Five Big Myths of Machine Learning and AI

AI has been in one form for more than 60 years, yet it is misunderstood more than ever. It is not due to a lack of effort. Since 2000, the number of active startups in the United States developing AI technology has increased by 14 to over $3 billion annually.

Even if they understand the distinction between AI and machine learning, CEOs, except the pioneers, are cautious about integrating AI and machine learning systems into their organizations.

AI seems appealing, but what is the practical usefulness of machine learning? Will true AI ever become a reality? AI has already proven its worth on the market and rapidly develops with business. The billions of dollars coming into AI startups already have an effect, resulting in potent tools utilizing cutting-edge cognitive technology and significantly improving your business’s decision-making processes. Additionally, technology is easier to utilize than ever before.

We have examined five of the most prevalent AI myths to help you grasp the reality of the AI world today. To set your firm up for success in the world of AI, we will debunk several common myths.

Myth 1: AI and ML Are the Same Things

At its most fundamental level, AI can be divided into general artificial intelligence (GAI) and narrow artificial intelligence (ANI).

Narrow AI is a group of technologies that rely on algorithms and are limited in scope, usually centered on a special duty. In contrast, GAI is designed to think independently. GAI research aims to create an AI that learns at a level with or above human intellect.

Not all forms of AI fall under the definition of machine learning. ML is just one example of the application of AI. ML comprises supervised, unsupervised, reinforcement, and deep learning systems. Supervised machine learning algorithms and models utilize labeled datasets and begin with understanding how the data is classified. In contrast, unsupervised models utilize unlabeled datasets and determine features and patterns from the data without explicit instructions or preexisting categorizations. The AI and ML space as a whole is continually growing. Understanding that these strategies may be used for business challenges as long as there is data to train them is crucial.

Myth 2: AI Is a Magic Wand

At its most basic level, data is the key to building a successful AI result, no matter what industry it’s used in or how complicated it is. AI would only exist with proper training, and models must be well-built to be useful. A spam filter must be taught how to differentiate between good and bad email messages. A voice-recognition AI assistant must listen to many hours of conversation before understanding what is being said. Most AI-powered factory floor projects need to look at a few million gigabytes of data every week to decide what might happen in the future.

Humans have to define the problem, find the right AI technology to solve it, train the tool with the right information, and then check that the results are correct. Even the most powerful AI tools must be carefully managed over time to stay on the rails.

The work is not done when an AI tool comes up with results. Many people who work in AI find that when an AI algorithm gives the wrong answer, they learn more than when it gives the right answer. Both consumers and businesses can see this effect. When an AI-based spam filter puts a message in the wrong folder, the user can retrain the tool by putting it in the right folder.

This shows the algorithm what it might have missed the first time around. As the tool learns from its mistakes, it gets better and better. If the spam filter had not been trained, it would not be any better the next time, and I would probably make the same mistake again.

Myth 3: You Need a Ph.D. to Understand AI and ML

Artificial intelligence and machine learning are challenging to understand just by looking at their names. In reality, these very complicated technologies are hard for the average person to understand.

However, you do not need an advanced degree to use smart technology, and no use case is too small. Still, knowing the difference between building an AI solution from the ground up and using AI tools already available in your organization is important. The first one is by far the hardest. Every day, the second is getting easier. Think about all the tech tools you use daily, like an email client or tools that help you get things done, like your digital assistant or spreadsheets. They are not easy technologies, but you can master them even if you do not know what is happening behind the scenes.

A similar phenomenon is occurring in AI as technologies become more available. There has been an increase in the breadth and quality of self-service analytics systems, allowing non-technical staff to analyze without relying exclusively on data scientists. Beginners can construct their machine-learning systems.

Myth 4: AI and ML will replace me

They are concerned that artificial intelligence will eliminate your job shortly. You are not alone. McKinsey reportedly predicted that by 2030, 375 million workers, or 14% of the global labor force, would need to “transition occupational categories” as machines increasingly capable of performing human-exclusive tasks.

Gartner projects that by 2020, 1; 8 million jobs will be lost due to the growing capacity of artificial intelligence. Because of this, many news reports about the world’s end are not very exciting. AI requires human development, deployment, management, and maintenance. That means employment opportunities. The same Gartner analysis estimates that the 1.8 million jobs lost will be countered by the creation of 2.3 million new jobs, resulting in a net increase of 500,000 jobs in 2020 and 2 million jobs in 2025.

Myth 5: Data Has to Be Perfect to Take Advantage of AI and ML

AI works best when it has a large amount of accurate data, but your company only needs some of this data to benefit from AI.

A technology that watches and analyses social media continuously collects data from other sources. Typically, an AI system that relies on data feeds such as ambient temperature, housing prices, and neighborhood demographics obtains all of this data from publicly accessible sources. There is no “too minor” need. Remember that a small improvement in a critical business vector can significantly affect the bottom line.

A system that lowers production errors by a fraction or accurately advises that a price increase of a few pennies might result in millions of dollars in avoided expenses or enhanced earnings. The difficulty lies primarily in locating these possibilities.

Conclusion:

Even though AI can be a game-changer that propels your organization to the next level, getting started with AI and machine learning can be something other than a Herculean task. There are lot of technologies out there that let you play around with AI in a sandbox, focusing on small “problem areas” that may have stopped your development in the past.

In a world full of uncertainties, let us help you to uncover the hidden opportunities and solutions lying on the ground. iVedha offers AI/ML services from start to finish. This lets clients create smart workflows that make them much more productive and less expensive. We are a service company that uses artificial intelligence to help organizations find new ways to do things. iVedha offers artificial intelligence solutions with human-like skills like reasoning, learning, and improving themselves without having to be pre-programmed. How we research, AI and Data Science projects differs from how we do research for software delivery projects in general.

Get a free consultation with our experienced experts to know more about our AI/ML services.