Collaborating Authors




ARTIFICIAL INTELLIGENCE 1. Introduction Introduction to Artificial Intelligence, Background and Applications, Turing Test and Rational Agent approaches to AI,…

Search and Learning for Unsupervised Text Generation New Faculty Highlights Extended Abstract

Interactive AI Magazine

The following article is an extended abstract submitted as part of AAAI's New Faculty Highlights Program. With the advances of deep learning techniques, text generation is attracting increasing interest in the artificial intelligence (AI) commu- nity, because of its wide applications and because it is an essential component of AI. Traditional text generation systems are trained in a supervised way, requiring massive labeled parallel corpora. In this paper, I will introduce our recent work on search and learning ap- proaches to unsupervised text generation, where a heuristic objective function estimates the quality of a candidate sentence, and discrete search algorithms generate a sentence by maximizing the search objective. A machine learning model further learns from the search results to smooth out noise and improve efficiency.

Global Machine Learning Courses Market Size 2023 Latest Report by Opportunities, Challenges, Manufacturers, Market Dynamics and Forecast to 2026 - Digital Journal


"Final Report will add the analysis of the impact of COVID-19 on this industry." Worldwide "Machine Learning Courses Market" 2023 report offers business strategy, covers complete scenario for market definition and overview including market segmentation (type, application) and market exchange rate. Furthermore, this report give information on Market Competition Analysis by Market Performance, Product and Service Analysis, Strategies for Company to Deal with the Impact of COVID-19, market size, share, Sales, Value, Price and Gross Margin. The Machine Learning Courses market report offers a full data about the position, extent of development, and possibilities of players on the viewpoint with forecast year 2026. Machine learning is a discipline that studies the actions of computers under non-specific programming conditions.

NASA unveils plan for next-gen telescope to search space for signs of life: reports

FOX News

Veteran NASA astronaut Tom Jones recaps the historic Artemis I mission after the Orion capsule made a successful return to earth and outlines what this means for the lunar return program. The Habitable Worlds Observatory was announced Monday at the latest American Astronomical Society meeting, and its goal is searching for signs of life on habitable exoplanets. said on Friday that the observatory will need a powerful coronograph, which is an instrument that allows scientists to study faint objects. Mark Clampin, the director of NASA's astrophysics division, reportedly said that the agency would approach the project as if it faced a strict launch window, building on previous technology used for the Nancy Grace Roman Space Telescope as well as Webb. FILE - In this April 13, 2017, photo provided by NASA, technicians lift the mirror of the James Webb Space Telescope using a crane at the Goddard Space Flight Center in Greenbelt, Maryland.

New Developments related to Minimax Optimization part1(Machine Learning)


Abstract: In this paper we study a class of constrained minimax problems. In particular, we propose a first-order augmented Lagrangian method for solving them, whose subproblems turn out to be a much simpler structured minimax problem and are suitably solved by a first-order method recently developed in [26] by the authors. Abstract: Nonconvex-nonconcave minimax optimization has been the focus of intense research over the last decade due to its broad applications in machine learning and operation research. Unfortunately, most existing algorithms cannot be guaranteed to converge and always suffer from limit cycles. Their global convergence relies on certain conditions that are difficult to check, including but not limited to the global Polyak-Łojasiewicz condition, the existence of a solution satisfying the weak Minty variational inequality and α-interaction dominant condition.

Understanding the Grover's Algorithm part2(Quantum Computing)


Abstract: Recent studies have been spurred on by the promise of advanced quantum computing technology, which has led to the development of quantum computer simulations on classical hardware. Grover's quantum search algorithm is one of the well-known applications of quantum computing, enabling quantum computers to perform a database search (unsorted array) and quadratically outperform their classical counterparts in terms of time. Given the restricted access to database search for an oracle model (black-box), researchers have demonstrated various implementations of Grover's circuit for two to four qubits on various platforms. However, larger search spaces have not yet been explored. In this paper, a scalable Quantum Grover Search algorithm is introduced and implemented using 5-qubit and 6-qubit quantum circuits, along with a design pattern for ease of building an Oracle for a higher order of qubits.

Working with the concept of Self-Imitation Learning part1(Machine Learning)


Abstract: Imitation learning (IL) enables robots to acquire skills quickly by transferring expert knowledge, which is widely adopted in reinforcement learning (RL) to initialize exploration. However, in long-horizon motion planning tasks, a challenging problem in deploying IL and RL methods is how to generate and collect massive, broadly distributed data such that these methods can generalize effectively. In this work, we solve this problem using our proposed approach called {self-imitation learning by planning (SILP)}, where demonstration data are collected automatically by planning on the visited states from the current policy. SILP is inspired by the observation that successfully visited states in the early reinforcement learning stage are collision-free nodes in the graph-search based motion planner, so we can plan and relabel robot's own trials as demonstrations for policy learning. Due to these self-generated demonstrations, we relieve the human operator from the laborious data preparation process required by IL and RL methods in solving complex motion planning tasks.

Working with Greedy Algorithms part1(Reinforcement Learning)


Abstract: In the present paper we identify those filtered probability spaces (Ω,F,(Fn),P) that determine already the martingale type of Banach space X. We isolate intrinsic conditions on the filtration (Fn) of purely atomic σ-algebras which determine that the upper ℓp estimates f pLp(Ω,X) Cp( Ef pX n 1 Δnf pLp(Ω,X)),f Lp(Ω,X) imply that the Banach space X is of the martingale type p. Abstract: We provide theoretical bounds on the worst case performance of the greedy algorithm in seeking to maximize a normalized, monotone, but not necessarily submodular objective function under a simple partition matroid constraint. We also provide worst case bounds on the performance of the greedy algorithm in the case that limited information is available at each planning step. We specifically consider limited information as a result of unreliable communications during distributed execution of the greedy algorithm. We utilize notions of curvature for normalized, monotone set functions to develop the bounds provided in this work.

Automated Dynamic Algorithm Configuration

Journal of Artificial Intelligence Research

The performance of an algorithm often critically depends on its parameter configuration. While a variety of automated algorithm configuration methods have been proposed to relieve users from the tedious and error-prone task of manually tuning parameters, there is still a lot of untapped potential as the learned configuration is static, i.e., parameter settings remain fixed throughout the run. However, it has been shown that some algorithm parameters are best adjusted dynamically during execution. Thus far, this is most commonly achieved through hand-crafted heuristics. A promising recent alternative is to automatically learn such dynamic parameter adaptation policies from data. In this article, we give the first comprehensive account of this new field of automated dynamic algorithm configuration (DAC), present a series of recent advances, and provide a solid foundation for future research in this field. Specifically, we (i) situate DAC in the broader historical context of AI research; (ii) formalize DAC as a computational problem; (iii) identify the methods used in prior art to tackle this problem; and (iv) conduct empirical case studies for using DAC in evolutionary optimization, AI planning, and machine learning.

Teaching - CS 221


CS 221 ― Artificial Intelligence My twin brother Afshine and I created this set of illustrated Artificial Intelligence cheatsheets covering the content of the CS 221 class, which I TA-ed in Spring 2019 at Stanford. They can (hopefully!) be useful to all future students of this course as well as to anyone else interested in Artificial Intelligence. You can help us translating them on GitHub!