I won't kill myself with worry about the fast pace of machine intelligence, if I was Hinton. We are living intelligence times. This was the objective we were working on for the last 80 years, when McCullough and Pitts designed the Turing-Complete "artificial neurons" in 1943. So why worry about it now?
We should take up the challenge and harness this power and nudge it into a more constructive direction. Moreover, informational complexity had to increase before we find a simpler solution. We should not confuse Intelligence with Informational complexity. Rather, the complexity of information created by LLMs has made it easier for us to solve the problem.
LLMs lack common sense, confuse correlation with causation, work with input information constraints and is far from intelligent. Eventually, a netizen will consider LLM's output with a pinch of salt and not take the result as biblical truth. LLMs will become the new search engines.
I asked the following questions to LLM about its limitations.
Why is it said the LLM and other AI tools lack common sense?
The reason why LLM (Language Model) and other AI tools are said to lack common sense is that they are designed to process and analyze vast amounts of data using statistical algorithms and machine learning techniques. These models can make predictions or generate text based on patterns in the data they are trained on, but they cannot understand the context, meaning, and implications of what they are processing.
Common sense, on the other hand, is the ability to understand and reason about everyday situations that people encounter in their lives. It includes a deep understanding of the world and how it works, as well as an ability to make judgments and decisions based on that understanding.
While AI models like LLM have made significant progress in natural language processing and other areas, they still struggle with common sense reasoning. This is because common sense requires a much deeper understanding of the world and human experience than what can be learned from data alone.
For example, an LLM might be able to generate a coherent paragraph about a fictional character, but it may not understand the social norms and expectations that govern how people behave in real-life situations. As a result, it might generate text that sounds awkward or inappropriate in context.
In summary, while AI tools like LLM are powerful and useful, they cannot reason about the world in the same way humans do, making them ill-equipped to deal with many of the complex and nuanced situations that humans encounter in their daily lives.
Is it fair to assume that LLM is a statistical parrot, which is not real learning?
It is not entirely accurate to describe LLM (Language Model) as a statistical parrot that does not involve real learning. While it is true that LLM and other AI tools are based on statistical models and machine learning algorithms, they involve a type of learning that is different from human learning.
LLM and other AI models learn from large datasets by identifying patterns and relationships in the data. They use these patterns and relationships to make predictions and generate new text based on what they have learned. While this is different from the way humans learn, it is still a form of learning that involves processing and analyzing information.
Moreover, LLM and other AI models can be fine-tuned and trained on specific tasks, allowing them to develop expertise in particular domains. For example, an LLM can be trained to generate legal documents, scientific papers, or other specialized forms of text.
That being said, LLM and other AI models are still limited in their ability to understand the world and make nuanced judgments based on that understanding. They lack the kind of common sense and reasoning abilities that humans possess, as I mentioned in my previous response. As a result, their output may be flawed or incomplete in certain contexts, and they require human oversight and intervention to ensure their accuracy and relevance.
Is it fair to assume that LLM does not learn like a child?
Yes, it is fair to assume that LLM (Language Model) does not learn like a child. While both children and LLM learn from exposure to information, the mechanisms by which they learn are fundamentally different.
Children learn by interacting with the world around them, through observation, exploration, and experimentation. They build up their understanding of the world over time, by incorporating new experiences and information into their existing knowledge and schemas. This type of learning is often called "inductive learning" or "constructivist learning" and involves a high degree of exploration and experimentation.
In contrast, LLM learns by analyzing vast amounts of text data using statistical models and machine learning algorithms. It identifies patterns and relationships in the data and uses these to make predictions and generate new text. This type of learning is often referred to as "supervised learning" or "machine learning," and it is based on mathematical and statistical models.
While both forms of learning involve processing and analyzing information, how they do so is fundamentally different. Children learn in a highly interactive and exploratory way, while LLM learns through statistical analysis and pattern recognition. As a result, LLM learning is highly dependent on the quality and quantity of the data it is trained on, and it requires human intervention to ensure that its output is accurate and relevant.
Is it fair to assume that LLMs only read, they don't comprehend?
Yes, to a certain extent, it is fair to assume that LLMs (Language Models) only read and do not comprehend in the same way that humans do. While LLMs can process and analyze large amounts of text data, they do not truly understand the meaning and context behind the words in the same way that humans do.
LLMs use statistical algorithms and machine learning techniques to identify patterns and relationships in the text they are processing. They can then use these patterns to generate new text that is similar in style and tone to the text they were trained on. However, LLMs cannot understand the underlying concepts and ideas behind the text, and they cannot make judgments or decisions based on this understanding.
For example, an LLM might be able to generate a coherent summary of a news article, but it may not understand the significance of the events described in the article or the broader context in which they occurred. Similarly, an LLM might be able to generate a persuasive argument for a particular point of view, but it may not truly understand the nuances and complexities of the issue at hand.
In summary, while LLMs are powerful tools for processing and generating text, they cannot truly comprehend the meaning and context behind the words they are processing. They can mimic human language to a certain extent, but they do not have the same level of understanding or reasoning abilities as humans do.
Is it fair to assume that LLMs have a linear thinking and are limited in their logic?
It is not entirely accurate to assume that LLMs (Language Models) have linear thinking and are limited in their logic, but their way of processing information is indeed different from human thinking.
LLMs use statistical algorithms and machine learning techniques to identify patterns and relationships in text data. These patterns and relationships are used to generate new text that is similar in style and tone to the input data. The process of identifying patterns and generating new text involves a type of logic, but it is not the same as the logic used by humans.
LLMs are not capable of true reasoning in the same way that humans are. They lack the kind of common sense and understanding of the world that humans possess. As a result, their output may be flawed or incomplete in certain contexts, and they require human oversight and intervention to ensure their accuracy and relevance.
While LLMs do not have linear thinking per se, they are limited by the algorithms and models that they use. They can only process information in the way that they have been programmed to do, and their output is based solely on the patterns and relationships that they have learned from their training data. This means that they are not capable of truly creative or innovative thinking, and they are limited by the quality and quantity of the data they are trained on.
In summary, while LLMs are capable of processing and generating text, they are not capable of true reasoning or creative thinking in the same way that humans are. Their logic is based on statistical models and algorithms, and they are limited by the quality and quantity of the data they are trained on.
Should logic involve questioning? Do LLMs question?
Logic can certainly involve questioning, as questioning is an important aspect of critical thinking and problem-solving. To arrive at logical conclusions, it is often necessary to ask questions and examine the evidence from multiple angles.
LLMs (Language Models), however, do not "question" in the way that humans do. They do not possess curiosity or a desire for knowledge, and they cannot actively seek out information or ask questions to clarify their understanding. Instead, LLMs process and analyze text data using statistical models and machine learning algorithms. They identify patterns and relationships in the data and use these to make predictions and generate new text.
While LLMs do not question in the same way that humans do, they can still be useful tools for generating questions and exploring complex topics. For example, an LLM might be able to generate a list of questions based on a given topic or generate hypotheses to be tested through further research. However, these questions and hypotheses would be based solely on the patterns and relationships that the LLM has learned from its training data, and they may not reflect the same level of critical thinking or creativity that would be present in questions generated by a human.
Is it fair to say that LLMs are limited by our knowledge of Statistical laws? For example, LLM can’t explain the mechanism generating power law, how normal statistical distributions and non-normal statistical distributions are connected, and how learning could be mechanism driven.
Yes, it is fair to say that LLMs (Language Models) are limited by our current knowledge of statistical laws and the limitations of machine learning algorithms. While LLMs are capable of processing and analyzing large amounts of text data, their ability to generate insights and explanations beyond the statistical patterns in the data is limited.
For example, LLMs may be able to identify and describe patterns of word usage in text data, but they may not be able to explain the underlying mechanisms that generate these patterns or how these mechanisms are related to other statistical distributions. This is because LLMs are based on statistical models and algorithms that are designed to identify patterns and relationships in data, but they do not have the same level of understanding and reasoning as humans.
Furthermore, the ability of LLMs to learn is limited by the quality and quantity of the data they are trained on. They can only learn what is present in the training data, and they may not be able to generalize beyond this data. This means that LLMs may struggle to understand complex concepts or generate new insights that are not present in the training data.
In summary, while LLMs are powerful tools for processing and generating text, they are limited by our current knowledge of statistical laws and the limitations of machine learning algorithms. They are capable of identifying patterns and relationships in data, but they may struggle to generate insights and explanations beyond these patterns.
LLMs are computationally heavy and hence power hungry at a time when we need to care about resources and carbon imprint.
Yes, LLMs (Language Models) can be computationally heavy and power-hungry, which can have implications for energy consumption and carbon footprint.
Training large language models requires a significant amount of computational resources, including high-performance computing clusters and specialized hardware such as Graphics Processing Units (GPUs). This can consume a large amount of energy, contributing to greenhouse gas emissions and climate change.
Furthermore, the use of LLMs in real-world applications, such as chatbots or language translation services, also requires significant computational resources to run the models in real time. This can be problematic for devices with limited processing power, such as smartphones or Internet of Things (IoT) devices, as well as in regions with limited access to electricity or reliable internet connections.
Efforts are underway to develop more energy-efficient language models and machine learning algorithms, such as federated learning and model pruning, which can reduce the amount of computational resources required to run these models. Additionally, some researchers are exploring alternative computing paradigms, such as neuromorphic computing, which mimic the structure and function of the brain to achieve greater efficiency.
In summary, while LLMs can be computationally heavy and power-hungry, efforts are underway to develop more energy-efficient language models and machine learning algorithms. As the use of LLMs becomes more widespread, it is important to consider the environmental impact of these models and work towards more sustainable computing practices.
Is It fair to say that LLMs are creating new information, and if we assume the old information was garbage, the newly processed information will be garbage out?
It is not accurate to say that LLMs (Language Models) create new information in the same sense that humans do. LLMs are statistical models that are trained on existing text data, and they generate new text by using patterns and relationships learned from this data. However, the information generated by LLMs is not entirely new, but rather a rearrangement or recombination of existing information.
Whether the information generated by LLMs is garbage or not depends on the quality of the data that the models are trained on and the accuracy of the models themselves. If the training data is biased or contains errors, the LLMs may reproduce or amplify these biases and errors in their output. Similarly, if the models are inaccurate or flawed, the output may be unreliable or misleading.
Therefore, it is important to carefully evaluate the quality and accuracy of LLM-generated text and consider the limitations and biases that may be present in the training data and the models themselves. While LLMs can be useful tools for generating text, they should not be relied upon as the sole source of information, and the output should be validated and verified through other means.
If modern finance can't reconcile efficiency and inefficiency about information and we have 50 years when benchmarks remain unbeaten, what does this tell us about available information's capability to be predictive and its ability to be reprocessed for something new and more capable?
The fact that benchmarks in finance have remained unbeaten for a long period may suggest that the available information and its predictive power are limited. It may also suggest that the current models and methods used in finance are insufficient to fully capture the complexity and dynamics of the financial markets.
It is important to note that the financial markets are highly complex and influenced by a wide range of factors, including economic indicators, geopolitical events, and investor sentiment, among others. While there are many sources of information available, such as market data, news, and social media, it is difficult to predict how these factors will interact and impact market outcomes.
Furthermore, the financial markets are subject to behavioral biases and irrational behavior, which can make it challenging to develop accurate and reliable models. These biases and behaviors can also contribute to the persistence of inefficiencies in the market, which may be difficult to exploit consistently over time.
In terms of reprocessing information for something new and more capable, there is ongoing research in the field of financial machine learning and artificial intelligence, which aims to develop more sophisticated models and methods for analyzing financial data. However, it is important to remain cautious and skeptical of claims of new and improved methods, as the financial markets are highly unpredictable and subject to change. Additionally, it is important to consider the ethical implications of using machine learning and AI in finance, such as potential biases and unintended consequences.
It’s fair to say that intelligence is not all synapsis, which means neural networks are constrained in their ability to enhance themselves and be perpetually intelligent. Correct?
Yes, it is fair to say that intelligence is not solely a function of the number of synapses in the brain. While neural networks, including those used in artificial intelligence, are designed to emulate the structure and function of the brain, they are not perfect replicas and have limitations in their ability to enhance themselves and achieve perpetual intelligence.
One of the limitations of neural networks is that they are typically designed to learn from a specific set of data or tasks, and their ability to generalize and adapt to new situations may be limited. Additionally, neural networks are subject to issues such as overfitting, where the model becomes too specialized to the training data and is unable to perform well on new data.
Furthermore, while neural networks can be optimized and improved through techniques such as backpropagation and regularization, there are limits to how much they can be enhanced. For example, increasing the number of neurons or layers in a neural network can improve its performance up to a certain point, but beyond a certain threshold, the benefits of additional complexity may be outweighed by the increased risk of overfitting and computational costs.
In summary, while neural networks are powerful tools for artificial intelligence and machine learning, they have limitations in their ability to enhance themselves and achieve perpetual intelligence. Research in the field of artificial intelligence is ongoing, and new techniques and models are being developed to overcome these limitations and push the boundaries of what is possible with neural networks.
Correlation does not imply causality. Is this statement at the heart of LLM outcomes?
The statement "correlation does not imply causality" is a common reminder in statistical analysis that just because two variables are correlated, it does not necessarily mean that one causes the other. This principle is relevant to LLM outcomes in the sense that LLMs are trained to identify correlations and patterns in large datasets, but they do not necessarily have an understanding of the causal relationships between variables.
LLMs work by identifying statistical regularities and patterns in data, which can be useful for making predictions or classifications. However, these patterns may not always correspond to meaningful causal relationships. For example, an LLM might identify a correlation between ice cream sales and crime rates, but it would not necessarily infer that ice cream causes crime. Instead, it is more likely that both variables are influenced by a third factor, such as temperature.
To overcome this limitation, it is important to supplement LLM analysis with other forms of knowledge, such as domain expertise or causal models. By combining statistical analysis with other forms of knowledge, it is possible to achieve a more nuanced understanding of complex phenomena and to make more accurate predictions and decisions.