10 Ways in Which AI Has Failed
Artificial Intelligence (AI) hasn’t just failed to meet expectations set by overly enthusiastic proponents; it has failed to meet specific needs and basic requirements. This list is about the failures that will keep AI from excelling and performing the tasks we need it to do. AI is currently an evolving technology that is partially successful at best.
One of the essential issues surrounding AI today is that people keep anthropomorphizing it and making it into something it isn’t. An AI accepts cleaned data as input, analyzes it, finds the patterns, and provides a requested output. An AI doesn’t understand anything, it can’t create or discover anything new, and it has no intrapersonal knowledge, so it can’t empathize with anyone about anything. An AI behaves as designed by a human programmer, and what you often take for intelligence is only a mix of clever programming and vast amounts of data analyzed in a specific manner. For another view of these and other issues, check out the article titled “Asking the Right Questions About AI.”
Even more important, however, is that people who claim that an AI will eventually take over the world fail to understand that doing so is impossible given current technology. An AI can’t suddenly become self-aware because it lacks any means of expressing the emotion required to become self-aware. An AI today lacks some of the essential seven kinds of intelligence required to become self-aware. Simply possessing those levels of intelligence wouldn’t be enough, either. Humans have a spark in them —something that scientists don’t understand. Without understanding what that spark is, science can’t recreate it as part of an AI.
AIs completely lack understanding
The ability to comprehend is innate to humans, but AIs completely lack it. Looking at an apple, a human more than just a series of properties associated with a picture of an object. Humans understand apples through the use of senses, such as color, taste, and feel. We understand that the apple is edible and provides specific nutrients. We have feelings about apples; perhaps we like them and feel that they’re the supreme fruit. The AI sees an object that has properties associated with it — values that the AI doesn’t understand, but only manipulates. The failure to understand causes AI as a whole to fail to meet expectations.
Interpreting, not analyzing
An AI uses algorithms to manipulate incoming data and produce an output. The emphasis is on performing an analysis of the data. However, a human controls the direction of that analysis and must then interpret the results. For example, an AI can perform an analysis of an x-ray showing a potential cancer tumor. The resulting output may emphasize a portion of the x-ray containing a tumor so that the doctor can see it. The doctor might not be able to see the tumor otherwise, so the AI undoubtedly provides an important service. Even so, a doctor must still review the result and determine whether the x-ray does indeed show cancer. An AI is easily fooled at times when even a small artifact appears in the wrong place. Consequently, even though the AI is incredibly helpful in giving the doctor the ability to see something that isn’t apparent to the human eye, the AI also isn’t trustworthy enough to make any sort of a decision.
Interpretation also implies the ability to see beyond the data. It’s not the ability create new data, but to understand that the data may indicate something other than what is apparent. For example, humans can often tell that data is fake or falsified, even though the data itself presents no evidence to indicate these problems. An AI accepts the data as both real and true, while a human knows that it’s neither real nor true. Formalizing precisely how humans achieve this goal is currently impossible because humans don’t actually understand it.
Going beyond pure numbers
Despite any appearance otherwise, an AI works only with numbers. An AI can’t understand words, for example, which means that when you talk to it, the AI is simply performing pattern matching after converting your speech to numeric form. The substance of what you say is gone. Even if the AI were able to understand words, it couldn’t do so because the words are gone after the tokenization process. The failure of AIs to understand something as basic as words means that an AI’s translation from one language to another will always lack that certain something needed to translate the feeling behind the words, as well as the words themselves. Words express feelings, and an AI can’t do that.
The same conversion process occurs with every sense that humans possess. A computer translates sight, sound, smell, taste, and touch into numeric representations and then performs pattern matching to create a data set that simulates the real-world experience. Further complicating matters, humans often experience things differently from each other. For example, each person experiences color uniquely. For an AI, every computer sees color in precisely the same way, which means that an AI can’t experience colors uniquely. In addition, because of the conversion, an AI doesn’t actually experience color at all.
An AI can analyze data, but it can’t make moral or ethical judgements. If you ask an AI to make a choice, it will always choose the option with the highest probability of success unless you provide some sort of randomizing function as well. The AI will make this choice regardless of the outcome.
In many situations, misjudging the ability of an AI to perform a task is merely inconvenient. In some cases, you may have to perform the task a second or third time manually because the AI isn’t up to the task. However, when it comes to consequences, you might face legal problems in addition to the moral and ethical problems if you trust an AI to perform a task that unsuited to it. For example, allowing a self-driving (SD) car to drive by itself in a place that doesn’t provide for this need is likely illegal, and you’ll face legal problems in addition to damage and medical charges that the SD car can cause. In short, know what the legal requirements are before you trust an AI to do anything involving potential consequences.
AIs cannot discover or create something
An AI can interpolate existing knowledge, but it can’t extrapolate existing knowledge to create new knowledge. When an AI encounters a new situation, it usually tries to resolve it as an existing piece of knowledge, rather than accept that it’s something new. In fact, an AI has no method for creating anything new, or seeing it as something unique. These are human expressions that help us discover new things, work with them, devise methods for interacting with them, and create new methods for using them to perform new tasks or augment existing tasks.
Devising new data from old
One of the more common tasks that people perform is extrapolation of data; for example, given A, what is B? Humans use existing knowledge to create new knowledge of a different sort. By knowing one piece of knowledge, a human can make a leap to a new piece of knowledge, outside the domain of the original knowledge, with a high probability of success. Humans make these leaps so often that they become second nature and intuitive in the extreme. Even children can make such predictions with a high rate of success.
The best that an AI will ever do is to interpolate data for example, given A and B, is C somewhere in between? The capability to successfully interpolate data means that an AI can extend a pattern, but it can’t create new data. However, sometimes developers can mislead people into thinking that the data is new by using clever programming techniques. The presence of C looks new when it truly isn’t. The lack of new data can produce conditions that make the AI seem to solve a problem, but it doesn’t. The problem requires a new solution, not the interpolation of existing solutions.
Seeing beyond the patterns
Currently, an AI can see patterns in data when they aren’t apparent to humans. The capability to see these patterns is what makes AI so valuable. Data manipulation and analysis is time consuming, complex, and repetitive, but an AI can perform the task with aplomb. However, the data patterns are simply an output and not necessarily a solution. Humans rely on five senses, empathy, creativity, and intuition to see beyond the patterns to a potential solution that resides outside what the data would lead one to believe.
A basic way to understand the human ability to see beyond patterns is to look at the sky. On a cloudy day, people can see patterns in the clouds, but an AI sees clouds and only clouds. In addition, two people may see different things in the same set of clouds. The creative view of patterns in the cloud may have one person seeing a sheep and another a fountain. The same holds true for stars and other kinds of patterns. The AI presents the pattern as output, but it doesn’t understand the pattern and lacks the creativity to do anything with the pattern, other than report that the pattern exists.
Implementing new senses
As humans have become more knowledgeable, they have also become aware of variances in human senses that don’t actually translate well to an AI because replicating these senses in hardware isn’t truly possible now. For example, the ability to use multiple senses to manage a single input (synesthesia) is beyond an AI.
Describing synesthesia effectively is well beyond most humans. Before they can create an AI that can mimic some of the truly amazing effects of synesthesia, humans must first fully describe it and then create sensors that will convert the experience into numbers that an AI can analyze. However, even then, the AI will see only the effects of the synesthesia, not the emotional impact. Consequently, an AI will never fully experience or understand synesthesia. Oddly enough, some studies show that adults can be trained to have synesthetic experiences, making the need for an AI uncertain.
Although most people know that humans have five senses, many sources now contend that humans actually have far more than the standard five senses. Some of these additional senses aren’t at all well understood and are just barely provable, such as magnetoception (the ability to detect magnetic fields, such as earth’s magnetic field). This sense gives people the ability to tell direction, similar to the same sense in birds, but to a lesser degree. Because we have no method of even quantifying this sense, replicating it as part of an AI is impossible.
AIs lack empathy
Computers don’t feel anything. That’s not necessarily a negative, but this chapter views it as a negative. Without the ability to feel, a computer can’t see things from the perspective of a human. It doesn’t understand being happy or sad, so it can’t react to these emotions unless a program creates a method for it to analyze facial expressions and other indicators, and then act appropriately. Even so, such a reaction is a canned response and prone to error. Think about how many decisions you make based on emotional need rather than outright fact. The lack of empathy on the part of an AI keeps it from interacting with humans appropriately in many cases.
Walking in someone’s shoes
The idea of walking in some else’s shoes means to view things from another person’s perspective and feel similar to how the other person feels. No one truly feels precisely the same as someone else, but through empathy, people can get close. This form of empathy requires strong intrapersonal intelligence as a starting point, which an AI will never have unless it develops a sense of self (the singularity). In addition, the AI would need to be able to feel, something that is currently not possible, and the AI would need to be open to sharing feelings with some other entity (generally a human, today), which is also impossible. The current state of AI technology prohibits an AI from feeling or understanding any sort of emotion, which makes empathy impossible.
Of course, the question is why empathy is so important. Without the ability to feel the same as someone else, an AI can’t develop the motivation to perform certain tasks. You could order the AI to perform the task, but there the AI would have no motivation on its own. Consequently, the AI would never perform certain tasks, even though the performance of such tasks is a requirement to build skills and knowledge required to achieve human-like intelligence.
Developing true relationships
An AI builds a picture of you through the data it collects. It then creates patterns from this data and, using specific algorithms, develops output that makes it seem to know you — at least as an acquaintance. However, because the AI doesn’t feel, it can’t appreciate you as a person. It can serve you, should you order it to do so and assuming that the task is within its list of functions, but it can’t have any feeling for you.
When dealing with a relationship, people have to consider both intellectual attachment and feelings. The intellectual attachment often comes from a shared benefit between two entities. Unfortunately, no shared benefit exists between an AI and a human (or any other entity, for that matter). The AI simply processes data using a particular algorithm. Something can’t claim to love something else if an order forces it to make the proclamation. Emotional attachment must carry with it the risk of rejection, which implies self-awareness.
Humans can sometimes change an opinion based on something other than the facts. Even though the odds would say that a particular course of action is prudent, an emotional need makes another course of action preferable. An AI has no preferences. It therefore can’t choose another course of action for any reason other than a change in the probabilities, a constraint (a rule forcing it to make the change), or a requirement to provide random output.
Making leaps of faith
Faith is the belief in something as being true without having proven fact to back up such belief. In many cases, faith takes the form of trust, which is the belief in the sincerity of another person without any proof that the other person is trustworthy. An AI can’t exhibit either faith or trust, which is part of the reason that it can’t extrapolate knowledge. The act of extrapolation often relies on a hunch, based on faith, that something is true, despite a lack of any sort of data to support the hunch. Because an AI lacks this ability, it can’t exhibit insight — a necessary requirement for human-like thought patterns.
Examples abound of inventors who made leaps of faith to create something new. However, one of the most prominent was Edison. For example, he made 1,000 (and possibly more) attempts to create the light bulb. An AI would have given up after a certain number of tries, likely due to a constraint. You can see a list of people who made leaps of faith to perform amazing acts online. Each of these acts is an example of something that an AI can’t do because it lacks the ability to think past the specific data you provide as input.