3D object detection based on multi-view cameras and bird’s-eye view (BEV) representation is a key task for autonomous driving, as it enables the perception systems to understand the surrounding scenes. However, most existing BEV representation methods rely on the projection matrix of camera intrinsic and extrinsic parameters, which requires a complex and time-consuming calibration process that may introduce errors and degrade the detection performance. Moreover, the calibration results may vary due to environmental changes and affect the stability of the detection system. To address this problem, we propose a calibration-free 3D object detection method that leverages a group-equivariant convolutional network to extract features from multi-view images and a projection network module to learn the implicit 3D-to-2D projection relationship for obtaining BEV representation. Furthermore, we employ contrastive learning to pre-train the projection network module without using manually annotated data. By exploiting the multi-view camera data through contrastive learning, our proposed method eliminates the need for tedious calibration, avoids calibration errors, and reduces the dependence on a large amount of annotated data for calibration-free 3D object detection. We evaluate our method on the nuScenes dataset and demonstrate its competitive performance. Our method improves the stability and reliability of 3D object detection in long-term autonomous driving.

When multiple agents collaborate to perform distributed operations, they can be modeled as cooperative games. Considering a network of agents work together and they can only communicate in a limited way (e.g., only to neighbor peers), the goal is to maximize the cooperation success globally, or maximize the total value and social welfare of the whole network. The type of cooperation is challenging since the game is not zero-sum. There are not any outside agents to serve as referees. The objective functions may be non-stationary and non-convex. In this paper, each agent is modeled as a content supplier or consumer. Each agent optimizes its own objective locally. We show that each agent self-organizes or converges to its "value" via the principles of quantum computing and game theories. We prove two theorems that can optimize an agent's own objective and simultaneously optimize the global social welfare of its peer network. The quantum intelligence game algorithms are unsupervised and self-organizing, where the weights expressed in quantum neural networks or transformers can be computed from a natural mechanism known as a quantum adiabatic evolution.

We demonstrate a machine learning method, namely lexical link analysis (LLA), which can be used to discover high-value information from financial data. LLA is an unsupervised learning method that does not require manually labeled training data. We also demonstrate how to form LLA in a game-theoretic framework. We show that with game theory: high-value information selected by LLA reaches a Nash equilibrium by superpositioning popular and anomalous information and at the same time generates high social welfare, therefore containing higher intrinsic value. We show the results of LLA of two sets of financial data validating and correlating with the ground truth.

We demonstrate a machine learning and artificial intelligence method, i.e., lexical link analysis (LLA) to discover high-value information from big data. In this paper, high-value information refers to the information that has the potential to grow its value over time. LLA is a unsupervised learning method that does not require manually labeled training data. New value metrics are defined based on a game-theoretic framework for LLA. In this paper, we show the value metrics generated from LLA in a use case of analyzing business news. We show the results from LLA are validated and correlated with the ground truth. We show that by using game theory, the high-value information selected by LLA reaches a Nash equilibrium by superpositioning popular and anomalous information, and at the same time generates high social welfare, therefore, contains higher intrinsic value.

We demonstrated a machine learning and artificial intelligence method, i.e., lexical link analysis (LLA) to discover different layers of semantic network that contribute to innovative ideas from big data. The LLA is an unsupervised machine learning paradigm that does not require manually labeled training data. Multilayer value metrics are defined based on game theory for LLA. We showed the following results: 1) the value metrics generated from LLA in a use case of an internet game and crowd-sourcing; 2) the results from LLA are validated and correlated with the ground truth; 3) the game-theoretic LLA can help an information provider to present the information in the most valuable way. The information presentation can solve a problem (e.g., a search request of innovation) that no other information providers can solve (i.e., expertise). In addition, it ties also to a broader context that the unique value can propagate through the consensus. Based on the game-theoretic LLA, an information provider should not always present expertise content or authoritative content but rather with a mixed strategy where each type of content is presented with certain probabilities for the best value overall.

We will demonstrate a distributed recursive method, i.e., Lexical Link Analysis (LLA) and an infrastructure, i.e., Collaborative Learning Agents (CLA) to discover high-value information. The combined system is a unified methodology of discovering high-value information from structured and unstructured heterogeneous data sources. We will demonstrate the LLA/CLA system using a crowdsourcing data source and show how it can be used to discover new knowledge for a widening range of applications and heterogeneous data types.