From chatbots to superintelligence: Mapping AI’s ambitious journey

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Is humanity on the brink of creating its intellectual superior? Some think we are on the cusp of such a development. Last week, Ilya Sutskever unveiled his new startup, Safe Superintelligence, Inc. (SSI), which is dedicated to building advanced artificial superintelligence (ASI) models — a hypothetical AI far beyond human capability. In a statement about launching SSI, he said “superintelligence is within reach,” and added: “We approach safety and capabilities in tandem.”

Sutskever has the credentials to aspire to such an advanced model. He was a founding member of OpenAI and formerly served as the company’s chief scientist. Before that, he worked with Geoffrey Hinton and Alex Krizhevsky at the University of Toronto to develop “AlexNet,” an image classification model that transformed deep learning in 2012. More than any other, this development kicked-off the surge in AI over the last decade, in part by demonstrating the value of parallel instruction processing by graphics processing units (GPUs) to speed deep learning algorithm performance.

Sutskever is not alone in his belief about superintelligence. SoftBank CEO Masayoshi Son said late last week that AI “10,000 times smarter than humans will be here in 10 years.” He added that achieving ASI is now his life mission.

AGI within 5 years?

Superintelligence goes way beyond artificial general intelligence (AGI), also still a hypothetical AI technology. AGI would surpass human capabilities in most economically valuable tasks. Hinton believes we could see AGI within five years. Ray Kurzweil, lead researcher and AI visionary at Google, defines AGI as “AI that can perform any cognitive task an educated human can.” He believes this will occur by 2029. Although in truth, there is no commonly accepted definition of AGI, which makes it impossible to accurately predict its arrival. How would we know?

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The same could likely be said for superintelligence. However, at least one prognosticator is on record saying that superintelligence could arrive soon after AGI, possibly by 2030.

Despite these expert opinions, it remains an open question whether AGI or superintelligence will be achieved in five years — or ever. Some, such as AI researcher Gary Marcus, believe the current focus on deep learning and language models will never achieve AGI (let alone superintelligence), seeing these as fundamentally flawed and weak technologies that can advance only through the brute force of more data and computing power. 

Pedro Domingos, University of Washington computer science professor and author of The Master Algorithm, sees superintelligence as a pipe dream. “Ilya Sutskever’s new company is guaranteed to succeed, because superintelligence that is never achieved is guaranteed to be safe,” he posted to X (formerly Twitter).

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What comes next

One of these viewpoints might prove to be correct. No one knows for certain if AGI or superintelligence is coming or when. As this debate continues, it’s crucial to recognize the chasm between these concepts and our current AI capabilities. 

Rather than speculating solely on far-future possibilities that are fueling exuberant stock market dreams and public anxiety, it’s at least equally important to consider the more immediate advancements that are likely to shape the AI landscape in the coming years. These developments, while less sensational than the grandest AI dreams, will have significant real-world impacts and pave the way for further progress.

As we look ahead, the next several years will likely see AI language, audio, image and video models — all forms of deep learning — continue to evolve and proliferate. While these advancements may not achieve AGI or superintelligence, they will undoubtedly enhance AI’s capabilities, utility, reliability and application.

That said, these models still face several significant challenges. One major shortcoming is their tendency to occasionally hallucinate or confabulate, essentially making up answers. This unreliability remains a clear barrier to widespread adoption at present. One approach to improve AI accuracy is retrieval augmented generation (RAG), which integrates current information from external sources to provide more accurate responses. Another could be “semantic entropy,” which uses one large language model to check the work of another. 

No universal answers about AI (yet)

As bots become more reliable over the next year or two, they will be increasingly incorporated into business applications and workflows. To date, many of these efforts have fallen short of expectations. This outcome is not surprising, as the incorporation of AI amounts to a paradigm shift. My view is that it is still early, and that people are still gathering information and learning about how best to deploy AI. 

Wharton professor Ethan Mollick echoes this view in his One Useful Thing newsletter: “Right now, nobody — from consultants to typical software vendors — has universal answers about how to use AI to unlock new opportunities in any particular industry.”

Mollick argues that a lot of the progress in implementing generative AI will come from workers and managers who experiment with applying the tools to their areas of domain expertise to learn what works and adds value. As AI tools become more capable, more people will be able to advance their work output, creating a flywheel of AI-powered innovation within businesses.  

Recent advancements demonstrate this innovation potential. For instance, Nvidia’s Inference Microservices can accelerate AI application deployments, and Anthropic’s new Claude Sonnet 3.5 chatbot reportedly outperforms all competitors. AI technologies are finding increased application across various fields, from classrooms to auto dealerships and even in the discovery of new materials.

Progress is likely to steadily accelerate

A clear sign of this acceleration came from Apple with their recent launch of Apple Intelligence. As a company, Apple has a history of waiting to enter a market until there is sufficient technology maturity and demand. This news suggests that AI has reached that inflection point. 

Apple Intelligence goes beyond other AI announcements by promising deep integration across apps while maintaining context for the user, creating a deeply personalized experience. Over time, Apple will enable users to implicitly string multiple commands together into a single request. These may execute across multiple apps but will appear as a single result. Another word for this is “agents.” 

During the Apple Intelligence launch event, SVP of software engineering Craig Federighi described a scenario to showcase how these will work. As reported by Technology Review, “an email comes in pushing back a work meeting, but his daughter is appearing in a play that night. His phone can now find the PDF with information about the performance, predict the local traffic, and let him know if he’ll make it on time.” 

This vision of AI agents performing complex, multi-step tasks is not unique to Apple. In fact, it represents a broader shift in the AI industry towards what some are calling the “Agentic era.”

AI is becoming a true personal assistant

In recent months there has been increasing industry discussion about moving beyond chatbots and into the realm of “autonomous agents” that can perform multiple linked tasks based on a single prompt. More than just answering questions and sharing information, this new crop of systems use LLMs to complete multi-step actions, from developing software to booking flights. According to reports, Microsoft, OpenAI and Google DeepMind are all readying AI agents designed to automate more difficult multi-step tasks. 

OpenAI CEO Sam Altman described the agent vision as a “super-competent colleague that knows absolutely everything about my whole life, every email, every conversation I’ve ever had, but doesn’t feel like an extension.” In other words, a true personal assistant. 

Agents will serve applications across enterprise uses as well. McKinsey senior partner Lari Hämäläinen describes this advancement as “software entities that can orchestrate complex workflows, coordinate activities among multiple agents, apply logic and evaluate answers. These agents can help automate processes in organizations or augment workers and customers as they perform processes.”  

Start-ups focused on enterprise agents are also appearing — such as Emergence, which fittingly just came out of stealth mode. According to TechCrunch, the company claims to be building an agent-based system that can perform many of the tasks typically handled by knowledge workers.

The way forward

With the pending arrival of AI agents, we will even more effectively join the always-on interconnected world, both for personal use and for work. In this way, we will increasingly dialog and interact with digital intelligence everywhere. 

The path to AGI and superintelligence remains shrouded in uncertainty, with experts divided on its feasibility and timeline. However, the rapid evolution of AI technologies is undeniable, promising transformative advancements. As businesses and individuals navigate this rapidly changing landscape, the potential for AI-driven innovation and improvement remains vast. The journey ahead is as exciting as it is unpredictable, with the boundaries between human and artificial intelligence continuing to blur.

By mapping out proactive steps now to invest and engage in AI, upskill our workforce and attend to ethical considerations, businesses and individuals can position themselves to thrive in the AI-driven future.

Gary Grossman is EVP of technology practice at Edelman and global lead of the Edelman AI Center of Excellence.


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