Future Proof Your Career Knowing These First

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To Future proof your career given the SkillsFuture funding from Budget 2024, we have to first be aware of the developments in the world to come. Only then can we position ourselves appropriately with the relevant skillsets to survive, if not thrive in future. One theme shaking things up is artificial intelligence (AI). Ongoing theme of increasing pragmatism year in year out to do more with less, keeps everyone on their toes. I have young children and what am I going to nudge them towards in future?

Let us start by talking about the elephant in the room – artificial intelligence. We must understand the stages of artificial intelligence development and where we are right now. I asked Google Gemini for the stages and the answer came:

There are different ways to conceptualize the stages of artificial intelligence (AI) development, but two common frameworks are based on capability and on development process.

By Capability

Artificial Narrow Intelligence (ANI): This is the type of AI we encounter most often today. ANIs are highly skilled at specific tasks, but they lack the general intelligence to apply their knowledge to new situations. Examples include chess-playing computers, spam filters, and facial recognition software.
Artificial General Intelligence (AGI): This hypothetical future stage of AI would result in machines with human-level intelligence. AGIs would be able to learn and apply their knowledge across a wide range of tasks and environments, just like humans can.
Artificial Super Intelligence (ASI): This even more speculative stage proposes that AI could surpass human intelligence altogether. ASIs might be able to exhibit creativity, social skills, and scientific breakthroughs beyond human capacity.

By Development Process

Rule-based Systems: These early AIs relied on pre-programmed rules to make decisions. They are not very adaptable and struggle with situations outside their specific programming. Examples include basic chatbots and some tax software.
Machine Learning: This approach uses algorithms to learn from data, enabling AI systems to improve their performance on a specific task over time. This is a major driver of progress in AI today, with applications in image recognition, natural language processing, and self-driving cars.
Deep Learning: This is a subfield of machine learning inspired by the structure and function of the human brain. Deep learning models, often called artificial neural networks, are able to learn complex patterns from data, allowing them to tackle tasks like image and speech recognition with high accuracy.

It’s important to note that these are frameworks, not strict categories. AI research is constantly evolving, and there is a lot of overlap between these stages. However, they provide a helpful way to understand the current state of AI and the potential directions for future development.

I believe as of the timing of this write up, we are at ANI and coming 5 years time as Jensen Huang predicts, AGI should be available to the masses. Right now we have GPTs, LLMs and image/video generative AIs to work with. One can future proof your career by having a mindset to start using the available technology in current work processes. And this will involve crafting good prompts for the AI to aid us.

Table of Contents

Which areas of work will artificial intelligence have significant impact on?

I posed the above question to Google Gemini and the answer came:

Artificial intelligence (AI) is poised to significantly impact a wide range of work sectors, creating both challenges and opportunities. Here’s a breakdown of the areas likely to be most affected:
Repetitive Tasks: AI excels at automating routine, well-defined tasks. This includes data entry, assembly line work, and some aspects of customer service. These jobs are at risk of being replaced by AI.
Data Analysis: AI can analyze vast amounts of data much faster and more accurately than humans. This will transform fields like finance, healthcare, and marketing, where data-driven decision-making is crucial.
Manufacturing and Logistics: AI-powered robots will play an increasingly prominent role in manufacturing and logistics, improving efficiency and safety.
Transportation: Self-driving cars and trucks are a prime example of AI’s potential to disrupt transportation. This technology could have a major impact on the trucking industry and taxi services.
Creative Fields: AI is already being used for tasks like composing music and writing different kinds of content. While unlikely to fully replace human creativity, AI can become a valuable tool for creative professionals.

Overall, the impact of AI on work will depend on how effectively humans adapt and collaborate with this technology. New job opportunities will likely emerge in areas like AI development, maintenance, and data analysis. The key will be for workers to develop the skills and knowledge to thrive in this evolving landscape.

Future Proof Your Career by learning to Code? Rush in with eyes open please.

>>> techwireasia.com/03/2024/nvidias-ceo-jensen-huang-ai-will-take-over-coding-making-learning-optional/

Snippet:

Huang is shaking things up, proclaiming that the era of prioritizing coding skills is over. Now, he suggests, we should focus on fields like agriculture and education. The rise of generative AI and natural language processing technologies is set to revolutionize our approach to technology, potentially redirecting the countless hours previously dedicated to learning programming languages towards gaining a deeper understanding of these critical areas.

Huang is on a quest to render technology so user-friendly that programming becomes an innate skill for everyone, achievable through the simplicity of our native languages. He envisions a future where the magic of artificial intelligence makes everyone a programmer, without the need for specialized coding languages.

However, Huang quickly points out that this doesn’t spell the end for coding. A foundational understanding of coding principles remains essential, particularly for leveraging AI programming effectively. He’s advocating for a shift towards upskilling, ensuring that individuals grasp the ‘how’ and the ‘when’ of employing AI in problem-solving.

Jensen Huang is Co-founder, President and CEO of Nvidia as of the timing of this blogpost.

I am not saying that one should not pursue learning to code but do so with the right reasons to avoid the broken promises of education. If it is to understand coding principles without expectations that it will lead to a job, that is fine. Nothing is more dangerous than assuming that adding skills leads to guaranteed employability in the sector with higher pay. When I was pursuing my engineering degree in 2000, triple-E was hot but by the time graduation came, the sector saw lower employment prospects. Broken promises of education is real.

The I, T, π, m Career Journey

This concept was made known to me incidentally over a discussion a while back. I thought that the stages denoted by those symbols are very apt in relation to the skills upgrading one’s endeavors to future proof your career.

I – By the time we graduate from tertiary education, we would have acquired 1 skill or a set of skills basically. It is quite narrow in application and thus pointed.

T – By virtue of working for some time now, probably sitting in some managerial meetings or senior level decision making exposure, we would have come across and got acquainted with other skills like accounting, marketing, business development etc. So we become a T, having some broad understanding, not so much depth in those specifics.

π – By taking on new skills like say myself, having graduated with Mechanical Engineering degree, took on CFP, ChFC and the CFA Charter, now have 2 “legs”.

m – I have since during Covid, taken up a diploma in digital marketing, and courses on photography, videography as well as understanding AI. It definitely goes beyond m now. I do know of engineers who have taken up law degrees and accountancy degrees.

By now you should catch the drift. To future proof your career is to keep learning and adding new capabilities.

Future Proof Your Career - Kids Edition

Some of you are parents are also wondering which areas to nudge your kids towards in future. This I had a conversation with my own spouse. Best to use a Venn diagram to find a sweet spot. Mind you, whether the kids are passionate about that path, is another conversation for another time. Here are some thoughts:

The global paycheck for the work that you do keeps falling with increased competition (pragmatism do more with less, and of course high salaries draw in more learners in that field). You want sustainable high income that is not seasonable with trends.
The job has to reward for years of experience. Thus far, I can only think of medicine where experience do count for something.
The job must and has to be done locally. Work from anywhere will be subjected to global competition (See Fiverr). Cooking, is work that must be done locally. So is dental work.
The job must require a license to conduct/carry out. That is a small barrier to entry and leaves the government in charge of controlling the numbers (job protection). It also signifies a certain amount of professionalism and standards control.
The job must not be significantly impacted by AI. This one is going to be tough, but at some point, everyone will have AGI to train and utilize for themselves.
The signatory must mean something. The engineer signs off papers on a project and will be accountable as well as responsible. At the end of the day, tasks and duties can be delegated away to a machine. But accountability and responsibility cannot. It does not make sense in a court of law for a machine algo to be liable. A human should be.

Final Words

I trust that Jensen Huang is right that AGI is on the way. The timing might be off but reasonable. Things are expensive at first but history has shown that costs will come down to be afforded by the masses. Right now, machine learning for hospitals and corporates are expensive for their use cases. But just like mapping out the human genome use to cost a bomb, now genetic testing for individuals are much more affordable. So will AGI be made available for the masses to train their own use case. Hence, I will not be pushing my kids to learn coding. Brush up their communication skills is a definite (eg enroll them in toastmasters club in future) to be able to communicate with machines in natural language. At this current juncture, veterinarian science seems to hit the sweet spot amongst the considerations above. Things are fluid and subject to change. I saw a machine peel the skin of a grape and stitch it back. For now, surgeons still charge less fees than what that machine will charge. Who knows what the future holds? Deploy your SkillsFuture funds thoughtfully.

Make your own play. (Wah long time never used this phrase.)