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Innovative AI: How Adept's Action Transformer is Shaping Our Future

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Chapter 1: The Vision for AI Integration

The potential of artificial intelligence lies in creating agents, both digital and physical, capable of acting in the real world under human guidance.

AI's aspiration is to develop systems that mimic human abilities. While models like GPT-3 excel in text generation and Stable Diffusion in image creation, neither can physically engage with their environment. For the past decade, AI firms have been striving to produce intelligent agents that can bridge this gap, and recent advancements signal a shift in this direction.

One recent development is Google's PaLM-SayCan (PSC), a robot utilizing the PaLM language model, which is currently among the most sophisticated available. PSC can decipher user requests in natural language and convert them into high-level tasks, which can then be broken down into actionable steps. The robotic component of PSC can execute these actions in the physical realm to meet user demands. Despite its limitations, which I discussed in detail, Google's PSC exemplifies the merging of cutting-edge AI with robotics—where the digital meets the physical.

Moreover, physical robots are not the only AI entities capable of directly interacting with the world. Another promising area of research involves digital agents that can engage with software through open-ended tasks. Unlike GPT-3 or DALL·E, which operate within a narrow range of actions due to their lack of any motor functions, OpenAI's Video PreTraining (VPT) model represents a significant advance. VPT learned to navigate the game Minecraft by observing human players, thereby somewhat mimicking their actions.

Currently, systems like VPT are rare due to the nascent stage of this technology. Developing such systems is challenging: while GPT-3 can alter information about the world, VPT can also change the state of the digital environment. VPT showcases greater autonomy and symbolizes a step closer to achieving general intelligence.

However, today's focus is on a new startup, Adept, which aims to create "useful general intelligence." Founded earlier this year, Adept has attracted talented individuals from Google, DeepMind, and OpenAI— including the original creators of the transformer model from 2017. Their goal is not to produce an AGI that could replace human jobs but to develop an intelligent interface that facilitates collaboration between humans and the digital world. As researcher David Luan states, "We want to establish a natural language interface—a natural language frontend for your computer."

Adept has unveiled its inaugural AI model: the Action Transformer (ACT-1). While technical specifications are still forthcoming, it's described as "a large-scale transformer trained to utilize digital tools." Adept intends for ACT-1 to serve as a bridge between users and software applications, acting as a natural human-computer interface (HCI).

ACT-1 has been showcased in several brief demo videos, demonstrating its ability to process high-level requests in natural language and execute them—similar to Google's PSC. Its tasks can span various software tools and websites, involving multiple steps and degrees of complexity. Notably, ACT-1 is capable of undertaking tasks that users might find challenging. Its versatility positions it as a multitasking meta-learner, allowing users to focus on more complex problems while delegating the execution of tasks to ACT-1.

However, the effectiveness of ACT-1 hinges on a significant "if." If ACT-1 were flawless, users could rely on it without hesitation. But if it were to fail, how could users determine when to trust it, especially for tasks outside their expertise? This highlights the growing importance of effective prompting—an essential skill in the future of AI interaction.

Chapter 2: Challenges of Trust and Reliability

One of the primary drawbacks of transformer-based models like GPT-3 is their unreliability in high-stakes environments, such as mental health therapy. These models, including ACT-1, are trained on vast internet datasets and are designed to predict the next action based on previous inputs. Consequently, they often lack common sense, the ability to discern intent, and a deep understanding of the world.

While ACT-1 aims to serve a different function than GPT-3, it shares similar limitations. For example, in a demonstration, a user requests ACT-1 to find a house in Houston under $600,000. Although ACT-1 successfully identifies a suitable property, it fails to grasp the underlying intent of the request, which could involve personal motivations or external context.

This brings us back to the critical "if." If users rely on ACT-1 for tasks they are unfamiliar with, they may inadvertently develop a blind trust in the AI, which could lead to dependency on an unreliable system.

It is worth noting that we already depend on various levels of abstraction in technology, which, if disrupted, could leave us vulnerable. However, the structures we rely on typically possess a degree of trustworthiness—such as the aviation industry—where societal incentives promote safety. In contrast, many deep learning-based systems lack this inherent reliability.

Alternatively, a more informed user might hesitate to trust the AI blindly, yet they would still struggle to assess whether ACT-1 performed the task correctly. This challenge mirrors the experience with GPT-3, where users occasionally find themselves verifying outputs to ensure accuracy.

If skepticism around ACT-1 grows strong enough, users may opt not to engage with it. However, this raises another concern: what if society becomes heavily reliant on such natural language interfaces as it has with social media and smartphones? This scenario could have significant implications.

Until we achieve AI systems that inspire trust, as noted by Professor Gary Marcus, the promises of technologies like ACT-1 remain just that—promises. If ACT-1 does not operate reliably, it may only serve as a costly tool that users could manage on their own, often needing to correct errors in the process.

Chapter 3: The Future of Prompting in AI Interaction

The second major point of discussion is the significance of prompting in human-computer interaction. Prompting, which I previously addressed in "Software 3.0—How Prompting Will Change the Rules of the Game," refers to the use of natural language to instruct AI systems to perform specific tasks.

Prompting is essential for making generative AI models function as intended. For instance, to instruct GPT-3 to create an essay, one might say, "Compose a 5-paragraph essay discussing the risks of AI." Similarly, to generate an image using DALL·E, a prompt could be "A cat and a dog playing with a ball on a sunny day, in a vibrant and colorful style, HD." Both Google's PSC and Adept's ACT-1 operate on this principle.

Prompting offers a more intuitive alternative to traditional programming languages, which can be challenging to master. Learning a programming language like Python or C often requires extensive training, whereas prompting relies on natural language, making it accessible to a broader audience.

While some may compare prompting to no-code tools, there is a fundamental difference. No-code platforms simplify the coding process but still necessitate users learn the specifics of each tool. In contrast, ACT-1 serves as a meta-tool, allowing users to rely solely on their prompting skills to operate it effectively.

Prompting can be viewed as the culmination of a historical progression in human-computer interaction—from punch cards to machine code, assembly language, and high-level programming languages. Each step has aimed to simplify the complexity of computer communication for users.

Although intuitive, prompting is not an innate ability; it requires practice to master. It can be likened to adjusting one's communication style based on the audience, such as speaking differently to a child or employing rhetoric in political discourse. This nuanced form of interaction is essential for effective prompting.

Tech blogger Gwern suggests viewing prompting as a new programming paradigm. This perspective may alienate those unfamiliar with coding but underscores that prompting is a skill that necessitates practice. For example, effectively using GPT-3 often requires multiple attempts to achieve a satisfactory result.

Even as AI tools like GPT-3 or ACT-1 prove valuable, users must still learn to craft effective prompts, similar to how people currently engage with various generative models.

While prompting is not a cure-all, it represents a significant advancement in human-computer interaction, democratizing access to technology for those who may otherwise struggle.

Chapter 4: The Limitations of Prompting

Despite its advantages, prompting has one notable drawback: the inherent ambiguity of human language combined with a lack of context. Unlike programming languages, which have rigid syntax and clarity, natural languages are open to interpretation.

Humans can often infer meaning through shared knowledge and contextual cues, but AI systems like GPT-3 and ACT-1 lack this understanding. They are not privy to the nuanced context of individual interactions, which can lead to ambiguity in their responses.

When faced with vague prompts, ACT-1 may either make an educated guess or halt the task altogether. This limitation is not new and has been observed in large language models and robotic systems like PSC.

A potential solution involves training AI systems similarly to how we educate children, allowing them to grow and learn through interactions with the world. However, restricting their capabilities to tasks that require minimal context may limit their overall utility.

Ultimately, Adept's journey toward developing AI agents capable of real-world actions is ambitious. Like other leaders in the field, they face the challenge of creating AI that is both powerful and reliable.

Adept's vision highlights the critical role of prompt programming as a new paradigm for communicating with AI systems, revealing the benefits over traditional methods and the significant hurdles that remain.

Keep an eye on Adept, as the Action Transformer (ACT-1) heralds an exciting line of research that will continue to evolve in the coming months and years.

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