To Be Me or Not to Be Me in the Age of AI: Why Experience Still Matters

So, it’s here. The Age of AI. The Age Of the future. The time all of our forbearers fancied themselves into believing they lived in (looking at you Jetsons) and of course this statement will age horribly 50 years from now but I digress. Artificial Intelligence is here and it can do some amazing things. So amazing, the way we work and collaborate moving forward will be dramatically shifted. And this has massive implications for society as a whole. But what exactly is AI?

What Is AI, Really?

Artificial Intelligence is defined by Nvidia as “In its most fundamental form, AI is the capability of a computer program or a machine to think and learn and take actions without being explicitly encoded with commands. AI can be thought of as the development of computer systems that can perform tasks autonomously, ingesting and analyzing enormous volumes of data, then recognizing patterns in that data. The large and growing AI field of study is always oriented around developing systems that perform tasks that would otherwise require human intelligence to complete—only at speeds beyond any individual’s or group’s capabilities. For this reason, AI is broadly seen as both disruptive and highly transformational.”

Whether speech or basic cognition, any computer that can display a sense of human-level intelligence could be considered AI. At the core of it all are algorithms, which are step-by-step instructions for a program to execute that, when followed correctly, should have a predictable/reproducible outcome. Key to algorithms are clear steps, input of data and output of a solution. Algorithms aren’t limited to just computers either. Any step-by-step set of instructions can be defined as an algorithm including everyday normal routines.

So, in theory, if you can construct an algorithm centered around playing a game, like chess, you can have a computer “learn” to play by feeding it a chess-centered algorithm. And if you packaged it in a way the computer can process, it’ll become a Grand Master in a week. More seriously, mid 20thcentury computer scientists did their wizardry and found a way to develop simple and then game-based algorithms and thus AI was born. Dating back to the 1950’s, computer scientists have been creating AI algorithms to play basic board games as a means of computational study for CPUs. Early CPU performance testing involved running gaming algorithms on a CPU. At the time, the processing limitations of the middle 20th century affected the potential of AI development and funding was uncommon. In the 1980’s however, a new frontier was discovered with the advent of Machine Learning(ML).

ML is a subset of AI that uses statistical methods to accurately predict or classify data. ML relies on what’s called a model to make accurate classifications or predictions. What’s a model? A model is simply a computer program that has been trained on a set of data to perform actions without being told to do so by a human. And if you’re wondering, training is teaching a model to learn from a combination of input data and its own previous attempts to improve overall performance.

So, where does ML fit in? Without going too deep in the technical weeds (if we haven’t already) ML relies on models to accurately perform tasks, usually classification or prediction.

Models themselves get their data from human input. Some data is explicitly labeled, which means it has information that helps identify said data like a tag. Data that doesn’t have information associated with it is known as unlabeled or raw data. (Essentially, if it has Metadata it’s labeled, if not then no, in my technical opinion/observation.)

When a ML model trains by learning from labeled data, this is known as Supervised Learning. When a ML model trains by learning from unlabeled data, that is called Unsupervised Learning. Supervised Learning is best suited for binary classification, such as when you need a True/False or Yes/No output response. While Unsupervised is geared towards finding patterns and anomalies, which is great for detecting abnormal behavior or discovering unknown connections. Each training method has its own strengths and drawbacks.

Diving further, a subset of ML is called Deep Learning and is what facilitates the ChatGPT model. Deep Learning is similar to ML with the exception being that the number of layers data passes through is exponentially deeper. These layers form a Neural or Neuronal Network, which resembles the interconnected neuron pathways of the human brain, hence the Neural in the name. ML typically uses 1 or 2 layers, whereas Deep learning can reach into the thousands. The added layers increase computational performance for Deep Learning algorithms via expanded number of neuronal nodes.

AI Is Here to Stay—Now What?

Well, since your halfway to becoming an AI aficionado, you’re probably going to be asked questions like; Is AI going to affect my company bottom line? Does it even help? Is the ROI worth the time and investment?
 
Short answer: Yes. 

Long answer: Yes, to all three questions and it’s going to change the way businesses operate.

AI’s Real Impact on Work & Skills

Content with it all.

For better or for worse, AI commoditizes workplace skills. AI has the capability to analyze/generate videos, analyze documents and reports, even fully-featured games all with a single, well-worded prompt. And that is just a sliver of its offerings.  It’s disrupting more than just the creative space well. The potential to generate a variety of content, not just creative, is tremendous not just for artists but for any human with access to the internet. In fact, you don’t even need to be online to use AI, just use a locally installed model. This unlocks a level of capability that would have taken entire teams before and puts it in a single person’s hands. Any person can be their own team now. AI has essentially commodified baseline skill-sets for many industries and will continue to do so.

Changing Job Standards

Remember those reports we used to hand to interns just so they would have something to look at? Yeah, that work methodology is no longer viable. AI takes those reports, summarizes them and creates beautiful dashboards, and makes you coffee, in the blink of an eye. The bar for entry-level work has been raised significantly. How your company captures the generative potential of AI is crucial. Companies should focus on maximizing their employees time by developing an expectation that employees and new-hires will have some familiarity with AI and thus AI should be used to enhance their role, not replace it. AI should not do their job for them but give their current role a noticeable efficiency boost. With the potential for increased productivity and more time devoted to money-making solutions, companies should view AI as another tool-in-the-toolbelt for employees and new-hires alike. Familiarity with AI tools the company uses should be considered integral to the hiring process moving forward.

Why Human Experience Still Wins

What makes you…well, you? Answer: your experiences. AI has created an inflection point for companies. The skills of yesteryear that were hard to come by and took years to develop (i.e. , artistry, programming) seem to now be easily accessible however the experience to utilize them to their full capacity is still mainly based on one’s experience. Anybody can tell ChatGPT to create a spaceship game, but what happens when your game breaks? When your Spaceship is clipping through planets? Or if the AI deletes your database?

An experienced consultancy group, like HBITS, would pre-emptively implement scheduled database backups and thus be able to restore said deleted database. While ChatGPT may suggest a person to do so, it doesn’t do it for you in anticipation of its own errors or unforeseen circumstances. AI isn’t that aware…. yet.  

So, while AI has massive upside, the capability gaps are still noticeable and that can only be filled with experience garnered from humans. Companies should place a higher premium on learned experience, not just from the enterprise but including Freelancing and Self-employment, because quality experience is valuable in all forms. Especially when you consider paying someone thousands of dollars just to use ChatGPT.

What Businesses Should Do Now

So what to do next? Embrace the newest technologies and upskill. Like the old saying goes, “If you can’t beat ‘em, join ‘em”. AI isn’t going anywhere. Researchers are currently working on the next level of AI called General AI that promises all types of B-Movie Sci-Fi horror. Just wait. In the meantime, we at HBITS suggest to arm yourself with the knowledge of how AI works and what it can do to increase certain efficiencies in your company/role. Protect yourself and your career with a top-notch certification or related experience. That’s why we partnered with our affiliate to offer AI centered certifications to help people stay competitive in the job market. Check out the banner link down below.

Let us know your thoughts about the article in the comment section below. Thanks for reading!!!

DISCLAIMER: NO AI WAS HARMED IN THE MAKING OF THIS BLOG POST.