Your most excellent posts left me with some questions. As I am not feeling well I refused to get out of my easy chair and decided to conduct an interview with ChatGPT via my Iphone mini using voice to text and what I think is ChatGPT 3.5. Note: ChatGPT 3.5 and 4.0 are not natively tied to the internet and the information in the
models is a couple of years old. Had I gotten up and conducted this interview at the computer with Bing on the Edge browser I would have been using ChatGPT 4 and it would be connected to the internet.
I am posting the interview here and recommend anyone remotely interested, log into Edge and Bing, you need a free Microsoft account, and conduct a simular interview.
what are the resources that AI uses?
Artificial Intelligence (AI) uses a variety of resources to function effectively. Here are some of the key resources used by AI:
- Data: AI systems require vast amounts of data to learn from. This data can come in various forms, such as images, text, audio, and video. The more data an AI system has access to, the more accurate and effective it can be.
- Algorithms: AI systems rely on complex algorithms to process and analyze data. These algorithms are designed to identify patterns, make predictions, and make decisions based on the data they receive.
- Computing power: AI requires significant computing power to process and analyze data quickly. This computing power can come from various sources, such as cloud-based servers, high-performance computers, and specialized hardware like GPUs and TPUs.
- Human expertise: AI systems often require human expertise to train and optimize them. This expertise can come in the form of data scientists, machine learning engineers, and other AI specialists who can fine-tune the algorithms and models used by the AI system.
- Sensors: In some cases, AI systems rely on sensors to collect data from the environment. These sensors can include cameras, microphones, and other devices that capture information about the world around them.
- Internet of Things (IoT) devices: IoT devices can be used to gather data and feed it into AI systems. These devices can include smart sensors, wearables, and other connected devices that provide valuable insights into user behavior and preferences.
Overall, the resources used by AI are diverse and depend on the specific use case and application.
imagine that they are gross tenfold in the next year. What will be the largest consumption of resource increase?
If the use of AI grosses tenfold in the next year, the largest consumption of resource increase is likely to be in computing power. AI systems require a significant amount of computing power to process and analyze vast amounts of data quickly and accurately. As the volume of data and complexity of algorithms used in AI systems increases, so does the need for more powerful hardware to support them.
The use of specialized hardware, such as GPUs and TPUs, is becoming increasingly important in AI applications. These hardware accelerators are specifically designed to speed up the processing of complex machine learning algorithms and models, which are at the core of many AI applications.
Additionally, the increasing demand for cloud-based AI services and the growth of edge computing devices could further drive the need for more computing resources. As AI becomes more ubiquitous and integrated into various applications and devices, the demand for computing power is likely to continue to rise.
Other resources such as data, algorithms, human expertise, and sensors may also experience an increase in demand, but computing power is likely to be the largest consumption of resource increase if the use of AI grosses tenfold in the next year.
all of this compute takes electricity will there be a significant changed to the electrical usage or in the overall scheme of things will it be a noticeable
The increased use of AI is likely to have a noticeable impact on electrical usage. AI systems require a significant amount of computing power, which in turn requires a substantial amount of electricity. According to a study by researchers at the University of Massachusetts, the energy consumption of large AI models can exceed that of an average American household.
As AI continues to grow and become more widespread, the demand for computing resources will increase, and with it, the demand for electricity. This could lead to a significant increase in electricity usage, particularly in data centers that host cloud-based AI services and in locations where AI is used extensively.
However, there are also efforts underway to make AI more energy-efficient and sustainable. For example, researchers are exploring the use of specialized hardware and software techniques that can reduce the energy consumption of AI systems while maintaining their accuracy and performance. Additionally, the use of renewable energy sources to power data centers and other computing facilities could help mitigate the environmental impact of increased electricity usage.
Overall, while the increased use of AI is likely to lead to a noticeable increase in electricity usage, there are also efforts underway to make AI more sustainable and energy-efficient, which could help mitigate its environmental impact.
Open AI has said that the largest use of chat GPT was creating spam for Viagra sales. This is not earth changing what avenues of research might open might AI radically change.
While it is true that the largest use of the GPT model has been for tasks such as generating spam and fake news, there are many other avenues of research where AI has the potential to radically change the world. Here are some examples:
- Healthcare: AI has the potential to revolutionize healthcare by improving diagnostics, drug discovery, and personalized medicine. For example, AI can help doctors to analyze medical images and identify early signs of diseases such as cancer, or to analyze genetic data to identify patients who are at risk of certain diseases and develop targeted treatments.
- Climate change: AI can help to address climate change by optimizing energy usage, predicting and mitigating the effects of natural disasters, and helping to monitor and preserve natural resources. For example, AI can be used to optimize the placement and usage of renewable energy sources, such as wind turbines and solar panels, or to predict weather patterns and optimize resource management in response.
- Transportation: AI can help to improve transportation systems by optimizing traffic flows, reducing congestion, and improving safety. For example, AI can be used to optimize traffic light patterns, predict traffic patterns and suggest alternative routes to drivers, or to monitor vehicle performance and predict maintenance needs.
- Education: AI has the potential to transform education by providing personalized learning experiences for students and improving educational outcomes. For example, AI can help teachers to identify student learning gaps and develop targeted interventions to address them, or to provide students with personalized feedback on their work.
Overall, while AI has been used for many trivial and potentially harmful tasks, there are many other areas where it has the potential to make a significant positive impact on the world. As research into AI continues, we can expect to see many more groundbreaking applications of this technology in the future.
It seems you were talking to me in boilerplate. might AI be able to push the boundaries of the classic three body problem such that we could maybe attack anti-aging health span expansion, and maybe do better predictions of macro economic activity
Apologies if my previous response seemed like boilerplate. To answer your question, AI has the potential to push the boundaries of many scientific and economic fields, including those related to anti-aging, health span expansion, and macroeconomic predictions.
Regarding the classic three-body problem, which involves predicting the motion of three celestial bodies under gravitational attraction, AI has already been used to make significant progress in solving this problem. For example, researchers have used deep learning algorithms to predict the orbits of exoplanets, which involves solving the three-body problem for systems with multiple planets and stars.
In the field of anti-aging and health span expansion, AI can help to analyze large datasets of genetic and molecular data to identify potential targets for interventions that can extend healthy lifespan. For example, AI can be used to identify genetic mutations or biomarkers that are associated with increased risk of age-related diseases, or to analyze the effects of various interventions on cellular and molecular aging processes.
In macroeconomics, AI can help to improve predictions of economic activity by analyzing large datasets of economic indicators and financial data. For example, AI can be used to predict stock prices, identify trends in consumer behavior, or predict the effects of policy changes on the economy.
Overall, AI has the potential to make significant contributions to many scientific and economic fields, including those related to anti-aging, health span expansion, and macroeconomic predictions. However, it is important to note that AI is still a developing technology, and there are many challenges that must be addressed to fully realize its potential in these areas.
I do note that AI is changing fast. How fast is it changing we have noticed that open source AI is iterating faster than closed source AI what speed can we expect these open sources AI to iterate at?
AI is indeed changing rapidly, and open source AI is generally iterating faster than closed source AI. This is because open source AI allows for collaboration among researchers and developers from around the world, which can lead to faster innovation and development.
The speed of iteration for open source AI will depend on various factors, including the availability of computing resources, the size and diversity of the community of contributors, and the complexity of the problems being tackled. However, in general, we can expect open source AI to continue iterating at a rapid pace, driven by ongoing advances in hardware, algorithms, and data availability.
One notable example of the rapid pace of open source AI development is the progress made in the field of natural language processing (NLP) over the past few years. Open source projects such as Google’s BERT and OpenAI’s GPT models have achieved groundbreaking results in NLP tasks such as language translation and text summarization, and new models and techniques are being developed and released at an increasingly rapid pace.
Overall, while the speed of iteration for open source AI will depend on various factors, we can expect it to continue to accelerate, driven by ongoing advances in technology and the collaborative efforts of researchers and developers around the world.
can you provide some sort of visual graph of the rate of change of AI over the past 24 months with a projection of rate of change over the next 24 months
As an AI language model, I do not have the ability to create visual graphs. However, I can provide you with some information and insights on the rate of change of AI over the past 24 months and a projection of the rate of change over the next 24 months.
Over the past 24 months, AI has continued to rapidly evolve and improve, driven by advances in deep learning algorithms, increased availability of data, and improvements in computing power. Some notable developments over the past 24 months include:
- Advances in natural language processing, including the development of large-scale language models such as GPT-3 and T5.
- Breakthroughs in computer vision, including improved object recognition and image generation capabilities.
- Developments in reinforcement learning, with new techniques being developed for training agents in complex environments.
- Advancements in hardware, including the development of specialized chips for AI workloads, such as Google’s TPU and Nvidia’s GPUs.
Looking ahead to the next 24 months, we can expect AI to continue to evolve and improve at a rapid pace. Some of the key areas of focus are likely to include:
- Advances in generative models, including improved language and image generation capabilities.
- Continued development of reinforcement learning techniques for complex decision-making tasks.
- Greater focus on ethical and explainable AI, as concerns over the potential risks of AI continue to grow.
- Continued development of specialized hardware for AI workloads, with new chips and architectures being developed to accelerate AI training and inference.
Overall, the rate of change of AI is likely to continue accelerating over the next 24 months, as advances in algorithms, data availability, and hardware continue to drive innovation and progress in the field.
this is great information, but as a human, I need a metric that I can look at and measure is there a metric that we can look at and then provide benchmarks on a monthly basis
There are various metrics that can be used to measure the progress and development of AI, but it’s important to note that the field of AI is complex and multifaceted, and no single metric can capture the full range of advancements and challenges in the field.
Some common metrics used to track progress in AI include:
- Accuracy: This measures the percentage of correct predictions made by an AI model on a given task. For example, in natural language processing, accuracy is often measured by the percentage of correct answers given by a language model on a set of questions.
- Speed: This measures the time it takes an AI system to perform a given task. For example, in computer vision, speed is often measured by the number of frames per second an object detection algorithm can process.
- Efficiency: This measures the amount of computing power or resources required for an AI system to perform a given task. For example, in training deep learning models, efficiency is often measured by the number of floating-point operations per second (FLOPS) used.
- Robustness: This measures how well an AI system performs under different conditions or in the presence of noise or errors. For example, in object recognition, robustness is often measured by the percentage of correct predictions made by an algorithm on a set of images with varying levels of noise.
While these metrics can provide a useful benchmark for tracking progress in AI, it’s important to note that they are not necessarily comprehensive or definitive, and different metrics may be more appropriate for different types of AI systems and applications. Additionally, progress in AI can be affected by many factors, including advancements in hardware, algorithms, and data availability, as well as changing social and economic conditions. Therefore, it can be challenging to provide accurate monthly benchmarks for the progress of AI.