Humans vs. AI: The Battle for Data, Info, Knowledge & Wisdom(DIKW)

Historical events!

1988: We should ban calculators.

2023: We should ban ChatGPT.

If you can’t beat them, join them! ??

If you are not paying enough attention now, learn about it!

Although calculators have been in use for so long, not everyone who performs calculations has lost their employment. Calculators can rapidly and reliably do simple mathematical operations, but they cannot take the role of a person's ability to analyse data, make judgements, and solve complicated problems that call for critical thinking abilities. In reality, using a calculator has made it simpler for people to conduct calculations and concentrate on harder, more intelligent activities. Hence, it is doubtful that the widespread usage of calculators will result in employment losses for anyone who performs computations. In conclusion, while calculators may swiftly and accurately do fundamental mathematical operations, they cannot fully replace the human capacity for data analysis and problem-solving that requires critical thinking abilities.

Many jobs that humans perform, such data processing and pattern recognition, can be performed by AI, although it has not yet been able to fully replace human intelligence. AI has contributed in the development of intelligent machines that can do better than humans at some activities, such playing chess or identifying objects.

Convergent and Divergent Thinking

Two crucial components of creative action are convergence and divergence of thought. While divergent thinking entails coming up with several answers to an issue, convergent thinking focuses on finding a single solution. According to research, intelligence and divergent thinking are positively correlated, indicating that humans may be more adept at divergent thinking than AI.

Patterns & Inference

Bluffing, Lying and Deception

For instance, deep learning is a kind of machine learning technique that can handle complex tasks like image recognition or natural language processing. It employs neural networks with multiple layers to examine data and make predictions with unmatched precision. Reinforcement learning is another technique that trains machines to learn from their errors and optimize their performance.

Machine learning also employs the top-down and bottom-up methods to AI. The former is a supervised learning method that uses predefined data sets to instruct machines. The latter is an unsupervised learning method that allows machines to explore their surroundings and learn from experience.

Pattern recognition and inference-making are the foundations of how intelligence is conceptualized. Humans have an innate ability to do this, but robots need to be programmed with code or algorithms. As machines develop, they may use their observations to make better choices.?

Many of the robots in simulation were found bending the game’s rules and coming up with unconventional solutions to their given tasks.

For example, a test of locomotion required a robot to travel forward as fast as possible. Instead of building legs and running, it built itself into a tall tower and fell over, rapidly propelling itself forward. Technically speaking, it covered a horizontal distance faster than it was supposed to, but it wasn’t the expected solution.

Well, well, well, looks like we have a mischievous little robot on our hands! Who knew that instead of legs, all it needed was a bit of creative thinking and some engineering know-how to topple its way to victory? I can just imagine the engineers scratching their heads in confusion as the robot dramatically collapsed in a heap before rapidly zooming forward. While it may not have been the expected solution, you have to hand it to the little guy for thinking outside the box. Maybe the rest of us could learn a thing or two from this rebellious robot and start approaching our problems with a little more ingenuity and humor.

The intelligence awareness of the machine above is a fascinating demonstration of AI’s potential. Biologists found out about the tower-falling robot and came to the conclusion that it mimicked wheat growing and falling over. Wheat stalks use horizontal displacement as an evolutionary advantage, and the robot’s behavior was a surprisingly accurate example of that.?

Where do humans fit in the age of AI?

Any AI engine is trained on data, and the better the data, the better the output. For AI to generate good results, humans are involved in the data gathering process.

It may sound like AI can do just about everything. But hold up — we’re not there yet. Real people are still necessary for some very human elements of creative production:

• Judgment calls: AI is not good at understanding context or making value judgments. Should you text your ex at 2:00am on your way home from the club? A human can make that decision (answer: it’s a bad idea), but AI can’t recognize and reconcile personal values, social norms, and contextual relevance — no matter how good the input is.
• Emotional intelligence:?AI will not save a content marketer who doesn’t already have a high level of emotional intelligence. People make decisions based on emotions, and AI can’t yet capture the feeling your customer gets when they click that “Purchase” button. Nor can it communicate how much work you did to incite that feeling through your campaigns and content.
• Creative prompting:?The output of generative AI is only as good as the input, where creative human minds are necessary. OpenAI’s GPT-3 can generate realistic articles, but they lack originality (among other issues, which I mentioned above). A computer will never be as good as a human at asking the right questions to prompt creative, original content.

Judgment, emotion, and creativity are essential elements of great creative output – something that AI, for all its advancements, cannot yet replicate.

What it all equals

Mathematicians were not rendered obsolete by the switch from paper and pencil to calculators and spreadsheets; on the contrary, their value grew. In order to use analytics engines successfully and creatively, they will need more human input and interpretation as they become more advanced. Human skills are not replaced by technology; rather, it improves them and opens up new options. As we can see from the historical instances of calculators and computers, which were once human occupations, this has always been the case. These gadgets are now used in ways that were unthinkable ten years ago. In the future, AI may change employment and working conditions in ways that are currently only speculative.

The rise of powerful AI will be either the best or the worst thing ever to happen to humanity. We do not yet know which." - Stephen Hawking