The Power of Natural Language: Using AI to Build Smarter Machines and Tools

In the heart of the MIT-IBM Watson AI Lab, two master’s of engineering students are harnessing the power of natural language to unlock new possibilities in AI technology. This blog delves into the fascinating world of Irene Terpstra and Rujul Gandhi’s research, showcasing how they are leveraging the rich tapestry of human language to build smarter AI systems, from chip design to robot communication and low-resource language processing.

Unveiling the Secrets of Chip Design: A Dance of Language and Algorithms

Terpstra’s project focuses on revolutionizing computer chip design by integrating the knowledge of large language models with the optimization power of reinforcement learning. Imagine an AI system that can analyze existing literature on chip modifications, understand human instructions in natural language, and then, through trial and error, design new chips that achieve specific performance goals. That’s the ambitious vision Terpstra is pursuing.

This journey unfolds in three key steps:

1. Analyzing Language Models: Terpstra is delving into the reasoning capabilities of pre-trained language models like ChatGPT and Llama 2 to understand how their knowledge can be applied to chip design. This involves deciphering the “why” behind their suggestions and identifying actionable insights.

2. Bridging the Gap with Code: The NGspice circuit simulator serves as the translator, taking the natural language prompts from Terpstra’s system and converting them into instructions for the chip design in code format. This allows the AI to manipulate and iterate on different designs.

3. Reinforcement Learning Takes the Wheel: A reinforcement learning algorithm takes the baton from the language model, receiving the code-based chip designs and evaluating their performance against the desired goals. It then guides the AI to refine its designs through continuous loops of experimentation and optimization.

The ultimate aim is to create an AI system that can autonomously design high-performance chips, drawing on the combined power of natural language understanding and reinforcement learning. Terpstra’s work paves the way for a future where chip design is faster, more efficient, and driven by the rich knowledge within natural language.

From Text to Action: Enabling Seamless Communication with Robots

While Terpstra focuses on the inner workings of machines, Gandhi tackles the crucial challenge of human-robot communication. Her project aims to bridge the gap between our natural language and the formal logic robots often operate on.

Imagine telling your robot to “clean the house until all the dishes are done,” and it flawlessly understanding your intent, breaking down the task into sub-steps, and even asking for clarification if it encounters any uncertainties. This is the vision Gandhi’s research strives to realize.

Here’s how it works:

1. Decoding the Language: Gandhi’s system, powered by the T5 encoder-decoder model, dissects natural language instructions into their smallest logical units, known as atomic propositions. This breakdown ensures the robot understands the core meaning of your instructions, not just the surface-level words.

2. Sub-tasks and Dependencies: By identifying sub-tasks within your instructions, like “load dishwasher” or “wipe down counters,” the system can efficiently manage the execution process. Moreover, Gandhi’s research takes into account logical dependencies like “until” or “after,” allowing the robot to understand the sequence of actions you expect.

3. Flexibility and Collaboration: By leveraging real-world data of how humans naturally communicate, Gandhi’s system is adaptable to various phrasing and even open to seeking clarification if it encounters ambiguity. This fosters a collaborative human-robot interaction, where you can guide the robot and the robot can ask for help when needed.

Beyond household tasks, Gandhi’s research holds immense potential for diverse applications. Imagine robots seamlessly assisting in healthcare, construction, or even disaster response, all thanks to their ability to understand and respond to natural language instructions.

Whispers in the Wind: Unlocking the Secrets of Low-Resource Languages

Not all languages have the luxury of abundant data or even a written form. Gandhi’s passion lies in developing speech models that can process and understand such low-resource languages, opening doors to a world of previously unheard voices.

Imagine interacting with your digital devices or receiving vital information in your native language, even if it’s not widely spoken or documented. This is the dream Gandhi is chasing.

The key lies in leveraging the inherent patterns within spoken language itself. Instead of relying on written vocabulary, Gandhi’s system analyzes sound sequences and identifies frequently occurring patterns, inferring potential words and concepts.

These inferred words then form a pseudo-vocabulary, providing a foundation for further analysis and application development. With this approach, even languages with limited data can be unlocked, allowing for translation, voice assistance, and other crucial language technologies to reach previously neglected communities.

A World Painted with Words: The Endless Possibilities of Natural Language

Through their individual research journeys, Irene Terpstra and Rujul Gandhi offer a glimpse into the transformative potential of natural language in shaping the future of AI. From the intricate architecture of computer chips to the nuanced conversations with robots and the whispered voices of low-resource languages, their work highlights the power of using our own language to unlock new avenues of innovation.

Embracing the Horizon: A Future Shaped by Natural Language

In navigating the realms of chip design, human-robot communication, and low-resource language processing, Irene Terpstra and Rujul Gandhi exemplify the transformative potential woven into the fabric of natural language. Their endeavors not only shed light on the current state of AI technology but also unveil the vast possibilities that lie ahead.

As we delve into the intricate dance of algorithms and language, we witness the emergence of an AI landscape where communication is not just a transaction but a nuanced interplay between human intent and machine understanding. Terpstra’s vision of autonomous chip design and Gandhi’s pursuit of seamless human-robot collaboration reflect a future where technology harmonizes with the richness of human expression.

In concluding this journey through the power of natural language, we stand at the precipice of an era where words paint the canvas of innovation. The endless horizons painted by Terpstra and Gandhi beckon us towards a future where AI systems not only comprehend our language but engage in a dialogue that transcends the boundaries of conventional human-machine interactions. As we navigate this evolving landscape, the resonance of words becomes the driving force behind the next wave of AI advancements, promising a future shaped by the eloquence of natural language.