It seems the gap between man and machine is being closed daily. Machines are taught to do human work and therefore they should be able to “think” like humans to solve human problems. Computers are very good at certain things and unable to adapt to aspects like human emotions which in real life play a huge role.

Moreover, thinking like a computer aids in writing code that is clear, concise, and easily understandable by both humans and machines. It involves considering factors like algorithmic efficiency, memory management, and code readability, which are crucial for producing high-quality software.

On the other hand, programmers should also think beyond the constraints of a computer's capabilities. They need to understand the user's needs, anticipate potential user interactions, and design software that is user-friendly and intuitive. This requires empathy, creativity, and an understanding of human psychology, which are distinctly human traits.

1. Feb 1 (Reuters) – “ChatGPT, the popular chatbot from OpenAI, is estimated to have reached 100 million monthly active users in January 2023, just two months after launch, making it the fastest-growing consumer application in history, according to a UBS study.”

Advances in generative artificial intelligence (AI) continue at an unprecedented pace, large language models (LLMs) are emerging as transformative tools with the potential to redefine the job landscape. The World Economic Forum’s Future of Jobs Report 2023 predicts that 23% of global jobs will change in the next five years due to industry transformation, including through artificial intelligence and other text, image, and voice processing technologies.

The change will not affect all individuals equally. There could be devastating results for some, whilst for others there may be a great opportunity to grow in their jobs and become more relevant and useful to their employer. The good news is that there is much that can be done to stay ahead of the curve. Time is however not on the employee’s side and action must be taken today, that is if you did not start yesterday.

The recent advancements in these tools, like GitHub’s Copilot, Midjourney, and ChatGPT, are expected to cause significant shifts in global economies and labour markets. These technological advancements coincide with a period of considerable labour market upheaval from economic, (COVID-19) geopolitical including the Ukraine invasion and Israel and Hamas disgrace to the entire world, green transition, and technological forces.

2. In the research paper titled, Jobs of Tomorrow: Large Language Models and Jobs WHITE PAPER SEPTEMBER, they state that, “Using both machine learning and manual methods, job tasks are individually rated concerning their potential exposure to the adoption of LLMs, thereby classifying them into one of four categories: 1. High potential for automation: Going forward, the task will be performed by LLMs, not humans. 2. High potential for augmentation: Humans will continue to perform the task, and LLMs will increase human productivity. 3. Low potential for automation or augmentation: Humans will continue to perform the task with no significant impact from LLMs. 4. Unaffected (i.e. non-language tasks).

3. In the Media space, the foundation of journalism remains editorial judgment. Large language models have and continue to play a crucial role. Editorial judgment is the ability to maintain a keen awareness of the deep informational needs of an audience or society, identifying stories that meet those deep needs, verify, and contextualize those stories, and then communicate them to audiences in clear and engaging forms. AI Content Creators will build on the work of Interface Designers to use LLM knowledge to rapidly produce in-depth content across various fields, from articles and books to teaching material and media storylines.

As generative AI introduces a new paradigm of collaboration between humans and AI, it will redefine how work is conducted and reshape the nature of various job roles.

For instance, a new term coined to cover one such job is “Prompt Engineers” These workers will be critical to developing, refining, and reframing prompts or inputs for LLMs to reach more optimal results, evolving skill sets, covering algorithm design, custom chip development, server infrastructure, and power systems engineering. Not the classical definition of an engineer, not a guy working in a laboratory with a white coat and some rats in cages – but let us call him/her an “engineer” for now.

Interface and Interaction Designers to craft user-friendly interfaces for LLMs, acting as user experience (UX) designers. These designers will need to adapt LLMs to different user inputs or specific tasks, such as developing personalized AI assistants, tutors, or coaches.

Data Curators and Trainers will ensure continued high quality of training dataset and performance by LLMs. They will also monitor the data integrity through rigorous quality checks, with a specialized workforce dedicated to curating internet text; and

Ethics and Governance Specialists to address biases and ethical concerns in LLMs. AI Safety Officers, ethicists, and regulators at company and government levels are expected to play a crucial role in testing and ensuring ethical AI deployment.

4. Machine learning and so-called Bot Trading: Many South Africans, thousands of Americans, and various others flocked into investing in Mirror Trading International (MTI). The company claimed that they had acquired a robot equipped with algorithms that enable them to make 99% winning trades on the cryptocurrency market. Billions were lost when it turned out to be a false claim and that it was a Ponzi scheme. Bot trading is devoid of distinctly human traits such as an understanding of human psychology, greed, fear, and empathy, which are all drivers of market performance. These attributes influence markets as much if not more than mere numbers. Numbers alone have been the cause of a value trap in many stocks.

Now there is an early warning emanating from Porter Stansberry. Some of his comments relate to similar events from which we can learn.

The 1997 emerging market collapse… the Japanese 1998 banking crisis… The Dot-Com blow-up of the 2000s…The demise of General Electric… the bankruptcy of General Motors… and the 2008 financial crisis, along with the fall of Fannie Mae & Freddie Mac.

History is replete with examples of what happens when a disruptive technology and the promise of market disruption meet excess capital.

5. Not all is gloom with the advent form AI- far from it.

Every day, an estimated 91 billion gallons of clean water are lost worldwide due to leaking pipes. In a world where freshwater supplies are limited and purifying polluted water takes energy, that’s a significant loss, affecting both our wallets and the environment.

Engineers at the U.K.’s Integrated Civil and Infrastructure Research Centre (ICAIR) are developing a solution: a crew of tiny robots that will repair pipes from the inside. By its nature, most pipe leaks are in areas that are hard for people to access, like inside walls or underground. Fixing them requires digging or demolition to uncover the pipe beforehand, and construction or landscaping work to restore the area afterward. These tiny robots are equipped with a camera and sound recorder to relay information about the situation inside a water pipe.

South Africa is well known for the state of disrepair in our water infrastructure. We now have daily water interruptions all over the country as our infrastructure was not sufficiently maintained and our pipes were not retired and replaced at the end of their useful expectant life span. These new technological advances cannot reach our shores soon enough. It could save the country billions of Rand.

* Kruger is an independent analyst.

PERSONAL FINANCE

#machine-learning

Run on numbers: Who’s the Boss? Man, or machine?

Youth talks tech: Machine learning, and what it really means

If schools are to become digitally transformed public-private push is vital