Today we're looking into the technologies that are driving Artifical Technologies like ChatGPT forward and where they may take us.

When we talk about advances in artificial intelligence, most people think about bigger datasets and more powerful versions of the computers we use today - huge datacentres with unimaginable processing power that give the guise of computer-based intelligence. However, two radically different approaches to computing are opening up new possibilities for how machines process information and thus where the future of true Articifcal Intelligence resides.

Traditional computers - including those running today’s AI systems - work like incredibly fast calculators, processing information one step at a time using binary code (1s and 0s). Whilst they’re getting more powerful every year, they’re still limited by this fundamental way of working - if you set such a computer to search 100 rooms for an object, they would check one room at a time, as quickly as they could but nevertheless in a linear fashion. This brings us to two groundbreaking alternatives: quantum and neuromorphic computing.

Quantum computers harness the strange properties of quantum physics that exist at the atomic level. Unlike regular computers that can only try one solution at a time, quantum computers can explore countless possibilities simultaneously. In our example above, instead of checking one room at a time a quantum computer can assess all rooms simultaneously. Making the problem more complex - imagine trying to find the quickest route between 100 cities - a traditional computer would need to check each possible route one by one whilst a quantum computer could assess millions of routes at once. This could revolutionise how AI systems tackle complex problems like drug discovery or climate modeling where there are nearly infinite possibilities to explore.

Meanwhile Neuromorphic computing takes its cues from nature’s most sophisticated computer - the human brain. These systems are built with electronic components that mimic neurons and synapses, allowing them to process information in parallel rather than sequentially. Just as your brain effortlessly processes sight, sound, and touch simultaneously while using less power than a light bulb, neuromorphic computers aim to achieve this same efficiency. This could be particularly valuable for AI applications that need to learn and adapt in real-time, such as robots that need to navigate changing environments or recognise objects in an imperfect chaotic world.

Both technologies are still in their early stages. Quantum computers currently require extreme cooling and are prone to errors, while today’s neuromorphic systems are far less complex than actual brains and therefore far less capable. However, both represent fundamentally new approaches to computing that could overcome many limitations of current AI systems.

Hence looking to the future, quantum computers might make it possible for AI to solve optimisation problems that would take traditional computers centuries to complete. Neuromorphic systems, meanwhile, could lead to AI that learns more naturally and uses far less energy than current approaches.

In short these are not just faster versions of existing computers - they’re entirely new ways of processing information that could help AI systems tackle problems in ways that better mirror how nature itself computes and solves problems. Although it may be years before these technologies reach their full potential, they are already beginning to complement traditional computing in specialised applications and scientific advances are making that future ever closer.

Rather than competing with each other, these different approaches to computing will likely find their own niches in advancing how machines process information and learn from their environment. The future of AI may not lie in just making our current computers faster, but in embracing these fundamentally different ways of computing.

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