Machine learning is currently overhyped, but in the long term it will deliver dramatic improvements in our jobs, lives and societies.
Organizations are getting disappointed that their investments in machine learning algorithms are not paying off because of their lack of understanding.
Machine learning (ML) is an iceberg of massive proportions with a lot of potential to change the world if used correctly.
Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
Machine learning is an important component of the growing field of data science. Through the use of statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights within data mining projects. These insights subsequently drive decision making within applications and businesses, ideally impacting key growth metrics.
Machine learning is everywhere in our daily life activities from spam filters, personalized ads, smart replies and fraud prevention.
Here are some popular examples of machine learning use cases in our daily life:
In order to use machine learning in a business context, organizations need unbiased data, and not just any data. You will need to match the data with the intent of the machine learning models. This requires intention and design.
Organizations shouldn’t buy into the shininess of machine learning without understanding the technology. Instead, they must consider any machine learning strategy in light of the value of their data, using machine learning to extract the data’s potential.
Source: Towards Data Science
Machine learning models can only predict items based on the data given.
Here is how organizations can use machine learning in a 5 step process:
Machine learning (ML) extracts meaningful insights from raw data to quickly solve complex, data-rich business problems. Machine learning algorithms learn from the data iteratively and allow computers to find different types of hidden insights without being explicitly programmed to do so.
Despite its numerous advantages, machine learning isn’t perfect. Here are 8 limitations that are harming the progress of machine learning:
Numerous machine learning engineers, at varying points in time, have claimed it’s the same hype-cycle being repeated and not doing exciting tasks:
Source: Chemical & Engineering News
Ultimately, machine learning engineers are disappointed because they are lured into the job with aspirations of putting big machine learning models into production and doing cutting edge algorithm research only to find out that their company is not ready for machine learning and that they only hired you so they can say to their clients, “Yes, we have a machine learning expert onboard”.
The quiet revolution of machine learning looks nothing like the way movies predicted. Machine learning is steadily creeping into areas of decision-making that were previously exclusive to humans. Because it is so hard to spot, you might not have even noticed how much of your life is influenced by machine learning algorithms.