The latest concept taking the world of Artificial Intelligence (AI) by storm is the development of intelligent systems that can be trained to perform a vast array of tasks instead of just one. These are collectively referred to as Artificial General Intelligence (AGI) and recent innovations have been inching toward the end goal of surpassing natural or human intelligence.
While performing multiple tasks, AGI is said to be capable of learning and adapting to instances that it has not been explicitly trained on, thus making it somewhat similar to human intelligence. However, a full-fledged intelligent system that can facilitate various highly distinct tasks such as generating music while at the same time being able to generate video and text content is yet to be developed.
In the meantime, we will try to decipher what goes into an intelligent system that utilizes AGI in its existing form and functionality. Let us begin by clearly defining AGI.
What is Artificial General Intelligence?
AGI is an enhanced type of artificial intelligence that can be trained on a wide variety of tasks and capabilities with the ultimate aim of reaching superintelligence. Superintelligence is a concept wherein the AI model is trained to generate synthetic data to train itself in a bid to surpass the versatility and adaptability of human intelligence.
The most popular and successful AI solutions have been trained on Deep Learning (DL) models such as the GPT-3-based ChatGPT. While this AI chatbot is solely purposed to perform one activity, which is to produce insightful textual responses to users' prompts based on the data it has been trained on.
Even the most cutting-edge AI-based tools and applications today can technically only be defined as Narrow AI. Narrow AI means that the AI model is trained on a specific type of data and is meant to perform a narrow set of tasks. So the application can either generate and mix music or generate textual responses or generate art from scratch. But the capability of a single application performing all of these tasks and more while adapting to changing situations and varying input data is still a slightly distant possibility.
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How Advanced is AGI Today?
One of the most advanced AI models today is GPT-4, and while it cannot truly be classified as AGI, it has demonstrated exceptional flexibility, accuracy, and versatility in generating the most appropriate content based on user prompts. A fast-improving area of research in AGI is reinforcement learning.
Reinforcement learning algorithms are developed to enable intelligent systems to learn from their own experiences with varied data and apply them to improve their performance over time. This workflow is similar to the way humans and certain advanced organisms adapt abilities through trial and error and shows significant prospects for developing intelligent systems that can perform multiple complex tasks.
Only with significant advancements in cognitive adaptability, Natural Language Understanding (NLU), and Robotic Process Automation (RPA) can we take larger steps toward realizing true AGI. Additionally, AI models need to be trained on millions of datasets in varied iterations along with exhaustive trial and error to make intelligent systems with superintelligence a reality.
Pioneering Intelligent Systems Advancing AGI Research
Although research in AGI is still in its nascent stage in terms of the end goal of achieving 'superintelligence', there have been significant breakthroughs in the right direction. A Cornell University research paper detailed how researchers tested the capabilities of an early iteration of GPT-4 while it was still in active development at OpenAI. Compared to other Large Language Models (LLM) out in the chatbot arena, this one showed the most potential in terms of computing and data processing capacity.
GPT-4, is among a cohort of advanced LLMs such as Google's PaLM and ChatGPT, that has reportedly shown that general intelligence and the capability for varied industry-agnostic implementation is on the rise. Other than this one, there are a number of other disruptive advancements that have helped the AI industry step toward a true AGI future:
1) AlphaGO: This AI model was developed by DeepMind and has demonstrated remarkable abilities to defeat some of the highest-ranked players of the strategy board game, Go. While it still technically occupies the Narrow AI domain, the ability of this model to analyze large volumes of data about Go moves and strategies applied by international champions with advanced machine learning techniques is worth a mention.
Through extensive analysis of the game, training, and reinforcement learning algorithms, the model has officially surpassed human ability in Go. While AlphaGO represents an impressive milestone in AI development and research, it still does not possess the theoretical general intelligence capabilities of AGI.
2) AlphaFold: In the medical research field, AlphaFold has proven to be a pioneering AI tool that primarily helps with protein structure prediction. Using state-of-the-art deep learning algorithms, this AI tool has successfully carried out protein folding, which is a process that held back the field of computational biology because of the sheer computational capacity required for it.
AlphaFold has also demonstrated cross-domain generalization by being able to generalize across various protein structures, even differentiating between and predicting structures that the model hasn't been trained on. This represents an important step for AGI, i.e. the ability to apply learnings from a large set of data to a completely unfamiliar setting or scenario.
3) SingularityNET: SingularityNET is an open-source decentralized platform for AGI researchers to share resources, research insights, and collaborate across the world. The widespread democratization of AI research and innovation is one of the predominant end goals of this initiative. Moreover, it offers a token-based ecosystem for the inventive implementation of blockchain technologies combined with AI.
By encouraging increased collaboration and a sense of total ownership, AGI researchers can come together and share their expertise in implementing cutting-edge AI solutions. Users can leverage the tools provided by SingularityNET to create, share, and monetize AI at scale across distant geographies.
4) Microsoft Cognitive Toolkit: The Cognitive Toolkit (CNTK) developed by Microsoft is a deep learning framework that provides efficient tools to build neural networks. The massive computational resources and high-volume datasets required for training models toward AGI capabilities are supported by the CNTK framework.
CNTK can enable researchers to put complex models through large-scale training scenarios so that sufficient experimental iterations can develop the AI models to put these learnings to unforeseen situations and scenarios at some point. This platform also supports transfer learning by providing pre-trained models and tools for fine-tuning and reusing learned features.
Image: MuZero's self-play and training technique
Source: DeepMind
5) MuZero: Another AI model designed by DeepMind, MuZero combines deep learning algorithms with tree search techniques. MuZero has demonstrated ground-breaking advancements in AI such as the ability to learn and plan in environments whose data it has not been trained on. The generalization capability of AGI is mirrored by this model as it gains an understanding of unforeseen scenarios through self-play.
Compared to other AI and deep learning algorithms, MuZero requires far smaller datasets and iterations to learn the dynamics of new environments.
6)OpenCog: The cognitive architecture framework provided by OpenCog allows for the seamless design and implementation of AGI systems in the near future. All the aspects of the system including memory allocation, learning mechanisms, pattern recognition, and so on can be configured over this platform.
OpenCog serves as a framework for organizing and integrating distinct cognitive processes and uses probabilistic inference to fill in the information gaps. The solutions derived from one cognitive process can be applied to better implement other processes and to collaborate freely.
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AGI is the Future of Intelligent Systems
Unlike narrow AI systems that are capable of undertaking only one task at a time, a single AGI solution has the potential to undertake several functions. AGI-driven intelligent systems can even learn to carry out tasks that it hasn't been trained on, opening up possibilities for surpassing human intelligence in the near future. AGI is an important area of research because of its power to address complex problems and its ability to be autonomous and highly adaptable and scalable. If you are looking to enhance the potential of your digital solution with AI, you can book a free consultation with our AI development experts.
