There are various Machine Learning Trainings ranging from Machine Learning with TensorFlow, R Machine Learning Solutions and Artificial Intelligence Training to Machine Learning Specialist and Python Machine Learning.
Like anything new and exciting, Machine Learning also comes with a few myths and misconceptions.
- Machine Learning and AI(Artificial Intelligence) can do it all
“All of the biggest technological inventions created by man; the airplane, the automobile, and the computer, says little about his intelligence, but speaks volumes about his laziness” - Mark Kennedy.
- We humans get dependent on technology very easily, as per our comfort and convenience. As soon as machine learning and AI became the talk of the town, it was not surprising to see people expecting machine learning to be able to do everything and perform every task.
- It is inarguably true that machine learning has opened unimaginable opportunities for humans, but it is also important to keep healthy and realistic expectations from the same.
- Medical sector has also realized that machine learning cannot solve every problem.
- Although we need to keep calm, experts like Amir Shapiro, who is a faculty member at Ben Gurion University of the Negev, apprises that machine learning is proficient at identifying patterns and making categorizations of data, and there is certainly plenty of room for innovation in factors like real-time control and motion planning.
- Machine Learning and AI(Artificial Intelligence) will replace humans
“I visualize a time when we will be to robots what dogs are to humans, and I’m rooting for the machines.” —Claude Shannon.
- One of the biggest threats that we face with Machine Learning is that it might replace humans.
- Researchers have proved that machine learning still struggles in areas where humans excel, as it lacks experience and knowledge.
- Machine learning would make our jobs easier and free us to focus on those aspects of the job that were always most important, but which were also the most difficult to address.
- If we want to reap the benefits from machine learning, we must start thinking about how machine learning can be meaningfully applied to specific areas of government, business and society.
- Machine learning tools give us valuable opportunities to expand our knowledge, and are not here to replace us.
- With AI ethics, it will be a sound place for humans and machine to coexist.
- Technical misunderstandings about Machine Learning
- There are different approaches programmers follow to learn Machine Learning like bottom-up and to-down.
- It is important for you to understand if you want are interested in using Machine Learning as a tool to solve problems or to research in the field.
- There are various Machine Learning Trainings ranging from Machine Learning with TensorFlow, R Machine Learning Solutions and Artificial Intelligence Training to Machine Learning Specialist and Python Machine Learning.
- There is no space for pre-existing knowledge in Machine Learning
- IT Professionals feel that Machine Learning comes with a blank slate and derives knowledge from the algorithm.
- One of the concepts applied in Machine Learning is representing existing human knowledge in a form that can be represented by a machine.
- The second concept is the extraction of the knowledge from the user data.
- Machine Learning and AI(Artificial Intelligence) are exactly same
- Technically, machine learning is a subset of AI.
- AI focuses on increasing the chances of success, whereas Machine Learning is more about gaining accuracy. Hence, AI leads to intelligence and Machine Learning leads to knowledge.
- The best part is that both the technologies complement each other.
- Machine Learning Experts tend to learn AI and vice versa.
About the author: Nisha Negi is a Technical Content Writer at Multisoft Virtual Academy. She writes blog posts and articles on various technical subjects. She is an experienced IT professional and bears immense knowledge of the latest technology. She stays current with all the ongoing and upcoming certifications. Her way of expression is contemporary and crisp.