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Computers versus Brain, who's the winner? [Part 1/3]

July 15, 2025•
AIBig DataTechnologyMachine Learning

Computers versus Brain, who's the winner? [Part 1/3]

DATA AND BIG DATA

When you share a picture online or look for a direction on Google Maps, or any other applications or websites, you are making and sending many pieces of information throughout the world's wild network (the internet), to which we are all connected.

You can generate various types of information from various sources, the little pieces of information are what we call data.

Now, imagine the amount of information or data that is produced by billions of daily internet users from all around the world.

How many images are posted on Instagram? Or how many posts on Facebook and Twitter do people share every day? How many videos are uploaded on YouTube per hour? Or how many times do people click the search button to look for something on Google in a second? You got the image here!

The answer to the previous question is billions, and that's what we call big data (scientific term). The definition of big data is larger and more complex data sets, especially from new data sources.

What Do Big Companies Do With All This Data?

So what do big companies do with this huge amount of information generated every day from their users in a very short amount of time?

If you have never asked this question or didn't know about this before, then the information below may channel you into a very interesting horizon.

It's known that these companies have several ample supercomputers and servers overseas, which means that they store the data, and that also means that they manipulate it and process it. But why?

"Data is more valuable than gold"

So you would ask what these big companies do with this huge amount of data and how they make a fortune out of it. Albeit, it's not easy as millions of raw data are generated each second nowadays.

These companies need to ingest and store the portions of data in the several computers they have all around the world and treat it in real-time to get insights from it. Consequently, more challenges will show up during the process!

Big Data Challenges

The Curse of Velocity!

Indeed, it is an arduous challenge for the engineers and specialists to get what they aspire for, as they go through a lot of constraints. Here are the famous 5 Vs of Big Data:

1. Volume

Volume refers to the amount of data that is generated.

Based on new statistics, the total amount of data created and consumed globally is forecast to increase rapidly, reaching 64.2 zettabytes just in 2020. Over the next three years up to 2025, global data creation is projected to grow to more than 180 zettabytes.

2. Velocity

Velocity refers to the high speed of accumulation of information from various sources (phones, computers, networks, social media, etc.).

Therefore more complexity is created, since companies are compelled to ingest it all, process it, and file it, to be able to retrieve and respond to it, as fast as possible because "Many types of data have a limited shelf-life where their value can erode with time, in some cases, very quickly".

Forsooth, a real-time big data analytics solution will be required to help businesses to make the right decisions at the right time, to achieve business goals and thrive, or in the future just to survive.

3. Variety

Variety refers to the nature of data that is structured, semi-structured and unstructured data, and also to heterogeneous sources.

4. Veracity

Veracity refers to the level of trustiness or messiness of data, so it relates to the assurance of the data's quality, credibility, and accuracy (The data in the real world is hard to know what's right and what is wrong).

5. Value

Value amounts to how worthy the data is of positively impacting a company's business.

In a survey conducted among global marketers in late 2020, it was found that 41 percent of respondents saw an increase in revenue growth and improved performance owing to the use of AI in their marketing campaigns. Another 38 percent attributed creating personalized consumer experiences to AI use for marketing purposes.

These are the famous 5Vs of big data. Hence, there are also more issues and challenges scientists counter while trying to get insights and the valuable matter from it.

Having said this, scientists can overcome these problems and challenges by implementing many technologies like BI solutions, data analysis, and AI algorithms.

Where Do Data Stand in This Conflict

The data is the fuel for every program, software running today on our devices, networks, etc.

Artificial intelligence with all its implementations is nothing without a big amount of optimized, clean, and classified data to train.

So with technologies and complicated algorithms, nowadays we can see a lot of products of AI we use every single day.

AI Applications in the Real World

Arguably, big companies use big data and AI to make money by selling products, such as:

  • Self-driving cars
  • Face recognition software
  • Robotic solutions
  • Automatic Machine Translation
  • Demographic and Election Prediction algorithms
  • Virtual Assistants (Siri, Google Assistant, etc.)
  • Fraud Detection software
  • Or by selling the data itself to other companies

This technology can go beyond the examples mentioned and can cover many fields.

In a matter of fact, AI is everywhere:

  • Cinematography: Deep fake, intelligent sound predictions
  • Advertisement: Real-time bidding (RTB) predictions
  • Medical field: From medical imaging to analyzing genomes to discovering new drugs, to cancer and tumor detection, to Alzheimer and brain research

The entire healthcare industry is in a state of transformation and AI technologies are boosting the improvements.

AI in Healthcare

AI and all its various implementations helped human beings defy many difficulties, overcome many issues, and solve existing gaps that many generations before couldn't.

For example, the challenges that the COVID-19 pandemic created for many health systems also led many healthcare organizations around the world to start field-testing new AI-supported technologies, such as:

  • Algorithms designed to help monitor patients
  • AI-powered tools to screen COVID-19 patients
  • Deep learning models capable of classifying brain tumors using a single 3D MRI scan

The Score

With that being said, the first point goes to computers.

Computers 1 - 0 Brain

But this is just the beginning of this conflict. Find out who's the winner in the second part of this article series!