From early days, a key and controversial question inside the artificial
intelligence community was whether Artificial General Intelligence (AGI) is
achievable. AGI is the ability of machines and computer programs to achieve
human-level intelligence and do all tasks that a human being can. While there
exist a number of systems in the literature claiming they realize AGI, several
other researchers argue that it is impossible to achieve it. In this paper, we
take a different view to the problem. First, we discuss that in order to
realize AGI, along with building intelligent machines and programs, an
intelligent world should also be constructed which is on the one hand, an
accurate approximation of our world and on the other hand, a significant part
of reasoning of intelligent machines is already embedded in this world. Then we
discuss that AGI is not a product or algorithm, rather it is a continuous
process which will become more and more mature over time (like human
civilization and wisdom). Then, we argue that pre-trained embeddings play a key
role in building this intelligent world and as a result, realizing AGI. We
discuss how pre-trained embeddings facilitate achieving several characteristics
of human-level intelligence, such as embodiment, common sense knowledge,
unconscious knowledge and continuality of learning, by machines.

本文提出实现人工通用智能需要建立一个智能世界，同时指出预训练嵌入在构建这种智能世界和实现人类水平智能方面发挥关键作用。

嵌入世界与人工通用智能

The Embeddings World and Artificial General Intelligence

Time-varying stochastic optimization problems frequently arise in machine
learning practice (e.g. gradual domain shift, object tracking, strategic
classification). Although most problems are solved in discrete time, the
underlying process is often continuous in nature. We exploit this underlying
continuity by developing predictor-corrector algorithms for time-varying
stochastic optimizations. We provide error bounds for the iterates, both in
presence of pure and noisy access to the queries from the relevant derivatives
of the loss function. Furthermore, we show (theoretically and empirically in
several examples) that our method outperforms non-predictor corrector methods
that do not exploit the underlying continuous process.

本文针对机器学习中涉及到的时间变化的随机优化问题，以及其在离散时间的解法中存在的连续性问题，提出了基于预测 - 校正算法的优化方法，并给出了理论与实验证明，相较于不使用该算法的其他方法，本文提出的算法能更好地利用连续过程，提高最优解的准确性和鲁棒性。