Back in the day, programmers would tell computers how to solve problems by defining rules in their programs. But now, programmers tell computers how to learn on their own to solve problems. Machine learning and machine learning algorithms, at their core, are the programmatic applications of statistics that enable computer programs to identify patterns in data and make highly accurate predictions using these patterns.
The simplest example goes like this: Consider feeding hundreds of photographs into a computer system, and labeling some of them “this is a rabbit” and others “this is not a rabbit.” The computer breaks each image down into dozens of metadata indicators, and creates patterns in its memory. If identified in any new datasets, it connects back to its initial training. So, once this is done, the same program will be able to:
- Analyze an image with a lot of animals, and accurately tell the number of rabbits in it.
- Sort out a list of images in the order of health and aesthetical scoring of the rabbits in them.
- Accurately tell you whether it’s a giant rat or a tiny rabbit you’re staring at.
Now, this is fascinating enough, even though it’s the equivalent of a “hello world” example! Let’s tell you more about machine learning now.
For all practical purposes, machine learning is AI
A lot of confusion ensues in almost every discussion about artificial intelligence. That’s because even supposedly knowledgeable people about AI use terms such as “machine learning,” “artificial intelligence,” “deep learning,” “cognitive computing,” “neutral networking learning,” “natural language processing,” etc. interchangeably.
Are they right in doing so? Absolutely not. We won’t go into individual definitions in this guide. At this point, just understand that machine learning, for all practical purposes, is what’s driving what marketers like to sell as “artificial intelligence.”
Machine learning is about two core components:
- Massive datasets, called training data.
- Complex algorithms that are coded to adjust and improve their behaviors based on comparisons of real outcomes (historical reality) with estimated outcomes (simulations).
You get your programmers to do this, and you would have used machine learning to create a program that, to the end user, would seem like an intelligent entity. Hence, the term “artificial intelligence.”
Machine learning is data first, and algorithms next
Like we said, ML is, essentially, based on two pillars — training data and machine learning algorithms.
Data, however, is the stronger pillar of the two. Whereas there’s a lot of buzz around how deep learning is making machine learning algorithms better and better (and that’s true), it’s actually good data that makes good machine learning algorithms. Machine learning can be implemented with a very basic algorithm, but can’t work with merely basic data. So, for any businesses looking to invest in their ML development capabilities, the money is best spent first in furthering the data capabilities, and then the algorithm capabilities.
Machine learning depends on good data for good results
As much as you’d want to believe machine learning is a magical “data in, intelligence out” black box, it’s not. Remember the good old adage — “garbage in, garbage out”? Well, it remains so with ML.
Your machine learning algorithms will only deliver results as good as the data you use to train them. For instance, consider a business that updates its customer classification metrics every six months. Also, it frequently keeps on adding new categories. Now, if such a business uses past 24 months of customer data, along with the categorization labels information from the past, the machine learning algorithm is going to create highly generalized models because of too many variables and too little consistent data.
This makes robust data maintenance and meta=tagging the prerequisites of rich training data.
Deep learning is great, but it’s not the holy grail
Deep learning is an approach to implement machine learning algorithms. Deep learning tries to emulate the complex decision-making methodology of the human brain with its never-ending branches and nodes of neural networks. Deep learning has tremendous applications in automating some aspects of decision making in machine learning algorithms.
However, it’s too early to say that deep learning is the future of machine learning. Though it’s been proven effective especially in image and video data processing and analysis, it requires a lot of data cleansing and transformation efforts, meaning that it certainly is not an easy pick for a programmer.
Machine learning algorithms will not take over the world
The global conspiracy theory engine is working overtime, churning rumors and stories about how machine learning is the beginning of the era where artificial intelligence, some fine day, will begin to wipe out humans from the Earth. That’s veritable crap; breathe easy.
For the sake of a healthy discussion, let’s consider the scenario where machine learning algorithm-powered software and hardware solutions start exhibiting erratic behaviors, and harming stakeholders in the process (for instance, too frequent doses of antibiotics to a patient). Such an outcome is highly likely to be a result of one of these factors:
- Inappropriate or insufficient training data used in algorithm training.
- Programmer’s biases can relegate ML algorithms into a self-fulfilling prophecy of sorts; that’s because the algorithm will keep on propagating the bias in its future learning and training data.
- Systemic errors in the program.
This is where the need for programmers to create exhaustive scenarios and test cases comes to the fore. Also, application of machine learning technologies to highly sensitive areas of work, such as patient health monitoring and care, need to be managed with sufficient human intervention in the mix. In such scenarios, ML becomes an enabler for freeing up human time for use in more value-adding activities, but certainly not for replacing humans.
Provided entrepreneurs and IT wizards understand machine learning, there’s tremendous potential of using ML algorithms to drive superb business use cases. There are already several platforms available for startups and small and medium-sized businesses (of course, enterprises, too) that help them leverage the power of machine learning-based program modules to build more sophisticated tools.
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