The recent movements at OpenAI leadership have made headlines worldwide, and the executive team has been reshuffled. But one question that was overlooked is why this was the case. Some have suggested that Sam Altman, the CEO of OpenAI, was replaced because he had overseen the creation of AGI or Artificial General Intelligence.
Sam Altman was reportedly fired from OpenAI, then joined Microsoft, a backer of OpenAI, which is the creator of ChatGPT, which has become one of the most popular services of all time. And now he (Sam Altman) is back to OpenAI, the company he co-founded and is backed by Elon Musk. The Titan of Tesla also has his own AI company named xAI, which has recently launched Grok to compete with OpenAI’s ChatGPT.
According to some reports, Sam Altman’s initial firing was developing a model named Q* (pronounced Q star). According to Reuters, several staff researchers wrote a letter to the board of directors of OpenAI warning of a powerful artificial intelligence discovery that they said could threaten humanity, which is considered AGI or close to AGI.
Some at OpenAI believe Q* (pronounced Q-Star) could be a breakthrough in the startup’s search for what’s known as artificial general intelligence (AGI), one of the people reported to the news agency Reuters. OpenAI defines AGI as autonomous systems that surpass humans in the most economically valuable tasks.
“Is this a tool we’ve built or a creature we have built?”
Sam Altman, the CEO of OpenAI
What is AGI?
Artificial General Intelligence (AGI), also known as strong AI or full AI, refers to a type of artificial intelligence that possesses the ability to understand, learn, and apply knowledge in a broad range of contexts in a manner that is indistinguishable from a human being. This contrasts with narrow AI, designed to perform specific tasks within a particular domain or set of parameters.
The likelihood of developing Artificial General Intelligence (AGI) is a subject of considerable debate among experts. Significant technical hurdles exist to overcome, such as creating algorithms capable of generalized understanding and reasoning across disparate domains. While there has been substantial progress in narrow AI, which performs specific tasks with proficiency, this does not necessarily translate to the broader cognitive abilities that AGI would require. Some experts believe that AGI is a possibility in the future, given the rapid advancement in machine learning and computational power. However, others argue that AGI may be decades away or even potentially unattainable due to the complex nature of human intelligence. The prediction of AGI’s emergence is thus highly speculative, and the time frame for its potential realization remains uncertain.
Q-Learning
Q-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the environment and can handle problems with stochastic transitions and rewards without requiring adaptations. Q-learning is widely used in various fields, from robotics to gaming, because of its simplicity and effectiveness in making decisions without a predefined model of the environment.
Q* Model
The Q has nothing to do with quantum. In reinforcement learning, particularly Q-learning, Q∗ represents the optimal action-value function. This function gives the expected utility (or total expected rewards) of acting in a state and following the optimal policy.
Q∗ , in simple terms, refers to the best possible action-value function in reinforcement learning. It tells you the highest expected reward you can get for any given state and action, assuming you continue to make the best possible choices afterward.