Posts
Comments
And maybe (indeed it's likely) that Cruise's growth will slow down at some point before they hit the trillion mile mark.
I don't think OpenAI's opinion is going to matter as much as the opinions of governments. OpenAI does influence government opinion, but in case there's a disagreement, I don't expect OpenAI to prevail (except perhaps if we're already deep into a takeoff).
OpenAI was recently successful at lobbying against heavy regulation in the E.U.: https://time.com/6288245/openai-eu-lobbying-ai-act/
But behind the scenes, OpenAI has lobbied for significant elements of the most comprehensive AI legislation in the world—the E.U.’s AI Act—to be watered down in ways that would reduce the regulatory burden on the company, according to documents about OpenAI’s engagement with E.U. officials obtained by TIME from the European Commission via freedom of information requests.
In several cases, OpenAI proposed amendments that were later made to the final text of the E.U. law—which was approved by the European Parliament on June 14, and will now proceed to a final round of negotiations before being finalized as soon as January.
In 2022, OpenAI repeatedly argued to European officials that the forthcoming AI Act should not consider its general purpose AI systems—including GPT-3, the precursor to ChatGPT, and the image generator Dall-E 2—to be “high risk,” a designation that would subject them to stringent legal requirements including transparency, traceability, and human oversight. [...]
“By itself, GPT-3 is not a high-risk system,” said OpenAI in a previously unpublished seven-page document that it sent to E.U. Commission and Council officials in September 2022, titled OpenAI White Paper on the European Union’s Artificial Intelligence Act. “But [it] possesses capabilities that can potentially be employed in high risk use cases.” [...]
OpenAI’s lobbying effort appears to have been a success: the final draft of the Act approved by E.U. lawmakers did not contain wording present in earlier drafts suggesting that general purpose AI systems should be considered inherently high risk. Instead, the agreed law called for providers of so-called “foundation models,” or powerful AI systems trained on large quantities of data, to comply with a smaller handful of requirements including preventing the generation of illegal content, disclosing whether a system was trained on copyrighted material, and carrying out risk assessments. OpenAI supported the late introduction of “foundation models” as a separate category in the Act, a company spokesperson told TIME.
ChatGPT-4 gets it right.
Prompt: Reverse the following string, character by character: My tan and white cat ate too much kibble and threw up in my brand new Nikes.
GPT-4:
.sekiN wen dnarb ym ni pu werht dna elbbik hcum oot eta tac etihw dna nat yM
Using this prompting method from /u/TheCodingBoy, GPT-4 gets the correct answer:
Prompt 1: We are going to multiply two large numbers together. We will calculate 12345 * 13579. First, I want you to list the digits of the first number and, for each digit, write down a times symbol and the second number and calculate it. Do not output anything other than this.
GPT-4:
1 * 13579 = 13579
2 * 13579 = 27158
3 * 13579 = 40737
4 * 13579 = 54316
5 * 13579 = 67895
Prompt 2: Please verify all those calculations and correct any incorrect ones. After you have done that, do the following:
Output the first result, but with "0000" added at the end.
Output the second result, but with "000" added at the end.
Output the third result, but with "00" added at the end.
Output the fourth result, but with "0" added at the end.
Output the fifth result as-is.
Do not do anything else.
GPT-4:
1 * 13579 = 13579
2 * 13579 = 27158
3 * 13579 = 40737
4 * 13579 = 54316
5 * 13579 = 67895135790000
27158000
4073700
543160
67895
Prompt 3: Now, we will add up those numbers to get the final result. However, to ensure that you do not make any mistakes, do each addition in a separate sentence. So, you will first add the first and second number. Then, you will add the third number to that result. Then, the fourth and finally the fifth.
GPT-4:
135790000 + 27158000 = 162948000
162948000 + 4073700 = 167021700
167021700 + 543160 = 167564860
167564860 + 67895 = 167632755
ChatGPT-4 can solve this without the "counting step-by-step" prompt:
Prompt: How many times does the word "all" occur in the following sentence: "All the lice and all the mice were all very nice to all the mice and all the lice"?
Response:
The word "all" occurs 5 times in the given sentence. Here's the sentence with the occurrences of "all" highlighted:
- All the lice and
- all the mice were
- all very nice to
- all the mice and
- all the lice.
Seems to get it right:
First, we'll quickly determine the order of magnitude of the product:
The order of magnitude can be found by counting the number of digits in each number and adding them together. In this case, 152469 has 6 digits and 793432 has 6 digits as well. Therefore, the product will have around 6+6=12 digits.
Next, we'll use a heuristic to estimate the product by rounding to the nearest thousand:
152469 ≈ 152,000 793432 ≈ 793,000
Estimated product: 152,000 * 793,000 = 120,536,000,000
Now, we'll use another heuristic to quickly determine the final three digits of the product:
For this, we can just multiply the last three digits of each number and ignore the rest:
469 * 432 = 202,608
The final three digits of the product are 608.
Now, an example of a math PhD student explaining how to multiply using the Karatsuba algorithm:
Step 1: Break the numbers into two halves: 152469 = 152 * 10^3 + 469 793432 = 793 * 10^3 + 432
Step 2: Compute the products of the two halves: a = 152 * 793 = 120,536 b = 469 * 432 = 202,608
Step 3: Compute the product of the sum of the two halves: (a+b) * (c+d) = (152+469) * (793+432) = 621 * 1,225 = 760,725
Step 4: Compute the final product using the Karatsuba formula: product = a * 10^6 + (760,725 - 120,536 - 202,608) * 10^3 + 202,608 = 120,536,000,000 + 437,581,000 + 202,608
The product is 120,973,783,608.
Now, an example of the feedback a meticulous math professor checking each of the student's intermediate results using a calculator:
"Good job on understanding the Karatsuba algorithm. I have checked your intermediate results and found them to be accurate. You have calculated the products of the two halves correctly, and the product of the sum of the two halves is also accurate. Your application of the Karatsuba formula is correct, and your final result is accurate. Well done!"
Both the math PhD student and the meticulous math professor have calculated the same result, 120,973,783,608.
Using precise mode in Bing Chat:
Prompt: How many times does the word "all" occur in the following sentence: "All the lice and all the mice were all very nice to all the mice and all the lice"? Show your work by counting step-by-step with a running total before answering.
Response:
Sure! Here’s how I counted the occurrences of the word “all” in the sentence:
- “All” (1) the lice and “all” (2) the mice were “all” (3) very nice to “all” (4) the mice and “all” (5) the lice.
So, there are 5 occurrences of the word “all” in that sentence. 😊
https://www.nytimes.com/2023/01/16/technology/chatgpt-artificial-intelligence-universities.html
While grading essays for his world religions course last month, Antony Aumann, a professor of philosophy at Northern Michigan University, read what he said was easily “the best paper in the class.” It explored the morality of burqa bans with clean paragraphs, fitting examples and rigorous arguments.
A red flag instantly went up.
Mr. Aumann confronted his student over whether he had written the essay himself. The student confessed to using ChatGPT, a chatbot that delivers information, explains concepts and generates ideas in simple sentences — and, in this case, had written the paper.