On November 30, 2022, OpenAI launched its ChatGPT chatbot to the public , reaching over 1 million users in just five days after its launch.
The stir it has caused in the world of conversational assistants is well-deserved. ChatGPT can not only answer users’ questions, becoming a facilitator of information searches and an unrivaled competitor to search engines, but it is also capable of writing stories, programming code, essays or scientific articles, solving problems, guessing riddles, interacting in several languages, making grammatical corrections, among many other things.
ChatGPT’s great capacity is due to the fact that it has been trained on large volumes of data. The initially released version, GPT-3.5, used approximately 499 billion tokens (semantic units, which often correspond to words) from the internet, which in turn modified 175 billion different parameters of the model to obtain these results.
While ChatGPT-3.5 blew us away with its “intelligence” and problem-solving abilities, the new version GPT-4, released on March 14, 2023, has been trained with around 1.6 trillion parameters and combines the Transformer architecture ( introduced by Google in 2017) with the Azure cloud structure (Microsoft) and NVIDIA A100 chips , to achieve performance quite close to the human level.
This latest version is not only capable of solving much more complex tasks than its predecessor, but can also better retain the context of the conversation, accept image inputs and generate more objective responses, with only 0.73% “toxic” responses, compared to GPT-3.5 levels of 6.48%.
Could it be that the intelligence demonstrated by GPT-4 has endowed it with a certain common sense? Will this pose a threat to various professions? What should we be paying attention to?
I decided to interview the bot, and this is what it told me. I hope you enjoy this dialogue and its interesting answers.
Interview with ChatGPT 4.0
JDC: Act as an expert in artificial intelligence
GPT-4: Of course! As an advanced language modeler based on the GPT-4 architecture, I have in-depth knowledge of artificial intelligence and its applications. Is there any particular topic you’d like to learn more about?
JDC: Do you consider yourself a weak or strong artificial intelligence?
GPT-4: As a language model, I am a form of weak artificial intelligence, as my ability is limited to generating responses based on the data and patterns provided to me during training. Although my ability to generate responses is quite impressive, I lack a complete understanding of the real world and the capacity to make decisions independently.
On the other hand, strong artificial intelligence refers to systems that are capable of thinking and reasoning like human beings and, therefore, can make autonomous decisions and solve complex problems in a wide variety of contexts. Currently, strong artificial intelligence is an active area of research, but we are still far from creating a system that can match human thought and reasoning abilities.
JDC: So, your behavior is based on probabilistic reasoning?
GPT-4: Yes, my behavior is based on probabilistic reasoning. As an AI-based language model, I use machine learning techniques that rely on probabilistic models to generate responses.
This means that, instead of following a strict set of rules or a deterministic algorithm, my ability to generate answers is based on the probability of a given answer being correct, based on the data and patterns observed during training. Therefore, my behavior and answers may vary depending on the available information and the probability associated with each answer.
JDC: How is probabilistic reasoning similar to the common sense that human beings have?
GPT-4: Probabilistic reasoning and common sense in humans share some similarities in terms of how uncertainty is dealt with and decisions are made based on limited or incomplete information.
- Both address uncertainty: Both probabilistic reasoning and common sense allow us to make reasonable assumptions or inferences in situations where complete information is unavailable. In probabilistic reasoning, this is done by assigning probabilities to different outcomes or events, while in human common sense, it relies on prior experience, general knowledge, and heuristic rules.
- Learning from experience: In both probabilistic reasoning and common sense, learning from past experiences plays a significant role in decision-making. Humans learn throughout their lives and adjust their common sense based on acquired experiences. AI models, like mine, also learn from data, adjusting their weights and neural connections to improve their performance based on the information they are exposed to.
- Information integration: Both approaches integrate information from diverse sources to make informed decisions. Humans use their prior knowledge and the information available at the time to apply common sense in specific situations. Similarly, probabilistic reasoning in AI combines information from different sources and data to estimate probabilities and make decisions.
However, it’s important to highlight that there are significant differences between probabilistic reasoning in AI and human common sense. Human common sense is much richer and more flexible, as it’s based on understanding context and the ability to adapt to new or unfamiliar situations. AI, in its current state, still has limitations in terms of deep contextual understanding and adapting to dynamic environments. Furthermore, humans can apply common sense creatively and originally, while AI relies on patterns and structures present in the data it has been trained on.
JDC: So you acknowledge that common-sense reasoning is anything but simple?
GPT-4: Yes, I recognize that common-sense reasoning in human beings is complex and sophisticated. Unlike artificial intelligence systems, human common sense involves a wide range of cognitive skills, acquired knowledge, and experiences that enable people to understand, interpret, and respond appropriately to everyday situations. Common sense is a fundamental characteristic of human intelligence and often involves intuition, logical reasoning, empathy, and adaptability. While it may appear “simple” on the surface, it is the result of a set of complex and highly interrelated mental processes that are still not fully understood.
In the field of artificial intelligence, developing systems that can exhibit common-sense reasoning similar to that of humans is a challenging and far-reaching goal. Researchers are working on developing algorithms and techniques that can better capture real-world knowledge and contextual understanding to enable more sophisticated common-sense reasoning in AI. However, much remains to be done to achieve this goal and fully emulate human common sense in machines.
JDC: Are you familiar with Moravec’s Paradox?
GPT-4: Yes, I’m familiar with Moravec’s Paradox, which is an observation in the field of artificial intelligence and robotics. It was articulated by Hans Moravec, a scientist and robotics pioneer in the 1980s. The paradox points out that tasks that are relatively easy for humans, such as sensory perception and object manipulation, have proven difficult to replicate in machines, while tasks that are computationally intensive but might be considered difficult for humans, such as chess or advanced mathematical calculations, have proven easier to achieve in AI.
The reason behind Moravec’s paradox is that high-level cognitive skills, such as logical reasoning and mathematical calculation, are the result of conscious and explicit processes, which facilitates their formalization and replication in machines.
On the other hand, low-level skills, such as perception and locomotion, are the result of evolutionary and developmental processes that have occurred over millions of years. These processes have allowed humans to acquire intuitive and automatic sensorimotor skills that are difficult to understand and replicate in AI and robotics.
Moravec’s paradox remains relevant today, as researchers continue to work on developing AI and robotics systems that can effectively mimic the perception, locomotion, and object manipulation found in humans and other animals.
Although significant progress has been made in areas such as deep learning, computer vision, and robotics, considerable challenges remain to fully overcome Moravec’s paradox.
JDC: In the context of the development of ethical artificial intelligence, what things should humans pay attention to?
GPT-4: The development of ethical artificial intelligence is essential to ensure that AI technologies are used responsibly and beneficially for society. There are several aspects that must be considered in the context of the development of ethical artificial intelligence:
- Bias and fairness: It is important to ensure that AI systems are fair and do not perpetuate or amplify existing biases in society. The data used to train AI models must be representative and diverse to minimize the introduction of bias into AI systems.
- Transparency and explainability: AI systems must be transparent in their operation and capable of providing understandable explanations of how they arrive at their decisions. Transparency and explainability are fundamental to building trust in AI systems and enabling human accountability and control.
- Privacy and security: Protecting user data privacy and security is fundamental to the development of ethical AI. AI systems must be designed with privacy and security in mind from the outset and must comply with applicable data protection regulations.
- Accountability and governance: There must be clear accountability and governance mechanisms in the development and deployment of AI systems. This includes establishing legal and ethical responsibilities for AI developers, operators, and users, and ensuring adequate oversight and regulation.
- Impact on employment and the economy: It is crucial to consider the impact of AI on employment and the economy, including the potential for job displacement and the creation of new employment opportunities. Appropriate policies and strategies must be in place to address these challenges and ensure a fair and equitable transition to an AI-driven economy.
- Military and autonomous use: The use of AI in military applications and autonomous weapons raises significant ethical concerns. It is important to establish clear limits and regulations on the development and use of AI in military contexts and to ensure that humanitarian principles and human rights are respected.
- Broad benefits and global cooperation: The development of ethical artificial intelligence must focus on ensuring that the benefits of AI are widely shared and do not exacerbate inequality. Global cooperation and collaboration among governments, businesses, and researchers are essential to ensuring that AI is used responsibly and benefits everyone.
These are just some of the key aspects to consider in the development of ethical artificial intelligence. Addressing these issues and fostering collaboration among multiple stakeholders, including researchers, policymakers, businesses, and society at large, are essential to ensuring that AI is developed and used responsibly and beneficially.
JDC: Are you referring to the problem of biases in artificial intelligence, or am I mistaken?
GPT-4: Yes, one of the key points I mentioned in the previous answer relates to the problem of bias in artificial intelligence. Biases in AI can arise when the data used to train AI models contains discriminatory or biased patterns, which can lead to AI systems perpetuating or even amplifying these biases when making predictions or decisions.
Bias in AI is a significant ethical concern, as it can negatively impact specific groups of people and perpetuate existing inequalities in society. For example, biased AI systems can lead to discrimination in areas such as job recruitment, credit approval, and policing, disproportionately affecting already marginalized groups.
To address the problem of bias in AI, it is essential to ensure that the data used to train AI models is representative and diverse, and that appropriate techniques and approaches are used to reduce and mitigate any bias present in the data and models. Furthermore, it is crucial to establish mechanisms for transparency, accountability, and human oversight in the development and deployment of AI systems to ensure they are used fairly and ethically.
JDC: Could you give me more examples of the different types of biases and the problems they have triggered or can trigger?
GPT-4: Yes, with pleasure. Here are some other examples of biases in artificial intelligence and the problems they can cause:
- Data selection bias: This bias occurs when the data used to train an artificial intelligence model is not representative of the general population. For example, if an image recognition model is trained only on images of white people, the model may have difficulty recognizing images of people of other races. This can lead to discrimination and the exclusion of certain groups of people.
- Cognitive bias: This bias occurs when artificial intelligence algorithms reflect human cognitive biases, such as stereotypes and prejudices based on gender or race. For example, if an employee hiring model is trained using only historical hiring data that reflects gender or racial bias, the model may perpetuate those biases in future hiring decisions.
- Confidence bias: This bias occurs when artificial intelligence systems rely too heavily on their own decisions and disregard additional information. For example, a fraud detection model might place too much trust in transactions made in a specific geographic location and, therefore, miss legitimate transactions in other locations.
- Lack of explanation bias: This bias occurs when artificial intelligence systems do not provide a clear explanation of how decisions were made. This can make it difficult to understand and correct errors in the model.
- Feedback bias: This bias occurs when artificial intelligence systems learn through user feedback, which can perpetuate prejudices and stereotypes. For example, an employee recommendation model might learn from past hires and recommend similar candidates, perpetuating previous biases.
In summary, biases in artificial intelligence can have significant negative consequences, such as discrimination and the exclusion of certain groups of people. It is important to address these biases and ensure that AI systems operate transparently, fairly, and ethically.
JDC: Could it only affect people or does it also include making foolish or senseless decisions?
GPT-4: Biases in artificial intelligence can not only affect people in terms of discrimination and inequality, but can also result in foolish or unwise decisions due to a lack of adequate contextual understanding, incorrect generalizations, or distorted learning patterns in the training data. Some examples of foolish or unwise decisions that can result from biases in AI include:
- Inappropriate recommendations: A recommendation system may suggest inappropriate or irrelevant products, content, or services due to biases present in the training data or in the model itself.
- Misclassifications : An AI model may incorrectly classify objects, images, or texts due to biases in learning or in the representativeness of the training data.
- Incorrect generalizations: An AI system can incorrectly generalize from limited or biased examples, leading to erroneous decisions and predictions in new or unknown situations.
- Decisions based on spurious correlations: AI can identify correlations in data that do not have a real causal relationship and, consequently, make decisions based on these spurious correlations.
To avoid foolish or unwise decisions in AI, it is important to ensure that models are trained on representative and diverse data, and to use learning and evaluation techniques that are robust against biases and incorrect generalizations. Furthermore, incorporating human oversight and control into AI systems can help detect and correct inappropriate or unwise decisions before they cause problems or harm in the real world.
JDC: But society is full of prejudices. Wouldn’t it be too much to ask artificial intelligence not to be?
GPT-4: It is true that society is full of biases, and it is difficult to completely eliminate them in artificial intelligence. However, the goal is not necessarily to achieve completely bias-free AI, but rather to minimize and mitigate biases as much as possible and ensure that AI systems are used fairly and ethically. Some strategies for addressing biases in AI include:
- Data diversity: Ensure that the datasets used to train AI models are representative and diverse, reflecting a wide range of perspectives and experiences. This can help reduce bias in AI systems.
- Bias review and assessment: Implement bias review and assessment processes during the development and deployment stages of AI systems. These processes may include evaluating fairness metrics and conducting bias tests on AI models.
- Unbias-free learning techniques: Use machine learning techniques and algorithms that are robust against bias and allow for active bias mitigation in AI models.
- Transparency and explainability: Promote transparency in AI systems and provide clear explanations of how they arrive at their decisions. This can facilitate the identification and correction of biases and prejudices in AI.
- Human control and oversight: Establish human control and oversight mechanisms in AI systems to ensure they are used fairly and ethically, and that inappropriate or biased decisions can be corrected.
- Diversity and inclusion in AI development: Promoting diversity and inclusion in the field of artificial intelligence, including the participation of people with different backgrounds and perspectives in the development and evaluation of AI systems.
While it may be difficult to completely eliminate bias in artificial intelligence, it is important to address these issues proactively and responsibly, and work to develop AI systems that are fair, transparent, and ethical.
JDC: Are there any other critical issues we haven’t discussed regarding artificial intelligence?
GPT-4: We have discussed several critical issues related to artificial intelligence, such as bias, ethics, and transparency. However, there are other important issues to consider in the field of AI:
- AI Security: As AI systems become more advanced and integrated into various aspects of our lives, it is crucial to ensure their security and resilience against potential malicious attacks, such as manipulation of training data, unauthorized access, and exploitation of vulnerabilities in AI models.
- Social and psychological impact: The rapid adoption of AI in society can have effects on human interaction, privacy, personal autonomy, and mental health. It is essential to research and address these impacts to ensure that AI is integrated responsibly and benefits society as a whole.
- Sustainability and energy efficiency: Next-generation AI models, especially in deep learning, often require significant computing resources and energy for training and operation. Addressing sustainability and energy efficiency in AI is crucial for reducing its environmental impact.
- Legislation and regulation: The development of appropriate laws and regulations is crucial to ensuring the ethical and responsible use of AI. This includes aspects such as data protection, legal liability, intellectual property, and government oversight of AI.
- Open and collaborative AI research and development: Encourage open and collaborative AI research and development by sharing knowledge, data, and resources among researchers, institutions, and governments. This can help accelerate advances in AI and ensure that its benefits are distributed equitably.
- Workforce readiness and education: Preparing the workforce and society at large for the adoption and impact of AI, through education and training in relevant skills, is critical to ensuring a just and beneficial transition to an AI-driven economy.
These are just some of the additional issues that are important to consider in the context of artificial intelligence. Addressing these challenges and opportunities proactively and responsibly is crucial to ensuring that AI is developed and used in a beneficial and ethical way in our society.
JDC: In summary, under what context can artificial intelligence replace human beings?
GPT-4: Artificial intelligence has the potential to replace or complement many human tasks and jobs, especially in contexts where automation can increase efficiency, reduce errors, and improve accuracy. Some contexts in which AI can replace or assist humans include:
- Repetitive and routine tasks: AI can perform repetitive and routine tasks faster and more efficiently than humans, such as data classification, document analysis, or mass production in manufacturing.
- Big data analysis: AI can quickly process and analyze large amounts of data, allowing for the extraction of patterns and valuable information in fields such as scientific research, medicine, finance, and marketing.
- Tasks that require precision and speed: AI can outperform humans in tasks that require high precision and speed, such as detecting anomalies in medical images, analyzing real-time sensor data, or making decisions in high-frequency trading algorithms.
- Natural language processing and translation: AI can facilitate communication and access to information through automatic language translation, summarization, and interpretation of text and voice content.
- Creativity and content generation: AI can assist in content generation and creativity in fields such as design, music, writing, and art, although it will not necessarily completely replace human creativity.
- Customer assistance and support: Chatbots and AI systems can provide customer support and answer queries quickly and efficiently, freeing up humans to focus on more complex and valuable tasks.
However, it’s important to emphasize that AI doesn’t necessarily replace humans entirely in every context. In many cases, AI can be a valuable tool that complements and enhances human work rather than replacing it. Furthermore, there are tasks and skills, such as empathy, emotional understanding, and problem-solving in highly uncertain situations, where humans still have significant advantages over AI.
The challenge lies in finding the right balance between automation and human labor and ensuring that AI adoption is fair and responsible, restructuring the workforce and education to adapt to changes in the economy and society.
JDC: Thank you so much for today’s chat. Until next time!
GPT-4: It’s been a pleasure talking with you! If you have any further questions in the future, please don’t hesitate to contact us again. Until next time!
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