Intelligence that will eat the world.

Our bet

Shunya Research is an independent research lab based in Bangalore, India. We work on embodied intelligence, physical AI, and neuromorphic systems. We believe true generalization requires more than text and images. It requires an agent to explore and experience the physical realm, building context out of pragmatics and not just semantics.

Scaling has delivered impressive results, but the returns from LLM scaling have not justified the investment and data poured in. Terabytes of VRAM, billions in capital, and almost all of humanity's knowledge, and yet the AI that solves cancer is nowhere in sight. All of this points to something fundamental missing in our approach to artificial general intelligence.

Our bet is on neuroscience-based world models that learn through experience. We also believe that by imitating the biological architecture of the brain and of sensorimotor systems, we can make AI systems far more sample- and energy-efficient. Neuromorphic computing and neuroscience are underexplored in this sense.

Current shortcomings

Academia used to be where unconventional ideas got a home. That isn't the case anymore. Publication counts and committee-driven funding made most institutions risk-averse; they would rather fund something safe than back a question that takes a decade to answer. This is not a shortcoming of individual researchers but a failure of the system as a whole.

Non-LLM approaches to scaling intelligence have been met with scrutiny and near-zealous skepticism, treated less as a research direction and more as a distraction. Yet pioneers of the field such as Yann LeCun, Richard Sutton, and Jürgen Schmidhuber have consistently held that text-based scaling of current architectures will not generalize.

A few problems we currently face:

• No continual learning.
• Sample-inefficient learning.
• Agents cannot reason about the consequences of their actions in physical spaces, i.e. no causal understanding.
• Energy inefficiency. The human brain runs on about 20 watts.

Our solution

Our approach rests on three principles.

1. World modelling paired with RL. Agents that learn by exploring their environments. Paired with RL, they learn reward-driven behaviour and not just statistical patterns in the environment they are exploring.
2. Neuromorphic systems. Spiking neural networks and neuromorphic chips are extremely energy efficient while remaining on par with ANNs on similar tasks; their sparsity is what buys the low energy usage. We are exploring non-backprop solutions that enable on-chip learning of SNN models. See Research.
3. Copy nature. The smartest beings in the universe that we know of are humans. Our minds are incredibly sample efficient, can continually learn, and outclass AI on energy efficiency. Our goal is to study biological designs such as those of Drosophila melanogaster and C. elegans to understand bio-neural circuits and quite literally copy them.

Goals

Research is tedious and rigorous and takes a long time. Despite that, we aim to achieve the following by next year:

• Publish our first technical results on event-based vision and spiking neural networks.
• Open-source tooling built for our own research workflow, starting with formal mathematics.
• Test and report our attempts at on-chip learning for SNNs using evolution strategies.
• Explore sustainable continual learning methods for world models.

Longer term, as a lab:

• Build embodied agents that are sample efficient, can continually learn, and see actual industry deployment.
• Establish Bangalore and India as a hub for frontier AI research.
• Demonstrate neuromorphic hardware-software co-design at a scale competitive with ANN baselines on real robotic tasks.

Conclusion

We are in an age where technology touches every aspect of our lives. AI has proven useful in many fields, be it novel protein synthesis, efficient aerodynamic design, or superhuman medical diagnosis. It has the potential to usher in an actual utopia for the human race. We do not think today's dominant paradigm gets us there. Closing that gap is what Shunya is built to do.

We are inspired by Bell Labs, Google DeepMind, and Sakana AI in how we approach research. If this resonates, we want to hear from you, whether that is mentorship, funding, or compute.

Ultimately, it is about building intelligence that will eat the world.

shunyaresearch.labs@gmail.com

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