Is Machine Learning Overhyped?

Is Machine Learning Overhyped?

Is Machine Learning Overhyped?

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. 

What is Machine Learning and Why It's Important?

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Source: Wordstream

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. 

What Are Some Popular Applications of Machine Learning In Daily Life?

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Source: Certybox

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:

  • Using voice search to catch up on updates. You pick up your smartphone and murmur, “Okay Siri. Open Facebook”.
  • Uploading a picture on social media and are surprised that the app tags all your friends correctly without any help.
  • Waking up in the morning by an alert from Google Assistant, reminding you of a commitment later in the day.
  • Laughing at the funny filters used by your friends, amused at how realistic the filters and objects look on their faces.
  • Scrolling through your feed, watching ads and finding products that you like. “Great! I really wanted this product. Glad that it showed up in my feed.”

How to Use Machine Learning In A Business Context?

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.

Machine_Learning_Business_Context.png

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:

  • Step 1: Adjust Mindset. Believe you can practice and apply machine learning.
  • Step 2: Pick a Process. Use a systemic process to work through problems.
  • Step 3: Pick a Tool. Select a tool for your level and map it onto your process.
  • Step 4: Practice on Datasets. Select datasets to work on and practice the process.
  • Step 5: Build a Portfolio. Gather results and demonstrate your skills.

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.

What Are The Limitations of Machine learning?

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Source: Gartner

Despite its numerous advantages, machine learning isn’t perfect. Here are 8 limitations that are harming the progress of machine learning:

  • 1. Lack of Data
  • 2. Time Consuming
  • 3. Weak Interpretation of Results
  • 4. High Error-Susceptibility
  • 5. Ethical Issues
  • 6. Absence of Skilled Resources
  • 7. Deficient Infrastructure
  • 8. Slow Results and Bias

Why Are Machine Learning Engineers Disappointed?

Machine_Learning_Engineer_Salary.jpeg

 

Source: Freelancermap

Numerous machine learning engineers, at varying points in time, have claimed it’s the same hype-cycle being repeated and not doing exciting tasks:

  • You discover that data is sparse and that the company actually doesn’t have the right features to do what it wants. Ultimately, real-world problems show a high degree of non-linearity and the easy canned ML solutions that you learned in college don’t really apply.
  • You find out you are a SQL junkie and you write SQL all day long to retrieve data. 90% of the hype is around getting the data right and building dashboards.
  • You find out you are a SQL junkie and you write SQL all day long to retrieve data. 90% of the hype is around getting the data right and building dashboards.
  • You just do data analysis in excel or R or Python because no advanced machine learning currently exists.
  • You find out that you have a manager who is apprehensive about machine learning because he/she feels it is a black box and he/she doesn’t know what is going on inside it.

Machine_Learning_Survet.jpeg

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”.

Conclusion

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.

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Azamat Abdoullaev

Tech Expert

Azamat Abdoullaev is a leading ontologist and theoretical physicist who introduced a universal world model as a standard ontology/semantics for human beings and computing machines. He holds a Ph.D. in mathematics and theoretical physics. 

   

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