Now that we have AI
how are we going to use it?
We’ve just crashed through a new ceiling in social development. Some think we have just opened the Pandora’s box of ‘superhuman’ capabilities by using generative AI tools that are emerging, practically on a weekly basis.
What can generative AI do?
Generative AI has demonstrated impressive capabilities in generating content like text, music, and images at a volume and speed that surpasses human ability.
What makes us ‘superhuman’?
There is no definition of what a ‘superhuman’ must possess. It is a fact though, that one imagined superhuman trait is the ability to muster knowledge beyond the limits of an average human being. And by this definition, the very first AI model already possesses “superhuman” capabilities.
Up until now, we measure intelligence of humans through an intelligence quotient (IQ) score. The majority of people fall in the 85 to 115 average intelligence score, whilst an individual with a score of 160 and above is deemed to be a genius. The IQ tests measure logic, spatial awareness, verbal reasoning, and visual abilities and these tests do not measure knowledge in any specific subject area or that anyone can study for.
Crystallized and Fluid Intelligence
Intelligence tests are designed to measure crystallized and fluid intelligence (originally proposed by psychologists Raymond Cattell (1945)and John Horn (1965) suggesting that intelligence is composed of different abilities that interact and work together to produce overall individual intelligence). Crystallized intelligence involves an individual’s knowledge and skills acquired throughout a lifetime while fluid intelligence is the ability to reason, problem-solve and make sense of abstract information (today commonly referred to as soft-skills).
Crystallized intelligence, is directly related to learning and experience and tends to increase as people grow older. It is a direct result of accumulation of knowledge and the ability to relate facts and experiences gained in order to fathom what actions need to be taken to solve an actual problem. On the other hand, fluid intelligence is necessary for a younger person facing a situation for the first time. The first pain when trying to grasp a mesmerising dancing flame will remain ingrained in a child’s brain.
It is often claimed that intelligence declines with age however, research suggests that while fluid intelligence begins to decrease after adolescence, crystallized intelligence continues to increase throughout adulthood.
AI already surpasses any human in crystallized intelligence!
Yet, if we refer to the quality, creativity, or depth of understanding involved in the generation process, that’s a different story. As of now, AI does not truly understand the content it generates. It does not possess consciousness, emotions, or a personal history that informs its outputs. So, while an AI bot might write a convincing story, it’s not drawing from personal experience or genuinely understanding the narrative. In terms of creativity, AI can generate novel combinations of known patterns, but it does not invent completely new concepts or ideas. The creativity demonstrated by AI is different from human creativity. It is more about recombining existing ideas in new ways, rather than coming up with entirely new concepts.
The older we get, the more obsolete we become!
In short, as we get older, we lose out to generative AI. Millions of jobs that rely on experiential regurgitation will disappear.
Similar to the large number of jobs lost to the data processing revolution brought about by the computer in the 1960s and 1970s, we can understand what is going to happen to the millions of current jobs that require minimal creativity, but a lot of deep knowledge. The new generation of ‘superhumans’ must necessarily strengthen the fluid intelligence beyond adolescence, being able to interpret correctly what highly advanced AI bots are able to churn within seconds.
AI concerns shape graduate career choices
From illustrations to translations, AI is proving a formidable adversary — young people should choose their career paths carefully lest they are deemed obsolete even before they graduate!
The ‘gods’ that develop the AI universe
Already, we have some very able human beings who have designed and developed these pioneering tools that up to some years back were only science fiction for Hollywood movies. These ‘gods’ are not your everyday nerd, and as the trend accelerates, adventurous individuals will embark on careers that will lead them to be at the very top of the food-chain in society.
What will make you a superhuman ‘god’?
Being an Artificial Intelligence (AI) and Machine Learning (ML) engineer involves a variety of skills that are required.
Mathematics
These individuals will not shy away from math. It is essential to have a strong foundation in mathematics, particularly in Linear Algebra, that offers the understanding of concepts such as vectors, matrices, and linear transformations, as these are used extensively in machine learning algorithms. Calculus, provides the tools to delve into concepts like derivatives, integrals, and limits which are important for understanding how many machine learning algorithms work, especially for optimization methods used in training models. The third important area is Statistics and Probability since machine learning relies heavily on statistical concepts, so principles such as probability distributions, statistical tests, and maximum likelihood estimators need to become second nature.
Computer Science
Knowledge in computer science is fundamental. Algorithmic design that is the ability to develop programs in a computer language such as Python, and data structures to efficiently store and manipulate big data is crucial for machine learning. Without an understanding of computer architecture, that illustrates the basics of how computers work, including memory management and parallel processing, will mean an inability to help optimize machine learning models. Designing, implementing, and maintaining complex systems is a key part of a machine learning engineer’s job, known as software engineering.
Machine Learning
Supervised learning such as linear regression, logistic regression, support vector machines, decision trees, and neural networks, and unsupervised learning such as k-means clustering, hierarchical clustering, and principal component analysis are a must. So are the principles of reinforcement learning and deep learning. Understanding the principles behind these algorithms, their strengths and weaknesses, and how to apply them to real-world problems is crucial. Deeper still are topics such as natural language processing, computer vision, and robotics.
Ethics
With the rise of AI technologies, ethical considerations are becoming increasingly important. Understanding potential biases, privacy issues, and the societal impacts of AI technologies is critical. As everything around us becomes connected through the internet of things, and we will experience our appliances talking to each other through AI bots without being central in the conversation, human kind will become more reliant on the ‘gods’ who need to ascertain that this immense power is handled well for the advancement and benefit of all humanity.
What will the IQ test of the future check?
If we want to live in the elysium of superhumans, we must possess a global capacity of reasoning out problems, have an insatiable hunger to learn new things, and think abstractly in order to survive the emerging world. If we don’t and end on the wrong side of the superhuman divide— then we are doomed.
