BY EDISON MARTE SICAD
A WELL-KNOWN entrepreneur opined that most of us are already cyborgs. With his definition that a cyborg is one who lives with technological enhancement or whose day-to-day activities are embedded with technological elements, he pointed out that our use of gadgets — and with the almost impossible scenario of living without social media activities — already transformed us (for better or for worse) into cyborgs.
Further, our daily interactions are already facilitated by technology that have also influenced and shaped our cognitive habits. We have learned more — maybe even faster and more efficient — with the use of these machines. But as we learn more, we get to realize that there is still more to learn. Human as we are, our curiosity is insatiable and thus, we ask, “can a machine really learn?”
What is machine learning?
Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. — IBM
This is just one of its many definitions. If you search the internet for more definitions, you will notice that definitions would depend on the purpose or objective of the research or organization. And that is where it gets more exciting or scary: the purpose or objective.
Machine learning, coined by Arthur Samuel in 1952, became more popular in the late 1990s when IBM’s Deep Blue supercomputer beat world champion Garry Kasparov in chess. This man-versus-machine game proved to us that machines are capable of human-like intelligence.
In relation to this, what is Human Learning?
Learning is the acquisition of habits, knowledge and attitudes. It involves new ways of doing things, and it operates in an individual’s attempts to overcome obstacles or to adjust to new situations. It represents progressive changes in behaviour. .. . It enables him to satisfy interests to attain goals.—Crow and Crow (1973).
Learning, as a complex process of brain function, is commonly accepted as a human activity. So, saying human learning could be redundant. But not anymore. We have created — based on our learning and continuous search for knowledge — another definition of learning that originates from us but can go way beyond our capacity and ability to understand, monitor, and control.
At present, AI can defeat (human) world class champions in games such as, Poker, Checkers, Go, and Dota 2. AI can also “paint or draw” with such creativity that the myth of Prometheus now has a version 2.0.
And in the field of education, our writing processes (and outputs — to the delight of some students and chagrin of some professors) are now aided by apps such as Quillbot and Grammarly.
INSTRUCTION: Write a poem about two tigers in the jungle who fall in love over an antelope
hunt. (From the YT channel of KARE 11)
OUTPUT:
Two tigers in the jungle, fierce and bold,
Roamed the forest in search of gold,
An antelope hunt was their quest,
With stealth and grace, they put their skills to the test.
But as they chased their prey, they stopped in their tracks,
For they saw each other, and their hearts beat fast,
One tiger’s stripes were black, the other’s were gold,
Their eyes locked, their gazes bold.
In that moment, they both knew,
That the hunt was no longer their priority true,
For they had found something far more rare,
A love that only they could share.
Together they prowled, and together they ate,
Their bond grew stronger, with each passing day,
Two tigers in love, in the jungle so wild,
Their love for each other, always kept them smiling.
So if you ever roam through the jungle one day,
And hear the roar of two tigers at play,
Just know that it’s the sound of true love’s embrace,
Two tigers, forever lost in each other’s grace.
—This poem was written by ChatGPT in mere seconds. ChatGPT is a chatbot model that “talks” to a person in a conversational manner—with the ability to write anything you want.
It can write a Filipino Oration Piece or Talumpati, as well as answer Math and Science problems. It can also create an algorithm, e.g., a program feature for log-in on a website. It can “chat” with you 24/7.
QUESTIONS:
“Why think that much (and that hard) when a computer can give you instant answers?”
“What else is left for us to think about that can advance learning when a machine can exponentially do it for us?”
From speech to text and driverless cars to Non-fungible token (NFT) art to quantum computing, it would seem that we, humans, must find ways of learning in a new way.
And this learning in a new way (which may not really be “better” if compared to a machine) preoccupies me for the past few years.
And so, I came up with this insight:
This idea could be (an)other interpretation or one of the many applications of yin and yang or Gestalt theory: All knowledge (or everyone’s opinion) is part of learning: the contradictions or layers of interpretations complement to enable a person to grasp the holistic value of “contrasting realities.”
The discourse is dialectic and thus discourages a cancel culture. And so, we listen with the intent to learn (in the most idealistic or grandest of manner possible) about life and existence in general and the issues or contentions in particular.
IN CONCLUSION, inasmuch that it feels good when we are proven correct and it feels (way) better to have proven someone wrong (for who doesn’t aber?), the concept of learning (to arrive at the right conclusion)—with the corollary human embellishment of reward and/or punishment as a consequence—is still an evolutionary process: a fluid state of mind certainty that may have an ironic and extinctive outcome: Artificial Intelligence./PN
