It seems like most new technology today comes with a lot of hype. Just a few years ago, the press was full of predictions that we’d be awash with Internet of Thing sensors that would transform the way we live. We’ve heard similar claims for technologies like virtual reality, blockchain, and self-driving cars. I’ve written a lot about the massive hype surrounding 5G—in my way of measuring things, there isn’t any 5G in the world yet, but the cellular carriers are loudly proclaiming its everywhere.

The other technology with a hype that nearly equals 5G is artificial intelligence. I see articles every day talking about the ways that artificial intelligence is already changing our world, with predictions about the big changes on the horizon due to AI. A majority of large corporations claim to now be using AI. Unfortunately, this is all hype, and there is no artificial intelligence today, just like there is not yet any 5G.

It’s easy to understand what real 5G will be like—it will include the many innovations embedded in the 5G specifications like frequency slicing and dynamic spectrum sharing. We’ll finally have 5G when a half dozen new 5G technologies are on my phone. Defining artificial intelligence is harder because there is no specification for AI. Artificial intelligence will be here when a computer can solve problems in much the way that humans do. Our brains evaluate available data on hand to see if we know enough to solve a problem. If not, we seek the additional data we need. Our brains can consider data from disparate and unrelated sources to solve problems. There is no computer today that is within a light-year of that ability—there are not yet any computers that can ask for specific additional data needed to solve a problem. An AI computer doesn’t need to be self-aware—it just has to be able to ask the questions and seek the right data needed to solve a given problem.

We use computer tools today that get labeled as artificial intelligence, such as complex algorithms, machine learning, and deep learning. We’ve paired these techniques with faster and larger computers (such as in data centers) to quickly process vast amounts of data.

One of the techniques we think of artificial intelligence is nothing more than using brute force to process large amounts of data. This is how IBM’s Deep Blue works. It can produce impressive results and shocked the world in 1997 when the computer was able to beat Garry Kasparov, the world chess champion. Since then, the IBM Watson system has beat the best Jeopardy players and is being used to diagnose illnesses. These computers achieve their results by processing vast amounts of data quickly. A chess computer can consider vast numbers of possible moves and put a value on the ones with the best outcome. The Jeopardy computer had massive databases of human knowledge available like Wikipedia and Google search—it looks up the answer to a question faster than a human mind can pull it out of memory.

Much of what is thought of as AI today uses machine learning. Perhaps the easiest way to describe machine learning is with an example. Machine learning uses complex algorithms to analyze and rank data. Netflix uses machine learning to suggest shows that it thinks a given customer will like. Netflix knows what a viewer has already watched. Netflix also knows what millions of others who watch the same shows seem to like, and it looks at what those millions of others watched to make a recommendation. The algorithm is far from perfect because the data set of what any individual viewer has watched is small. I know in my case, I look at the shows recommended for my wife and see all sorts of shows that interest me, but which I am not offered. This highlights one of the problems of machine learning—it can easily be biased and draw wrong conclusions instead of the right ones. Netflix’s suggestion algorithm can become a self-fulfilling prophecy unless a viewer makes an effort to look outside of the recommended shows—the more a viewer watches what is suggested, the more they are pigeonholed into a specific type of content.

Deep learning is a form of machine learning that can produce better results by passing data through multiple algorithms. For example, there are numerous forms of English spoken around the world. A customer service bot can begin each conversation in standard English, and then use layered algorithms to analyze the speaker’s dialect to switch to more closely match a given speaker.

I’m not implying that today’s techniques are not worthwhile. They are being used to create numerous automated applications that could not be done otherwise. However, almost every algorithm-based technique in use today will become instantly obsolete when a real AI is created.

I’ve read several experts that predict that we are only a few years away from an AI desert—meaning that we will have milked about all that can be had out of machine learning and deep learning. Developments with those techniques are not leading towards a breakthrough to real AI—machine learning is not part of the evolutionary path to AI. At least for today, both AI and 5G are largely non-existent, and the things passed off as these two technologies are pale versions of the real thing.