7 myths about Artificial Intelligence and the reality behind them

Artificial intelligence (AI) is everywhere in our daily life —from self-driving cars and personal assistants, to chatbots and CRM systems. 

According to research, the artificial intelligence market is predicted to reach $266.92 billion by 2027, which is almost ten times more than in 2019. Moreover, according to recent surveys, nine out of 10 businesses have stated that they are currently investing in AI. 

Alongside the growing popularity of AI, the number of myths is growing as well. In this article we will present to you the most common ones and the reality behind them.

Myth 1: Natural Language Processing and AI are the same

Artificial intelligence (AI) is a broad concept, and natural language processing is a part of it. AI algorithms allow machines to analyze and process huge amounts of data in order to detect patterns, and learn and improve. AI allows a machine to simulate human intelligence in solving a variety of real word problems. NLP itself has a number of subsets, including natural language understanding, which refers to machine reading comprehension, and natural language generation, where a machine can express numerical data into human words. Natural language processing makes it possible for computers to extract keywords and phrases, understand the intent of language, translate to another language, or generate a response to the input. NLP only solves problems regarding text and language, but doesn’t cover tasks such as decision making, visual perception, etc.

Myth 2: All NLP understands the context

Natural language processing (NLP) applies to both text and speech. There are two techniques to help computers understand text: syntactic and morphological analysis, and semantic analysis.

Syntactic analysis examines text using basic grammar rules to identify sentence structure, how words are organized, and how words relate to each other, which means that this type of NLP does not understand the contextual meaning of the text. The same goes for morphological analysis, for tasks like identifying the tense and conjugation of words in order to group all variants of a word together.  

Semantic analysis, which is used in Iris.ai tools, focuses on capturing the meaning of text. First, it studies the meaning of each individual word (lexical semantics). Then, it looks at the combination of words and what they mean in context (relational and discourse semantic analysis).

Myth 3: AI can understand and analyze any data 

AI to date can’t understand the data that is too broad or has a layout that the system was not designed to analyze. Currently, we have narrow AI and the data should be structured to the AI similarly to how humans deal with the task for automation. It’s important that data is relevant to the problem being solved and is specific to a set of use cases and a domain of knowledge. Rather than process everything, an AI system needs information and content that has been carefully curated and is of high quality -this is especially true for training your algorithm. If the data is faulty, chances are your results will contain a mistake too, no matter what the system.

Myth 4: AI will take your job

Artificial intelligence has been developed to the point that it can perform some easy and common tasks. AI and automation have the potential to seriously disrupt the labour market. However, there are a number of more complex tasks and decisions in business and everyday life that still require human skills, expertise and creativity. AI can play an important role in helping humans make better decisions and to work in newer and smarter ways.

Myth 5: AI can solve any problem

In reality, cognitive technologies can’t solve problems for which they weren’t designed. Currently, we only have access to narrow AI which focuses on one problem. We are striving to develop artificial general intelligence, which focuses on building intelligent systems that can handle any task or problem in any domain. The promise of AGI remains unfulfilled for now, but the drive toward the technology pushes the abilities of AI. AI typically performs tasks that include visual perception, speech recognition, decision-making, and translation between languages. Cognitive AI technologies have the ability to simulate how a human being would deal with ambiguity and nuance, but they are nowhere near learning new problem areas. Any artificial intelligence program is only as good as the data on which it is trained. Humans still need to define the cases and scenarios that the AI program will operate under. An AI program will work within those cases and scenarios, but it will not define new scenarios to operate in. 

Myth 6: AI Equals Robots

I was looking for an appropriate photo to add to this article and started my search with “AI” as a keyword and… most of the results were representing humanoid robots! While there might be some overlap between the two, AI and robots are two separate concepts. Robots are just the most obvious form of AI, but even then, not every robot relies on AI. Robots are physical devices programmed to handle difficult, repetitive 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 robots may possess.

Myth 7: Most companies don’t need AI

It’s important to understand the difference between building an artificial intelligence solution from the ground up and implementing existing AI tools. The first one is extremely difficult, but the second is getting easier every day. AI tools are becoming increasingly accessible. Even without our knowledge, we use them daily at work- an email to a client, digital assistants or spreadsheets. 

Some businesses might think that the problem is too insignificant to solve it with AI technology. However, applied AI solutions do not need to be complex and “big”. There are many tools that could help companies in many areas of their business and make the processes more efficient and easier. 

Key Takeaways

Artificial intelligence is getting more and more used across all organizations and in our private life. This growing popularity creates many myths. It’s important to learn about that technology to distinguish the truth from common misconceptions.