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Artificial Intelligence: Debunking the Top 10 AI Myths

By Claudio Buttice
Published: July 22, 2020
Key Takeaways

AI is a hot technology, but many people have misconceptions on what exactly it entails. Here we take a look at some of the myths surrounding artificial intelligence and examine the facts.

Why is everybody talking about artificial intelligence (AI), yet we still do not see friendly robots like Data from "Star Trek" walking among humans? Did we remember to add RoboCop's Second Prime Directive to their scripted patterns so they can "Protect the innocent" instead of exterminating humanity as soon as they gain full sentience?


Today, even in 2020, there's a lot of confusion about what AI, machine learning (ML) and deep learning (DL) actually are, what "intelligent machines" can do, and what the current state of AI technologies actually is.

It's time to enjoy some good old debunking, so let's bust the 10 most common myths about AI. (For more on the potential future of AI, check out Is the AI Revolution Going to Make Universal Income a Necessity?)


1. AI consists of intelligent robots or androids that look like humans.

Too much "Blade Runner" for everyone here, hmm? Well, we’re already past the fictional “November 2019” of that movie now! Although there's a lot of general confusion between robotics and AI, they are two completely different science fields which serve different purposes. Robots are physical devices served by actuators and sensors to perform a wide range of tasks, such as building, carrying or dismantling products in factories.

AI is software programmed in such a way that it is autonomous enough to make decisions and learn from its mistakes. Although some robots may eventually be enhanced by AI algorithms, the "intelligence" part is just one additional ability AI may possess.

2. AI, machine learning and deep learning are all the same thing.

Although they're all parts of the same larger AI system, they're three different things. Basically, machine learning is the method through which AI learns from external sources, as in using algorithms to discriminate data and determine its correct behaviors. Deep learning is just one possible technique used in practical applications of machine learning. It is based on neural networks (NNs) and is used to tell the AI what its probability is of making the right decision.

3. AI learns completely on its own.

Despite some exaggerated hype about AI that was allegedly able to learn on its own, it is still impossible to find an AI-powered system that has any real-world application that can grow from zero knowledge without human assistance. Any system that has to deal with hidden information or uncertainty of any kind cannot be "understood" by AI, which still needs to be fed input and data by humans. Also, every bit of information must have a clear purpose, something that AI cannot guess without external sources (not in the beginning, at least).


4. AI is always better than human employees.

It is true that many jobs have already been taken over by AI. But they’re all simple and repetitive tasks that are now automated. Although they may be more efficient than humans in some instances, current AI technologies are pretty basic and can rarely substitute a human employee in any area that requires creativity, empathy, or critical thinking. Some very “humane” things like face-to-face communication cannot be replaced by any machine, too. No AI is able to create knowledge itself and think independently, and even the most intelligent machines are still much less… intelligent than simple animals.

5. The power needed to perform all future deep-learning operations is unsustainable.

It is undeniable that AI requires a lot of additional computing power to be trained and perform all its complex deep-learning operations. In a future where most enterprises will make use of AI to some extent, this problem may grow to epic proportions, making its use potentially unsustainable.

However, AI may actually provide us with the perfect solutions to help us tackle the climate change. It can help farmers push yields per hectare, improve energy production by reducing power grids' waste and inefficiency, enhance mobility through smart vehicles and drive the adoption of clean energy solutions. Also, by the time AI is widely adopted, we will have better, more powerful computers (or even quantum ones) to support this tech.

6. It's easy for an enterprise to rent the computing power needed to fuel AI operations.

... if AWS, Google, Microsoft and Alibaba Cloud weren't currently centralizing the vast majority of the computing power available in the world. So AI developers currently have just two choices: renting it at exceptionally high prices or purchasing their own super-expensive hardware.

However, there's a chance that this myth-debunking can be... debunked in the near future. A new company called Tatau developed a blockchain-based supercomputing platform that can solve the issue. Their solution allows the aggregation and reselling of the combined resources of a globally distributed network of GPU-based machines.

Imagine cryptocurrency miners, gamers or other high-performance computers dedicating their compute power toward AI development. AI companies can tap into this underexploited source of GPU power to train their machine learning models at a much cheaper price. Note that this new platform may also provide an answer to the problem highlighted in point 5 since it promotes efficient use of currently untapped resources.

7. You need immense amounts of data to train AI.

Not necessarily. Sure, you need a lot of data and computing power to train an AI from scratch. And, albeit to a lesser extent, you need terabytes of data to train an AI to perform a complex task such as driving a car. However, depending on the field of application of the AI, pre-trained neural networks are flexible enough to be retrained only in some specific areas. The basic data framework may come from a larger, more general data set, with only the last part of the network needing to be replaced to "fill in the blanks" specific to that given use case.

8. AI will replace existing BI tools, making any previous technology obsolete.

That's a bit of a stretch, to say the least. The majority of modern business intelligence (BI) solutions are highly scalable and often customizable, so that any future AI-based model can be easily integrated directly inside their platforms. Companies always prefer to implement only those solutions which come without any risk for workflow disruption, and AI technologies have adapted to this need. Therefore, most AI platforms are implemented via the web so no replacement is necessary or, in the worst-case scenario, can be safely implemented in phases.

9. Artificial neural networks are like biological networks but mechanical.

No artificial neural network (ANN) can even hope to reach a fraction of the complexity of the human brain. It's like comparing the complexity of a military aircraft to a kite just because they can both fly. Despite many years of clinical and scientific research, we still fail to understand biological neural networks to their full extent since neurons fulfill so many different tasks with the human body (think about the difference between a sensory and a motor neuron) and even transmit information through many different pathways (using electricity, chemical potential and neurotransmitters).

The majority of AI actually employed by enterprises are just Narrow AI that possess simple abilities to react to stimuli. They are equipped with little or no memory or data storage capabilities, and only use historical data to inform decisions. Strong AI and Deep AI that can apply their intelligence and knowledge to solve problems are still largely theoretical, and are still nothing but models with no practical application. To put things in perspective, the Fujitsu-built K, one of the most advanced strong AI, needed 40 minutes to simulate the equivalent of just one second of brain activity!

10. AI will eventually become intelligent enough to understand that humans are dangerous to it and must be exterminated.

Well, we can't actually debunk this myth since it's not a myth. It's a reality. Brace yourselves, because resistance is futile!

Jokes aside, simply put, AI has nowhere near the intelligence needed to understand the world around itself and make autonomous, rational decisions.

Each algorithm is developed to perform one task and is not able to do anything outside that, let alone reach the ability to think independently. Computers use the "brute force" of their superior computational powers to find a solution to relatively simple issues, but they lack the understanding, perception depth, and strategic complexity to have a purpose outside the one they're programmed for.

So rest easily, because AI is just going to be nothing but our artificial helpers and servants for a long, long time.


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Written by Claudio Buttice | Data Analyst, Contributor

Profile Picture of Claudio Buttice

Dr. Claudio Butticè, Pharm.D., is a former clinical and hospital pharmacist who worked for several public hospitals in Italy, as well as for the humanitarian NGO Emergency. He is now an accomplished book author who has written on topics such as medicine, technology, world poverty, human rights, and science for publishers such as SAGE Publishing, Bloomsbury Publishing/ABC-Clio, and Mission Bell Media. His latest books are "Universal Health Care" (2019) and "What You Need to Know about Headaches" (2022).

A data analyst and freelance journalist as well, many of his articles have been published in magazines such as Cracked, The Elephant, Digital Journal, The Ring of Fire, and Business Insider. Dr. Butticè also published pharmacology and psychology papers on several clinical journals, and works as a medical consultant and advisor for many companies across the globe.

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