As David Shapiro mentions, AGI is projected to become a reality in the next 1.5 years.
In Brief
The anticipation is that artificial general intelligence could emerge within the next year and a half, driven by robust funding, accessible frameworks, and the evolution of language models into agents capable of cognitive functions.
In a recent video breakdown, David Shapiro argues that the timeframe could indeed suffice for AGI to take shape.
As mentioned, we’ve crossed a significant threshold, indicating that AGI might become a reality in 1.5 years. The interplay of ample funding, open-source frameworks, and the adaptation of language models into cognitive entities is key. A recent analysis by David Shapiro offers an intriguing perspective on these developments, highlighting three crucial factors that may not be immediately apparent.
If we consider the potential effects of the three factors Shapiro outlined regarding AI’s advancement, this period could very well be sufficient for AGI to emerge globally.
To steer clear of superficial debates about terminology, let's clarify things right away.
- The term 'artificial general intelligence' can be defined in countless ways, many of which can be contradictory or differ significantly. This often requires an iterative clarification of the terms used in these definitions.
- For a straightforward approach, we’ll leave philosophical disagreements aside and apply what we call the 'duck test': If an AI can engage like a human, handle intellectual tasks seamlessly, and adapt to new scenarios with human-like behavior, we will categorize that AI as artificial general intelligence (AGI).
- That the phrase “AGI When we say 'will appear in 1.5 years,' we are implying the creation of an AI that meets the 'duck test' standard.
David Shapiro presents a compelling case that a year and a half is indeed a feasible timeline for the development of AGI. AGI is based on three bases.
1) Corporations are genuinely optimistic about the remarkable possibilities of AI. As a result, we can expect substantial financial backing in AI innovation over the next 18 months, aimed at significantly lowering the costs associated with 'intelligent inference' for consumers - like what you might have on your smartphone - given the current hefty expenses involved in training large models. He cites an interesting point: 'We believe that GPT-5 is currently running on 25,000 GPUs, which amounts to about $225 million in NVIDIA hardware, and the costs for inference are likely much lower than the figures we've observed.' Morgan Stanley report 2) New frameworks for developing applications based on language models, such as [example], not only provide API access to the model but also:
- Enable the model to utilize external data sources. LangChain - Transform the model into an agent that can interact with its surroundings.
3) Innovative systemic paradigms (for instance, MM-REACT) have emerged that integrate ChatGPT with expert systems to facilitate multimodal reasoning and action when tackling complex issues. Within these frameworks, cognitive action pathways can be crafted, which allow for generating responses to users via a fusion of ChatGPT's reasoning capabilities.
If all three aforementioned elements - affordable intellectual inference, the conversion of models into interactive agents, and the establishment of cognitive workflows - align effectively, then in 18 months, discussions about what constitutes AGI may become irrelevant due to the advanced capabilities of the AI itself.
These abilities will be so sophisticated and human-like that defining AGI will no longer pose any challenges. comprehension problems AGI: Major Tech Companies in the U.S. Hold the Key to Humanity's Future. DeepMind's newly developed adaptive AI agent, Ada, approaches human-level intelligence. and expert actions.
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