The father of AI, Geoffrey Hinton, recently made a post against the recent move to make OpenAI a for-profit company, a sharp turn from the company's initial roadmap.
According to BusinessInsider publication on a recent video of Geoffrey saying, "I'm particularly proud of the fact that one of my students fired Sam Altman".
Why were the kings battling over the safety of AGI (Artificial General Intelligence)? The question that pops up first is whether AI is a bad thing.
To understand this fundamental question, let's rewind time to the beginning.
What is Artificial Intelligence?
According to Britannica, artificial intelligence (AI) is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from experience.
While this might be common knowledge to many people, it gets interesting as we dive into different types of intelligence.
Types of Artificial Intelligence?
- Narrow AI (Weak AI)
Narrow AI, also known as Weak AI, is designed to perform specific tasks within a limited domain. This type of AI is highly specialized and excels at particular functions but lacks general intelligence.
How it works:
Narrow AI systems are trained on vast amounts of data relevant to their specific task. They use various machine learning techniques, including deep learning, to recognize patterns and make decisions within their domain of expertise.
Current status:
We've made significant progress in narrow AI, the most common type of AI used today.
Examples include:
- Virtual assistants like Siri, Alexa, and Google Assistant
- Recommendation systems on platforms like Netflix and Amazon
- Image and speech recognition software
- Spam filters in email services
- AI-powered game opponents
These systems often outperform humans in their specialized tasks but are limited to those specific domains.
- General AI (Strong AI)
General AI, or Strong AI, refers to AI systems that possess human-like intelligence and can understand, learn, and apply knowledge across various tasks and domains.
How it works
General AI would theoretically use cognitive architectures that mimic human-like reasoning and problem-solving abilities. It involves complex neural networks, advanced language models, and sophisticated decision-making algorithms that can adapt to new situations.
Current status
We are still far from achieving true General AI. While we've made significant strides in various aspects of AI, creating a system matching human-level intelligence across all domains remains a considerable challenge.
Current research focuses on developing more flexible and adaptable AI systems, but a fully realized General AI is likely decades away if achievable.
- Artificial Superintelligence (ASI)
Artificial Superintelligence refers to AI systems surpassing human intelligence in specific tasks and across all fields, including scientific creativity, general wisdom, and social skills.
How it works
ASI is mainly theoretical at this point. It would likely involve self-improving AI systems that can rapidly enhance their capabilities, leading to an "intelligence explosion."
Current status
ASI remains in the realm of science fiction and philosophical speculation. We are nowhere near creating such systems, and there is ongoing debate about whether developing ASI is possible or desirable.
- Reactive Machines
Reactive Machines are the most basic type of AI systems. They can only react to current situations and can't form memories or use past experiences to inform current decisions.
How it works:
These systems operate based on predefined rules and respond to inputs in real time without any concept of the wider world or past events.
Current status:
While limited, reactive machines are used in various applications, such as:
- Chess-playing AIs like Deep Blue
- AI systems in video games that respond to player actions
- Simple chatbots that provide predefined responses to specific inputs
- Limited Memory AI
Limited Memory AI systems can use past experiences to inform future decisions. They have short-term memory capabilities that allow them to improve over time.
How it works
These systems use a combination of pre-programmed knowledge and observations from recent past events to make decisions. They can learn from historical data to improve their responses.
Current status
Limited Memory AI is widely used in various applications, including:
- Self-driving cars that use sensor data and recent observations to navigate
- Chatbots that maintain context within a conversation
- Recommendation systems that learn from user behaviour.
Theory of Mind AI
Theory of Mind AI refers to systems that can understand and interpret human emotions, beliefs, and intentions. This type of AI would be capable of more complex social interactions and decision-making based on understanding others' mental states.
How it works
Theoretically, these systems would use advanced natural language processing, computer vision, and cognitive models to interpret human behaviour and respond appropriately.
Current status:
We are making progress in this area, but true Theory of Mind AI is still in the early stages of research. Current developments include.
- Emotion recognition in facial expressions and voice
- Sentiment analysis in text
- Early attempts at creating empathetic chatbots
However, we're still far from AI systems that truly understand and model human mental states.
- Self-Aware AI
Self-aware AI represents the most advanced form of AI, where machines have consciousness, self-awareness, and a sense of existence.
How it works
It is mainly theoretical, but it would involve AI systems with a level of introspection and understanding of their thought processes and existence.
Current status
Self-aware AI remains in the realm of philosophical discussion and science fiction. We have no clear path to creating such systems, and there's significant debate about whether machine consciousness is even possible.
While we have made remarkable progress in Narrow AI and are advancing in areas like Limited Memory AI and aspects of Theory of Mind AI, we are still far from achieving General AI or more advanced forms of artificial intelligence.
The field continues to evolve rapidly, with new knowledge every day, and with the use case so tempting and life-changing, why are people so scared and sceptical about the rise of AI?
The Dark AI Tunnel
AI presents a dark tunnel with high risk to society. From deep fake scams to privacy surveillance to information war by states, the stakes increase as we become more encumbered with AI.
Manipulation and Misinformation: AI-powered tools can create compelling fake content, including deep-fakes and synthetic text. This technology enables the spread of disinformation at an unprecedented scale, manipulation of public opinion, election interference, and erosion of trust in media and institutions.
Privacy and Surveillance: AI enhances surveillance capabilities, leading to mass data collection and analysis of personal information. This raises concerns about potential authoritarian control, suppression of dissent, and erosion of personal privacy in both public and digital spaces.
Job Displacement: As AI automates more tasks, it could result in widespread unemployment in specific sectors, widening economic inequality, and necessitating large-scale workforce retraining and education reform.
Bias and Discrimination: AI systems can perpetuate and amplify existing societal biases, leading to unfair treatment in areas like hiring, lending, and criminal justice. This reinforces stereotypes and social inequalities, potentially excluding marginalized groups from AI-driven services.
Cybersecurity Threats: AI can be weaponized for cyber attacks, creating more sophisticated and harder-to-detect malware, enabling automated hacking at scale, and increasing the vulnerability of critical infrastructure.
Autonomous Weapons: AI in military applications raises concerns about lowered barriers to armed conflict, lack of human oversight in warfare decisions, and potential for accidental escalation of disputes.
Environmental Impact: The energy consumption of AI systems contributes to an increased carbon footprint from data centres, resource depletion for hardware manufacturing, and e-waste from rapid technological turnover.
Dependence and De-skilling: Over-reliance on AI systems may lead to atrophy of human skills and critical thinking, vulnerability to AI system failures or manipulations, and loss of human agency in decision-making.
Ethical and Existential Risks: As AI becomes more advanced, we face challenges in aligning AI goals with human values, potential loss of control over superintelligent systems, and philosophical questions about the consciousness and rights of AI.
Social Fragmentation: AI-driven personalization and recommendation systems can result in echo chambers and polarization of views, decreased exposure to diverse perspectives, and weakened shared societal narratives and experiences.
Sam's 6.6 Billion Dollars Funding
Sam Altman's recent funding of $6.6 billion raises questions about the future of AI, the new stakeholders, and how the future of OpenAI be defined.
Many believe that the military use case would become a critical playbook in the future of AI; one has to imagine if there will be holdbacks.
Would Sam be able to stir the ship while following ethics, or would the future be like the arms races during the Cold War?
The war on AI has entered a new dimension as big techs are spending billions on building their own AGI tailored to particular metrics, but OpenAI has cracked the code and is getting recognition.
Microsoft is backing OpenAI and hoping to have a slice of the pie when it goes mainstream; Mata, facebook's parent company, is putting much effort into developing Llama 3.
Like the code war, it seems the tech industry is slowly becoming a shark tank with lesser getting sucked in, or workers pouched.
What Does the Future Hold
The future of AI promises both exciting and scary possibilities. As AI technology advances, we can expect deeper integration into daily life, more sophisticated human-AI collaboration, and potential breakthroughs in science and medicine. However, this progress will also bring complex ethical, economic, military, and societal issues to the forefront.
The future of AI will be shaped by our ability to harness its potential while mitigating risks.
Collaboration between various stakeholders and the government to ensure AI development aligns with human values and benefits society is a far-fetched dream.
As we move forward, balancing innovation with responsible development will be crucial in creating a future where AI enhances human capabilities and improves lives globally without becoming the tool of rich, powerful states or individuals who use it for self-preservation and not for the progress of humanity.
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