3 types of artificial intelligence, but only 2 are valid – VentureBeat

For all of the visions of robots taking over the world, stealing jobs, and outpacing humans in every facet of existence, we havent seen many cases of AI drastically changing industries, or even our day-to-day lives, just yet. For this reason, media and AI deniers alike question whether true broad-scale AI even exists. Some go as far as to conclude that it doesnt.

The answer is a bit more nuanced than that.

Current AI applications can be broken down into three loose categories: Transformative AI, DIY (Do It Yourself) AI, and Faux AI. The latter two are the most common and therefore tend to be the measure by which all AI is judged.

The everyday AI applications weve seen most of so far are geared toward accessing and processing data for you, making suggestions based on that data, and sometimes even executing very narrow tasks. Alexa turning on yourmusic, telling you whats happening in your day, and reporting on the weather outside are good examples. Another is your iPhone predicting a phone number for a contact you dont already have saved.

While these applications might not live up to the image of AI we have in our heads, it doesnt mean theyre not AI. It just means theyre not all thatlife-changing.

The kind of AI that will take over the world or at least have the most dramatic effect on how people live and work is what I think of as Transformative AI. Transformative AI turns data into insights and insights into instructions. Then, instead of simply delivering those instructions to the user so he or she can make more informed decisions, it gets to work, autonomously carrying out an entire complex process on its own based on what it has learned and continues to learn along the way.

This type of AI isnt yet ubiquitous. The most universally known manifestation of this is likely the self-driving car. Self-driving cars are an accessible example of what it looks like for a machine to take in constantly changing information, process it, and act on it, thereby eliminating the need for human participation at any stage.

Driving is not a fixed process that is easily automated. (If it were, AI wouldnt be necessary.) While there is indeed a finite set of actions involved in driving, the data set the AI must process shifts every single time the passenger gets into the car based on road conditions, destination, route, oncoming and surrounding traffic, street lanes, street closures, proximity to neighboring vehicles,a pedestrian stepping out in front of the car, and so on. The AI must be able to take all of this in, make a decision about it, and act on it right then and there, just like a human driver would.

This is Transformative AI, and we know its real because its already happening.

Now imagine the implications of this technology applied elsewhere. Most people will likely experience Transformative AI through their jobs or industries before it directly affects the way they live. In business, the massive amount of big data that companies are collecting will be the fuel that AI uses to single-handedly power processes currently handled by entire teams, and it will do so with far greater precision and efficiency.

Were seeing this in the marketing space, as brands like Cosabella and Dole Asia have replaced their digital account teams and agencies built onartificial intelligence platforms.

But these are still early days, and it will be a while before these types of stories are commonplace. In the meantime, well mostly see different manifestations of DIY AI and Faux AI.

DIY AI is any artificial intelligence platform whose end goal is to make you, the user, more informed so that you can then do the remaining workyourself. This type of AI can take in and process large amounts of data to produce insights, but thats the end of the line for it. Put another way, its practical and prescriptive but not curative.

Nevertheless, it can be extremely valuable to companies and organizations that have been relying on data scientists to make sense of their data manually. Even the most talented data scientists need far more time to process, analyze, and make recommendations from data than a machine does. A few of the many reasons for this is that humans require things like sleep, food, and weekends off. A more significant reason is that humans simply dont have the same processing power that machines do.

An example of DIY AI is Salesforces Einstein. In an ad placed in the New York Times in early May, Salesforce described how Einstein qualifies leads, predicts when customers are ready to buy, and helps close more deals. In other words, the AI is reading companies CRM data, making sense of it, and setting up salespeople for more success than theyd have if they had to wade through the same data on their own. But the execution elements of the sales process are ultimately still DIY for the user.

Its worth noting that DIY AI isoften bolt-on, meaning that the AI is essentially bolted onto an existing technology. It then acts as the brain that makes a once dumb (or static) system smart (or insightful). For the sake of comparison, Transformative AI must be built from the ground up, meaning there are no parts of the technology that arent AI-driven.

The final category of AI were seeing is the one that spoils it for everyone: Faux AI. While DIY AI might seem lackluster or boring, Faux AI is pretending to be something that its not. As with any new technology that creates hype and intrigue, AI has inspired companies to prey on the publics lack of understanding. Many of the companies doing this are re-positioning their predictive and automation technologies as AI, when really they are just offering rules-based applicationsthat arent governed by machine learning.

Not to single out any chatbots, but there are a few culprits in that space. They look and act like AI agents, but they are not really using machine learning. They are pretenders.

Programmatic ad buying is a good example of an insight-driven, predictive technology that many people confuse with AI and which often passes itself off as the same. Because programmatic technology has been around for over a decade, learning that it is AI (which its not) can leave people feeling like artificial intelligence isnt so special.

The way AI will evolve and begin infiltrating our lives is two-fold.

Some of the more robust DIY AI out there isactually Transformative AI in training. The data being collected and processed will train algorithms over time so that theyre ultimately equipped with all the information they need to begin acting on that data (assuming theyve been programmed to do so). And technologists whoare just getting started on their platforms will build them with AI from the ground up, rather than bolting training wheels on after the fact. The result will be active sources of Transformative AI that ultimately shape up into what we imagine AI can be ideally, in the most positive way possible.

Or Shani is the CEO of Albert, an AI marketing platform.

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3 types of artificial intelligence, but only 2 are valid - VentureBeat