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The most common misconceptions about artificial intelligence

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The concept of artificial intelligence (AI) is so broad that there are many misconceptions.

Artificial intelligence has a great future and will be one of the essential tools for the organizations of tomorrow.

Millions of people have lost their jobs due to the effects of Covid-19 pandemic and now machines will take even more jobs away from workers, says World Economic Forum.

Artificial intelligence will soon rule all aspects of our lives, from online shopping and smart cities, quantum computing, blockchain, autonomous vehicles and cybersecurity.

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Source: IBM

Today’s artificial intelligence as well as machine learning, deep learning and neural networks are guided by statistics, big data analysis, processes, methods, techniques and algorithms.

In this article, we’ll demystify 5 common misconceptions about artificial intelligence.

Misconception # 1: artificial intelligence mimics the human mind

Artificial intelligence systems shouldn’t work exactly like the human mind.

While intelligent machines may outperform humans on some particular benchmarks, AI systems still fall far short of more general human capabilities.

Unlike human intelligence, smart algorithms, bots, cobots, robots and smart digital devices need to be constantly fed with real world data.

The human mind will never beat real AI machines in terms of data processing, computing power, and speed of execution.

Misconception # 2: Artificial Intelligence Knows Everything

The reality is that machines are not yet at the stage where they can make their own decisions about their area of ​​application.

Artificial intelligence can make better decisions, but it still doesn’t know everything.

The use of artificial intelligence can boost sales and marketing campaigns by quickly analyzing large data sets enabling businesses to make instant decisions.

However, it’s important to clarify that AI models still depend on countless human brain hours.

Misconception n ° 3: artificial intelligence is impartial

Since cognitive bias is almost inevitable, the most delicate part of data preparation is to limit this bias as much as possible.

Often a model reproduces a confirmation bias that it inherited from its human creators.

Numerous studies have shown that errors can occur during training, benchmarking and testing of models. This highlights the danger of over-trusting data that hasn’t been thoroughly verified, even if the dataset comes from touted institutions.

Misconception # 4: artificial intelligence and machine learning are the same

While artificial intelligence and machine learning are very much related, they are not quite the same thing.

Machine learning and artificial intelligence are often associated with slogans and catchphrases used for marketing purposes, just like those used for classic commercial products.

Artificial intelligence (AI) is a broader concept for creating intelligent machines capable of simulating human thought capacity and behavior, while machine learning is an application or a subset of AI that enables people to machines to learn from data without being explicitly programmed.

Misconception # 5: Artificial Intelligence Will Destroy All Jobs

Artificial intelligence will destroy some jobs but also create new opportunities for workers.

Jobs that require creativity, high emotional intelligence, warmth and understanding, coding and relationship building will survive the takeover of artificial intelligence.

Artificial intelligence (AI) will most likely replace receptionists, manufacturing workers, proofreaders, bookkeeping clerks, basic retail jobs, receptionists and cashiers.

Conclusion

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Source: McKinsey & Company

Artificial intelligence remains a vague concept, almost 65 years after its creation. Today’s artificial intelligence applications are very context specific. True artificial intelligence is not explicitly data driven, unlike data science. Nonetheless, AI has enjoyed great success in many different fields including robotics, speech recognition, facial recognition, social media, data science, healthcare, and finance.

Humans rely on an innate prelinguistic foundation knowledge of space, time, and many other essential world properties for learning and understanding language. If we want AI-powered robots to similarly master human language, we’ll first need to endow them with the overarching principles humans are born with. In order to assess understanding of machines, we must begin by assessing their understanding of fundamental principles, which we might call “infantile metaphysics.”

The next wave of advances in AI is moving towards the development of emotional intelligence.


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