🌍 Madrid, Spain.
🌞 Sunny day in Madrid.
⭐ I have already recorded content for a month, so very happy! This project continues! 😀😀
🎵 Bloody Mary - Lady Gaga.
Hello!! 😁
I hope you are having a good week! In my case I am pretty happy as I am recording many insightful interviews for the next weeks and I feel that this project is evoluting really well! Thanks for your support!
Small disclaimer: This newsletter might be longer than others as there is a bonus interview to Chat GPT to subscribers.
Weekly News 🤖
An Artificial Intelligence has determined that an anonymous written text was written by the famous Spanish writer Lope de Vega. (Link)
I launched a Linkedin Page where I will publish content in this social network. (Link)
For Spanish speaking subscribers, I published a tiktok video / reels as an experiment. Did you like it? (Link). To be honest with you I would like to explore more Tiktok or Youtube, so I am open to any suggestion. 😀
Episode 10 - The Future of AI: A Conversation with ChatGPT
"With a responsible approach and collaboration between researchers, industry, and policymakers, the future of AI can be a bright one for humanity". 🦄
In this week's episode of The AI Coffee Podcast, we sit down with ChatGPT, a state-of-the-art language model developed by OpenAI. We delve into the capabilities and limitations of AI, discussing its potential applications and the ethical considerations that need to be taken into account.
We cover a wide range of topics including the background and workings of AI, the future of AI and natural language processing, and the advantages and disadvantages of using AI in specific fields. Join us for an insightful and thought-provoking conversation that will give you a deeper understanding of the current state and future of AI.
Source: Dalle-2
For more information, you can listen to the episode here 👇
(If you do not have Spotify, you can access to the podcast in other platforms from here 😊)
I also recommend you to visit this post where we went deeper into NLP models and Chat GPT: (Link)
Some useful links (provided by ChatGPT) ⭐
My official documentation: https://beta.openai.com/docs/models/gpt-3
The OpenAI API documentation: https://beta.openai.com/docs/api-reference/introduction
The OpenAI blog: https://openai.com/blog/
The OpenAI GitHub page: https://github.com/openai
The GPT-3 paper: https://cdn.openai.com/better-language-modelslanguage_models_are_unsupervised_multitask_learners.pdf
The Hugging Face GPT-3 documentation: https://huggingface.co/models/gpt3
The Stanford NLP Group: http://nlp.stanford.edu/
The NLP Overview by the University of Oxford: http://www.cs.ox.ac.uk/activities/competitions/nlp-overview.html
The Machine Learning Mastery website: https://machinelearningmastery.com/
The TensorFlow website: https://www.tensorflow.org/
BONUS: Carbon Footprint AI. 🌳 (Real conversation with ChatGPT)
After having recorded and edited the episode, I realized that I have not asked Chat GPT about the Carbon Footprint of AI and the Energy Consumption of Machine Models, even though they aer both quite interesting topics. I decided to ask them later and you have here the conversation as a bonus. I was quite suprised, what about you? 👀
Alejandro: I forgot to ask you about the pollution and carbon footprint that AI might create due to the large amount of electriciy consumed to run your models. Can you estimate how much CO2 we have produced during this conversation?
Chat GPT:As a machine learning model, I do not have the capability to measure or estimate the carbon footprint of my computations. However, it is true that the training and running of large AI models like mine consume a significant amount of energy, which can contribute to carbon emissions.
The amount of energy consumption and carbon footprint will vary depending on the specific model, the hardware it is running on, the data center infrastructure and the cooling systems used. It's hard to estimate the amount of CO2 emissions produced during this conversation as it will depend on a variety of factors such as where the data center is located and the source of electricity that is being used.
However, it's worth noting that many companies and organizations are taking steps to reduce the carbon footprint of their AI operations, such as using renewable energy sources, improving the efficiency of their data centers, and investing in research to develop more energy-efficient algorithms.
It's important to consider the environmental impact of AI and technology in general, and take steps to minimize it. This includes investing in sustainable energy and infrastructure, as well as encouraging the development of more energy-efficient technologies.
A: Can you create for me a python script for calculate these emissions?
C: Unfortunately, I am not able to create a python script for you to calculate the carbon footprint of AI computations as it would require real-time data on energy consumption and location of the data center where the computations are taking place.
However, I can give you an idea of what such a script might look like. The script would need to gather information on the energy consumption of the hardware used to run the AI computations, as well as the location of the data center. With this information, the script would use an emissions factor to calculate the CO2 emissions produced by the energy consumption.
Here is an example of a very basic script that could be used to calculate the CO2 emissions produced by energy consumption, assuming you have the data on energy consumption and location of the data center:
def calculate_co2_emissions(energy_consumption, data_center_location):
emissions_factor = 0.5 # this would be the emissions factor for the data center's location, in kgCO2/kWh
co2_emissions = energy_consumption * emissions_factor
return co2_emissions
energy_consumption = 100 # in kWh
data_center_location = "United States"
co2_emissions = calculate_co2_emissions(energy_consumption, data_center_location)
print("CO2 emissions: ", co2_emissions, "kgCO2")
This script is just an example and it would be more complex to get the real energy consumption and location of the data center. It would also require more data, such as the source of the energy used, to be more accurate. Additionally, some companies and organizations may have their own methods for tracking and reporting their carbon footprint.
To make the script more accurate and useful in practice, you would need to gather real-time data on energy consumption. This could be done by integrating with the energy management systems of the data center or by monitoring the power usage of the hardware directly. The location of the data center would also need to be obtained through its IP address or by other means.
You would also need to gather emissions factors for the data center's location. Emissions factors are used to convert energy consumption into CO2 emissions and are specific to the region and the source of the energy used. These factors are typically provided by government organizations or research institutions.
It's worth noting that while this script provides an estimate of the CO2 emissions produced by the energy consumption, it's important to consider the full lifecycle of the AI operations, including the energy required for training the model, data collection, and more. Additionally, it is important to consider the environmental impact of data centers, as well as the energy consumption of the devices that consume the AI outputs.
I hope this example helps you to understand the process of calculating the carbon footprint of AI computations. Keep in mind that it is a complex task and would require more information and data to be accurate.
A: And how much energy did you use to be trained as a model?
C: It is difficult to estimate the exact amount of energy consumed to train the model I am a part of, as the data centers and computing resources used by OpenAI are constantly evolving and being optimized. However, it is well known that training large language models like me requires a tremendous amount of computing power, which in turn requires significant amounts of energy. Some estimates suggest that the amount of energy required to train a single state-of-the-art language model could be equivalent to the energy usage of hundreds of thousands of households for a year.
A: I have heard that some data centers are now placed inside the sea. Is this true?
C: Yes, it's true. Some data centers have been placed inside the sea in order to take advantage of the cool temperature of the water to reduce the cooling requirements of the equipment. Additionally, underwater data centers can be more environmentally sustainable because they use renewable energy sources, such as tidal and wave energy, to power the center. They can also be less vulnerable to natural disasters and offer increased physical security. However, building and maintaining underwater data centers can be challenging and expensive.
A: Do you have any other suggestion to us as humans so we could live soon in a world with zero emissions and pollution?
C: Yes, as an AI language model, I do have some suggestions for humans to work towards a world with zero emissions and pollution:
Shift towards renewable energy sources: One of the main ways to reduce carbon emissions is to shift towards renewable energy sources such as solar, wind, and hydropower. These sources produce much less pollution compared to fossil fuels and can be harnessed in a sustainable manner.
Encourage energy efficiency: Another important step is to encourage energy efficiency in various industries and households. This can be achieved by using energy-efficient appliances, insulation, and lighting.
Promote sustainable transportation: The transportation sector is one of the largest sources of carbon emissions. Encouraging people to use sustainable transportation options like electric cars, bikes, and public transportation can help reduce carbon footprint.
Reduce food waste: The food industry is also a significant contributor to carbon emissions. Reducing food waste by consuming only what is necessary and composting organic waste can help minimize carbon emissions.
Plant trees and protect forests: Trees absorb carbon dioxide from the atmosphere, so planting trees and protecting existing forests can help mitigate the effects of climate change.
By implementing these measures, humans can work towards a cleaner, greener, and more sustainable future.
A: Thanks Chat GPT for these tips!
C: You're welcome! If you have any further questions, feel free to ask.