New neural model enables AI-to-AI linguistic communication.

In a major leap forward for artificial intelligence (AI), a team at the University of Geneva (UNIGE) has successfully developed a model that mimics a uniquely human trait: performing tasks based on verbal or written instructions. and later pass them on to others. This achievement addresses a long-standing challenge in AI, marking a milestone in the evolution of the field.

Historically, AI systems have excelled at processing vast amounts of data and performing complex computations. However, they have consistently fallen short in tasks that humans perform intuitively—learning a new task from simple instructions and then describing the process for others to copy. The ability to not only understand but also to communicate complex instructions is evidence of advanced cognitive functions that remain a distinctive feature of human intelligence.

The UNIGE team’s progress goes beyond simply implementing the task to generalize human-like language. It involves an AI model capable of absorbing instructions, performing specified tasks, and then communicating with a ‘sister’ AI to describe the process in linguistic terms, mimicking it. can be enabled. This development opens up unprecedented possibilities in AI, especially in the realm of human-AI interaction and robotics, where effective communication is crucial.

The challenge of replicating human cognitive abilities in AI

Human cognitive skills demonstrate the remarkable ability to learn and communicate complex tasks. These abilities, which are deeply rooted in our nervous systems, allow us to understand instructions quickly and communicate our understanding to others in a coherent manner. Simulating this complex interplay between learning and linguistic expression in AI has been a considerable challenge. Unlike humans, traditional AI systems require extensive training on specific tasks, often relying on large data sets and iterative reinforcement learning. AI’s ability to intuitively understand a task with minimal instruction and then clarify that understanding is still lacking.

This difference in AI capabilities highlights the limitations of existing models. Most AI systems operate within the limitations of their programmed algorithms and datasets, lacking the ability to extrapolate from training or make predictions. Consequently, the ability for AI to adapt to new scenarios or communicate insights in a human-like manner is significantly limited.

The UNIGE study represents an important advance in overcoming these limitations. By engineering an AI model that not only executes tasks based on instructions, but also communicates those tasks to another AI entity, the UNIGE team has demonstrated a significant advance in AI’s cognitive and linguistic capabilities. The development suggests a future where AI can more closely mimic human-like learning and communication, opening the door to applications that require such dynamic interaction and adaptation.

Bridging the gap with natural language processing

Natural Language Processing (NLP) is at the forefront of bridging the gap between human language and AI comprehension. NLP enables machines to understand, interpret and respond to human language in a meaningful way. This subfield of AI focuses on the interaction between computers and humans using natural language, with the goal of reading, understanding and understanding human languages ​​in a meaningful way.

The core principle of NLP lies in its ability to process and analyze large amounts of natural language data. This analysis is not limited to understanding words in a literal sense, but extends to understanding context, emotion and even the subtle nuances within language. By leveraging NLP, AI systems can perform many tasks, from translation and sentiment analysis to complex interactions such as conversational agents.

Central to this advance in NLP is the development of artificial neural networks, which take inspiration from biological neurons in the human brain. These networks mimic the way human neurons transmit electrical signals, processing information through interconnected nodes. This architecture allows neural networks to learn from input data and improve over time, much like the human brain learns from experience.

The connection between these artificial neural networks and biological neurons is a key component in advancing the linguistic capabilities of AI. By modeling the neural processes involved in the comprehension and production of human language, AI researchers are laying the foundations for systems that can process language in ways that mirror human cognitive functions. The UNIGE study exemplifies this approach, using advanced neural network models to simulate and simulate the complex interplay between language comprehension and task processes involved in human cognition.

The UNIGE Approach to AI Communication

A team from the University of Geneva attempted to develop an artificial neural network that mirrors human cognitive abilities. The key was to develop a system that could not only understand language, but also use it to communicate learned tasks. Their approach began with an existing artificial neuron model, S-Bert, known for its language comprehension abilities.

The UNIGE team’s strategy involves combining S-Bert, which consists of 300 million neurons already trained in language understanding, with a smaller, simpler neural network. This small network was tasked with replicating specific areas of the human brain involved in language processing and production – Wernick’s area and Broca’s area, respectively. The cerebellum’s area of ​​the brain is important for language comprehension, while Broca’s area plays an important role in speech production and language processing.

The fusion of these two networks aims to simulate the complex interactions between these two brain regions. Initially, the convolutional network was trained to mimic Wernicke’s area, honing its ability to understand and interpret language. Subsequently, he trained to mimic the functions of Broca’s area, enabling language production and articulation. Notably, the entire process was performed using conventional laptop computers, demonstrating the accessibility and scalability of the model.

Experience and its effects

The experiment involved feeding the AI ​​written instructions in English, which then had to perform the indicated tasks. These tasks varied in complexity, from simple actions such as pointing to a location in response to a stimulus, to more complex tasks such as perceiving and responding to subtle contrasts in visual stimuli.

The model mimicked the movement or gesturing intent by simulating human responses to these tasks. Specifically, after mastering these tasks, the AI ​​was able to describe them linguistically on another network, which was a duplicate of the first. This second network, upon receiving instructions, successfully replicated the tasks.

This achievement is the first instance where two AI systems have communicated with each other purely through language, a milestone in the development of AI. The ability of one AI to instruct another to complete tasks through linguistic communication alone opens new frontiers in AI interaction and collaboration.

The implications of this development extend beyond academic interest, promising substantial advances in fields that rely on advanced AI communication, such as robotics and automated systems.

Robotics and the possibilities beyond

This innovation significantly affects the field of robotics and extends to various other fields. The potential applications of this technology in robotics are particularly promising. Humanoid robots equipped with these advanced neural networks can understand and execute complex instructions, increasing their functionality and autonomy. This capability is critical for robots designed for tasks that require adaptation and learning, such as healthcare, manufacturing, and personal assistance.

Furthermore, the technology’s implications extend beyond robotics. In fields such as customer service, education, and healthcare, AI systems with improved communication and learning capabilities can offer more personalized and efficient services. The development of more complex networks based on the UNIGE model provides opportunities to build AI systems that not only understand human language but also interact in a way that mimics human cognitive processes, allowing users to There are more natural and intuitive experiences.

These advances in AI communication point to a future where the gap between human and machine intelligence diminishes, leading to advances that could redefine how we interact with technology. The UNIGE study, therefore, is not only a testament to the emerging capabilities of AI but also a beacon for future explorations in the realm of artificial cognition and communication.

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