Implicit neural 3D representation has achieved impressive results in surface or scene reconstruction and novel view synthesis, which typically uses the coordinate-based multi-layer perceptrons (MLPs) to learn a continuous scene representation. However, existing approaches, such as Neural Radiance Field (NeRF) and its variants, usually require dense input views (i.e.

To coordinate, intelligent agents might need to know something about themselves, about each other, about how others view themselves and others, about how others think others view themselves and others, and so on. Taken to an extreme, the amount of knowledge an agent might possess to coordinate its interactions with others might outstrip the agent's limited reasoning capacity (its available time, memory, and so on). Much of the work in studying and building multiagent systems has thus been devoted to developing practical techniques for achieving coordination, typically by limiting the knowledge available to, or necessary for, agents. This article categorizes techniques for keeping agents suitably ignorant so that they can practically coordinate and gives a selective survey of examples of these techniques for illustration. Certainly, people who know much (or think they know much) are sometimes subject to cockiness, confusion, paralysis, resignation, or other unpleasant states.

The Cornell RoboCup Project was created to teach systems engineering concepts and practices to students to prepare them for designing, integrating, and maintaining highly complex systems. Another objective of the project is to explore the interplay between AI, dynamics, and control theory. This article describes the Cornell RoboCup team, which won the RoboCup-99 small-league championship in Stockholm, Sweden. Another objective of the project is to explore the interplay between AI, dynamics, and control theory. This article describes the Cornell RoboCup team, which won the RoboCup-99 small-league championship in Stockholm, Sweden.

A classifier is a tool in data mining that takes a bunch of data representing things we want to classify and attempts to predict which class the new data belongs to. Orange, an open-source data visualization and analysis tool for data mining, implements C4.5 in their decision tree classifier. Support vector machine (SVM) learns a hyperplane to classify data into 2 classes. The balls represent data points, and the red and blue color represent 2 classes.

The network effects and economic incentives around these open systems and their data can be more powerful than current centralized companies because they are open standards that anyone can build on in the same way the protocols of the internet like TCP/IP, HTML, and SMTP have achieved far greater scale than any company that sits atop them. This open data has the potential to commoditize the data silos most tech companies like Google, Facebook, Uber, LinkedIn, and Amazon are built on and extract rent from. And it creates an open data layer for AIs to train on. Simplified, AI is driven by 3 things: tools, compute power, and training data.

At the core of cluster analysis is the concept of measuring distances between a variety of different data point dimensions. For example, when considering k-means clustering, there is a need to measure a) distances between individual data point dimensions and the corresponding cluster centroid dimensions of all clusters, and b) distances between cluster centroid dimensions and all resulting cluster member data point dimensions. While k-means, the simplest and most prominent clustering algorithm, generally uses Euclidean distance as its similarity distance measurement, contriving innovative or variant clustering algorithms which, among other alterations, utilize different distance measurements is not a stretch. It is thus a judgment of orientation and not magnitude: two vectors with the same orientation have a cosine similarity of 1, two vectors at 90 have a similarity of 0, and two vectors diametrically opposed have a similarity of -1, independent of their magnitude.

To coordinate, intelligent agents might need to know something about themselves, about each other, about how others view themselves and others, about how others think others view themselves and others, and so on. Taken to an extreme, the amount of knowledge an agent might possess to coordinate its interactions with others might outstrip the agent's limited reasoning capacity (its available time, memory, and so on). Much of the work in studying and building multiagent systems has thus been devoted to developing practical techniques for achieving coordination, typically by limiting the knowledge available to, or necessary for, agents. This article categorizes techniques for keeping agents suitably ignorant so that they can practically coordinate and gives a selective survey of examples of these techniques for illustration.