This is an Op-ed about the future of the internet and, while speculative, it's an example and an attempt to demonstrate how Artificial Intelligence at scale in a human would or could have disastrous impacts without AI regulation and AI ethics to protect us. GPT-3 stands for Generative Pre-trained Transformer. As you likely already know GPT-3 is an autoregressive language model that uses deep learning to produce human-like text. It is the third-generation language prediction model in the GPT-n series (and the successor to GPT-2) created by Microsoft-funded OpenAI (that was supposed to be a not for profit firm). In 2021 we've had a NLP-explosion year in terms of Artificial Intelligence activity.

Future Internet involves several emerging technologies such as 5G and beyond 5G networks, vehicular networks, unmanned aerial vehicle (UAV) networks, and Internet of Things (IoTs). Moreover, future Internet becomes heterogeneous and decentralized with a large number of involved network entities. Each entity may need to make its local decision to improve the network performance under dynamic and uncertain network environments. Standard learning algorithms such as single-agent Reinforcement Learning (RL) or Deep Reinforcement Learning (DRL) have been recently used to enable each network entity as an agent to learn an optimal decision-making policy adaptively through interacting with the unknown environments. However, such an algorithm fails to model the cooperations or competitions among network entities, and simply treats other entities as a part of the environment that may result in the non-stationarity issue. Multi-agent Reinforcement Learning (MARL) allows each network entity to learn its optimal policy by observing not only the environments, but also other entities' policies. As a result, MARL can significantly improve the learning efficiency of the network entities, and it has been recently used to solve various issues in the emerging networks. In this paper, we thus review the applications of MARL in the emerging networks. In particular, we provide a tutorial of MARL and a comprehensive survey of applications of MARL in next generation Internet. In particular, we first introduce single-agent RL and MARL. Then, we review a number of applications of MARL to solve emerging issues in future Internet. The issues consist of network access, transmit power control, computation offloading, content caching, packet routing, trajectory design for UAV-aided networks, and network security issues.

GPT-3 is probably a foundational layer for the future because it was developed using huge quantities of training data and computer power to reach state-of-the-art general-purpose performance. This has been brought to the attention of the AI community by Percy Liang. Foundational models create "a single point of failure, so any defects, any biases which these models have, any security vulnerabilities . . .

Artificial Intelligence has the potential to bring immense opportunities, but it also poses challenges. Artificial intelligence (AI) is dominating the R&D agenda of the leading Internet industry. The Silicon Valley and other startup hubs are buzzing about artificial intelligence and the issue has come at the top of policymakers' agenda including the G20, the ITU, and the OECD, where leaders gathered this week in Paris. AI isn't new, but its recent acceleration can be explained by its convergence with big data and IoT, and the endless applications and services it allows. In the market, this translates into investments across all industries as stakeholders try to understand the potential of AI for their own businesses. For instance, at the beginning of the year, Ford motors announced a plan to invest $1 billion over the next five years in Argo AI, an artificial intelligence startup that is focused on developing autonomous vehicle technology.