Why non-techies will take over the world thanks to AI

It is hard to remember that there was a time when whatever the printed media published was all we got. The same goes for TV and radio. We trusted the people in charge of the Media to make decisions on our behalf, hoping they would pick the right content for us. The cadence, format, timing, and depth were all decided by “experts” who knew what they were doing. News shows, stories, and music were curated or designed to serve many segments simultaneously. By trying to cater to everybody, the content didn’t serve anybody in particular. The few lucky people with a voice in the media had a disproportionate amount of power to influence the thinking of the masses and favor or dismiss ideas even if they didn’t intend to do it on purpose. 

But one day, the internet and the World Wide Web showed up. A technology designed to share scientific ideas became the new channel for anybody who wanted to say something to be able to do it without anybody in the middle. The beauty of the internet is not only that a regular person could create content that was better than traditional media, but that new formats were created by common people who didn’t have any background or experience in media. 

I’m emphasizing media so much because there is a parallel with what’s going on in engineering nowadays. Only companies that can afford to have a team of engineers can invest in developing new products for the market. This is similar to big broadcasting studios being the only ones capable of producing content in the 20th century. Engineering a product is costly and requires a couple of iterations and time to be ready in the best case. For that reason, products need to be designed to serve many segments at once. The one-size-fits-all content produced by old-school media had the same economic reason as mass-produced products: Cost. Continuing with the parallelism, the design decisions that shape a product are always made by experts who are supposed to know better.  But what protects those design decisions from personal bias or being influenced by commercial reasons? Maybe engineers must use a suboptimal provider because of a partnership or a regulation. In some cases, engineers make a decision simply because that is the skillset available in the team at the moment. This is not different from content generated by traditional media, which, while supposedly neutral, suffers from an intentional or unintended bias and influence from partner companies, owners, circumstances, or pure personal preferences from its executives. 

But one day, a new generation of capable Artificial Intelligence appeared. Suddenly, a person who doesn’t know how to code could generate and publish the source code of a simple website. Many people disregarded that achievement as a toy example. “Real engineering requires real imagination, creativity, and experience,” they said. That’s what copywriters said a year earlier, but now they were losing their jobs as people realized they could iterate with a writing-able AI to craft messages at almost no cost. Traditional media also disregarded Websites that published news in the early 2000s as “amateur” to realize later that was the future.  In every single one of these instances, specialists are replaced by common people with a passion for the topic. 

We don’t need to predict to understand what the future looks like as it is happening now. Advancements in manufacturing from the last decade allow for ultra-short productions. You can now create custom-printed circuit Boards with electronic components assembled at cost, CNC mechanical parts on demand, and 3D print structures and encasings at production quality. The only thing stopping a non-technical person from creating its products is the ability to design engineered blueprints or source code for the components. That’s where AI comes in. 

While some AI forecasts paint a grim future, giving non-technical people the ability to create a well designed product (engineering-wise) is equivalent to giving musicians an electric guitar, the possibilities are infinite. What makes good products is not that they are technically sound; that is just a pre-requirement. Instead, a good product is something that people want to use. While many engineers have the ability to understand the user, there is usually some tension between what the engineer thinks the user needs and what the user wants. 

Our job as engineers is far from over; the mission is to provide non-engineers working with AI with the building blocks to create anything they can come up with. Returning to the example of self-publishing media, the online platforms that allowed anybody to post anything didn’t just appear from nowhere; they were also created and maintained by teams of engineers. 

The building blocks enabling everybody to build new Products will not be hobby-grade Do-It-Yourself kits. They will be fully engineered, tested, standardized, and continuously improved production-ready pieces of modular technology that AI will base its designs on to create blueprints and source code. Think of AI writing Python. Python’s standard library has been carefully crafted by thousands of engineers. AI writing a Python script is the culmination of all that effort. Now, extrapolate that to electronic, mechanical, and structural components. In a few years, we’ll see the rise of modular systems that AI will use to assemble almost anything in the real world. There will be many systems like this, but that’s a good thing; competition is healthy. 

Same as the creation of the internet spawned a series of new jobs. Inevitably, we will see the rise of the Product+AI job descriptions. This person will be very well-versed in the different building blocks that AI can use to design products and their cost, the kind of prompt needed to describe the requirements to AI, and the best way to test and iterate the design results. And when everything looks good, this person will know how to send it to be produced for the market. The idea to launch cycle will be reduced from months to days without sacrificing quality. The concept of going to an electronics store to purchase a gadget designed by a big corporation to serve thousands of users with the exact same design will give way to a model where everybody gets the solution that solves their specific problem and requirements. This solution will be ordered online and shipped within the week after it has been designed, produced, and packaged in a local facility, with all the building blocks on the shelf waiting to be used. Welcome to the future =). 

Irma is a Battery Powered, NFC provisioned, wirelessly charged BLE Mesh Network with I2C peripherals.

The idea behind Irma is to lay a low power Mesh Network capable of communicating peripherals wirelessly with a central (but not necessarily unique) main function via sub/pub. All the Irma blueprints are designed to run on the Irma Architecture.

Dr. Brown from Back to the Future was one of the reasons I decided to be an engineer. Not because he invented a time machine (pfff) but because of the machine he created to feed his dog (Einstein). If you haven’t seen that movie, stop reading this and go watch it.

Funny thing is that while some of my other friends in Engineering school wanted to become engineers because they wanted to go to the moon or something as far fetched as building a sentient cyborg, I just wanted to build cool gadgets for myself.

In this Blog Post I present a quick draft of how could we build a dog feeder using technology I’m familiar with: Microcontrollers, sensors, a mesh network and off the shelf mechanical components.

The basic idea is to have a mechanical timer/relay like those used in gardens advertise if it is time to do something or not. The way I would implement this is by reading whether current is passing or not (sensor N). Yes, I would have to translate from 220V to 5V but that’s not a big deal. I could just use a 220v to 5V transformer ( also known as a phone charger) to generate an input signal.

After that, I would check whether there is food in the main container. I was thinking about a sonar (sensor B) , I’ve worked with sonar sensors in the past and they are pretty reliable once you calibrate them. Here, it would know if there’s food based on how long it takes the ping to come back. A value of 2 is equivalent to 20% of the container being empty. Sonars don’t rely on light so I assume it will work with some food dust around.

In addition to checking if there’s food I need to verify whether there is food in the plate or not (sensor I) . I would place a small light sensor on the bottom of the plate. If light is detected, that means there is no food on the plate.

If all looks good I would start the motor that drives a dispenser screw (actuator C) for it to drop some food to the pipes that are already pressurized by the air pump (actuator D). Similar to when you vacuum your own home, it will drive the food kernels through the pressurized pipes all the way to the element F.

A vibration sensor (sensor E) detects whether food is hitting the walls of the pipes. If no vibration is detected that means no food is in the pipes and something must be wrong: Either no pressure or no food. We output an error message to the message board (actuator M) and exit the program. Sorry dogs, no robot serviced food for you today.

Once food makes it to the intersection F, it will go either left or right. Actuator F is just a ramp that moves to one side or the other letting the food fall in Chi Chi’s plate or in Hugo’s plate.

Maybe this is an overkill, but there’s yet another sensor in the pipe that detects whether food is passing through all the way to the plate. This is to confirm that F did its job properly. I could just instead check sensor I to figure out if the plate is filled in.

Because dogs also need water to live, specially after eating that processed food, we check the level of the water using a floating magnet. A magnetic field magnet checks the intensity of the field, infers how close the magnet is (sensor K) and determines the level of the water. If water is needed, the water valve Q is opened for a certain amount of seconds to fill the bottle.

And all this so we can focus on the Flux Capacitor…

The idea in a nutshell: To install a series of levers, buttons, lights and sounds actuators around the house or apartment for dogs to be entertained while owners are not around. Ideally, the smart home would randomly generate new ways to call the dog’s attention and learn from what is effective or not.

Why would we want this? Let me use my own experience as an example : My dear dog “Chicharron” has destroyed about 15 books, 8 DVDs, a passport, a couple hats , a couch and even a snake plant with his teeth. At first I thought he was just a bad dog, then I got the feeling that he was just hungry so I started feeding him much more. He still chewed on random things all the time (I get he is young and his teeth are growing). We gave him a massive amount of chewable toys, all colors and sized but nothing really changed. I’d like to believe he is just bored. Chewing things seem to be the way he copes with boredom because he never does it when we are around.

Doing some research I found a Swedish Dog games designer called Nina Ottosson, in her website she mentions the following:

 “All dogs need to use their head sometimes in order to feel good, and some dogs have a greater need than others. If they don’t get to channel their energy into an organized activity, they can sometimes create their own “fun”, which is not always appreciated by the owners, such as chewing on things, or just get hyperactive”.

Worth mentioning: Nina Ottosson has a full line of Dog Activity Games that look great. I’m ordering a couple today for Chicharron. However what I’m proposing is a little bit more on the crazy Emmet “Doc” Brown side from “Back to the Future” with electronics, lights and mechanical components. If you remember the opening scene of the first movie, there is a full contraption that feeds “Einsteing” (his dog). I like to imagine that in this alternate and fictional movie universe, the “Doc” would have also invented and developed a way to stimulate “Einstein’s” brain (pun intended).

First of all we need to think about the Dog Interface/Experience (DI/DX):

• The levers and buttons needs to be rugged enough to withstand chewing.
• Dog should be able to interact with its Paws, Tongue, Teeth or Snout.
• Light actuators should be within the dog’s line of sight.
• Reward dispensers should make it easy for the dog to retrieve its payoff (and communicate explicitly that a reward was dispensed).
• Sensors should not only include levers and buttons but also microphones (to pick up dog sounds like whining, crying or barking) and cameras for computer vision to be able to recognize bad habits (like taking things from the top of the table or opening the Trash can).
• The feedback shouldn’t only come from the light actuators but also from any other smart home device (like living room lights, colors, blinds, etc)

Plan of Action

We are going to design an hypothetical system that will give cues to the dog(s) to take an action. The dogs will have to be trained to respond to the cues via the input gadgets (rope pulls, cranks, buttons) for them to acquire a reward (could be a treat or a sound). The system will get feedback (how and when the dog reacted to the cues) via the Microphone, Computer Vision enabled camera and the Movement sensors. A “Dog Actions Classifier” will interpret the feedback inputs and recommend the next cue.

Design Notes

• The system should NOT be connected to the internet and that is OK. This is not intended for dog owners to play remotely with their dogs… This is literally, the Smart Home playing with the dog and learning from it.
• Maybe it is not explicitly depicted in the diagram but all the components should communicate wirelessly. This would avoid the problem of dogs chewing on the cables.
• The only components that need to be dog-ruggedized are the input gadgets : the cord pull, crank buttons, etc
• Also, not explicitly depicted in the diagram but there is a micro controller that orchestrates all the gadget in an asynchronous way. Every gadget should still work even if that controller is down.
• The most basic Classifier should be pre-trained, however it would be fascinating to have it train against every dog’s particularities. Also, to randomly come up with new cue-action-reward combos to discover new ways to entertain the dog.
• The dog will reinforce the system the system will reinforce the dog.

Further Improvements

+ Connect the system to the internet for owners to play remotely with its dog. Although I suspect owners will rapidly loose interest after doing so a couple of times. Hence the importance of having the smart home interact with the dog directly

+ Maybe implement something similar for cats? Do they care?

Top of mind conclusions

The chances of somebody investing more than a $100 in a toy or a system to entertain a dog are practically zero (I usually wouldn’t). Nevertheless, as I’m writing this post, Chicharron just destroyed today the living room rug that costs much more than a $100. Could the case be made that the ROI for such implementation is positive? How do you know what your dog is going to destroy in advance? Also, is this a more humane way to keep dogs from destroying things? (opposed to keep them outside or in a cage). Or, are we creating obsessive compulsive animals that require constant feedback? Are dogs even interested in an interactive experience?

I’ll put this concept (Dog Gamification) in consideration for a prototype build. I might be able to assemble a very primitive version of it with some Arduinos and Raspberry Pis and some cheap sensors that I find online. That will be part 2 : Stay tuned.

This idea follows the Zombie Home Manifest

Let’s say you have a brand new idea. Let’s be honest, maybe you should just forget about it… Why? Because by definition you are absolutely biased by your own background, assumptions and context. You are to such idea what a mom is to her baby. You won’t see anything wrong with it, you’ll love it and you’ll do anything it takes to demonstrate the world that you are right (even if you aren’t).

As I was doing the laundry last Sunday, I realized that not because I do it every week, the clothes are getting cleaner, they are just the same every week. Same as the Jackpot player that pulls the lever once or a thousand times, the probabilities don’t really increase as it resets every time however subconsciously there is indeed a sense of progress. How many hours have you spent thinking about that awesome idea you had a year ago? How do you know that every day you don’t just reset your vision and replay the same scenarios on how that idea will succeed?

In order to break that mental ping pong you need to run a very simple mental exercise:

“Assume that exactly the same idea was already imagined by somebody smarter that you and with more funding than you… but it failed.”

By doing so, the following happens :

• You stop day-dreaming that you have the killer, world changing idea that will be sold out the first day and focus in what is important which is your product.
• It brings you closer to the hard reality that you might fail as well but it also gives you the chance to demonstrate that your execution and tactics are much better.
• When somebody gives you feedback, you’ll actually listen instead of trying to “correct” them as they could be giving you valuable information that the other guys might have ignored.
• You become more cold blooded with trivial features as making a series of wrong choices could have been the reason of the other guy’s demise .
• You stop pretending you can be everything for everybody. You want to live to see the next day even if that involves only serving one segment at a time.

As the saying goes: “It is not the idea, it is the execution what matters”. Execution is the art of smuggling an idea from the Imaginary world to the Real world. The crystallization of something ethereal. We should focus on being the Marco Polo of ideas, you and only you know your silk road.

Every time I meet a film director I ask them the same question: If I gave the same script and budget to 5 different directors… would I get the same movie? The answer is unequivocally “no”.

So, why ideas would be different? Ideas being the equivalent of scripts. I would bet you would not be able to replicate Facebook even if you were sent back in time to that dorm at that moment in history same as you wouldn’t be able to replicate Clockwork Orange by Stanley Kubrick even if you had the exact same crew and script.

That is actually good news. Because you could be the “One” that executes right, that non original idea you just got. : )

There is a very curious thing that happens when you are trying to learn a new (spoken) language, you run out of words pretty fast. When I was just learning english I wanted to use the work “good” for practically everything. Food was “good”, a movie was “good”, my seat was “good” and generally speaking life was “good”. People would definitely understand me but I felt kind of trapped in this one dimensional linguistic space. Problem is, that ideas are everything but one dimension.

Transplanting that concept to Smart Home, I was originally planning to write this blog under the constraints that every proposed idea was to be built with existing plug and play components available in the market. However… I found myself trying to work around the limited catalogue of existing components and basically coming up with one dimensional ideas. On the other hand, I could put my hat of electronic engineer, grab my multimeter and my soldering iron and build them myself with resistors, micro controllers, etc, however that would be like cheating, and I would be violating the principle of “The skill to setup a zombie home should be equivalent to the skills to connect any other home appliance.” .

For that reason, I’m going to propose here a list of elements that will fill an hypothetical box of components that I’ll use in every project.

Assume that each one of these components can just talk to each other. A way to visualize their communication is to see them as actors in a play and they are following a script. Let’s say I want to create a gadget that identifies bad smells in the kitchen.

The play would go like this:

Every five minutes, Sarah “The Controller” listens to Joe “The Air Quality sensor” yelling out loud his readings. When Sarah hears that the air quality is not good, she suggests Bill “The Fan” to start and run until further notice. In the meantime, Sarah suggests Mary “The color cube” to turn red. In addition to that, she writes in a notebook (a flash memory) what just happened and goes to sleep. Five minutes after, Sarah wakes up and pays attention to what Joe (the Air Quality guy) is saying again and figures out if something has changed. It the readings look better now, she suggests Bill (the fan) to stop and Mary (the color cube) to turn green. Sarah goes to sleep again. She repeats this action every 5 minutes.

You might be thinking this is stupid and childish… maybe =), but I’ve designed quite a couple software components in my life and realized early on that when you turn all the components into people, your social skills kick in and you start seeing what everybody is going and not going to do, who has enough information, who are you using a lot, who is being ignored, etc. The result is a much more well though and rounded up piece of software. Just for the sake of simplicity, I’m going to use this paradigm to explain the algorithms that run each one of the gadgets. You just need to assume that such algorithms (or plays) are already loaded in the gadget or in the controller so you don’t need to program them.

Conclusion: I just established a series of hypothetical components that by act of magic just talk to each other as if they were just people. Instead of software we’ll use some sort of play script that describes their interaction.