software libraries to the open source community, including Google, Facebook, Twitter, and even Walmart Labs.

Decreasing Cost to Build & Launch

There are two main parts to this driver. First, as mentioned above, the very practical implication of the increasing availability of quality software libraries means that building a product has never been cheaper (or faster). Second, we’re seeing tremendous growth in cloud environments, like AWS (Amazon Web Services). Any developer in the world can now provision a load-balanced, highly available, fault tolerant, n-tier server environment with a few mouse clicks and only pay for the actual usage.

Shifting Needs & Sources of funding

This is a potential disruption disruption to Silicon Valley. Given 1 & 2 above, tech companies need less funding to get started. Startups no longer need to go to a VC and give away a boatload of equity when the company is pre-revenue or still in seed stage. It’s pretty easy and cheap to get pretty far, whereas the past required funding by a VC just to build out the basic concept of an idea. The other major part of this is the tremendous growth of Crowdfunding sites, like Kickstarter and Indiegogo: got an idea for a product? Post it on a crowdfunding site and let the community fund the idea to start. Although not every project on crowdfunding sites gets funded, and not all the projects are worthwhile, we have seen some major success stories – like Pebble and Oculus – that are providing evidence of the power of these platforms to rewrite the role of venture funding.

Increasing Interest & Investment in Machine Intelligence

It seems everyone is doing something with AI lately. Watson winning on Jeopardy, Facebook opened an AI Lab, Baidu opening an AI Lab (and hiring very senior talent away from Google), Google investing in AI companies at a rapid pace to build out its capabilities. Everyday it seems there is a new advancement made, software library released, or news story about it.

Maturing Hardware Possibilities & Capabilities

For years the only hardware on the market came from large, well established players because building hardware is hard, and expensive. That is really starting to change, rapidly. There are two main reasons this is happening: first, crowdfunding sites are enabling people to sell products to people before they’re even built, offsetting the cost component. The second is the rise of manufacturing capabilities, everything from low cost options in China to specialized firms that will help bring a hardware vision to life by providing experience & expertise to novice hardware companies. One great example of this is the home automation company Smart Things, which recently sold to Samsung, they started on Kickstarter and raised the funds to build out their hardware platform.

Increasing Technology Adoption

This might seem incongruent with the rest of the list, as the other items are focused more on the nuts & bolts of tech, whereas this one is more of a consumer lens. Why this matters is that people are adopting tech to handle more and more parts of their lives. It started with the web, now we’re all carrying really powerful smartphones and rely on them more and more. There is an increasing comfort people have using tech. This is a virtuous cycle that leads more people to start building tech products because the audience is growing and it is becoming cheaper & easier to do so.

What do you think? Any that you’d disagree with?