AI has come a long way in the last 20 years, and some companies are now building solutions to rival human intelligence itself. Unlike narrow AI, which powers the solutions you use every day like Siri and self-driving cars, strong AI uses deep learning and other techniques to build AI that can match a human brain and its cognitive powers.
Is it possible? Should it be? AI researchers are working to answer these questions, and in this article, we’ll try to do the same. We’ll cover the theoretical capabilities of strong AI, the tech behind it today, and the ethical implications of a machine that can match another human.
What is strong AI?
Definition and features
Strong AI aims to build an AI that can rival human intelligence. That means building a robot or machine that can do any intellectual task a human can do. This was previously the realm of Star Trek or science fiction novels, but as our tech advances, so do the possibilities.
The features of what would be called strong AI include general problem-solving at a human intelligence level. Strong AI can learn and adapt across different domains and apply human-level reasoning. In the future, these solutions will also be creative and have emotional intelligence.
Strong AI vs weak AI
Weak AI, also known as narrow AI, is designed for very specific tasks. It can’t adapt to situations outside of its original programming. Strong AI aims to replicate (or surpass) the human brain, so it has far fewer limitations. If you’re looking to hire AI developers, you’ll likely need them to support weak AI development rather than provide a general AI solution.
Theoretical foundations of strong AI
Philosophical roots
Strong AI raises the question of whether machines can really “think” or “feel” like humans. Several thought experiments have tackled this idea throughout history, the most famous being the Turing Test. Alan Turing invented the Turing Test in the late 1940s as a way to test a machine’s emotional and general intelligence against a human brain. It has since been featured in many science fiction stories, most notably in Blade Runner and Ex Machina.
But variations of this idea have been around for hundreds of years. The Chinese Room thought experiment has its roots in the 1700s and argues that a machine will never be able to think, feel, or experience life like a human. Despite these thought experiments, research in our time is still testing if strong AI is possible.
Cognitive science and neuroscience
Since it’s so closely tied to human thinking, strong general AI research is informed by both neuroscience and computational models. Both reveal how the brain processes information and how it learns and adapts.
For example, studying neural networks and synaptic plasticity—the brain’s ability to strengthen or weaken connections based on experience—helps AI researchers design algorithms that can learn over time and mimic human intelligence.
Technological challenges of strong AI
Computational power and infrastructure
The biggest challenge with building this next-gen AI is the resources required. Many are worried about the current hardware but more importantly about energy efficiency and the environmental impact of building and maintaining an AI of this scale. Quantum computing is a potential solution to the training data, but for now, infrastructure is the biggest technological challenge for strong AI development.
Algorithms and learning paradigms
The level of language understanding required of an AI that can replicate the human brain is huge. Developers need to create unsupervised, reinforcement learning models that can solve problems at a human level, which is a massive task. Trying to generalize knowledge to this extent. where outsourced AI development can support a solution that can transfer learning across different domains, remains a big challenge.
Data quality and diversity
One of the main goals of the Turing Test was to test how a machine could be ethical and avoid bias. Artificial intelligence today still poses risks, and a human-level general AI is one of them. AI researchers have to constantly consider algorithmic bias while also simulating real-world scenarios to test their machine learning.
Ethical considerations in strong AI development
Risks and unintended consequences
One of the biggest concerns for future general AI development is that these systems will override our control over them. If we can build a machine that’s as capable, if not more capable than humans, it will threaten to turn against its creators. This is another doomsday scenario that’s been explored in sci-fi for decades. Even if a general AI doesn’t bring about a Skynet-like takeover of the world, it will still bring serious operational and economic problems if our systems are governed by a machine that can think for itself.
Developer moral responsibilities
Building general AI intelligence at this scale also raises more ethical questions about the machine itself. Should developers design a machine that can feel? If it can feel, shouldn’t this machine have rights or moral considerations? Isaac Asimov wrote a fictional set of “Three Laws of Robotics” in 1942, and today, machine learning developers are having to ask if they should apply these rules to their research.
Regulatory and governance frameworks
There’s also the question of how to govern a sentient machine. Governments need to consider policies that prepare for this possibility and can safeguard the development process, the workforce, and the country as a whole. AI ethics boards already exist in many places to control general AI development.
Applications of strong AI
Solving global problems
If developers can add general AI with human capabilities to the types of AI they can build, they can theoretically support humans with big world problems. This new strong AI could help with climate change modeling and mitigation or be tasked with finding medical breakthroughs through advanced medical research.
Advancing technology and human knowledge
This level of AI research can also change what we consider innovative in the last few years. They could potentially use their advanced deep learning to discover new scientific theories we haven’t even thought of yet. Or they could figure out how to better educate humans to transform our educational institutions. They will also transform certain industries like computer science and data science.
Current state and future of strong AI
Main players and projects
Because of the complexity and nature of these solutions and their cognitive abilities, the research stages for general AI have rarely been made public. Right now, we know that a few private companies and public academic initiatives are working on AI: OpenAI, DeepMind and the biggest tech companies like Google and Microsoft.
Predictions and timelines
It’s impossible to predict when, if ever, general AI will be as capable as the human brain. There are many infrastructural and regulatory hurdles to a true strong AI that’s better than the current weak AI. Even an optimistic estimate for a strong AI would be several decades away from where we are today.
Open questions
The main open question around general artificial intelligence is if it’s even ethical or wise to pursue this technology. Weak AI has already brought many concerns across many industries and strong AI would multiply those concerns. If you’re going to hire generative AI developers or support research in this field, it’s best to balance progress and the pursuit of knowledge with caution.
Conclusion
Strong AI is being developed, and if it’s ever achieved a general AI will change the very fabric of human society as we know it. Anyone working with these solutions must work with other companies and departments and apply ethical considerations at every stage of the development process.
Here are some questions to ask yourself: How do you feel about AI that can mimic another human? What’s our future looking like? How far have we come? Where are we going? Keep asking yourself these questions and stay updated with these technologies and how they’re being controlled and used.
FAQs
What’s the difference between strong AI and weak AI?
Narrow AI, also known as weak AI, is designed to perform very specific tasks. Narrow AI can’t deviate from its programmed tasks. It can’t think, feel, or adapt like a human. That’s why narrow AI has been used for automation and to make manual labor tasks easier for humans.
Strong AI or general AI aims for human-level cognition. It wants an AI that can adapt, creatively consider, and have emotional intelligence. An example of narrow AI is a simple chatbot that you can ask about a recent online order. A general AI would be a humanoid robot that can perceive and respond to your very specific situation like another human would.
How long until strong AI?
Experts are very far apart on a timeline for strong AI. Some say we’ll see general AI in a few decades. Others say it will be much longer.
Significant progress has been made in narrow AI over the last couple of decades, but creating an AI that can generalize and be conscious like a human is a much bigger challenge. There are many technical hurdles to overcome, including systems that can generalize across domains and do abstract thinking. Developers need to think about the theoretical challenges of general AI, and governments around the world are regulating and responding. The answer to this question is we’re not close. But maybe one day we will be.
What are the dangers of strong AI?
The risks of strong AI are many. One major concern is losing control of one of these machines, specifically where a superintelligent AI might do things not aligned with human values or intentions. Strong AI could also bring economic disruption by automating many jobs and causing mass unemployment, which we’ve seen already with weak AI. We must develop AI responsibly and ensure ethical considerations, safety protocols, and robust regulations are in place to mitigate those risks.
Why strong AI?
In theory, general AI can solve some of humanity’s biggest problems. It could accelerate disease research by processing vast amounts of medical data to find new treatments or cures. It could be key to solving environmental challenges like climate change, optimizing energy consumption, and developing sustainable solutions. Being able to generalize human-like reasoning across many fields could lead to huge technological advancements and improvements in life. That’s why researchers are still chasing general AI. But note that all of the above is theoretical.
Can strong AI be conscious?
Current AI systems can simulate human behavior, but they don’t have actual awareness or subjective experience. They are just simulations. Consciousness involves complex things like self-awareness and emotions, which are not yet understood or replicated by machines. AI can simulate behaviors associated with consciousness, but there is no scientific consensus on whether machines can actually experience it. And that’s one of the biggest debates in general AI research.
How can strong AI be regulated?
Regulating a general AI will need highly specific and detailed regulations to ensure it’s used safely and ethically. That will need international cooperation to create global standards. Those ethical guidelines will need to cover transparency, fairness, and accountability.
