A battle is brewing, and the future of our world is at stake!
On one side, we have machine learning: precise, consistent, logical, and unemotional.
On the other side, we have messy, inconsistent, and emotional human learning.
Which will come out on top?
In this blog post, we will explore Machine learning and Human learning, their differences, their similarities – and if our future is at stake.
Model-Based Decision-Making Vs. Model-Free Decision Making
The world of decision-making is complex, and having two contrasting ways to make decisions can often leave people – and systems – in unique situations.
Model-based decision-making is an approach that uses highly accurate models to make predictions and decisions about a scenario that a system has “experienced” before.
This system can then make an optimal decision based on pre-programmed criteria (making the most money, finding the fastest route, etc.)
This leads to decision making – which is usually good at one thing and one thing only.
Model-Free decision-making is simple; it’s how you and I make decisions every single day.
What time should I leave my house to pick up my kids from soccer practice? What time do I need to go to bed to feel good tomorrow?
While it may seem like machine learning is taking over the world, a majority of the decisions made in your life – are made by you.
While model-free decision-making usually takes many more things into account than model-based decision-making, this type of decision-making lacks optimality and struggles to understand the cost-to-risk relationship clearly, as humans are biased by their biology and past experiences.
So, which is better?
Well…
That depends on what you’re doing.
Why Machine Learning Is Not Like Human Learning (yet)
As fascinating and advanced as machine learning algorithms are, they are still far from the complex ways humans learn.
Many don’t realize that machine learning is modeled after human learning.
The architecture of a neural network is designed based on the neuron patterns in your brain.
What’s even worse is that the neural networks we’ve been able to build have more neurons than your brain!
IBM has built a simulator with 530 billion neurons, about 7x as many as the 86 billion neurons you have right now inside your brain.
So, if machine learning is built like a brain, it has more power than a brain…
What makes it different?
One difference is that machines can often only optimize towards one criterion, usually an algorithm’s goal or objective.
They cannot consider other criteria nor reflect upon them as humans can.
This makes it harder for machines to make decisions about their overall objectives – which are often much bigger than the optimized criteria.
And, probably the biggest difference, is that AI is not sentient..yet.
A machine learning algorithm can’t understand its decisions; it just makes them.
Without feelings or perception, these algorithms aren’t flexible and can’t change course.
Even with 7x the brain power as a human, in those particular situations, human decisions will always be superior – because they get it.
Reference:
How is Machine Learning Similar to Human Learning?
Machine learning is fascinating because it mimics the same structure our brains use.
Neural networks, a type of machine learning technology, were engineered based on modeling neurons and the synaptic architecture of the brain.
This design allows machines to learn like humans do – by recognizing patterns from experience to improve.
In other words, these networks work similarly to how our minds work – we watch and analyze patterns in our environments so that we’re able to make better decisions for ourselves in the future!
Will Machine Learning Eventually Replace Humans?
Machines have been increasingly taking on human tasks over the years, and as technology develops, it is equally valid to say that machines are getting smarter.
One concern that has been stirring up more and more conversations is the idea of machine learning eventually replacing humans in the workforce.
This thought can be scary at first – what would our lives look like if robots took all of our jobs?
However, looking at this objectively and in a historical context reveals something interesting.
For every job these machines take, like manual roles traditionally held by humans, another higher-paying automation job is created.
Even if you’re still worried, employers will have to grab up employees for their soft skills like creativity, communication, and problem-solving.
Machines are dazzlingly efficient with their ability to crunch through data, but where humans shine is when we use technology to amplify our creative strengths.
Therefore, although machine learning will continue to influence how we work, it does not necessarily mean that robots will replace us.
Instead, understanding how these machines work and perfecting human-machine collaboration may become a sought-after skill in the ever-changing job market.
Why We Will Always Need Human Learning
The world moves quickly, and technology often finds ways to improve our everyday lives.
Still, there is something that technology will never replace: the scope, compassion, flexibility, and creativity of human learning.
Computer algorithms can learn from data sets and gain insights into what someone is likely to do next, but humans like you and me can discover and develop fresh perspectives that inspire creative solutions.
Would we have the iPhone or send rockets to the moon without human inventiveness and creativity?
Human learning isn’t deterministic; it explores what could be.
It is more valuable than brute-force problem-solving because it takes advantage of our capacity for innovation and desire to optimize and improve through creativity.
From teaching new generations to develop their ideas to advancing our understanding of science, human learning gives us the tools to generate further information about things we didn’t even know we needed!
No matter how sophisticated artificial intelligence becomes, the complex insights provided by humans through learning will remain irreplaceable – ultimately giving us an edge in how we solve problems and live our lives.
With all that said – there will be things that AI will take from us.
However, I’m sure we’ll be glad to see it go, as we can move on to where we shine – in the creative realm.
Can Machine Learning Surpass Human Learning?
Machine learning has already passed human learning and understanding in some aspects.
When it comes to brute force, optimization, and capacity – Machine learning has passed human learning (and it’s not close).
As we quoted earlier, IBM has already developed a computer with 7x the human brain’s neurons.
How creative do you think that computer is?
Would you rather have that supercomputer’s review of a new album from your favorite artist or best friend?
Again, while Machine learning has and will pass humans in certain aspects – it’ll never pass us in creativity and innovation.
Human Learning vs. Machine Learning: Who learns patterns the fastest?
If we’re talking about learning patterns, it’s challenging to deny machines’ obvious advantage over humans.
Machines can process data at a rate and level of accuracy much faster than any human ever could.
Machine learning is based on convergence, and with the current state of machine learning, it isn’t based on assumptions.
So, in a situation with thousands of experiences (think a huge dataset), a machine learning algorithm would learn at a rate that would demolish human learning.
However, if we expand this to one-off situations and applying those experiences to other realms – humans will dominate.
Machines are great at studying the past and providing highly accurate wisdom – if nothing were to change.
Humans commonly make the same mistakes repeatedly and are terrible at studying the past.
But we create the future.
Other This Or That Articles
We’ve written a couple of other articles that are very similar to this one; check out:
- Machine Learning vs. Automation:
- Full Stack Developer vs. Machine Learning Engineer
- Heuristic Algorithm vs. Machine Learning
- Data Science vs. Bioinformatics
- Data Science vs. Operations Research
- Data Science vs. Economics
- Machine Learning vs. Programming
