Even though we see lots of articles about AI, few of us really have even a vague idea of how it works. It is super complicated, but that doesn’t mean we can’t explain it in simple terms.

I don’t work in AI, but I did work as a Computational Scientist back in the early 1980’s. Back then I became aware of fledgling neural network software and pioneered its applications in formulation chemistry. While neural network technology was extremely crude at that time, I proclaimed to everyone that it was the future. And today, neural networks are the beating heart of AI which is fast becoming our future.

To get a sense of how neural networks are created and used, consider a very simple example from my work. I took examples of paint formulations, essentially the recipes for different paints, as well as the paint properties each produced, like hardness and curing time. Every recipe and its resulting properties was a training fact and all of them together was my training set. I fed my training set into software to produce a neural network, essentially a continuous map of this landscape. This map could take quite a while to create, but once the neural network was complete I could then enter a new proposed recipe and it could instantly tell me the expected properties. Conversely, I could enter a desired set of properties and it could instantly predict a recipe to achieve them.

So imagine adapting and expanding that basic approach. Imagine, for example, that rather than using paint formulations as training facts, you gathered training facts from a question/answer site like Quora, or a simple FAQ. You first parse each question and answer text into keywords that become your inputs and outputs. Once trained, the AI can then answer most any question, even previously unseen variations, that lie upon the map that it has created.

Next imagine you had the computing power to scan the entire Internet and parse all that information down into sets of input and output keywords, and that you had the computing power to build a huge neural network based on all those training facts. You would then have a knowledge map of the Internet, not too unlike Google Maps for physical terrain. That map could then be used to instantly predict what folks might say in response to anything folks might say – based on what folks have said on the Internet.

You don’t need to just imagine, because now we can do essentially that.

Still, to become an AI, a trained neural network alone is not enough. It first needs to understand your written or spoken language question, parse it, and select input keywords. For that it needs a bunch of skills like voice recognition and language parsing. After finding likely output keywords, it must order them sensibly and build a natural language text or video presentation of the outputs. For that you need text generators, predictive algorithms, spelling and grammar engines, and many more processors to produce an intelligible, natural sounding response. Most of these various technologies have been refined for a long time in your word processor or your messaging applications. AI is really therefore a convergence of many well-known technologies that we have built and refined since at least the 1980’s.

AI is extremely complex and massive in scale, but unlike quantum physics, quite understandable in concept. What has enabled the construction of AI scale neural networks is the mind-boggling computer power required to train such a huge network. When I trained my tiny neural networks in the 1980’s it took hours. Now we can parse and train a network on well, the entire Internet.

OK, so hopefully that demystifies AI somewhat. It basically pulls a set of training facts from the Internet, parses them and builds a network based on that data. When queried, it uses that trained network map to output keywords and applies various algorithms to build those keywords into comprehensible, natural sounding output.

It’s important we understand at least that much about how AI works so that we can begin to appreciate and address the much tougher questions, limitations, opportunities, and challenges of AI.

Most importantly, garbage in, garbage out still applies here. Our goal is for AI should be to do better than we humans can do, to be smarter than us. After all, we already have an advanced neural network inside our skulls that has been trained over a lifetime of experiences. The problem is, we have a lot of junk information that compromises our thinking. But if an AI just sweeps in everything on the Internet, garbage and all, doesn’t that make it just an even more compromised and psychotic version of us?

We can only rely upon AI if it is trained on vetted facts. For example, AI could be limited to training facts from Wikipedia, scientific journals, actual raw data, and vetted sources of known accurate information. Such a neural network would almost certainly be vastly superior to humans in producing accurate and nuanced answers to questions that are too difficult for humans to understand given our more limited information and fallibilities. There is a reason that there are no organic doctors in the Star Wars universe. It is because there is no advanced future civilization where organic creatures could compete the AI medical intelligence and surgical dexterity of droids.

Here’s a problem. We don’t really want that kind of boring, practical AI. Such specialized systems will be important, but not huge commercially nor sociologically impactful. Rather, we are both allured and terrified by AI that can write poetry or hit songs, generate romance or horror novels, interpret the news, and draw us images of cute dragon/butterfly hybrids.

The problem is, that kind of popular “human like” AI, not bound by reality or truth, would be incredibly powerful in spreading misinformation and manipulating our emotions. It would feedback nonsense that would further instill and reinforce nonsensical and even dangerous thinking in our own brain-based neural networks.

AI can help mankind to overcome our limitations and make us better. Or it can dramatically magnify our flaws. It can push us toward fact-based information, or it can become QANON and Fox “News” on steroids. Both are equally feasible, but if Facebook algorithms are any indication, the latter is far more probable. I’m not worried about AI creating killer robots to exterminate mankind, but I am deeply terrified by AI pushing us further toward irrationality.

To create socially responsible AI, there are two things we must do above all else. First, we must train specialized AI systems, say as doctors, with only valid, factual information germane to medical treatment. Second, any more generative, creative, AI networks should be built from the ground up to distinguish factual information from fantasy. We must be able to indicate how realistic we wish our responses to be and the system must flag clearly, in a non-fungible manner, how factual its creations actually are. We must be able to count on AI to give us the truth as best as computer algorithms can recognize it, not merely to make up stories or regurgitate nonsense.

Garbage in garbage out is a huge issue, but we also face a an impending identity crisis brought about by AI, and I’m not talking about people falling in love with their smart phone.

Even after hundreds of years to come to terms with evolution, the very notion still threatens many people with regard to our relationship with animals. Many are still offended by the implication that they are little more than chimpanzees. AI is likely to cause the same sort of profound challenge to our deeply personal sense of what it means to be human.

We can already see that AI has blown way past the Turing Test and can appear indistinguishable from a human being. Even while not truly self-aware, AI systems can seem to be capable of feelings and emotion. If AI thinks and speaks like a human being in every way, then what is the difference? What does it even mean to be human if all the ways we distinguish ourselves from animals can be reproduced by computer algorithms?

The neural network in our brain works effectively like a computer neural network. When we hear “I love…” our brains might complete that sentence with “you.” That’s exactly what a computer neural network might do. Instead of worrying about whether AI systems are sentient, the more subtle impact will be to make us start fretting about whether we are merely machines ourselves. This may cause tremendous backlash.

We might alleviate that insecurity by rationalizing that AI is not real by definition because it is not human. But that doesn’t hold up well. It’s like claiming that manufactured Vitamin C is not really Vitamin C because it did not some from an orange.

So how do we come to terms with the increasingly undeniable fact that intellectually and emotionally we are essentially just biological machines? The same way many of us came to terms with the fact that we are animals. By acknowledging and embracing it.

When it comes to evolution, I’ve always said that we should take pride in being animals. We should learn about ourselves through them. Similarly, we should see computer intelligence as an opportunity, not a threat to our sense of exceptionalism. AI can help us to be better machines by offering a laboratory for insight and experimentation that can help both human and AI intelligences to do better.

Our brain-based neural networks are trained on the same garbage data as AI. The obvious flaws in AI are the same less obvious flaws that affect our own thinking. Seeing the flaws in AI can help us to recognize similar flaws in ourselves. Finding ways to correct the flaws in AI can help us to find similar training methodologies to correct them in ourselves.

I’m an animal and I’m proud to be “just an animal” and I’m equally proud to be “just a biological neural network.” That’s pretty awesome!

Let’s just hope we can create AI systems that are not as flawed as we are. Let’s hope that they will instead provide sound inputs to serve as good training facts to help retrain our own biological neural networks to think in more rational and fact-based ways.