Revolutionary A.I.: Merging Neuroscience and Breakthroughs

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Applying Neuroscience to Deep-Learning A.I. Models: Bridging the Gap

With the rise of artificial intelligence (A.I.) and the excitement surrounding technologies like ChatGPT, it’s easy to overlook the potential pitfalls. While A.I. holds the promise of intelligent machines that understand their environments and continuously learn, we must not forget the importance of applying neuroscience to deep-learning A.I. models. Unfortunately, these two disciplines have remained isolated for decades, hindering progress in creating truly intelligent A.I. systems.

The Untapped Potential

Neuroscience and A.I. have a shared history. In the 1930s, theories on how neurons learn inspired the first deep-learning models. In the 1950s and ’60s, Nobel Prize-winning research on the brain’s perceptual system led to advancements in convolutional neural networks, a key component of A.I. deep learning. However, many recent breakthroughs in neuroscience have not been incorporated into today’s A.I. systems, leaving A.I. professionals unaware of these advances and their potential impact.

For instance, the human brain, with its incredible computational power, consumes only about 20 watts of power for an average adult, while A.I. models like ChatGPT consume massive amounts of electricity. In fact, a single A.I. model can emit as much carbon as five cars in their lifetimes, and the energy use increases exponentially with each training cycle. Additionally, the computational resources required to train these models have been doubling every 3.4 months since 2012, making the current A.I. trajectory unsustainable.

The Superiority of the Brain

The human brain not only consumes a fraction of the energy used by large A.I. models, but it is also truly intelligent. Unlike A.I. systems, the brain can understand the structure of its environment, make complex predictions, and carry out intelligent actions. Moreover, humans learn continuously and incrementally, while A.I. models require retraining to correct mistakes. This highlights the need to bridge the gap between neuroscience and A.I. to create more efficient and sustainable A.I. systems.

Bridging the Gap

The cultural differences between neuroscientists and A.I. practitioners have hindered collaboration and communication. Neuroscientists need to explain their concepts from a big-picture standpoint, helping A.I. professionals understand the relevance of their findings. Additionally, there is a need for more researchers with hybrid A.I.-neuroscience roles to facilitate interdisciplinary collaboration and translate neuroscientific findings into brain-inspired A.I.

Recent breakthroughs have shown that applying brain-based principles to large language models can significantly increase efficiency and sustainability. By mapping neuroscience-based logic to A.I. algorithms, data structures, and architectures, A.I. systems can learn quickly with minimal training data, mimicking the capabilities of the human brain. Several organizations, including government agencies, academic researchers, and companies like Intel, Google DeepMind, and, are making progress in applying brain-based principles to A.I.

The Future of A.I.

To ensure the future of A.I. is sustainable and efficient, it is crucial to bridge the neuroscience-A.I. gap. A.I. systems must interact with scientists, assisting in experiments that push the boundaries of human knowledge. These systems should enhance human capabilities by learning alongside us and helping us in various aspects of our lives. By fostering interdisciplinary research, commercialization, education, policies, and practices, we can utilize A.I. to improve the human condition.

In conclusion, the potential of A.I. lies in the integration of neuroscience principles into deep-learning models. By leveraging the brain’s superior intelligence and energy efficiency, we can create A.I. systems that are not only more powerful but also sustainable and efficient. Bridging the gap between neuroscience and A.I. is vital for the advancement of both fields and the development of intelligent machines that truly understand and assist us in our daily lives.


  1. Applying neuroscience principles to A.I. models can significantly improve efficiency and sustainability.
  2. The human brain consumes far less energy than large A.I. models and possesses true intelligence.
  3. Collaboration between neuroscientists and A.I. practitioners is essential to bridge the gap and translate neuroscientific findings into brain-inspired A.I.
  4. Recent breakthroughs have demonstrated the potential of brain-based principles in increasing A.I. performance.
  5. A future where A.I. systems enhance human capabilities and contribute to scientific advancements requires interdisciplinary research and collaboration.
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