Defining the Five Tribes and the Master Algorithm

You’ve no doubt seen and heard lots of hype about AI and its potential impact. If you’ve seen movies such as Her and Ex Machina, you might be led to believe that AI is further along than it is. The problem is that AI is actually in its infancy, and any sort of application such as those shown in the movies is the creative output of an overactive imagination. The following sections help you understand how hype and overestimation are skewing the goals you can achieve using AI today.

You may have heard of a concept called the singularity, which is responsible for the potential claims presented in the movies and other media. The singularity (when computer intelligence surpasses human intelligence) is essentially a master algorithm that encompasses all five “tribes” of learning used within machine learning. To achieve what these sources are telling you, the machine must be able to learn as a human would — as specified by the eight kinds of intelligence discussed earlier. Here are the five tribes of learning:

• Symbologists: The origin of this tribe is in logic and philosophy. It relies on inverse deduction to solve problems.
• Connectionists: This tribe’s origin is in neuroscience, and the group relies on backpropagation to solve problems.
• Evolutionaries: This tribe originates in evolutionary biology, relying on genetic programming to solve problems.
• Bayesians: This tribe’s origin is in statistics and relies on probabilistic inference to solve problems.
• Analogizers: The origin of this tribe is in psychology. The group relies on kernel machines to solve problems.

REMEMBER

The ultimate goal of machine learning is to combine the technologies and strategies embraced by the five tribes to create a single algorithm (the master algorithm) that can learn anything. Of course, achieving that goal is a long way off. Even so, scientists such as Pedro Domingos at the University of Washington are working toward that goal.

To make things even less clear, the five tribes may not be able to provide enough information to actually solve the problem of human intelligence, so creating master algorithms for all five tribes may still not yield the singularity. At this point, you should be amazed at just how little people know about how they think or why they think in a certain manner.

REMEMBER

Any rumors you hear about AI taking over the world or becoming superior to people are just plain false.

Considering Sources of Hype

Many sources of AI hype are out there. Quite a bit of the hype comes from the media and is presented by people who have no idea of what AI is all about, except perhaps from a sci-fi novel they read a few years back. So it’s not just movies or television that cause problems with AI hype — it’s all sorts of other media sources as well. You can often find news reports presenting AI as being able to do something it can’t possibly do because the reporter doesn’t understand the technology. Oddly enough, many news articles are now written entirely by AI like ChatGPT, so what you end up with is a recycling of the incorrect information.

Some products should be tested much more before being placed on the market. The article “12 Famous AI Disasters” at CIO.com (www.cio.com/article/190888/5-famous-analytics-and-ai-disasters.html) discusses twelve AI products, hyped by their developers, that fell flat on their faces. Some of these failures are huge and reflect badly on the ability of AI to perform tasks as a whole. However, something to consider with a few of these failures is that people may have interfered with the device using the AI. Obviously, testing procedures need to start considering the possibility of people purposely tampering with the AI as a potential source of errors. Until that happens, the AI will fail to perform as expected because people will continue to fiddle with the software in an attempt to cause it to fail.

WARNING

Another cause of problems stems from asking the wrong person about AI — not every scientist, no matter how smart, knows enough about AI to provide a competent opinion about the technology and the direction it will take in the future. Asking a biologist about the future of AI in general is akin to asking your dentist to perform brain surgery — it simply isn’t a good idea. Yet many stories appear with people like these as the information source.

TIP

To discover the future direction of AI, ask a computer scientist or data scientist with a strong background in AI research.

Managing User Overestimation

Because of hype (and sometimes laziness or fatigue), users continually overestimate the ability of AI to perform tasks. For example, a Tesla owner was recently found sleeping in his car while the car zoomed along the highway at 90 mph (www.bbc.com/news/world-us-canada-54197344). However, even with the user significantly overestimating the ability of the technology to drive a car, it does apparently work well enough (at least, for this driver) to avoid a complete failure.

WARNING

Be aware that cases exist in which auto drive failed and killed people. (See the article at www.washingtonpost.com/technology/interactive/2023/tesla-autopilot-crash-analysis.)

However, you need not be speeding down a highway at 90 mph to encounter user overestimation. Robot vacuums can also fail to meet expectations, usually because users believe they can just plug in the device and then never think about vacuuming again. After all, movies portray the devices working precisely in this manner, but unfortunately, they still need human intervention. Our point is that most robots eventually need human intervention because they simply lack the knowledge to go it alone.