rewrite this title 10 Common Myths About Artificial Intelligence That People Believe

There is significant confusion today about the current state of machine learning, deep learning, and artificial intelligence (AI) technologies. Due to misinformation spreading rapidly online, many myths have emerged under the umbrella of AI. Here are 10 AI myths that are often mistaken for facts.

1. AI consists of intelligent robots and androids that resemble humans.

Robotics and artificial intelligence are often confused. However, these are two completely different scientific fields serving distinct purposes. Robots are physical devices that perform a wide variety of tasks in factories, such as production, transportation, and assembly. AI, on the other hand, refers to the software programmed to make decisions and learn from mistakes. While some robots may incorporate AI algorithms, the “intelligence” part is only one aspect of AI.

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

Although they are all components of an AI system, AI, machine learning, and deep learning are three different concepts. Machine learning is the method by which AI learns from external sources, using algorithms to differentiate between data and determine correct behaviors. Deep learning is just one of the techniques used in the practical applications of machine learning. It is based on artificial neural networks and works to determine the probability of successful decisions for AI.

3. AI learns entirely on its own.

You may have heard exaggerated claims that some AIs can learn and develop on their own. However, the reality is that no AI-powered system currently exists that can grow from a state of zero knowledge without human help. AI cannot make predictions; it only works with external sources and past data. It cannot conceptualize abstract information like humans can.

4. AI is better than human employees.

The pandemic required interventions to reduce close contact between people. AI-powered automation became a hero, not only helping prevent the spread of the virus but also providing much-needed resilience to many sectors, such as supply chains, that were struggling with quarantines and restrictions. While it’s true that AI systems have become permanent for many jobs, many of these systems only perform simple, repetitive tasks that can be automated. Although they can be more efficient than humans in some situations, AI technologies cannot replace a human employee in any field that requires creativity, empathy, dexterity, or critical thinking.

5. The power needed for future deep learning operations is unsustainable.

A lot of power is required to train AI and perform complex deep learning operations. It may seem that in a future where many businesses will utilize AI to a certain extent, this problem could become serious, potentially making its use unsustainable. However, AI actually provides excellent solutions to help us overcome problems like climate change. AI can help farmers increase crop yields per hectare, improve energy production by reducing waste and inefficiency in the electricity grid, lower carbon footprints and greenhouse gas emissions, and support strategic planning and decision models for how to combat climate change. With additional advancements in computing, such as quantum computing, it won’t be long before we have the power and resources to run even more demanding AI systems.

6. It’s easy for companies to rent the computing power needed to support AI operations.

This would likely be true if Amazon Web Services, Google, Microsoft, and Alibaba Cloud hadn’t centralized the vast majority of the world’s available computing power. As a result, AI developers currently have only two options: renting at extremely high prices or buying their own super-expensive hardware.

7. You need massive amounts of data to train an AI.

When training an AI to perform a complex task like driving a car, terabytes of data are needed. However, depending on the AI’s application, pre-trained neural networks are flexible enough to be retrained in specific areas. In some sectors, only the final part of the network may need to be changed on top of the AI’s basic data framework. Therefore, you don’t always need a lot of data to train an AI.

8. AI will replace existing business intelligence tools and render previous technologies obsolete.

Most modern business intelligence (BI) solutions are highly scalable and customizable. This allows them to be easily integrated into any future AI-based model. Therefore, AI and BI solutions can work together. Companies prefer solutions that don’t disrupt their workflow. For this reason, most AI platforms are offered as a web service. This means that even in the worst-case scenario, they don’t require an overhaul and can be implemented securely to minimize workflow interruptions.

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

No artificial neural network even comes close to the complexity of the human brain. That would be like comparing the complexity of a military jet to a kite just because they can both fly. Despite years of clinical and scientific research, we still do not fully understand biological neural networks. In contrast, most of the AIs used by businesses have a simple ability to react to data triggers. They are equipped with little or no data storage capabilities and use only historical data for decisions. Powerful AI and deep AI that can use their intelligence and knowledge to solve problems are still largely theoretical because they have very few practical applications.

10. AI will eventually become smart enough to realize that humans are dangerous and should be destroyed.

Perhaps the most famous AI myth since Isaac Asimov’s “I, Robot” is that humanity will be exterminated. Every algorithm is developed to perform a specific task and cannot do anything outside of that. Computers use their superior computing power to solve relatively simple problems, but they lack the understanding, perceptual depth, and strategic complexity to have a purpose outside of what they are programmed for. It is likely that the end of humanity by AI will not be a real problem for several more centuries.

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