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I am not trying to justify my reasons for saying somebody’s notion is a delusion, for I am an AI/ML enthusiast. Logically misunderstanding makes room for misconceptions. Media and our cinematic narrative writers have overhyped AI for misinterpretation; they are to blame. To shatter myths is when you grasp reality and correct those misses by working on your comprehension.

1. AI is the silver bullet that will solve every problem.

This is untrue as the underlying truth is AI cannot interact with the environment or surroundings the same way humans do. The quality of humans thinking in one perception and applying it in another is beyond AI’s capability as of now. Therefore there are still complex problems that require a level of reasoning beyond its scope.

2. AI’s singularity is a truth.

Technically, it’s a state where AI is uncontrolled and irreversible, and we are not there yet, my friend. This notion is found among people who have been introduced to the concept of AI through fiction. Whatever example of AI you can see today is where it has been used to narrow its hunt for a specific task. If the corporate sector talks about implementing AI, they are not discussing deploying some terminator version. 

3. AI has recently occurred.

AI has been here way before you were born; its increasing traction is due to the massive amount of data, better computational powers, and improved algorithms in today’s era. This branch of science has evolved from its inception to the present day. Few people picture it as something that will happen in the future, yet its applications are now widespread and are transforming every industry you can think of. It’s hard for people to picture it, yet they use it without realizing it. When you browse websites, you see recommendations from some AI system working at the backend.

4. AI will take away jobs.

During the Industrial Revolution, many lost their jobs because industries preferred machines over humans to improve production and revenue. Is this to say that enterprises don’t require human intervention? No, as a result of this revolution, individuals were upskilled, and their working methods changed. There was now a demand for skilled labor to repair equipment and other related tasks. Similarly, if people lose their employment due to AI, it will open up opportunities for more. To provide you with a landscape perspective of AI opportunities for new jobs.

Rather than taking away jobs, AI is more likely to create new job roles, augment human capabilities, and improve productivity and efficiency across fields. 

Source: world economic forum.

5. AI always needs a lot of data.

Thinking over this tangent is a gap; if you don’t have it, stop thinking about AI— definitely a myth. My first suggestion would be to educate about AI to explore this fact. Little and no data is no problem at all. Get started— start collecting relevant data, and begin early. Data 3-6-month-old records sometimes provide you predictions certainly at the compromised accuracy but get better over time. Sometimes 1-month-old data can assist you in making forecasts, but conditions exist, such as how well patterns are represented inside data records. Then it’s a problem at hand that will decide what level of intricacies in data are required, and your data scientist is there to assist you.

6. AI and ML are the same

Artificial intelligence (AI) is a discipline of science that includes cutting-edge technologies such as machine learning, deep learning, robotics, etc. Therefore machine learning is one of the techniques of AI implementation. They are often used interchangeably. The ultimate objective of AI is for machines to mimic human intellect, allowing them to adapt, reason, and provide solutions. Machine learning-powered AI lets you use your data to your competitive advantage by analyzing all collected data and providing real-time recommendations.

7. AI is unbiased.

It’s not true. However, we’re getting there. Having unbiased data is always on the cards, which is imperative for building trust in AI. Due to the human factor involved getting 100% objective is a little hard to achieve, but efforts are always made to train AI/ML models on clean, unbiased data.

8. In AI/ML, the primary driver is a data scientist.

The data scientist will undoubtedly aid in developing a successful AI/ML model, but the domain expert always ensures its success. They are the primary drivers as they are well aware of what business problem is worth addressing and what factors influence the specific problem. A data scientist will assist in model formation in context to the problem at hand and leverage the data based on the relevance of domain experts’ insights. The area specialist can set the metrics for gauging AI/ML models’ success.

Simply put, A domain expert helps roadmap a problem to embedding it into production, where data scientists work on it to build a data-driven model.

“Emly labs” is working on an AI platform that empowers domain experts to drive the creation of a successful AI solutions.

9. Every business doesn’t need an AI strategy.

The impact of AI is so pervasive now because of the multiple success stories centered on different use cases. AI has been leveraged to augment humans in maintaining customer relationships, decision-making, and other innovation breakthroughs.t all comes down to properly investigating AI and determining what business challenges it can address. Incorporating it into your business strategy is a must; if it is a no today, it can be a yes tomorrow for different business problems. It makes no difference whether you are a small or large organization; if you are maintaining records of your transactions, there is a strong possibility that you can seek opportunities to apply AI/ML.

10. AI is there only to automate mundane tasks. 

While AI may absolutely automate tasks, the flip side is AI/human augmentation, where AI can be at your assistance, augmenting decision-making and enabling humans with creativity where they are more innovative. E.g., it can help enterprises predict customer or employee churn, thus helping management in monetary decision-making.

A way forward:

In this emerging era of AI/ML, getting educated on ML-driven AI is critical, as it will be a valuable tool for business experts. Leveraging AI/ML is secondary; understanding it is the only way to overcome your misconceptions and embrace what it can do for you in reality. 

EMLY Labs recognizes how essential it will be for business experts to have AI/ML as a skill set to be a pioneer of the future. We have developed a gamified approach to AI/ML learning — “Emly learn” (soon to be released) in which people may learn about the various phases of machine learning and apply it to specific problem-solving. For business experts the AI knowledge up to the application of AI level is sufficient.

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