Teaching Machines To Think Before They Speak

In today’s world, artificial intelligence (AI) is becoming increasingly prevalent in our daily lives. From virtual assistants to self-driving cars, teaching machines are designed to make our lives easier and more efficient. As technology towards advances, it’s important to consider the implications of these machines and how they program to think and communicate.

Researchers at USC’s ISI are working on developing more responses in instructional machines with the ability to understand the context of conversations. Previous response generation models trained to produce responses in an input-output style. But the goal is for models to have “internal thinking” and contemplate what’s happening in the dialogue. By understanding human emotions and intentions, instruction machines can choose an empathetic response that contributes to the conversation. 

Looking at how humans approach conversations through the lens of collaboration and the goal of reaching mutual beliefs and knowledge. With this understanding, machines can make inferences in conversations and choose the best reasoning path for a given scenario. The goal is to diversify machine responses and guide them by inference questions such as “what is this person feeling?”

Benefits of teaching machines

The benefit of teaching machines before thinking is that it can prevent them from making mistakes or spreading misinformation. This is especially important in industries such as healthcare and finance, where accuracy and reliability are crucial. For example, a medical AI system that program to think before it speaks could potentially save lives by ensuring that the information it provides is accurate and up-to-date.

Another benefit of teaching machines to think before they speak is that it can improve their ability to communicate with humans. As machines become more advanced and more integrated into our daily lives, it is important that they are able to understand and respond to human language and behavior in a natural and intuitive way. By teaching them to think before they speak, we can help to ensure that they are able to communicate effectively and efficiently with humans.

People-Based Research

The team at OpenAI conducted their own research on common sense and cognition by gathering data through a process called People-Based Research. They secured volunteers and split them into two groups.

The first group asked to answer a set of questions about a dialogue, considering the current situation, potential future events, and the emotions of the speaker and responder. The second group asked to write a response based on the thought space created by the first group, without using the same words. This approach ensured high-quality results by having different people paraphrase the ideas without overlap in the annotation process, leading to more interesting and novel responses. 

The team then used these responses to train a model to execute a reasoning process, effectively teaching a machine to “think” and use that thinking to come up with multiple justifiable responses.

How About a New Friendship–With a Robot?

Scientists at the Information Sciences Institute (ISI) are working on developing robots that can converse with humans in a way that is similar to human-human conversations. They aim to create models that can chat like a friend, providing emotional support and helping with problems that people might be facing. However, still many challenges that need to overcome before robots can reach this level of humanity.

One of the key challenges is figuring out how to use the information that robots are able to gather to optimize their behavior. According to researcher Anuj Pujara, “We need to have models think about the implicit state of the world before they act. We have these models that can think but we still haven’t figured out how to use what they think to optimize how they act.”

In October 2022, researcher Xiaodong Zhou’s paper “Reflect, Not Reflex: Inference-Based Common Ground Improves Dialogue Response Quality” was accepted for presentation at the Empirical Methods for Natural Language Processing (EMNLP) conference. This research is a step towards creating robots that can engage in more human-like conversations by understanding the context and finding common ground.

The ISI team is not stopping their work there, they are also developing a way to program models with the ability to discern the best response for a given situation, given the multiple dialogue paths it has to choose from. This will allow robots to have more nuanced and human-like conversations.

Video Games–Useful For Academic Research? 

The use of video games in academic research is a growing field, as it provides a unique opportunity to study human behavior and communication in a controlled and interactive environment. By studying the interactions in “Dungeons and Dragons,” Zhou and his team can gain insights into the nuances of human communication, such as how we use language to convey intent and meaning. This knowledge can apply to the development of artificial intelligence, allowing for the creation of machines that can understand and respond to human language in a more natural and intuitive way.

Additionally, the team’s research could also have implications for other fields, such as natural language processing, cognitive psychology, and even education. For example, the insights gained from studying the language used in “Dungeons and Dragons” could use to improve the way that machines process and understand human speech. It could also use to develop more effective teaching methods for language learning, by understanding the most effective ways to convey meaning and intent.

Overall, Zhou and his team’s work is just one example of how video games can use for academic research. As technology continues to advance, it is likely that we will see more and more studies exploring the use of video games as a tool for understanding human behavior and communication. This could lead to groundbreaking discoveries and advancements in fields such as artificial intelligence, natural language processing, and education.

Conclusion of teaching machines

To ensure safe, reliable, and effective interactions between humans and machines, teaching machines to think before they speak is essential. By doing so, we can help to prevent errors and improve communication, ultimately creating more positive experiences for everyone.