Hop onto the “AI wave” (Part 2)
Learn Data Science in 2023
A “Robot Lawyer” has been permitted to defend a traffic-ticket case in a US courtroom. Relax! it would not be like some orthodox sci-fi mecha robot(as they are using for the thumbnails of this news). Joshua Browder, CEO of DoNotPay, said the company’s AI-creation runs on a smartphone, listens to court arguments, and formulates responses for the defendant. The AI lawyer tells the defendant what to say in real time through headphones(source: CBS News).
The news is the hot pool for debates. Different speculations are mushrooming among enthusiasts and skeptics. Which way the wind blows, it’s just a matter of time. But yes! it is a historic moment from the perceptive of breakthroughs in AI and its impact on our society. Part 1 highlights some of the notable developments in the field in 2022. More advances are going to happen in leaps and bounds, this year. I believe that it is a great time and about time to dip toes into the tech.
Why?
While the news from around the world is good enough to spark personal curiosity to learn about AI, some pragmatic arguments are worth considering before diving further:
Job Crisis: As deep learning washes over the global economy, the risk of wiping out billions of jobs up and down the economic ladder is very real. Though our civilization has absorbed similar technology-driven shocks in the past, turning hundreds of millions of farmers into factory workers overnight in the 19th and 20th centuries, none of these changes ever arrived as quickly as AI.
When it comes to estimating the scale of AI-induced job losses, several research predictions range from terrifying to not a problem at all. While the debate about the numbers has been going on for almost a decade and doesn’t seem to settle soon, one thing is substantially clear it is going to change the game of every industry. So to keep up with that it’s important to keep honing your swords.
Autocratization: AI naturally trends towards winner-take-all economics. Deep Learning’s relationship with data fosters a virtuous cycle for strengthening the best products and companies:
To intercept this concentration paradox it is important to ensure that there is enough competition in the market. This democratization can be engineered through accessible education and upskilling individual dexterity at the masses.
Misinformation: AI is a novel and dynamic discipline. It’s not just the rapid chain-reaction breakthroughs that are dense to keep up with, the technical principles and some philosophical contexts require a better understanding to have a modish opinion about the technology. Consequently, it is easy to be prey to false or misleading information. For instance, last year the news of AI being sentient(an event that we discussed in Part 1) was a very common topic of discourse but someone who understands the tech knew that it’s just a model efficiently trained on good data. Thence, it’s important to learn AI for general awareness.
What?
When it comes to learning AI there’s a whole set of options from zero-coding to a professional research degree. AI is a broad umbrella term, generally used to encompass all sorts of human-designed intelligence that we can imagine. Maslow’s Hierarchy of Needs can offer a better way to understand the AI hierarchy of needs:
At the bottom of the pyramid is data collection. One needs a solid foundation of data before being effective with AI and Machine Learning. In fact, in an article published 10 years ago, HBR entitled Data Scientist: The Sexiest Job of the 21st century. 10 years later HBR reviewed it and published “Is Data Scientist Still the Sexiest Job of the 21st Century?” only to conclude that: “By 2019, postings for data scientists on Indeed had risen by 256%, and the U.S. Bureau of Labor Statistics, predicts data science will see more growth than almost any other field between 2019 and 2029”.
How?
The most significant advantage of this information age is the abundance of learning resources available at our fingertips but the challenge is to find the right motivation(which we have already established) and a favorable environment to learn, practice, and impart our learnings. We need not worry about that favorable environment if it is inbound in a culture. It’s easy to find such culture in a good college, university, or organization. The next best place is to find a social group or community online. And that is what we are going to do.
#DataScience23:
Let’s make “learning data science” a priority this year and turn it into a movement. For more information visit:
Bonus:
To get a practical intuition of what AI is capable of doing at present, watch this video by David Bombal:
References:
- https://www.cbsnews.com/news/ai-powered-robot-lawyer-takes-its-first-court-case/?ftag=CNM-00-10aab7e&linkId=196616664
- AI SUPERPOWERS by Kai-Fu Lee: https://amzn.eu/d/41k3ig5
- https://hbr.org/2022/07/is-data-scientist-still-the-sexiest-job-of-the-21st-century
- https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century
