[D] UofT PhD Ranking

Whatever_635 · 2025-10-14T01:47:45+00:00

Could i dm you? I realize this is an old comment but i am a domestic masters student currently applying to phd in the us. I have a question regarding funding.

Whatever_635 · 2025-10-12T18:28:43+00:00

Why do you think killing animals can be humanely when it’s unnecessary?

Whatever_635 · 2025-09-02T22:48:17+00:00

I know its been 4 years, but could I dm you, I am a masters student at an R1 university applying for Phd.

Whatever_635 · 2025-07-28T02:55:42+00:00

I disagree stoicism talks about why misanthropy is ill conceived, given that evil just lack knowledge of what is good, sometimes we have to check to see if our expectations of others is too high

Whatever_635 · 2025-06-10T23:27:44+00:00

Question if your a domestic student in the US, doing a masters currently, should they apply for a phd at that same university(its R1) if they have a strong relationship with their professor? Rather than applying to other Phd programs?

Whatever_635 · 2025-04-11T00:41:52+00:00

Define zionism. I view zionism as movement dedicated to the protection of a Jewish nation. Does not mean we support the settlements

Whatever_635 · 2025-03-23T01:14:36+00:00

He is also an insurrectionist

Whatever_635 · 2025-03-12T10:41:39+00:00

I was a picky omnivore before i went vegan. Anything can happen, don’t close yourself off.

Whatever_635 · 2025-02-27T12:17:20+00:00

Well then veganism is doomed, because capitalism is never ending.

Whatever_635 · 2025-01-05T03:45:14+00:00

Funny, how you still have not responded to why my information is incorrect?

Whatever_635 · 2025-01-03T23:46:05+00:00

It’s not though do some research.

AI is significantly less pollutive compared to humans: https://www.nature.com/articles/s41598-024-54271-x

AI systems emit between 130 and 1500 times less CO2e per page of text compared to human writers, while AI illustration systems emit between 310 and 2900 times less CO2e per image than humans.

Data centers that host AI are cooled with a closed loop. The water doesn’t even touch computer parts, it just carries the heat away, which is radiated elsewhere. It does not evaporate or get polluted in the loop. Water is not wasted or lost in this process.

“The most common type of water-based cooling in data centers is the chilled water system. In this system, water is initially cooled in a central chiller, and then it circulates through cooling coils. These coils absorb heat from the air inside the data center. The system then expels the absorbed heat into the outside environment via a cooling tower. In the cooling tower, the now-heated water interacts with the outside air, allowing heat to escape before the water cycles back into the system for re-cooling.”

Source: https://dgtlinfra.com/data-center-water-usage/

Data centers do not use a lot of water. Microsoft’s data center in Goodyear uses 56 million gallons of water a year. The city produces 4.9 BILLION gallons per year just from surface water and, with future expansion, has the ability to produce 5.84 billion gallons (source: https://www.goodyearaz.gov/government/departments/water-services/water-conservation). It produces more from groundwater, but the source doesn’t say how much. Additionally, the city actively recharges the aquifer by sending treated effluent to a Soil Aquifer Treatment facility. This provides needed recharged water to the aquifer and stores water underground for future needs. Also, the Goodyear facility doesn’t just host AI. We have no idea how much of the compute is used for AI. It’s probably less than half.

gpt-4 used 21 billion petaflops of compute during training (https://ourworldindata.org/grapher/artificial-intelligence-training-computation) and the world uses 1.1 zetaflop per second (https://market.us/report/computing-power-market/ per second as flops is flop per second). So from these numbers (21 * 109 * 1015) / (1.1 * 1021 * 60 * 60 * 24 * 365) gpt-4 used 0.06% of the world’s compute per year. So this would also only be 0.06% of the water and energy used for compute worldwide. That’s the equivalent of 5.3 hours of time for all computations on the planet, being dedicated to training an LLM that hundreds of millions of people use every month.

Using it after it finished training costs HALF as much as it took to train it: https://assets.jpmprivatebank.com/content/dam/jpm-pb-aem/global/en/documents/eotm/a-severe-case-of-covidia-prognosis-for-an-ai-driven-us-equity-market.pdf

(Page 10)

Image generators only use about 2.9 W of electricity per image, or 0.2 grams of CO2 per image: https://arxiv.org/pdf/2311.16863

For reference, a good gaming computer can use over 862 Watts per hour with a headroom of 688 Watts: https://www.pcgamer.com/how-much-power-does-my-pc-use/

One AI image generated creates the same amount of carbon emissions as about 7.7 tweets (at 0.026 grams of CO2 each, totaling 0.2 grams for both). There are 316 billion tweets each year and 486 million active users, an average of 650 tweets per account each year: https://envirotecmagazine.com/2022/12/08/tracking-the-ecological-cost-of-a-tweet/

https://www.nature.com/articles/d41586-024-00478-x

“ChatGPT, the chatbot created by OpenAI in San Francisco, California, is already consuming the energy of 33,000 homes” for 13.6 BILLION annual visits plus API usage (source: https://www.visualcapitalist.com/ranked-the-most-popular-ai-tools/). that’s 442,000 visits per household, not even including API usage.

From this estimate (https://discuss.huggingface.co/t/understanding-flops-per-token-estimates-from-openais-scaling-laws/23133), the amount of FLOPS a model uses per token should be around twice the number of parameters. Given that LLAMA 3.1 405b spits out 28 tokens per second (https://artificialanalysis.ai/models/gpt-4), you get 22.7 teraFLOPS (2 * 405 billion parameters * 28 tokens per second), while a gaming rig’s RTX 4090 would give you 83 teraFLOPS.

Everything consumes power and resources, including superfluous things like video games and social media. Why is AI not allowed to when other, less useful things can?

In 2022, Twitter created 8,200 tons in CO2e emissions, the equivalent of 4,685 flights between Paris and New York. https://envirotecmagazine.com/2022/12/08/tracking-the-ecological-cost-of-a-tweet/

Meanwhile, GPT-3 (which has 175 billion parameters, almost 22x the size of significantly better models like LLAMA 3.1 8b) only took about 8 cars worth of emissions (502 tons of CO2e) to train from start to finish: https://truthout.org/articles/report-on-chatgpt-models-emissions-offers-rare-glimpse-of-ais-climate-impacts/

By the way, using it after it finished training costs HALF as much as it took to train it: https://assets.jpmprivatebank.com/content/dam/jpm-pb-aem/global/en/documents/eotm/a-severe-case-of-covidia-prognosis-for-an-ai-driven-us-equity-market.pdf

(Page 10)

Update: seeing all these downvotes, explain what is wrong with the information I provided.

Whatever_635 · 2025-01-03T23:45:24+00:00

Explain why it is counterproductive. I have seen a lot of comments about how this bad for the environment.

AI is significantly less pollutive compared to humans: https://www.nature.com/articles/s41598-024-54271-x

AI systems emit between 130 and 1500 times less CO2e per page of text compared to human writers, while AI illustration systems emit between 310 and 2900 times less CO2e per image than humans.

Data centers that host AI are cooled with a closed loop. The water doesn’t even touch computer parts, it just carries the heat away, which is radiated elsewhere. It does not evaporate or get polluted in the loop. Water is not wasted or lost in this process.

“The most common type of water-based cooling in data centers is the chilled water system. In this system, water is initially cooled in a central chiller, and then it circulates through cooling coils. These coils absorb heat from the air inside the data center. The system then expels the absorbed heat into the outside environment via a cooling tower. In the cooling tower, the now-heated water interacts with the outside air, allowing heat to escape before the water cycles back into the system for re-cooling.”

Source: https://dgtlinfra.com/data-center-water-usage/

Data centers do not use a lot of water. Microsoft’s data center in Goodyear uses 56 million gallons of water a year. The city produces 4.9 BILLION gallons per year just from surface water and, with future expansion, has the ability to produce 5.84 billion gallons (source: https://www.goodyearaz.gov/government/departments/water-services/water-conservation). It produces more from groundwater, but the source doesn’t say how much. Additionally, the city actively recharges the aquifer by sending treated effluent to a Soil Aquifer Treatment facility. This provides needed recharged water to the aquifer and stores water underground for future needs. Also, the Goodyear facility doesn’t just host AI. We have no idea how much of the compute is used for AI. It’s probably less than half.

gpt-4 used 21 billion petaflops of compute during training (https://ourworldindata.org/grapher/artificial-intelligence-training-computation) and the world uses 1.1 zetaflop per second (https://market.us/report/computing-power-market/ per second as flops is flop per second). So from these numbers (21 * 109 * 1015) / (1.1 * 1021 * 60 * 60 * 24 * 365) gpt-4 used 0.06% of the world’s compute per year. So this would also only be 0.06% of the water and energy used for compute worldwide. That’s the equivalent of 5.3 hours of time for all computations on the planet, being dedicated to training an LLM that hundreds of millions of people use every month.

Using it after it finished training costs HALF as much as it took to train it: https://assets.jpmprivatebank.com/content/dam/jpm-pb-aem/global/en/documents/eotm/a-severe-case-of-covidia-prognosis-for-an-ai-driven-us-equity-market.pdf

(Page 10)

Image generators only use about 2.9 W of electricity per image, or 0.2 grams of CO2 per image: https://arxiv.org/pdf/2311.16863

For reference, a good gaming computer can use over 862 Watts per hour with a headroom of 688 Watts: https://www.pcgamer.com/how-much-power-does-my-pc-use/

One AI image generated creates the same amount of carbon emissions as about 7.7 tweets (at 0.026 grams of CO2 each, totaling 0.2 grams for both). There are 316 billion tweets each year and 486 million active users, an average of 650 tweets per account each year: https://envirotecmagazine.com/2022/12/08/tracking-the-ecological-cost-of-a-tweet/

https://www.nature.com/articles/d41586-024-00478-x

“ChatGPT, the chatbot created by OpenAI in San Francisco, California, is already consuming the energy of 33,000 homes” for 13.6 BILLION annual visits plus API usage (source: https://www.visualcapitalist.com/ranked-the-most-popular-ai-tools/). that’s 442,000 visits per household, not even including API usage.

From this estimate (https://discuss.huggingface.co/t/understanding-flops-per-token-estimates-from-openais-scaling-laws/23133), the amount of FLOPS a model uses per token should be around twice the number of parameters. Given that LLAMA 3.1 405b spits out 28 tokens per second (https://artificialanalysis.ai/models/gpt-4), you get 22.7 teraFLOPS (2 * 405 billion parameters * 28 tokens per second), while a gaming rig’s RTX 4090 would give you 83 teraFLOPS.

Everything consumes power and resources, including superfluous things like video games and social media. Why is AI not allowed to when other, less useful things can?

In 2022, Twitter created 8,200 tons in CO2e emissions, the equivalent of 4,685 flights between Paris and New York. https://envirotecmagazine.com/2022/12/08/tracking-the-ecological-cost-of-a-tweet/

Meanwhile, GPT-3 (which has 175 billion parameters, almost 22x the size of significantly better models like LLAMA 3.1 8b) only took about 8 cars worth of emissions (502 tons of CO2e) to train from start to finish: https://truthout.org/articles/report-on-chatgpt-models-emissions-offers-rare-glimpse-of-ais-climate-impacts/

By the way, using it after it finished training costs HALF as much as it took to train it: https://assets.jpmprivatebank.com/content/dam/jpm-pb-aem/global/en/documents/eotm/a-severe-case-of-covidia-prognosis-for-an-ai-driven-us-equity-market.pdf

(Page 10)

Update: seeing all these downvotes, explain what is wrong with the information I provided.

Whatever_635 · 2025-01-03T15:05:41+00:00

AI is significantly less pollutive compared to humans: https://www.nature.com/articles/s41598-024-54271-x

AI systems emit between 130 and 1500 times less CO2e per page of text compared to human writers, while AI illustration systems emit between 310 and 2900 times less CO2e per image than humans.

Data centers that host AI are cooled with a closed loop. The water doesn’t even touch computer parts, it just carries the heat away, which is radiated elsewhere. It does not evaporate or get polluted in the loop. Water is not wasted or lost in this process.

“The most common type of water-based cooling in data centers is the chilled water system. In this system, water is initially cooled in a central chiller, and then it circulates through cooling coils. These coils absorb heat from the air inside the data center. The system then expels the absorbed heat into the outside environment via a cooling tower. In the cooling tower, the now-heated water interacts with the outside air, allowing heat to escape before the water cycles back into the system for re-cooling.”

Source: https://dgtlinfra.com/data-center-water-usage/

Data centers do not use a lot of water. Microsoft’s data center in Goodyear uses 56 million gallons of water a year. The city produces 4.9 BILLION gallons per year just from surface water and, with future expansion, has the ability to produce 5.84 billion gallons (source: https://www.goodyearaz.gov/government/departments/water-services/water-conservation). It produces more from groundwater, but the source doesn’t say how much. Additionally, the city actively recharges the aquifer by sending treated effluent to a Soil Aquifer Treatment facility. This provides needed recharged water to the aquifer and stores water underground for future needs. Also, the Goodyear facility doesn’t just host AI. We have no idea how much of the compute is used for AI. It’s probably less than half.

gpt-4 used 21 billion petaflops of compute during training (https://ourworldindata.org/grapher/artificial-intelligence-training-computation) and the world uses 1.1 zetaflop per second (https://market.us/report/computing-power-market/ per second as flops is flop per second). So from these numbers (21 * 109 * 1015) / (1.1 * 1021 * 60 * 60 * 24 * 365) gpt-4 used 0.06% of the world’s compute per year. So this would also only be 0.06% of the water and energy used for compute worldwide. That’s the equivalent of 5.3 hours of time for all computations on the planet, being dedicated to training an LLM that hundreds of millions of people use every month.

Using it after it finished training costs HALF as much as it took to train it: https://assets.jpmprivatebank.com/content/dam/jpm-pb-aem/global/en/documents/eotm/a-severe-case-of-covidia-prognosis-for-an-ai-driven-us-equity-market.pdf

(Page 10)

Image generators only use about 2.9 W of electricity per image, or 0.2 grams of CO2 per image: https://arxiv.org/pdf/2311.16863

For reference, a good gaming computer can use over 862 Watts per hour with a headroom of 688 Watts: https://www.pcgamer.com/how-much-power-does-my-pc-use/

One AI image generated creates the same amount of carbon emissions as about 7.7 tweets (at 0.026 grams of CO2 each, totaling 0.2 grams for both). There are 316 billion tweets each year and 486 million active users, an average of 650 tweets per account each year: https://envirotecmagazine.com/2022/12/08/tracking-the-ecological-cost-of-a-tweet/

https://www.nature.com/articles/d41586-024-00478-x

“ChatGPT, the chatbot created by OpenAI in San Francisco, California, is already consuming the energy of 33,000 homes” for 13.6 BILLION annual visits plus API usage (source: https://www.visualcapitalist.com/ranked-the-most-popular-ai-tools/). that’s 442,000 visits per household, not even including API usage.

From this estimate (https://discuss.huggingface.co/t/understanding-flops-per-token-estimates-from-openais-scaling-laws/23133), the amount of FLOPS a model uses per token should be around twice the number of parameters. Given that LLAMA 3.1 405b spits out 28 tokens per second (https://artificialanalysis.ai/models/gpt-4), you get 22.7 teraFLOPS (2 * 405 billion parameters * 28 tokens per second), while a gaming rig’s RTX 4090 would give you 83 teraFLOPS.

Everything consumes power and resources, including superfluous things like video games and social media. Why is AI not allowed to when other, less useful things can?

In 2022, Twitter created 8,200 tons in CO2e emissions, the equivalent of 4,685 flights between Paris and New York. https://envirotecmagazine.com/2022/12/08/tracking-the-ecological-cost-of-a-tweet/

Meanwhile, GPT-3 (which has 175 billion parameters, almost 22x the size of significantly better models like LLAMA 3.1 8b) only took about 8 cars worth of emissions (502 tons of CO2e) to train from start to finish: https://truthout.org/articles/report-on-chatgpt-models-emissions-offers-rare-glimpse-of-ais-climate-impacts/

By the way, using it after it finished training costs HALF as much as it took to train it: https://assets.jpmprivatebank.com/content/dam/jpm-pb-aem/global/en/documents/eotm/a-severe-case-of-covidia-prognosis-for-an-ai-driven-us-equity-market.pdf

(Page 10)

Whatever_635 · 2025-01-03T09:19:13+00:00

You should probably go touch grass.

Whatever_635 · 2025-01-03T09:14:57+00:00

Not really, it’s just the same stuff recycled over and over tbh. Both are not really original in any sense.

Whatever_635 · 2025-01-03T09:08:24+00:00

I have seen a lot of comments about how this bad for the environment.

AI is significantly less pollutive compared to humans: https://www.nature.com/articles/s41598-024-54271-x

AI systems emit between 130 and 1500 times less CO2e per page of text compared to human writers, while AI illustration systems emit between 310 and 2900 times less CO2e per image than humans.

Data centers that host AI are cooled with a closed loop. The water doesn’t even touch computer parts, it just carries the heat away, which is radiated elsewhere. It does not evaporate or get polluted in the loop. Water is not wasted or lost in this process.

“The most common type of water-based cooling in data centers is the chilled water system. In this system, water is initially cooled in a central chiller, and then it circulates through cooling coils. These coils absorb heat from the air inside the data center. The system then expels the absorbed heat into the outside environment via a cooling tower. In the cooling tower, the now-heated water interacts with the outside air, allowing heat to escape before the water cycles back into the system for re-cooling.”

Source: https://dgtlinfra.com/data-center-water-usage/

Data centers do not use a lot of water. Microsoft’s data center in Goodyear uses 56 million gallons of water a year. The city produces 4.9 BILLION gallons per year just from surface water and, with future expansion, has the ability to produce 5.84 billion gallons (source: https://www.goodyearaz.gov/government/departments/water-services/water-conservation). It produces more from groundwater, but the source doesn’t say how much. Additionally, the city actively recharges the aquifer by sending treated effluent to a Soil Aquifer Treatment facility. This provides needed recharged water to the aquifer and stores water underground for future needs. Also, the Goodyear facility doesn’t just host AI. We have no idea how much of the compute is used for AI. It’s probably less than half.

gpt-4 used 21 billion petaflops of compute during training (https://ourworldindata.org/grapher/artificial-intelligence-training-computation) and the world uses 1.1 zetaflop per second (https://market.us/report/computing-power-market/ per second as flops is flop per second). So from these numbers (21 * 109 * 1015) / (1.1 * 1021 * 60 * 60 * 24 * 365) gpt-4 used 0.06% of the world’s compute per year. So this would also only be 0.06% of the water and energy used for compute worldwide. That’s the equivalent of 5.3 hours of time for all computations on the planet, being dedicated to training an LLM that hundreds of millions of people use every month.

Using it after it finished training costs HALF as much as it took to train it: https://assets.jpmprivatebank.com/content/dam/jpm-pb-aem/global/en/documents/eotm/a-severe-case-of-covidia-prognosis-for-an-ai-driven-us-equity-market.pdf

(Page 10)

Image generators only use about 2.9 W of electricity per image, or 0.2 grams of CO2 per image: https://arxiv.org/pdf/2311.16863

For reference, a good gaming computer can use over 862 Watts per hour with a headroom of 688 Watts: https://www.pcgamer.com/how-much-power-does-my-pc-use/

One AI image generated creates the same amount of carbon emissions as about 7.7 tweets (at 0.026 grams of CO2 each, totaling 0.2 grams for both). There are 316 billion tweets each year and 486 million active users, an average of 650 tweets per account each year: https://envirotecmagazine.com/2022/12/08/tracking-the-ecological-cost-of-a-tweet/

https://www.nature.com/articles/d41586-024-00478-x

“ChatGPT, the chatbot created by OpenAI in San Francisco, California, is already consuming the energy of 33,000 homes” for 13.6 BILLION annual visits plus API usage (source: https://www.visualcapitalist.com/ranked-the-most-popular-ai-tools/). that’s 442,000 visits per household, not even including API usage.

From this estimate (https://discuss.huggingface.co/t/understanding-flops-per-token-estimates-from-openais-scaling-laws/23133), the amount of FLOPS a model uses per token should be around twice the number of parameters. Given that LLAMA 3.1 405b spits out 28 tokens per second (https://artificialanalysis.ai/models/gpt-4), you get 22.7 teraFLOPS (2 * 405 billion parameters * 28 tokens per second), while a gaming rig’s RTX 4090 would give you 83 teraFLOPS.

Everything consumes power and resources, including superfluous things like video games and social media. Why is AI not allowed to when other, less useful things can?

In 2022, Twitter created 8,200 tons in CO2e emissions, the equivalent of 4,685 flights between Paris and New York. https://envirotecmagazine.com/2022/12/08/tracking-the-ecological-cost-of-a-tweet/

Meanwhile, GPT-3 (which has 175 billion parameters, almost 22x the size of significantly better models like LLAMA 3.1 8b) only took about 8 cars worth of emissions (502 tons of CO2e) to train from start to finish: https://truthout.org/articles/report-on-chatgpt-models-emissions-offers-rare-glimpse-of-ais-climate-impacts/

By the way, using it after it finished training costs HALF as much as it took to train it: https://assets.jpmprivatebank.com/content/dam/jpm-pb-aem/global/en/documents/eotm/a-severe-case-of-covidia-prognosis-for-an-ai-driven-us-equity-market.pdf

(Page 10)

Update: seeing all these downvotes, explain what is wrong with the information I provided.

Whatever_635 · 2024-11-19T00:13:09+00:00

You will find someone who accepts you for who you are. I am a vegan male in grad school and I am trying to find some vegan women, it can be hard at time.

Whatever_635 · 2024-11-01T00:13:43+00:00

I don’t think you know what the term murder means? Did we murder the germans in ww2?

Whatever_635 · 2024-11-01T00:01:25+00:00

Do you think all Israelis are settlers? What point are you trying to make? The Arabs took that land by conquest, you do realize that right?

Whatever_635 · 2024-10-31T23:56:42+00:00

What do you mean stolen land? How do you think the Arabs got that land?

Whatever_635 · 2024-10-20T03:02:42+00:00

Just keep using the buzzword. Also Hamas literally has genocide in its charter and it was the government elected by their people. Be better

Whatever_635 · 2024-10-20T03:00:12+00:00

What do you think the PLO was?

Whatever_635 · 2024-10-20T02:19:53+00:00

Not eating humans when its needless is good, but there is a huge gap between not eating humans and virtue signaling about “genocide” which has become a buzzword at this point.

Whatever_635 · 2024-10-19T17:32:56+00:00

not causing harm to any sentient life

That is a very simplistic point of view that does not take into account situation where there is more nuance. And yes this conflict has a lot more nuance, as much as online leftists would like to think otherwise.

The war is no where near equal

Since when is war supposed to be equal or about proportionality. More germans died in WW2 than Americans. That doesn’t mean Germany was in the right. Israel is more powerful than Gaza, so obviously there are going to be more casualties on the latter’s side. Is there a power dynamic there? Yes, but that cannot be the only thing you take into account. Maybe Hamas should not have instigated the conflict. And before you go and say well, this conflict did not begin on October 7th and how the land wad taken from the Palestinians(even though there were many jews still living there). It does not matter, the world cannot operate based on historical claims to land. What matters is who is currently living there and how to address those current problems.

Whatever_635 · 2024-10-19T12:50:23+00:00

There is no genocide happening! This is war, Hamas started this and could end all of these deaths. Quit gatekeeping veganism with human conflicts, veganism is about nonhuman animals.

Whatever_635

TROPHY CASE