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A Modern Self-Referential Weight Matrix That Learns to Modify Itself
Kazuki Irie · Imanol Schlag · Róbert Csordás · Jürgen Schmidhuber
Event URL: https://openreview.net/forum?id=lVUGfLpNpCF »

The weight matrix (WM) of a neural network (NN) is its program. The programs of many traditional NNs are learned through gradient descent in some error function, then remain fixed. The WM or program of a self-referential NN, however, can keep rapidly modifying all of itself during runtime. In principle, such NNs can meta-learn to learn, and meta-meta-learn to meta-learn to learn, and so on, in the sense of recursive self-improvement. Here we revisit such NNs, building upon recent successes of fast weight programmers (FWPs) and closely related linear Transformers. We propose a scalable self-referential WM (SRWM) that uses outer products and the delta update rule to modify itself.We evaluate our SRWM in a multi-task reinforcement learning setting with procedurally generated ProcGen game environments.Our experiments demonstrate both practical applicability and competitive performance of the SRWM.

Author Information

Kazuki Irie (Swiss AI Lab, IDSIA (USI & SUPSI))
Imanol Schlag (IDSIA)
Róbert Csordás (IDSIA)
Jürgen Schmidhuber (Swiss AI Lab, IDSIA (USI & SUPSI); NNAISENSE; KAUST)

Since age 15 or so, the main goal of professor Jürgen Schmidhuber has been to build a self-improving Artificial Intelligence (AI) smarter than himself, then retire. His lab's Deep Learning Neural Networks based on ideas published in the "Annus Mirabilis" 1990-1991 have revolutionised machine learning and AI. By the mid 2010s, they were on 3 billion devices, and used billions of times per day through users of the world's most valuable public companies, e.g., for greatly improved (CTC-LSTM-based) speech recognition on all Android phones, greatly improved machine translation through Google Translate and Facebook (over 4 billion LSTM-based translations per day), Apple's Siri and Quicktype on all iPhones, the answers of Amazon's Alexa, and numerous other applications. In 2011, his team was the first to win official computer vision contests through deep neural nets, with superhuman performance. In 2012, they had the first deep NN to win a medical imaging contest (on cancer detection). All of this attracted enormous interest from industry. His research group also established the fields of mathematically rigorous universal AI and recursive self-improvement in metalearning machines that learn to learn (since 1987). In 1990, he introduced unsupervised adversarial neural networks that fight each other in a minimax game to achieve artificial curiosity (GANs are a special case). In 1991, he introduced very deep learning through unsupervised pre-training, and neural fast weight programmers formally equivalent to what's now called linear Transformers. His formal theory of creativity & curiosity & fun explains art, science, music, and humor. He also generalized algorithmic information theory and the many-worlds theory of physics, and introduced the concept of Low-Complexity Art, the information age's extreme form of minimal art. He is recipient of numerous awards, author of over 350 peer-reviewed papers, and Chief Scientist of the company NNAISENSE, which aims at building the first practical general purpose AI. He is a frequent keynote speaker, and advising various governments on AI strategies.

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