Today, we are living in an increasingly intelligent society.
Over the years, humans have come up with multiple technology and inventions.
One of the most phenomenal innovation so far is machine learning.
You may not realize it, but it has existed for quite some time.
You can see them in Google search predictions up to the creation of artificial intelligence.
As this technology becomes mainstream, several critical questions arise.
Some believe that machine learning will make millions lose their job.
How true is that statement?
Hence, we are going to explore more about the pros and cons of machine learning.
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Machine learning pros
- Forecast consumer insights
- Boost organizational efficiency
- Improve healthcare diagnosis
- Enhance finance institutions
1. Forecast consumer insights
In an ideal world, businesses can forecast all of their customers wants and needs.
However, this is not what happens in the real world.
There are informational gaps that businesses must fill to succeed in their industry.
More often than not, those informational gaps require businesses to do extra research.
Typically, businesses rely on historical data to make forecasts.
However, there is an inherent flaw with historical data.
Today, we are seeing how data is growing at an exponential rate.
In fact, data that businesses learn will probably be redundant by the time they fully understand it.
To counter this problem, businesses must be agile and highly resourceful.
Hence, many businesses have adapted technological systems that help them do complex data analysis.
While technology is great, another important need arises for businesses.
They must learn from their customers in real time.
Every time a customer enters an input, businesses must immediately take it into account.
Even the slightest information can change the deduction that software makes.
Hence, machine learning can amplify the capabilities of the technologies of today.
Previously, technologies work based on a specified input and have a designated goal.
However, machine learning’s purpose is to learn from patterns and make its own deductions.
In fact, it can suggest an entirely different suggestion from the historical data.
To some extent, it can imitate how humans think and make its own decisions.
If developed, they can be part of some of the most intricate parts of our lives.
Digital home assistants such as Amazon Alexa and Google Home are excellent examples.
By incorporating them with machine learning, they can learn from our daily routine and make appropriate recommendations.
These devices learn from its main user’s voice and the data integrated into its system.
2. Boost organizational efficiency
Data breach and attacks on an organizational’s database are growing increasingly fast.
This should be a red flag for organizations out there.
Here’s how machine learning can play its role.
· Enforcing customized security layers
No organization is truly safe from security breaches.
Even cryptocurrency’s blockchain which is claimed to be highly secure has been hacked multiple times.
It is true that hackers are becoming more advanced.
However, these incidents may also be caused by employee’s carelessness.
In our world today, one wrong mistake could lead to millions worth of losses.
One wrong click on a junk email by an employee could lead to a massive database attack.
More importantly, businesses must understand that not all employees are technology savvy.
With this in mind, machine learning acts as a security mechanism against potential security mishaps.
Therefore, machine learning incorporated into the company’s system can learn from an employee’s browsing pattern.
The employee also gains access to a system that understands their tendencies to err in judgment.
· Reducing time needed to type documents
Just like artificial intelligence, there are other subareas of machine learning.
One of it is deep learning.
This is used by popular speech recognition software such as Dragon Naturally Speaking.
In short, this software can translate voice data into words.
In fact, it can learn from different accents.
The user can train the software to translate more accurately.
Over time, the software adapts itself to the user’s accent.
In a workplace, this enables employees to do work faster as the software can type faster than the average person’s ability.
It can also empower disabled employees as the software can be trained to execute tasks on a computer using voice instructions.
During meetings, employees can record the meeting using Amazon Alexa.
Then, they can replay it later on to record important details of the meeting using a deep learning speech recognition software.
3. Improve healthcare diagnosis
In our pursuit towards a higher life quality, healthcare is vital.
In fact, improved healthcare also contributes towards a longer lifespan.
Machine learning in healthcare can offer much-needed insights.
Its ability to learn from past data can help detect diseases at an early stage.
When it comes to diseases such as heart diseases, cancer and stroke, early detection makes a big difference.
However, doctors may not be able to detect it earlier on.
With machine learning, the technology can learn from past medical cases.
In fact, it can analyze thousands of medical cases from its database.
From there, it can instantly recognize a symptom even if the case seems remote.
In this regard, machine learning aims to assist medical practitioners by making up for areas that they lack.
For instance, going through past data manually can be time-consuming.
However, machine learning can enable faster diagnosis and initiate earlier prevention or medication.
In healthcare, scientists are experimenting with medical technologies that can perform surgical procedures.
These technologies are projected to be more precise.
They can also learn extensively from surgical videos to imitate the movements made.
Although these surgical robots have yet to become mainstream, machine learning will be key to establishing these robots in the industry.
The capabilities of these machines to learn may not necessarily replace a doctor’s expertise.
However, machine learning represents an important possibility in the world of science.
Its ability to learn exponentially and not being affected limitations of a human being can bring forth innovations in the healthcare industry.
4. Enhance financial institutions
One of the industries that can benefit from machine learning is the finance industry.
In fact, financial institutions are often searching for ways on how they can enhance their profitability.
This can include better wealth management, daily operations and security concerns.
Hence, this shows how machine learning can be present in different areas of an organization.
In the world of finance, data is constantly changing in real time.
So, global conflicts around the world can affect how financial institutions make their decision.
In fact, many governments are searching for financial instruments that can offer a good rate of return.
More importantly, this has to be balanced with the financial risks that come with it.
Today, more people are searching for financial institutions that can advise them on how they can invest and get the most out of their money.
For instance, people want to know what are the best retirement investments.
With machine learning, a financial institution employee can offer better suggestions to a prospect as the system can personalize the suggestion to the prospect’s needs.
Also, there are more methods of investing that are emerging in the market.
Furthermore, there is an increasing interest shown by financial institutions towards cryptocurrency.
This interest is directed more towards blockchain, which is the infrastructure behind most of these cryptocurrencies.
Financial institutions are already working with Ethereum to test out blockchain applications.
Therefore, machine learning will enable financial institutions to make better and more accurate forecasts of the potential uses of blockchain during the testing period.
Machine learning cons
- Exposed to human bias
- Cannot replace human intuition
- Lack of experts in machine learning
1. Exposed to human bias
Inherently, machine learning learns from data input.
This presents a major flaw.
If the data has embedded biases in it, the output will also be biased.
So, this can cause discrimination to happen.
Therefore, the input of any machine learning system must come from diverse sources.
However, this may not be possible in some areas that only has several experts in them.
Or, it can also concern professions that are primarily dominated by a certain race.
By nature, this means that there are going to be similarities in the data based on racial preference.
In fact, this may be a completely unintended bias.
Let’s take a look at the demographics of software developers.
The number of male software developers are much higher compared to the females.
Inherently, there is going a discrepancy in data if it is used for machine learning.
In addition, data is also susceptible to manipulation by a powerful entity.
We’ve seen how big scale frauds occurred in Enron and Volkswagen by the top management.
Hence, the issue of biases in data should not be taken lightly.
Machine learning can mold the way we live our lives.
With biased data, we will see increasing social injustice, exemplified.
Therefore, the integrity of the data sources should always be a priority.
So, proper regulations from the authority should be put in place for machine learning.
2. Cannot replace human intuition
As technology becomes more advanced, it is undeniable that we will have fears as to how powerful it can be.
To what extent should we allow technology to grow?
One of the biggest fear that people have with machine learning is that it will eventually master every human skill in the world.
Despite this, machine learning cannot replace human intuition.
As humans, we are equipped with emotional mechanisms that can’t simply be learned by a machine.
In fact, intuition is one the things that differentiate us from technologies.
Intuition may very well be the quality that makes us human.
Also, our intricate nature as human beings allows us to sympathize and empathize with others.
On the other hand, machine learning only considers the data that it has received.
In fact, it also aims to achieve a certain goal.
In a sense, it means that machine learning can be ruthless in the pursuit of achieving its goal.
For example, machine learning can filter through thousands of university applications.
Therefore, it will present the best candidates for placement based on their merits.
Despite this being useful, taking into other consideration such as the potential of a student is also important.
If it was up to machine learning, this potential might be unproven by previous achievements.
However, human intuition will enable a university officer to see the potential in a student.
Hence, we should look at machine learning as a technology that encourages innovation.
Inherently, machine learning may be faster, smarter and more precise than us.
Despite this, it will never be human.
Believe it or not, our nature as human beings make the world a better place.
It also makes us more adaptable and resilient towards change.
3. Lack of experts in machine learning
The potential for machine learning is huge.
However, there is no one size fits all approach when it comes to machine learning.
In fact, software developers have to create customized technology incorporating machine learning for different industries.
Similar to engineering, machine learning has different subsections.
This can spell trouble for the early progress of machine learning.
Today, we still lack experts in machine learning.
In fact, it can take some time before you can be considered an expert in machine learning.
The complex nature of machine learning makes it highly costly to hire an expert.
We can see this as only top companies such as Google, Amazon and Microsoft are making leaps and bounds in machine learning.
Hence, we can see that we are only in the early stages of machine learning.
However, this opens up an important window of opportunity.
Career-wise, talents in the field of machine learning are highly sought after.
More importantly, if you choose to pursue this field, you will have the opportunity to work with top companies in the world.
Also, you will have the opportunity to lead a wave of change brought about by machine learning.
Even with the lack of experts, this technology is showing incredible potential.
With a more qualified workforce to develop machine learning, imagine the progress that we can make.
Today, there are already some universities offering education in machine learning.
Inherently, more educational institutions and governments realize that machine learning will play an important role in the future.
So, does machine learning bring more harm than good?
During the early days of the Internet, very few believed in its potential.
One of the few who saw its massive potential was Jeff Bezos, the founder of Amazon.
Because of this, he became a billionaire.
When Jeff Bezos first started, can you imagine how many people didn’t believe him?
Probably, a lot of people.
It seems reasonable to not believe in Jeff Bezos at that time.
The Internet wasn’t as developed as it is today.
The computer itself was also so big it filled an entire room.
However, Jeff Bezos believed in what the Internet could be.
Today, we are facing a similar situation with machine learning.
This is a technology that can potentially change the way we live our lives.
As consumers, we will enjoy better products and services.
From a business perspective, it can transform operational efficiency.
With machine learning still in its early stages, it comes with certain limitations.
For instance, the integrity of the data may be uncertain.
Embedded biasness also can potentially occur.
Also, there is a shortage of experts in machine learning.
So, where is machine learning headed?
We are seeing major corporations investing serious money in machine learning.
This is a positive move for the progress of this technology.
In the years ahead, we can expect to see more innovations incorporating machine learning.
As we learn more about this technology, we will be able to overcome its limitations.
More importantly, we need to have favorable outlook towards machine learning.
Look at this technology as an opportunity for us to move forward.
The potential that it represents is too big for us to ignore.
Over the next few years, more discoveries will be made in this area.
Undoubtedly, we will benefit more them.
Also, this doesn’t mean that we overlook its limitations completely.
In fact, we need to learn more about the technology to continuously improve.
Hence, we see that the only direction that machine learning will move is, forward.