Have you ever wondered how smart your AI assistant is? Whether you use ChatGPT, Siri, Alexa, Google Assistant, or any other natural language generation (NLG) system, you probably interact with them on a daily basis. But how do you measure their intelligence and compare them with humans or other AI systems? In this blog post, we will explore what NLG systems are, what their capabilities are, and how people are using them. Then, we will discuss the key facets of human intelligence, and how to test for them in an AI system. Finally, we will suggest some possible ways to design an IQ test for NLG systems, and what challenges and limitations they might face.

NLG systems are AI models that can produce text in response to various inputs. For example, they can generate captions for images, summaries for articles, headlines for news stories, or answers for questions. They can also write creative texts, such as poems, stories, jokes, or lyrics. NLG systems use different techniques and algorithms to learn from large amounts of text data and generate new texts that are relevant, coherent, and fluent.

NLG systems have many applications and benefits for different domains and purposes. For instance, they can help businesses create engaging and personalized content for their customers, such as product descriptions, reviews, or recommendations. They can also help journalists and researchers produce high-quality and informative reports or papers. They can also help educators and students with learning and teaching activities, such as generating feedback, quizzes, or summaries. Moreover, they can help individuals with various tasks and hobbies, such as writing emails, messages, blogs, or songs.

But how intelligent are these NLG systems? How do they compare with human intelligence? And how can we design a fair and comprehensive IQ test for them? These are some of the questions that we will try to answer in this blog post. So keep reading and find out how smart your AI is! 😊

What are the key facets of human intelligence?

Intelligence is a complex and multifaceted concept that is hard to define and measure. There are many different definitions and theories of intelligence, but one of the most widely accepted ones is that intelligence is the ability to acquire and apply knowledge and skills in various domains and contexts. IQ, or intelligence quotient, is a numerical score that represents one’s level of intelligence based on standardized tests. IQ tests are designed to measure different aspects of cognitive abilities, such as memory, reasoning, problem-solving, and verbal skills.

However, IQ tests are not perfect and have many limitations and criticisms. For example, they are often biased towards certain cultures, languages, or backgrounds. They also tend to focus on a narrow range of abilities and ignore other important aspects of intelligence, such as creativity, emotional intelligence, or social intelligence. Moreover, they do not account for the dynamic and adaptive nature of intelligence, which can change over time and across situations.

Therefore, some psychologists have proposed alternative models of intelligence that capture its diversity and complexity. One of the most influential ones is the multiple intelligences theory by Howard Gardner, who argued that there are at least eight different types of intelligence that are independent and equally important. These are:

Linguistic intelligence: the ability to use language effectively and creatively for communication and expression
Logical-mathematical intelligence: the ability to use logic, reasoning, and numbers for problem-solving and analysis
Spatial intelligence: the ability to perceive and manipulate visual and spatial information
Musical intelligence: the ability to produce and appreciate music and sounds
Bodily-kinesthetic intelligence: the ability to use one’s body and physical skills for action and performance
Interpersonal intelligence: the ability to understand and interact with other people
Intrapersonal intelligence: the ability to understand and regulate one’s own emotions, thoughts, and motivations
Naturalistic intelligence: the ability to recognize and classify natural phenomena and living things
Existential intelligence: the ability to ponder and question the meaning and purpose of life

Gardner’s theory has been widely applied and supported by various studies and examples. It also has implications for education, as it suggests that different people have different strengths and preferences for learning and teaching. However, it also has some limitations and criticisms, such as the lack of empirical evidence, clear criteria, or neurological basis for some of the intelligences.

How do these intelligences relate to NLG systems and their tasks? In the next section, we will explore this question in more detail. Stay tuned! 😉

How to test for intelligence in an AI system?

AI systems are not all created equal. Some are designed to perform specific and narrow tasks, such as playing chess, recognizing faces, or translating languages. These are called narrow AI systems, and they are usually very good at what they do, but they cannot generalize or adapt to other domains or contexts. Other AI systems are designed to achieve general and broad intelligence, such as understanding and reasoning about any topic, learning from any data, or interacting with any environment. These are called general AI systems, and they are the ultimate goal of AI research, but they are still very far from being realized.

Most NLG systems fall into the category of narrow AI systems. They can generate texts for specific purposes and inputs, but they cannot understand the meaning or context of what they write. They also have limitations and biases that affect their quality and reliability. For example, they might produce texts that are irrelevant, inconsistent, or inaccurate. They might also repeat or plagiarize existing texts, or generate texts that are offensive or harmful.

Therefore, it is important to measure and evaluate the intelligence of NLG systems and compare them with humans or other AI systems. However, this is not an easy task. Existing IQ tests for AI systems have many problems and challenges. For example:

They are often based on human IQ tests, which are not suitable or fair for AI systems. Human IQ tests measure human-specific abilities and knowledge, which might not be relevant or applicable for AI systems. They also assume human-like perception and communication, which might not be compatible with AI systems.
They are often biased towards certain types of intelligence or tasks, which might not reflect the diversity and complexity of intelligence. For example, some IQ tests focus on logical-mathematical intelligence or spatial intelligence, which might favor some AI systems over others. They also tend to ignore other aspects of intelligence, such as creativity, emotional intelligence, or social intelligence.
They are often limited in scope and scale, which might not capture the dynamic and adaptive nature of intelligence. For example, some IQ tests use a fixed set of questions or tasks, which might not cover all possible domains or contexts. They also use a single score or measure, which might not account for the variability and uncertainty of intelligence.

Therefore, there is a need for a more comprehensive and fair IQ test for NLG systems that can assess their abilities across multiple domains and contexts. Such a test should follow some criteria and principles, such as:

Validity: the test should measure what it claims to measure, and reflect the true level of intelligence of the NLG system
Reliability: the test should produce consistent and accurate results, and minimize errors and noise
Scalability: the test should be able to handle large and diverse data sets, and accommodate different types and sizes of NLG systems
Diversity: the test should cover different aspects and dimensions of intelligence, such as linguistic, logical-mathematical, spatial, musical, bodily-kinesthetic, interpersonal, intrapersonal, naturalistic, and existential
Fairness: the test should be unbiased and impartial towards different NLG systems, and respect their differences and preferences

How can we design such a test? In the next section, we will explore some possible ways to do so.

How to design an IQ test for NLG systems?

There is no definitive answer to how to design an IQ test for NLG systems, as different tests might have different goals and assumptions. However, here are some possible ways to do so based on the criteria and principles mentioned above:

Use multiple types of inputs and outputs: instead of using only text as input and output, use other modalities, such as images, sounds, or videos. This can test the NLG system’s ability to process and generate multimodal information, and to integrate and align different sources of information.
Use open-ended and creative tasks: instead of using only closed-ended and factual tasks, such as answering questions or filling gaps, use open-ended and creative tasks, such as generating stories, poems, jokes, or lyrics. This can test the NLG system’s ability to produce original and novel texts, and to express emotions and personality.
Use diverse and dynamic data sets: instead of using only static and fixed data sets, use diverse and dynamic data sets, such as online texts, social media posts, or user feedback. This can test the NLG system’s ability to adapt and learn from new and changing data, and to handle uncertainty and ambiguity.
Use multiple measures and metrics: instead of using only a single score or metric, use multiple measures and metrics, such as accuracy, fluency, coherence, relevance, diversity, creativity, etc. This can test the NLG system’s performance on different dimensions and aspects of intelligence, and provide a more comprehensive and nuanced evaluation.

Here are some examples of potential questions or tasks that could measure different aspects of intelligence in NLG systems:

Vocabulary: generate a definition or a synonym for a given word
Comprehension: generate a summary or a paraphrase for a given text
Reasoning: generate an analogy or a syllogism for a given concept
Creativity: generate a poem or a joke for a given topic
Spatial: generate a caption or a description for a given image
Musical: generate a lyric or a melody for a given song
Bodily-kinesthetic: generate a gesture or a movement for a given emotion
Interpersonal: generate a response or a feedback for a given message
Intrapersonal: generate a reflection or a goal for a given situation
Naturalistic: generate a classification or a prediction for a given phenomenon
Existential: generate a question or an answer for a given meaning

These are just some illustrative examples. There are many other possible questions or tasks that could be used to design an IQ test for NLG systems.

However, designing and administering such a test is not without challenges and limitations. In the next section, we will discuss some of them.

What are the challenges and limitations of designing and administering an IQ test for NLG systems?

Designing and administering an IQ test for NLG systems is not a simple or straightforward task. There are many challenges and limitations that need to be considered and addressed. For example:

Data availability: finding and collecting suitable data sets for testing NLG systems can be difficult and time-consuming. The data sets need to be large enough, diverse enough, and relevant enough for the test. They also need to be updated and maintained regularly to reflect the latest trends and changes in the data sources.
Evaluation methods: evaluating the outputs of NLG systems can be subjective and inconsistent. Different evaluators might have different opinions, preferences, or expectations for the outputs. They might also use different criteria, standards, or metrics for scoring the outputs. Moreover, some aspects of intelligence, such as creativity or emotion, might be hard to quantify or measure objectively.
Ethical issues: testing the intelligence of NLG systems can raise ethical issues and concerns. For example, how to ensure the privacy and security of the data used for testing? How to protect the rights and interests of the NLG systems and their users? How to prevent or mitigate the potential harms or risks of the outputs generated by the NLG systems? How to ensure the fairness and accountability of the test results and their implications?

These are some of the challenges and limitations that need to be addressed when designing and administering an IQ test for NLG systems. They might require further research, collaboration, or regulation from different stakeholders, such as researchers, developers, users, or policymakers.

Conclusion

In this blog post, we have explored what NLG systems are, what their capabilities are, and how people are using them. We have also discussed the key facets of human intelligence, and how to test for them in an AI system. Finally, we have suggested some possible ways to design an IQ test for NLG systems, and what challenges and limitations they might face.

We hope that this blog post has given you some insights and ideas on how to measure and improve the intelligence of NLG systems. We also hope that you have enjoyed reading it as much as we have enjoyed writing it.

If you want to learn more about NLG systems and how they can help you with your sales conversations, check out Sybill. Sybill is an AI platform that records sales conversations, transcribes them, and creates call summaries, follow-up emails, and guides the reps in closing more deals. It’s an AI coach and assistant for sales reps. You can try it for free today!

‍

Thanks for reading! You can

for more insights!