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- 1 ChatGPT and Google’s Gemini have revolutionized the AI world and have made many tasks a piece of cake.
- 2 The continuous growth of AI technologies has created a sense of fear in the market.
OpenAI’s ChatGPT tool, with backers like Microsoft and LinkedIn co-founder Reid Hoffman, was largely credited with raising awareness about the existence of Artificial Intelligence (AI) and its range of applications between 2022 and 2023.
Despite this, the web and other media are overflowing with optimistic and apocalyptic hype about AI. On the contrary, the recent layoffs have created a sense of fear in the market. Some people have also started filing petitions to stop the adaptability of AI around the corporate world.
Myth#1: AI is approaching human intelligence
Although AI systems are approaching or outperforming humans in increasingly complex tasks such as generating musical melodies or playing the game of Go, they still need to be narrow and brittle, lacking true agency or creativity.
AI may not be chosen to create a melody or not use sounds to create music because it will not appeal to the masses. Researchers have instead developed tools to recognize melodic patterns and use them to project similar patterns based on their guidance.
AI systems that generate melodies cannot produce realistic speech, paint a picture, or play chess. People will likely prefer human sounds over AI because it makes them feel connected.
Myth#2: AI will make human labor obsolete
AI systems currently excel at narrow tasks, while occupations comprise many interrelated tasks. Although AI will shift jobs, as transformative technologies always have, it also enhances worker productivity and creates new types of jobs. The greater risk is that moving jobs may increase income inequality and create challenges for workers whose jobs are displaced or require new skills.
This challenge is more significant than any one organization can solve. We must work together on policies and programs that equip people for new jobs and allow for stable careers in a shifting landscape.
Myth#3: AI, machine learning, and deep learning are all the same thing
Although AI is a commonly used term, there is no widely accepted technical definition for it. One way to think of AI is as the study of making things intelligent.
On the other hand, machine learning (ML), a subfield of AI, is responsible for many recent advancements in AI. Rather than being programmed with specific rules, computers in ML learn and recognize patterns from examples.
There are various ML techniques, but deep learning is currently one of the most popular. Deep learning is based on neural network technology. This algorithm is inspired by the human brain’s architecture and can learn to recognize intricate patterns, such as identifying what “hugs” look like or what a “party” is like. Therefore, those three things are vastly different from each other.
Summary
The practical applications of “new” technology have never touched people’s professional and personal lives so significantly, quickly, and tangibly. AI’s rapid growth has also raised the possibility of new forms of progress and confused the opportunities they bring, such as simplifying hundreds of tasks or making average jobs obsolete.
Anurag is working as a fundamental writer for The Coin Republic since 2021. He likes to exercise his curious muscles and research deep into a topic. Though he covers various aspects of the crypto industry, he is quite passionate about the Web3, NFTs, Gaming, and Metaverse, and envisions them as the future of the (digital) economy. A reader & writer at heart, he calls himself an “average guitar player” and a fun footballer.
