How To Think Like A Bot In Four Steps


Data Analytics Courses

Ever wondered how artificial intelligence or robots receive, discern, and process information in order to give accurate and reliable output? Wonder no more. Bots use algorithms to guide them in discerning, and processing data as well as developing output.

But what makes the human mind so different, and in some ways, similar to a bot? What is an algorithm, and how can a human think like a bot?

Read on and find out how you can think like a bot in four steps.

Differences and Similarities

In order to figure out how a human can think like a bot, we first need to find out what makes the human mind so different, yet in some ways similar to a bot.

The “mind” of a robot is governed by certain algorithms. Therefore, robots make decisions purely on their logical capabilities. However, the human mind often makes decisions based on more than its logical capabilities.

In other words, humans, unlike bots, make decisions that sometimes give unpredictable responses due to emotions or the environment. These are the qualities that mainly differentiate us from robots. Nevertheless, much of the human mind and human decisions are based on logical inferences.

What are Algorithms?

Algorithms are the rules that process the “thoughts” of robots. These are the rules that make bots draw a specified conclusion; there are two broad categories of algorithms for machine learning: supervised models and unsupervised models.

You can learn more about these models and see how they apply in robotics by enrolling in data analytics courses in Singapore.

 The Four Steps

 1. Frame the Problem

Think about the problem and frame it in such a way that it can be described from the beginning to the final desired goal and all the possible variables and options that could be.

Consider a situation where you would like to arrive at work using the least amount of resource, within the shortest time.

The problem is to get you from home to work. While the basic variables are the different types of commute available for you, other variables such as what time you should leave can also come into consideration.

So, first, take time to frame the problem and the variables.

2. Design a Data Collection System and Specify the Data

Think about what data you have already. Design a system that will gather as much data regarding your problem as possible and think about where to store the data and what could manipulate that data.

Also, specify what data you will need to comprehend and model ideal commute times. Consider special circumstances and classify them as either predictable or unpredictable data. If unpredictable, include them as random variations in your model. However if predictable – and controllable – include them as co-variants.

3. Develop “Thinking” Model Starting From the End

Consider the end goal and develop your “thoughts” using the model considering the end from the start.

Provide your model with “interventions” that you would need to take at different stages in order to arrive at the desired goal. For instance, if you need to arrive at work at a certain time, add in interventions such as accounting for travel time and potential delays in traffic.

Also, consider the worst case scenario and model accordingly.

4. Repeat the Model with Different Scenarios to Affirm your Model.

To confirm a particular theory or result, it’s important to run the model multiple times through different scenarios ensuring that the model is reliable. This is true with people too. Once you can affirm your model, it becomes a reliable system that you can operate by, just like an algorithm.