Middle Age and Machine Learning: Lost Algorithms

There is little more tragic in life than seeing middle-aged men appear enthusiastic at corporate dropout workshops on artificial intelligence. You can nod your head politely, practice expression, look forward to Day 5, and boast that he has finally been “humbled with completing the AI ​​fundamentals of strategic decisions” on LinkedIn.

Still, in the boardrooms across the country, well-intentioned HR teams in their early 30s were armed with PowerPoint slides full of buzzwords such as “Reskill Revolution” and “Future-Ready-Ready Workforce,” declaring that everyone must learn AI and data science.

While most 50+ elderly people struggle to set up a VPN app and figure out the differences between micro USB and USB C on the phone, their training budget is strengthened with a vision of COO cramming a lot of AI with Coffee Boy to Benson, Benson and a two-week digital course. Spiritually before returning to sleep. Meanwhile, vector algebra, Bayesian probability, data engines that spent four years learning girls' names on the toilet wall, mathematics alumni, wondering whether it's worth spending young people in the lecture room with other shy nerds with the director.

Of course, this future calibration is not a whole new phenomenon… Remember, of course, that we were trying to digitize the future of the world before the cloud changed the future of digitalization. That would have been good, but then the stupid blockchain changed the future of the cloud. The future is changing so rapidly that it is now a melange of opportunities, fears and linked posts about opportunities and fears.

“Even us?” asks Vinay, a 52-year-old regional manager, the Excel sheet that Big Data ideas use to track Cricket match scores and 25 GB downloaded video collections.

“Yes,” and I'm sure it only comes from swallowing corporate policies without question, as there is usually a forced training time chutney.

Now let me clarify. This is not about intelligence. Men in their 50s can fully grasp complex ideas. They navigate life, career, marriage, stepchildren, teenager, cholesterol rise and loosely understand the concept of buying and selling fees at money changer shops. They survived the 90s throat modem pairing, floppy disks, and bosses who were unable to convey the difference between “replies” and “all replies” in Outlook. But asking them to learn AI is like asking a ballerina to learn rap.

Consider the case of my friend Bharat, a respected senior VP of a respected company in quite a industry. In fact, he is so respected that even Ola Driver kisses his phone with respect and holds it towards his forehead before refusing to ride. During his recent training session on machine learning, he nodded seriously for 20 minutes in terms such as “backpropagation” and “gradation descent.”

You know, you haven't learned the problem. The question is learning. Aging cannot be denied. Regardless of the number of miro cups and the number of walnuts eaten, most of us are in our 50s and can't learn from a truly important level. It's the same hard wiring that will become as simple as learning a foreign language, which is difficult for adults compared to 4 years old. So, if most people can't learn a foreign language within two years, how do you plan to learn AI to an acceptable level by doing a one-week course via Zoom? But in a parallel universe of fleeting LinkedIn glory and HR policy, reality is handcuffed and monkey aggged while PR is prioritized

So, when suddenly faced with terms like “hyperparameter tuning” or “unsupervised learning,” the 50-year-old brain begins its own version of denial-of-service attacks in these courses. Young people who still slid their baby teeth and learn differential calculus, and who have acquired the ten decimal point of their wisdom teeth, are expected to engulf one generation's lack of knowledge in a few weeks, giving them a huge luminous verche of radiant knowledge.

In all fairness, it's not our fault. We grew up in an era of carbon copy, remote-less television and mobile phones that required Rotary patience. Our go-to search engine was Office Peon, and the main data backup was a drawer marked “Misc.” Our complete school mathematics was learned without using Greek symbols. You can't suddenly throw us into the world of Tensorflow, Gans and binary classifiers, and you can't expect to nod as cleverly as a Deepmind intern. We've still recovered from being told we can't actually eat at the pivot table.

The truth is that our journey was not built on algorithms. Its components were people, instincts and years of experience. It cannot be boiled down to data frames. Our superpower is not AI. It's EI – emotional intelligence. You can't accurately predict churn, but from a single WhatsApp message you can tell that Jason in Sales is about to quit.

So, what is the solution?

Instead of trying to turn the wise old lion into an algorithm-purpose gazelle, do our strengths. Pair it with a data-savvy Gen ZS. Share the wisdom of the meeting room and share human empathy. Meanwhile, someone else with a ponytail and butterfly tattoo explains how to fine-tune the transformer model.

And personnel matters? Stop making sure your post-training feedback form needs to evaluate your “confidence to deploy AI Solutions deployments.” Return to the cubicle and stay a quiet anchor of stability that learns to write emails and create presentations using GPT, Claude, humanity and more. AI for us will be like a car. You may learn to drive, but there is no bloody way to understand how an internal combustion engine works.

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