Post by mhbruin on Apr 26, 2023 9:44:48 GMT -8
It's Not the End of the World As We Know It, and I Feel Fine
ChatGPT has come for software developers
When ChatGPT was released to the world in November, most of us marveled at its ability to write rap lyrics and cover letters and high-school English essays. But Adam Hughes, a software developer, was intrigued by artificial intelligence's much-ballyhooed aptitude for writing code. So he signed up for an account and asked ChatGPT to program a modified tic-tac-toe game, giving the game some weird rules so the bot couldn't just copy code that another human had already written. Then he quizzed it with the kind of coding questions he asks candidates in job interviews.
Whatever he threw at it, Hughes found that ChatGPT came back with something he wasn't prepared for: very good code. It didn't take him long to wonder what this meant for a career he loved — one that had thus far provided him with not only a good living and job security, but a sense of who he is. "I never thought I would be replaced in my job, ever, until ChatGPT," he says. "I had an existential crisis right then and there. A lot of the knowledge that I thought was special to me, that I had put seven years into, just became obsolete."
Coding, as an occupation, has long been considered a haven from the relentless advance of technology. Even as new gizmos replaced other jobs, the people who wrote the instructions for the machines felt untouchable. Universities rushed to expand their computer-science programs. Policymakers scrambling to futureproof the workforce stuck to one unwavering message: Learn to code! But in recent weeks, behind closed doors, I've heard many coders confess to a growing anxiety over the sudden advent of generative AI. Those who have been doing the automating fear they will soon be automated themselves. And if programmers aren't safe, who is?
Much has been written about how AI is coming for white-collar jobs. Researchers at OpenAI, which created ChatGPT, recently examined the degree to which large language models could perform the 19,000 tasks that make up the 1,000 occupations across the US economy. Their conclusion: 19% of workers hold jobs in which at least half their tasks could be completed by AI. The researchers also noted two patterns among the most vulnerable jobs: They require more education and come with big salaries. "We didn't think that would be the case," says Ethan Mollick, a professor of management at Wharton who studies innovation. "AI was always supposed to automate dangerous, dirty tasks — not the things we want to do."
But one white-collar skill set, the study found, is especially at risk for being automated: computer programming. The reason? Large language models like the one powering ChatGPT have been trained on huge repositories of code. Researchers at Microsoft and its subsidiary GitHub recently divided software developers into two groups — one with access to an AI coding assistant, and another without. Those assisted by AI were able to complete tasks 56% faster than the unassisted ones. "That's a big number," Mollick says. By comparison, the introduction of the steam engine in the mid-1800s boosted productivity at large factories by only 15%.
Tech companies have rushed to embrace generative AI, recognizing its ability to turbocharge programming. Amazon has built its own AI coding assistant, CodeWhisperer, and is encouraging its engineers to use it. Google is also asking its developers to try out new coding features in Bard, its ChatGPT competitor. Given the tech industry's rush to deploy AI, it's not hard to envision a near future in which we'll need half as many engineers as we have today — or, down the line, one-tenth or one-hundredth (Emad Mostaque, the CEO of Stability AI, has gone as far as predicting "there's no programmers in five years."). For better or worse, the rise of AI effectively marks the end of coding as we know it.
I Feel Fine Because I'm Retired.
I wrote into the Microsoft Chat Bot: "write a program to calculate the outcomes of throwing two diice"
I got this:
import random
def roll_dice():
return random.randint(1, 6)
def main():
outcomes = {}
for i in range(1, 7):
for j in range(1, 7):
outcome = i + j
if outcome not in outcomes:
outcomes[outcome] = 0
outcomes[outcome] += 1
for outcome in sorted(outcomes):
print(f"{outcome}: {outcomes[outcome]}")
Also, it Doesn't Produce Give Good Code. Here's Another Response
Sure! Here’s a Python program that calculates the probability of flopping a set when you start with a pair in Texas Hold’em:
def calculate_probability():
# There are 50 cards left in the deck after you are dealt your two cards
num_cards_left = 50
# There are three cards that will give you a set
num_set_cards = 3
# There are two cards left in the deck that will be dealt on the flop
num_flop_cards = 2
# Calculate the probability of flopping a set
probability = (num_set_cards / num_cards_left) * ((num_set_cards - 1) / (num_cards_left - 1)) * (num_flop_cards / (num_cards_left - 2))
return probability
print(f"The probability of flopping a set when you start with a pair in Texas Hold'em is {calculate_probability():.2%}")
This program calculates the probability of flopping a set when you start with a pair in Texas Hold’em by using basic combinatorics. The program assumes that there are 50 cards left in the deck after you are dealt your two cards, and that there are three cards that will give you a set (the two remaining cards of your rank and one card of another rank). The program then calculates the probability of getting one of these three cards on each of the three flop cards.
I hope this helps! Let me know if you have any other questions.
.....................
It gives the wrong answer. With any paired hole card combination, you have about a 11.8% chance of flopping exactly a set. This translates to about 7.5-to-1 odds against flopping a set.
ChatGPT has come for software developers
When ChatGPT was released to the world in November, most of us marveled at its ability to write rap lyrics and cover letters and high-school English essays. But Adam Hughes, a software developer, was intrigued by artificial intelligence's much-ballyhooed aptitude for writing code. So he signed up for an account and asked ChatGPT to program a modified tic-tac-toe game, giving the game some weird rules so the bot couldn't just copy code that another human had already written. Then he quizzed it with the kind of coding questions he asks candidates in job interviews.
Whatever he threw at it, Hughes found that ChatGPT came back with something he wasn't prepared for: very good code. It didn't take him long to wonder what this meant for a career he loved — one that had thus far provided him with not only a good living and job security, but a sense of who he is. "I never thought I would be replaced in my job, ever, until ChatGPT," he says. "I had an existential crisis right then and there. A lot of the knowledge that I thought was special to me, that I had put seven years into, just became obsolete."
Coding, as an occupation, has long been considered a haven from the relentless advance of technology. Even as new gizmos replaced other jobs, the people who wrote the instructions for the machines felt untouchable. Universities rushed to expand their computer-science programs. Policymakers scrambling to futureproof the workforce stuck to one unwavering message: Learn to code! But in recent weeks, behind closed doors, I've heard many coders confess to a growing anxiety over the sudden advent of generative AI. Those who have been doing the automating fear they will soon be automated themselves. And if programmers aren't safe, who is?
Much has been written about how AI is coming for white-collar jobs. Researchers at OpenAI, which created ChatGPT, recently examined the degree to which large language models could perform the 19,000 tasks that make up the 1,000 occupations across the US economy. Their conclusion: 19% of workers hold jobs in which at least half their tasks could be completed by AI. The researchers also noted two patterns among the most vulnerable jobs: They require more education and come with big salaries. "We didn't think that would be the case," says Ethan Mollick, a professor of management at Wharton who studies innovation. "AI was always supposed to automate dangerous, dirty tasks — not the things we want to do."
But one white-collar skill set, the study found, is especially at risk for being automated: computer programming. The reason? Large language models like the one powering ChatGPT have been trained on huge repositories of code. Researchers at Microsoft and its subsidiary GitHub recently divided software developers into two groups — one with access to an AI coding assistant, and another without. Those assisted by AI were able to complete tasks 56% faster than the unassisted ones. "That's a big number," Mollick says. By comparison, the introduction of the steam engine in the mid-1800s boosted productivity at large factories by only 15%.
Tech companies have rushed to embrace generative AI, recognizing its ability to turbocharge programming. Amazon has built its own AI coding assistant, CodeWhisperer, and is encouraging its engineers to use it. Google is also asking its developers to try out new coding features in Bard, its ChatGPT competitor. Given the tech industry's rush to deploy AI, it's not hard to envision a near future in which we'll need half as many engineers as we have today — or, down the line, one-tenth or one-hundredth (Emad Mostaque, the CEO of Stability AI, has gone as far as predicting "there's no programmers in five years."). For better or worse, the rise of AI effectively marks the end of coding as we know it.
I Feel Fine Because I'm Retired.
I wrote into the Microsoft Chat Bot: "write a program to calculate the outcomes of throwing two diice"
I got this:
import random
def roll_dice():
return random.randint(1, 6)
def main():
outcomes = {}
for i in range(1, 7):
for j in range(1, 7):
outcome = i + j
if outcome not in outcomes:
outcomes[outcome] = 0
outcomes[outcome] += 1
for outcome in sorted(outcomes):
print(f"{outcome}: {outcomes[outcome]}")
Also, it Doesn't Produce Give Good Code. Here's Another Response
Sure! Here’s a Python program that calculates the probability of flopping a set when you start with a pair in Texas Hold’em:
def calculate_probability():
# There are 50 cards left in the deck after you are dealt your two cards
num_cards_left = 50
# There are three cards that will give you a set
num_set_cards = 3
# There are two cards left in the deck that will be dealt on the flop
num_flop_cards = 2
# Calculate the probability of flopping a set
probability = (num_set_cards / num_cards_left) * ((num_set_cards - 1) / (num_cards_left - 1)) * (num_flop_cards / (num_cards_left - 2))
return probability
print(f"The probability of flopping a set when you start with a pair in Texas Hold'em is {calculate_probability():.2%}")
This program calculates the probability of flopping a set when you start with a pair in Texas Hold’em by using basic combinatorics. The program assumes that there are 50 cards left in the deck after you are dealt your two cards, and that there are three cards that will give you a set (the two remaining cards of your rank and one card of another rank). The program then calculates the probability of getting one of these three cards on each of the three flop cards.
I hope this helps! Let me know if you have any other questions.
.....................
It gives the wrong answer. With any paired hole card combination, you have about a 11.8% chance of flopping exactly a set. This translates to about 7.5-to-1 odds against flopping a set.