技術動向の調査として、ICML Workshop Uncertainty & Robustness in Deep Learningの中で、面白そうなタイトルを中心に読んで各論文を4スライドでまとめました。
最新版:https://speakerdeck.com/masatoto/icml-2021-workshop-shen-ceng-xue-xi-falsebu-que-shi-xing-nituite-e0debbd2-62a7-4922-a809-cb07c5da2d08(文章を修正しました。)
技術動向の調査として、ICML Workshop Uncertainty & Robustness in Deep Learningの中で、面白そうなタイトルを中心に読んで各論文を4スライドでまとめました。
最新版:https://speakerdeck.com/masatoto/icml-2021-workshop-shen-ceng-xue-xi-falsebu-que-shi-xing-nituite-e0debbd2-62a7-4922-a809-cb07c5da2d08(文章を修正しました。)
深層学習以降のAI研究の流れの中で、特に、基盤モデルにおけるchain of thought promptingやfactual groundingに焦点を当て、基盤モデルが論理的推論などの意識レベルの処理を学習したと言えるかについて考察する。
時間が許せば、深層学習によるpostdictionの可能性等についても論じる。
※다운로드하시면 더 선명한 자료를 보실 수 있습니다.
게임을 더 재미있게 만들기 위해서는 레벨 디자인을 잘 해야 하는데, 이를 도와주는 Puzzle AI를 만들어 가는 과정을 설명합니다. Puzzle AI를 이용해 신규 레벨의 난이도를 예측하여 유저가 만족할 수 있는 레벨로 디자인하는 방법을 알아봅니다.
반복적인 게임 레벨 디자인을 도와주고, 레벨에 대한 객관적인 평가를 하고, 레벨 디자이너와 개발자에게 도움을 줘서 결국에는 고객을 만족시켜 매출에 도움을 줄 수 있는 방법을 알 수 있습니다.
목차
1. 재미있는 게임이란?
2. 적절한 난이도의 게임을 만들기 위한 방법
3. 에이전트를 이용한 게임의 난이도 평가
4. 게임의 난이도 예측
5. 실제 적용
대상
머신러닝, 강화 학습, 게임 AI, 게임 개발, 게임 QA, 레벨 디자인에 관심 있는 분 누구나
■관련 동영상: https://youtu.be/OUg0xcgkhls
深層学習以降のAI研究の流れの中で、特に、基盤モデルにおけるchain of thought promptingやfactual groundingに焦点を当て、基盤モデルが論理的推論などの意識レベルの処理を学習したと言えるかについて考察する。
時間が許せば、深層学習によるpostdictionの可能性等についても論じる。
※다운로드하시면 더 선명한 자료를 보실 수 있습니다.
게임을 더 재미있게 만들기 위해서는 레벨 디자인을 잘 해야 하는데, 이를 도와주는 Puzzle AI를 만들어 가는 과정을 설명합니다. Puzzle AI를 이용해 신규 레벨의 난이도를 예측하여 유저가 만족할 수 있는 레벨로 디자인하는 방법을 알아봅니다.
반복적인 게임 레벨 디자인을 도와주고, 레벨에 대한 객관적인 평가를 하고, 레벨 디자이너와 개발자에게 도움을 줘서 결국에는 고객을 만족시켜 매출에 도움을 줄 수 있는 방법을 알 수 있습니다.
목차
1. 재미있는 게임이란?
2. 적절한 난이도의 게임을 만들기 위한 방법
3. 에이전트를 이용한 게임의 난이도 평가
4. 게임의 난이도 예측
5. 실제 적용
대상
머신러닝, 강화 학습, 게임 AI, 게임 개발, 게임 QA, 레벨 디자인에 관심 있는 분 누구나
■관련 동영상: https://youtu.be/OUg0xcgkhls
“실시간 급상승 검색어” 기능은 실시간으로 포털사이트 사용자들의 검색 질의(Query) 횟수를 기준으로 순위를 선정하여 시각적으로 보여줌으로써, 검색 결과를 통해 현재 어떤 것이 이슈화 되고 있는지 파악할 수 있어 많은 사용자에게 편리하게 사용되어져 왔다. 하지만, 분리된 포털사이트로 인한 검색결과의 차이와 실시간으로 쏟아지는 다량의 정보로 인해 사용자들은 한 번에 많은 데이터를 처리해야 되는 문제에 맞닥뜨리게 된다. 본 논문에서는 이러한 문제들을 해결하기 위해 사용자들이 한 번에 처리할 수 있도록 다량의 정보를 요약 및 분석해주는 기술이 필요하다고 판단되어, 실시간 급상승 검색어 데이터와 그에 파생된 많은 정보의 검색결과를 텍스트 랭크 알고리즘을 중심으로 활용하여 분석 및 요약하는 기술을 제안한다.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
45. -
MAP
A 0.8 0.9
B 0.2 0.5
C 0.5 0.1
Probability
Estimation
Supervised
Learning
11/01 11/02 11/03
A /ref/direct /filter /cart/add
B /ref/banner /item/* /cate/*
C /ref/search /shop/* /main/sale