Artificial intelligence is one of the most important technologies in use today, but misconceptions about it are spreading every day. This should not be surprising because myths and misconceptions about technologies that grow and spread as fast and as widely as artificial intelligence has will always attract various myths. These can range from people in certain industries losing their jobs to the end of humanity as we know it. Some of these myths can be very believable because they combine just a sprinkle of facts and lots of speculation. Below, we are going to look at the most common misconceptions about artificial intelligence and debunk them so you can get all the facts.
Artificial Intelligence Works Like the Human Brain
Artificial intelligence (AI) has progressed very fast in recent years. Because of how good it has become, there is a myth going around that it works like the human brain. This is not entirely true, especially when we focus on how we judge relevance and understand language.
When we think about language, most people who have used Siri, Google Assistant or Alexa understand that they can talk directly to a program. However, what these programs do is find associations between words, texts and phrases yet they do not understand the difficulties associated with grammar, content and, in some cases, context. This is one of the reasons why most of the writing done by artificial intelligence is usually unintelligible or dull.
In everyday communication, we understand what facts are relevant about people and things we interact with. AI has some way to go before it can understand the relevance of the information it gets and use this relevance to change the way it communicates. For example, saying someone “seems odd because they enjoy camping for weeks to observe the yearly bird migration” would allow a human to understand how the bird watching, camping and oddity come together. An AI might pick out some parts of this statement but still not be able to tell which parts are relevant enough to contribute to the oddity.
Intelligent Machines Can Learn on Their Own
There is some truth in this statement, but it is not fully correct. Intelligent machines can use trial and error to learn which paths to follow so they can be more efficient at their tasks. They can also learn how to make predictions based on the data they are presented with. However, humans have to provide the inputs that are required for them to perform these tasks and know how to do them better. Machines are also not yet able to implement the key components that are required to be called intelligence such as planning and problem-solving. Even though the results are always impressive, these results are only achieved after humans have done the hard work of designing these systems and feeding them the initial data they need.
Artificial Intelligence is Always Objective
Many people think that because these are machines, they are always objectives. The objectivity of an intelligent system depends on the people working on that system as well as the type and quality of data they feed it. If a computer or data scientist created a prejudiced algorithm and fed it AI biased data, the system will always be prejudiced and biased. In many cases, people do not notice until the output of these systems goes public.
The main point is that unless all scientists working on intelligent systems do not have any prejudices and their data is unbiased, the results produced by these systems will always be prejudiced.
Artificial Intelligence Can Figure Out Any Data
AI is a very powerful tool, especially when it comes to data analysis, but it cannot analyze all data on its own. It requires data engineers, analysts, and other specialists to label the data, clean or and otherwise organize it so the AI can work on it.
Data labeling and the whole cleaning process are very involved before the data can be ingested by a machine to do with it as you wish. To ensure AI is working efficiently and helping with data analysis, data scientists must understand data science methodologies that arm them with the skills and tools to better clean data for use with Ai systems.
To combine the world of data analytics, artificial intelligence, and machine learning, computer scientists and data analysts can go into artificial intelligence specializations that combine all three. Institutions like Worcester Polytechnic Institute offer online master of computer science in data science programs to arm graduates with the knowledge, skills, and tools to combine big data analytics with artificial intelligence. Such a specialization also gives you the skills to examine and come up with new approaches and perspectives that will be critical in new applications of artificial intelligence in data analysis and other hands-on projects.
Artificial Intelligence and Machine Learning are Interchangeable
Machine learning and artificial intelligence are so closely related that a lot of people end up using them interchangeably. They should not be used as substitutes for one another so let’s break down what each of them is and means.
Artificial intelligence is the science of making technologies that operate similarly to human intelligence. It is a more general and open term, and this is why its meaning is always being discussed and tweaked. Machine learning, on the other hand, is one subset of artificial intelligence. It explains the ability of machines to give recommendations after predicting various outcomes without additional instructions from humans.
Although the definition and application of machine learning are generally static, the definition of AI and what constitutes it is always changing. This is because of technological advancements in the field that are always changing things.
Artificial Intelligence Will Take Your Job
This is perhaps the most common and the most peddled myth about artificial intelligence. This myth has been around since humans discovered machines, and it grew in popularity during the industrial revolution. In the modern age, the fear of losing our jobs to a new intelligence or robots is grounded in very little fact. Artificial intelligence and other technologies associated with it are meant to work collaboratively with humans. AI is designed to help improve and enhance human abilities and efficiency rather than against humans.
AI is mainly designed to do the harder, more boring and repetitive tasks, while humans are left to concentrate on creative tasks that require skills that artificial intelligence does not have, such as problem-solving or communications skills.
Studies have already been done that show that some jobs will be replaced by AI. That always happens as technology changes. However, AI will help generate new jobs and drive their demand since there will exist new abilities and capabilities.
All Artificial Intelligence Is Equal
This is another myth that is perpetrated by the media, namely sci-fi films. There are three main types of artificial intelligence: AGI (Artificial General Intelligence), ASI (Artificial Super Intelligence), and ANI (Artificial Narrow Intelligence).
Of these three, ANI is the one most people will know about. This is artificial intelligence that is very good at one task, such as playing a game of Go, or answering online messages. This type of artificial intelligence is also very well suited to repetitive tasks. ANI is also great at performing tasks considered boring by humans, with a great example being a chatbot that answers the most commonly asked questions on a hotel booking website. These chatbots can do simple things like looking up answers on a database, looking up product details, and retrieving recent orders.
AGI is considered a step up from ANI, but it is not quite here yet. It is meant to mimic human behavior and intelligence. In theory, it should be able to solve problems creatively as well as come up with solutions in real-time and under pressure. We can see glimpses of Artificial General Intelligence in the autonomous vehicle industry where companies like Tesla are developing AI to help with driverless cars. This AI will be expected to drive like a human when its development is complete, to mimic real human driver behavior, make decisions, and solve problems in real time as a human driver would.
AGI is thought to be the lead up to Artificial Super Intelligence which is thought to be able to surpass our human reasoning and brainpower. This is the type of AI that is thought to bring the end of humanity and you can see this concept a lot in SCI-FI movies.
AI Cannot Be Creative
Many people do not associate AI with creativity, but you will be surprised to learn that AI has already been involved in many creative endeavors. AI has already generated valuable and unprecedented ideas, especially when it is combined with human intuition, creative thinking, and problem-solving skills.
A good showcase for AI’s creativity is in the automotive industry. Companies like Rolls-Royce are already using AI to improve their engine designs. Others are combining AI with simulation tools to help them come up with better and more aerodynamic car body designs. In both cases, AI helps predict the performance of the new products.
Additionally, AI is being used in component manufacturing. Instead of physically molding new products, automotive engineers can use AI to find out how certain design and material combinations would work. This has proven to be invaluable in replacing some engine components to make more efficient, faster, and overall better engines.
In pharmaceutics, AI is also being used to make predictions about how people will respond to certain medications. Using a medication’s properties and a patient’s metabolic data, AI can infer things such as absorption rates and other factors to determine the efficacy of these medications.
In the creative industry, AI is coming up with new and original pieces of music. Here, it identifies music genres, characterizes them, and then recombines different patterns and pieces of music to come up with original pieces. AI has been able to come up with original pieces in the styles of famous musicians and composers like Beethoven and Bach.
Artificial Intelligence is New
We normally associate artificial intelligence with computers because that is where we see it being applied most. However, modern computers have been around for about 60 years and the idea of artificial intelligence has been around since around 1845. An English writer and mathematician called Lady Ada Lovelace predicted that a machine would one day be able to compose complex and elaborate pieces of music. They predicted that the extent and complexity of the composition will depend on the machine used and the intelligence it possesses. As we have seen above, this has already happened.
Around the middle of the 20th century, Alan Turing and others were already setting the foundations for machine learning as we know it today. The Bombe machine that was used to crack the Enigma code is considered by many as one of the first machine learning computational tools. Even though Alan Turing and his team could not go as far with AI as they anticipated, since the technology did not exist in 1948, his foundations for machine learning were picked up when more powerful computers were introduced in the 1950s.
Cognitive AI Can Solve Problems Just as the Human Brain Can
Cognitive AI is a set of technologies that are meant to mimic how the human brain works as closely as possible. They have already been able to analyze sentences and identify images. However, as with other AI, they need human intelligence.
We can learn vital lessons about the strengths and weaknesses of cognitive AI by looking at how it is used by Meta (formerly Facebook). Meta used cognitive AI to analyze images on both Facebook (at the time) and Instagram to serve ads and to ban images that were not appropriate to serve to their audiences.
However, the same AI was unable to distinguish between real and fake news and this is why some malicious actors were able to post so much false news on Facebook without the AI picking up on it.
Artificial intelligence remains one of the most important technological advancements of our lifetimes. However, to understand how it can be used as well as its limitations, you should be able to differentiate fact from myth when thinking about or talking about AI.