In the ever-evolving landscape of artificial intelligence (AI), we are constantly on the lookout for the next big thing. Generative AI, with its transformative capabilities, has captured our imagination and become part of our daily lives. But what comes next? Recent discussions in the AI world have pointed to a trend known as Neuromorphic Computing, which could be the next key development following the era of Generative AI.
Generative AI: A Game-Changer
Generative AI has been nothing short of a game-changer in the world of technology. Platforms like ChatGPT have made advanced AI technologies accessible to a mass audience. These tools can understand plain language prompts, answer questions, write computer code, and even craft entire articles. The astounding success of ChatGPT, which reached 100 million users in just two months, has set a new benchmark, surpassing even the growth rates of platforms like TikTok, Netflix, or Spotify.
The appeal of Generative AI, however, extends far beyond its popularity among the general public. NASA is utilizing this technology to design spaceship components, and NVidia is pioneering its use in drug research and development. Nonetheless, Generative AI has its limitations, primarily linked to scalability. Current AI tools demand substantial computing power to operate, leading to off-site servers and latency in responses. Moreover, this high energy consumption is both expensive and environmentally detrimental.
So, What’s Next?
One promising solution to these challenges is the growing interest in neuromorphic computing. This approach involves building computers that mimic the architecture of the human brain. While the idea might seem philosophically reductive, the human brain can be technically abstracted into computation units (neurons) connected by fast-access local memory (synapses).
By designing a computer system inspired by this biological structure, we can significantly increase computing density, leading to reduced energy costs and a smaller storage footprint. The human brain consumes around 12 watts of power continuously, in stark contrast to a laptop computer’s 60 watts. Additionally, it can respond to stimuli in real-time and adapt by forming and reorganizing connections between neurons when exposed to new information.
Though we are still far from matching the 100 billion neurons in the human brain, Intel has made substantial progress by developing a neuromorphic computing board with the equivalent of 100 million neuron nodes. All this computing power fits within a chassis the size of just five standard servers.
Applications of Neuromorphic Computing
Neuromorphic computing may not serve the same applications as Generative AI, at least initially. According to GlobalData, its primary functions are expected to be in more human-like tasks, such as enhancing connectivity between prosthetics and human brains, improving autonomous vehicle navigation, and enhancing customer service.
In the long term, we can anticipate greater integration between neuromorphic computing and Generative AI and neural networks. IBM’s TrueNorth chip is an early example of this integration. It features 1 million digital neurons connected by 256 million digital synapses and has the capacity for neural network integration, enabling AI models to learn more rapidly and efficiently.
The Job Market and Key Players
The job market in neuromorphic computing has been on the rise since mid-June of 2021, with 2022 seeing an increase in senior postings in the field compared to previous years. Unsurprisingly, Intel and IBM are leading the charge in hiring for neuromorphic computing roles, with Ericsson and HP not far behind.
Whether neuromorphic computing integrates with Generative AI or charts its own path, it is undeniable that this technology will be a significant factor in shaping the future of technology. As we eagerly await what comes next after Generative AI, all signs point to the exciting possibilities of neuromorphic computing.
In conclusion, the trend of neuromorphic computing is poised to be the next key development following the era of Generative AI. This innovative approach, inspired by the architecture of the human brain, promises to bring increased computing power, reduced energy costs, and exciting new applications to the world of artificial intelligence.