What do you think of when you hear the words Artificial Intelligence?
For me as a kid it was robots. I imagined a future world filled with walking, talking robot companions. Robots to carry me to school. Robots to sit and play a game with me. You may remember The Jetsons. It was a cartoon which depicted a nice family who lived in a futuristic world of flying cars and friendly personal butler robots. I dreamed of one day living in a world like that.
And today flying cars are real and personal robots exist by the thousands. But I still mop my own floor and drive a pickup truck. Like most fiction it’s not exactly how the creators of the Jetson’s imagined.
While most of the technology seen in this futuristic show exists – it’s evolving a little differently than we thought. And It’s also nothing to joke about. AI is more than just robots. It’s software that is already changing the world. AI is not as complicated as is sounds and anyone can become an AI innovator.
What is Artificial Intelligence (AI)
WikiPedia defines A.I. a computers demonstrating intelligence. So in simple terms the computer is making some sort of decision based data it’s fed.
But all of this is nothing new. There’s been lots of work done at government and University levels research facilities since the 1950’s. The Ancient Greeks conceived a similar idea and semi-intelligent software has been around for a long time. So what is all the buzz? For starters A.I. has simply become a trendy buzzword. I have seen a number of startups suddenly emerge that claim use some semblance of AI in their product. Some of these are nonsense and a few are likely very good and will become useful.
In reality it is hard to find anyone with any kind of new truly functional, useful AI like is depicted in movies. This is just because the technology isn’t quite there yet. Even though we have lightning fast computers and Internet available, it’s not always cost-effective or feasible to process mass amounts of data in real time.
This is why you see prototypes with patches of intelligence displayed in closed environments but not as many fully functional human looking robots out there ready to greet you throughout your day.
Getting started on some basics: There are 2 main types of AI:
Applied: Most common – a very specific application such as a self-driving car or a voice activated computer. These are using libraries of data and get better as they go. These software programs typically use some sort of Machine Learning architecture where the engineer uses lots of machines each receiving inputs on the environment. In aggregate, or over time, the machines are able to find a pattern and use the pattern to make decisions.
General: I can’t think of a single ‘general’ AI that actually works in a production mode. If someone has one please let me know! An example would be a robot you could walk up to and it would just instantly be able to converse freely with you with no human input. Even the impressive humanoid Sophia isn’t really fully autonomous.
AI as a socioeconomic tool
Over the years I’ve had the opportunity to host and participate many robotics meetups and events with thousands of people and virtually every type of robot known to man. I’ve designed and brought to market several technologies, some of which failed and a few which made it through the gauntlet of consumer acceptance and adoption. For a working class kid from rural Vermont technology was an opportunity to transform myself and the world around me. Today I love my job helping others to innovate around robotics.
I am a capitalist so yes I want to also make a profit. That’s important to sustain innovation. But teaching people about AI, robotics and technology entrepreneurship is my social-economic-spiritual statement to the world, and gives me a purpose. Currently I’m working on BiBli which is a robotics platform to help people learn about all of this easily and inexpensively. It’s also really fun to collaborate with smart, talented people to make goofy robot inventions and even some potential breakthroughs.
The CEO of Google recently came out and said he thought AI was more of a game changer than electricity. Elon Musk has warned AI is more dangerous than nuclear weapons. I agree with both statements. Do we need to be afraid? No. Do we need to pay attention? Yes.
AI is here and already impacts your life daily. Marketing, fake news, hacking, the financial markets, security and safety screening, healthcare and education all use AI.
It’s really nothing new, nor that difficult to understand conceptually. If you are a real geek interested in the math and more advanced aspects of AI I would suggest starting with learning a about the basics of neural networks and or the different types of machine learning. Being versed in college-level math such as calculus or linear algebra could help you to understand but really all of this comes down to analyzing data.
If you have no programming experience, don’t worry that part isn’t important in the conceptual stages. If you are really interested you could spend a few nights a week learning Python which is a great starting point to learn software.
Simply start to think about how a robot could help you in your daily life
During Build-a-BiBli workshops we often give people a blank piece of paper and ask them to design their ‘dream robot’. What comes back is pretty typical. In the case of students their robot usually looks like a robot they’ve seen in movies, cleans their room and does their homework.
We talk about the power usage required to power a laundry folding arm and how difficult it would be to program a computer to do homework in any language on any subject. Eventually the robot design gets reduced down to a prototype idea that could actually be built and used.
An interesting note – In the case of adults there is almost always one person who wants a robot that will go get them a beer. In all cases, their robot is usually inferior – a sort of slave designed to serve them.
An Example of an AI Project in Real Life
Sometimes the easiest way to understand a concept is to see it in real life. For this example let’s take a look at Tesla’s self-driving cars. You may own a Tesla or hopefully have at least seen one drive by. Tesla isn’t the only autonomous driving vehicle out there but they are pioneers.
It’s a great example of a network of computers working together to get smarter. Teslas rely on cameras, sensors, GPS data and a driver to navigate through streets and highways. And while the self-driving is getting better and better it’s still not totally autonomous. The company has given no deadline for when they will be.
Think about why:
There are many Teslas in the world and it was recently reported that more than 1.3 Billion miles have been driven autonomously. They are all connected to the internet. Data from all of those miles have been fed into the Tesla software. Also the data from your own routes as a driver (hint: you repeat the same ones over and over) are also part of the math.
I would guess that when there is enough collective data to make that system perfect they will offer a software update that makes of the cars truly intelligent and self-driving will be a reality in the world. And they will likely make a pile of money in the process.
The Tesla fleet is a perfect example of neural network using machine learning to get smarter as they go. This is a real thing – in fact you can even see discussion about the Tesla neural network community here.
Get started inventing the next AI
There’s no calculus homework involved. You won’t need a soldering gun or engineering background to complete this challenge. You just need creativity:
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- Today, simply go about your day. Look at all of the devices in your life. Appliances, computers and vehicles are everywhere. Most are connected already to or bluetooth. Think about which of those could help you more in life if they were just slightly ‘smarter’.
- Then think about the types of data they could potentially collect if they all had inputs or sensors. Sensors are cheap and easy and just send a computer a signal that is either on, off or with some value. They can detect lots of different things. Temperature, distance, humidity, light, air quality, sound, hidden frequencies and more.
- The AI part: Finally think about they kind of patterns you might see if you looked at all of that data in aggregate. Think about a simple way in which a computer might use that pattern to easily make a guess on what will happen next.
- Also think about the various tones and lights and voices these devices use and how that makes you feel. Do you like the device? Do you trust it? Does it annoy you? Social robots are the same tech as a roaming laptop with eyes. But we see them much differently. Why? The personality of AI is going to be very important.
- Now think of a product that lots of people would also need, invent it and become the first trillionaire..
OK, maybe it’s not quite that easy. But it’s not as hard as you might think. No longer are the days where only big companies or trained scientists can invent the next breakthroughs. Every day people are solving problems and coming up with products from their garage or classroom.
Our future is not going to be exactly like The Jetsons – but still pretty amazing. AI is here to stay and it’s just getting started. We don’t need to fear it we need to harness it.
Countless breakthroughs are coming and trillions in new revenue.
Hopefully because of someone just like you!
