More posts around deep learning and google deep mind

 robotics

http://hutter1.net/ai/suaibook.pdf

https://arxiv.org/abs/1708.04782

https://github.com/google-deepmind/pysc2/blob/master/pysc2/lib/named_array.py

https://github.com/google-deepmind/deepmind-research/tree/master/tandem_dqn

https://www.youtube.com/watch?v=hCeJeq8U0lo&t=9s

https://arxiv.org/pdf/2105.14039

https://arxiv.org/pdf/2102.05182

https://openreview.net/forum?id=nPHA8fGicZk

https://github.com/google-deepmind/deepmind-research/tree/master/tandem_dqn


. Neuromorphic Computing

Technologies: Neuromorphic chips like Intel's Loihi or IBM's TrueNorth are designed to mimic the structure and function of the human brain, making them ideal for brain-like AI models.

Purpose: These chips are designed to support spiking neural networks (SNNs), which are more biologically plausible than traditional artificial neural networks.

2. Spiking Neural Networks (SNNs)

Algorithms: SNNs use spikes (events) to transmit information, similar to how neurons communicate in the brain. This allows for more efficient and brain-like computation.

Purpose: SNNs are particularly useful for continuous learning and energy-efficient computation.

3. Reinforcement Learning (RL)

Algorithms: Algorithms like Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Actor-Critic methods can be adapted for continuous learning environments.

Purpose: RL algorithms enable the model to learn from interaction with the environment over time, improving decision-making through trial and error.

4. Continual Learning (Lifelong Learning)

Technologies/Algorithms: Elastic Weight Consolidation (EWC), Synaptic Intelligence (SI), and Memory Aware Synapses (MAS) help models retain knowledge over time without forgetting previous knowledge (addressing the problem of catastrophic forgetting).

Purpose: These techniques are crucial for building a model that can learn continuously over years.

5. Self-supervised Learning

Algorithms: Techniques like Contrastive Predictive Coding (CPC) and SimCLR allow models to learn from unlabelled data by predicting parts of the data based on other parts.

Purpose: This is akin to how humans learn from raw sensory input without explicit labels, making it more aligned with how the brain learns.

6. Biologically Inspired Algorithms

Examples: Hebbian learning, inspired by the way synaptic connections strengthen based on experience, and Bayesian learning, which mimics the brain's probabilistic reasoning capabilities.

Purpose: These algorithms aim to replicate specific brain functions and learning processes.

7. Large-scale Data Ingestion and Processing

Technologies: Distributed computing frameworks like Apache Spark, Kubernetes, and cloud platforms (e.g., AWS, Google Cloud) for handling and processing massive amounts of data over time.

Purpose: To support the long-term and continuous absorption of data at scale.

8. Neuroscience-Inspired Architectures

Examples: Models inspired by cortical columns, the hippocampus, and other brain structures can help build more biologically plausible architectures.

Purpose: These architectures are designed to replicate specific brain functions, such as memory formation and spatial reasoning.

9. Hardware Acceleration

Technologies: GPUs, TPUs, and specialized AI accelerators like Google's Tensor Processing Units (TPUs) or FPGA-based systems.

Purpose: To support the computational demands of continuous learning over extended periods.

10. Ethics and Safety Considerations

Frameworks: Implementing ethical AI frameworks and ensuring safe exploration in RL to prevent harmful behaviors.

Purpose: Continuous learning AI systems need to be safe and aligned with ethical standards to prevent undesirable outcomes over long periods.



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https://www.reddit.com/r/MachineLearning/comments/1mhkx4/a_look_at_nupic_a_selflearning_ai_engine_part/


https://www.youtube.com/watch?v=VyMhZhwe3gc



https://github.com/htm-community/NAB



https://github.com/orgs/htm-community/repositories


https://stanfordmag.org/contents/keeping-secrets



https://www.numenta.com/blog/2023/07/28/top-5-questions-from-customers/




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https://www.maragu.dev/blog/go-is-my-hammer-and-everything-is-a-nail



https://www.jefftk.com/p/history-of-https-usage


https://htmchallenge.devpost.com/details/resources



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https://devpost.com/software/htm-models-adelaide




https://github.com/iizukak/ecg-htm




https://devpost.com/software/ecg-htm


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