Intelligence Augmentation - The Next-Gen AIMelanie Cook
Robotics and AI have integrated human and mechanical capabilities at work, with jobs lost and skills condensed to a keystroke. But human intelligence is far from obsolete.
With crowd-computing we have knowledge exchanges like Wiki, and real-time curated news. Semantic technology helps leaders to understand what is happening in the work place. But neurology shows that these leaders cannot make choices, and therefore take action, without emotion.
Augmented Intelligence takes human intuition and imagination, and combines it with AI’s ability to automate and scale, making the Intelligent Workplace hard to beat.
Modern Prospecting Techniques for Connecting with Prospects (from Sales Hacke...HubSpot
Sales is a difficult world to be in because buyers aren't putting up with salespeople anymore. Instead of helping and building relationships, sales reps are still focused on closing prospects - even when they aren't ready to buy! So buyers ignore them. Because of that, even great sales reps would be lucky to get on the phone with someone.
While buyers have evolved and become more sophisticated, sales reps and training programs have been slow to adapt to that change.
Learn actionable modern prospecting techniques you can apply immediately from two best selling authors and sales experts: Max Altschuler CEO of Sales Hacker, and Mark Roberge CRO of HubSpot.
The lack of visible female role models is pervasive in the tech industry, particularly on Wikipedia, where just under 17% of Wikipedia biographies were on women. That's why HubSpot wrote fourteen Wikipedia entries for remarkable women in tech to help inspire young women to reach positions at the highest levels of STEM.
Mobile-First SEO - The Marketers Edition #3XEDigitalAleyda Solís
How to target your SEO process to a reality of more people searching on mobile devices than desktop and an upcoming mobile first Google index? Check it out.
An immersive workshop at General Assembly, SF. I typically teach this workshop at General Assembly, San Francisco. To see a list of my upcoming classes, visit https://generalassemb.ly/instructors/seth-familian/4813
I also teach this workshop as a private lunch-and-learn or half-day immersive session for corporate clients. To learn more about pricing and availability, please contact me at http://familian1.com
How to Become a Thought Leader in Your NicheLeslie Samuel
Are bloggers thought leaders? Here are some tips on how you can become one. Provide great value, put awesome content out there on a regular basis, and help others.
ライフサイエンスデータベースの現状 〜データベース統合化のための技術的・政治的側面〜
Japan Museum Bioinformatics (Museomics) Working Group 第2回会合@東工大・緑が丘キャンパス
https://sites.google.com/site/museumbioinfo/meetings/201410xx
#museomejp
Briefly reviews International Conference on Weblogs and Social Media (ICWSM12) from my perspective.
The latter part written in Japanese, sorry for that.
Presented at Journal Paper Track, The Web Conference, Lyon, France, April 15, 2018
https://doi.org/10.1145/3184558.3186234
Abstract: Linked Open Data (LOD) technology enables web of data and exchangeable knowledge graphs through the Internet. However, the change in knowledge is happened everywhere and every time, and it becomes a challenging issue of linking data precisely because the misinterpretation and misunderstanding of some terms and concepts may be dissimilar under different context of time and different community knowledge. To solve this issue, we introduce an approach to the preservation of knowledge graph, and we select the biodiversity domain to be our case studies because knowledge of this domain is commonly changed and all changes are clearly documented. Our work produces an ontology, transformation rules, and an application to demonstrate that it is feasible to present and preserve knowledge graphs and provides open and accurate access to linked data. It covers changes in names and their relationships from different time and communities as can be seen in the cases of taxonomic knowledge.
We propose Crop Vocabulary(CVO) as a basis of the core vocabulary of crop names that becomes the guidelines for data interoperability between agricultural ICT systems on the food chain. Since a single species is treated in different ways, there are many different types of crop names. So, we organize the crop name discriminated by properties such as scientific name, planting method, edible part and registered cultivar information. Also, Crop Vocabulary is also linked to existing vocabularies issued by Japanese government agency and international organization such as AGROVOC. It is expected to use in the data format in the agricultural ICT system.
Presented in 45th Asia Pacific Advanced Network (APAN45) Meeting, Singapore (2018)
Presented as the invited talk at International Workshop on kNowledge eXplication for Industry (kNeXI2017). In this talk, I explain the experience and lesson learnt how to build ontologies. I am currently building the agriculture activity ontology (AAO). It describes classification and properties of various activities in the agriculture domain. It is formalized with Description Logics.
Presented at the Interest Group on Agricultural Data (IGAD) ,3 April, 2017, Barcelona, Spain
Abstract: n this talk, we present the current status of our agriculture ontologies that are developed to accelerate the data use in agriculture.
The agriculture activity ontology formalizes the activities in agriculture. We have developed it for three years. Now we are developing its applications. One application is to exchange formats between different farmer management systems. Another ontology is the crop ontology that standardizes the names of crops. The structure is simple but has links to many other standards in distribution industry, food industry and so on.
Now it is getting common for farmers to use IT systems to manage their activities. To realize incomparability among IT systems, we are building the vocabulary based on the agricultural activity ontology. The words in the vocabulary have logical definitions because the ontology is formalized based on description logic. As a result, the vocabulary has expendability to add new words and flexibility to generate custom vocabularies such like those for specific crops and regions.
【DLゼミ】XFeat: Accelerated Features for Lightweight Image Matchingharmonylab
公開URL:https://arxiv.org/pdf/2404.19174
出典:Guilherme Potje, Felipe Cadar, Andre Araujo, Renato Martins, Erickson R. ascimento: XFeat: Accelerated Features for Lightweight Image Matching, Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
概要:リソース効率に優れた特徴点マッチングのための軽量なアーキテクチャ「XFeat(Accelerated Features)」を提案します。手法は、局所的な特徴点の検出、抽出、マッチングのための畳み込みニューラルネットワークの基本的な設計を再検討します。特に、リソースが限られたデバイス向けに迅速かつ堅牢なアルゴリズムが必要とされるため、解像度を可能な限り高く保ちながら、ネットワークのチャネル数を制限します。さらに、スパース下でのマッチングを選択できる設計となっており、ナビゲーションやARなどのアプリケーションに適しています。XFeatは、高速かつ同等以上の精度を実現し、一般的なラップトップのCPU上でリアルタイムで動作します。
セル生産方式におけるロボットの活用には様々な問題があるが,その一つとして 3 体以上の物体の組み立てが挙げられる.一般に,複数物体を同時に組み立てる際は,対象の部品をそれぞれロボットアームまたは治具でそれぞれ独立に保持することで組み立てを遂行すると考えられる.ただし,この方法ではロボットアームや治具を部品数と同じ数だけ必要とし,部品数が多いほどコスト面や設置スペースの関係で無駄が多くなる.この課題に対して音𣷓らは組み立て対象物に働く接触力等の解析により,治具等で固定されていない対象物が組み立て作業中に運動しにくい状態となる条件を求めた.すなわち,環境中の非把持対象物のロバスト性を考慮して,組み立て作業条件を検討している.本研究ではこの方策に基づいて,複数物体の組み立て作業を単腕マニピュレータで実行することを目的とする.このとき,対象物のロバスト性を考慮することで,仮組状態の複数物体を同時に扱う手法を提案する.作業対象としてパイプジョイントの組み立てを挙げ,簡易な道具を用いることで単腕マニピュレータで複数物体を同時に把持できることを示す.さらに,作業成功率の向上のために RGB-D カメラを用いた物体の位置検出に基づくロボット制御及び動作計画を実装する.
This paper discusses assembly operations using a single manipulator and a parallel gripper to simultaneously
grasp multiple objects and hold the group of temporarily assembled objects. Multiple robots and jigs generally operate
assembly tasks by constraining the target objects mechanically or geometrically to prevent them from moving. It is
necessary to analyze the physical interaction between the objects for such constraints to achieve the tasks with a single
gripper. In this paper, we focus on assembling pipe joints as an example and discuss constraining the motion of the
objects. Our demonstration shows that a simple tool can facilitate holding multiple objects with a single gripper.
4. ディープラーニングま
での歴史
• 1958 Perceptron
(XOR Problem)
• 1980 Neocognitron [1][2]
• 1986 Backpropagation [3]
(Data and computation limitation)
• Mid 2000: Deep Learning
[1] 福島邦彦、位置ずれに影響されないパターン認識機構の神経回路のモデル --- ネオコグニトロン ---、1979
年、電子通信学会論文誌A, vol. J62-A, no. 10, pp. 658-665
[2] K. Fukushima、Neocognitron: A self-organizing neural network model for a mechanism of pattern
recognition unaffected by shift in position、1980年、Biological Cybernetics}, vol. 36, no. 4, pp. 193-202
[3] Rumelhart, David E.; Hinton, Geoffrey E., Williams, Ronald J. (8 October 1986). "Learning representations by
back-propagating errors". Nature 323 (6088): 533–536. doi:10.1038/323533a0
5. Deep Learning
Building High-Level Features using Large Scale Unsupervised Learning. Quoc
V. Le, Marc'Aurelio Ranzato, Rajat Monga, Matthieu Devin, Kai Chen, Greg S.
Corrado, Jeffrey Dean and Andrew Y. Ng. In Proceedings of the Twenty-Ninth
International Conference on Machine Learning, 2012.
Deep Learning, The Analytics Store, http://theanalyticsstore.ie/deep-learning/ (2015-07-15)
“the model has1 billion connections, the dataset has
10 million 200×200 pixel images downloaded from
the Internet…We train this network using model
parallelism and asynchronous SGD on a cluster with
1,000 machines (16,000 cores) for three days”
6. IBM Watson
• IBMが開発した質問応答システム
• 2011年1月13日にはトーマス・J・ワトソン研究所でワト
ソンの公開と「ジェパディ!」での人間と対戦デモが行
われ、前チャンピオンに勝つ。
• ワトソンは、10台のラックに搭載されたPower Systems
750で構成され、2880個のPOWER7プロセッサ・コアを
搭載し、オペレーティングシステムはLinux、処理性能
は80テラFLOPS(TFLOPS)で、インターネットには接続さ
れておらず、本・台本・百科事典(Wikipediaを含む)な
どの2億ページ分の構造的及び非構造的テキスト
データ(70GB程度、約100万冊の書籍に相当)を取り
込む。
https://ja.wikipedia.org/wiki/ワトソン_(コンピュータ)
7. -
Ferrucci, D et al. (2010), "Building Watson: An Overview of the DeepQA
Project", AI Magazine (AI Magazine) 31 (3), retrieved 2011-02-19
16. Linked Open Dataの構築
- LODAC: Linked Open Data for Academia -
LODAC SPECIES: 種名をベースに多様なDBをリンクで接続
博物館
標本
DB
種情報
DB
Taxon
Name DBGBIF BioSci.
DB
個別
研究
DB
名前数: 113118
トリプル数:14,532,449
Data from Source BIntegrated data
dc:references dc:references
dc:references dc:references
dc:references dc:references
dc:creator
dc:creator
crm:P55_has_current_location
crm:P55_has_current_location
crm:P55_has_current_location
dc:creator
Data from Source A
Work
Museum
Creator
Minimum Data to identify entitiesRaw Data for entities Raw Data for entities
LODAC Museum: 博物館・美術館のデータのLOD
検索拡張アプリ
CKAN (日本語):
データセット登録レジストリ
DBPedia Japanese
トリプル数: 40,059,131
館数:114, 収蔵品:約 177万
28. 自律移動椅子
H. Takeda, K. Terada and T. Kawamura: Artifact Intelligence: Yet Another Approach for Intelligent
Robots, in Proceedings of the 11th IEEE International Workshop on Robot and Human
Interactive Communication (ROMAN 2002)