【講演】デジタルヘルスシンポジウム @京大 12/4/2015 「IoT時代のクラウドとAIの展望
This talk covers these three topics:
1. Current Market Trend of Mobile IoT Health Care Devices and their business models
2. Is mobile cloud is applicable for medical services?
3. Future vision of machine translation and data mining technologies for Clinicaland Pharmaceutical field
【講演】デジタルヘルスシンポジウム @京大 12/4/2015 「IoT時代のクラウドとAIの展望
This talk covers these three topics:
1. Current Market Trend of Mobile IoT Health Care Devices and their business models
2. Is mobile cloud is applicable for medical services?
3. Future vision of machine translation and data mining technologies for Clinicaland Pharmaceutical field
[Azure Council Experts (ACE) 第6回定例会] Microsoft Azureアップデート情報 (2014/06/18-2014...Naoki (Neo) SATO
Microsoft Azure 最新アップデート情報
http://a-c-e.biz/activity/update
第6回定例会およびワーキンググループのご案内
http://a-c-e.biz/20701
[Azure Council Experts (ACE) 第5回定例会] Microsoft Azureアップデート情報 (2014/04/16-2014/06/18)
http://www.slideshare.net/satonaoki/20140618aceazureupdate
[Azure Council Experts (ACE) 第4回定例会] Microsoft Azureアップデート情報 (2014/02/19-2014/04/16)
http://www.slideshare.net/satonaoki/20140416aceazureupdate
[Azure Council Experts (ACE) 第3回定例会] Windows Azureアップデート情報 (2013/12/07-2014/02/19)
http://www.slideshare.net/satonaoki/20140129-ace-azureupdate-31375612
[Azure Council Experts (ACE) 第2回定例会] Windows Azureアップデート情報 (201311/24-2013/12/06)
http://www.slideshare.net/satonaoki/20131206-ace-azureupdate
[Azure Council Experts (ACE) 第5回定例会] Microsoft Azureアップデート情報 (2014/04/16-2014...Naoki (Neo) SATO
Microsoft Azure 最新アップデート情報
http://a-c-e.biz/activity/update
第5回定例会およびワーキンググループのご案内
http://a-c-e.biz/20191
[Azure Council Experts (ACE) 第4回定例会] Microsoft Azureアップデート情報 (2014/02/19-2014/04/16)
http://www.slideshare.net/satonaoki/20140416aceazureupdate
[Azure Council Experts (ACE) 第3回定例会] Windows Azureアップデート情報 (2013/12/07-2014/02/19)
http://www.slideshare.net/satonaoki/20140129-ace-azureupdate-31375612
[Azure Council Experts (ACE) 第2回定例会] Windows Azureアップデート情報 (201311/24-2013/12/06)
http://www.slideshare.net/satonaoki/20131206-ace-azureupdate
Chris Anderson and Yochay Kiriaty - Serverless Patterns with Azure FunctionsServerlessConf
Presented at ServerlessConf NYC 2016.
The future of cloud development is Serverless. Sure, there will always be those whom insist on provisioning and managing VMs, but in few short years majority of developers will default to Serverless architecture when building cloud applications. Join Chris Anderson and Yochay Kiriaty for this demo heavy session describing existing and emerging Serverless patterns.
Published on Feb 11, 2014
Introduction for Vagrant and Docker provider.
This presentation is prepared for Docker Meetup Tokyo 2014 #1 in 12, Feb, 2014 at National Institute of Informatics.
Copyright 2014, NTT Data Corporation.
https://www.slideshare.net/miurahr/introduction-to-vagrantdockernttdata
Generative AI: Redefining Creativity and Transforming Corporate LandscapeOsaka University
The advent of Generative AI is redefining the boundaries of creativity and markedly transforming the corporate landscape. One of the pioneering technologies in this domain is the Reinforcement Learning from Human Feedback (RLHF). Combined with advancements in LLM (Language Model) has emerged as a notable player. LLM offers two primary interpretations: firstly, as a machine capable of generating highly plausible texts in response to specific directives, and secondly, as a multi-lingual knowledge repository that responds to diverse inquiries.
The ramifications of these technologies are widespread, with profound impacts on various industries. They are catalyzing digital transformation within enterprises, driving significant advancements in research and development, especially within the realms of drug discovery and healthcare. In countries like Japan, Generative AI is heralded for its potential to bolster creativity. The value generated by such AI-driven innovations is estimated to be several trillion dollars annually. Intriguingly, about 75% of this value, steered by creative AI applications, is predominantly concentrated within customer operations, marketing and sales, software engineering, and R&D. These applications are pivotal in enhancing customer interactions, generating innovative content for marketing campaigns, and even crafting computer code from natural language prompts. The ripple effect of these innovations is palpable in sectors like banking, high-tech, and life sciences.
However, as with every innovation, there are certain setbacks. For instance, the traditional business model of individualized instruction, as seen in the context of professors teaching basic actions, is on the brink of obsolescence.
Looking ahead, the next five years pose pertinent questions about humanity's role amidst this technological evolution. A salient skillset will encompass the adept utilization of generative AI, paired with the discernment to accept or critique AI-generated outputs. Education, as we know it, will be reimagined. The evaluative focus will transition from verifying a student's independent work to gauging their ability to produce content surpassing their AI tools. Generative AI's disruptive nature will compel us to re-evaluate human value, reshaping the paradigms of corporate management and educational methodologies
To be or not to be an academic, big enterprise, startup job that is the qu...Osaka University
"Who said it first is not important." Who gets there first is."
(Takeo Kanade, Circa 1990s)
Before joining a Big Enterprise, Check these
Empathy with the company's vision and mission.
Senior management prepares their own presentation materials (with high IT literacy).
There are executives who joined the company mid-career from outside.
There is a good employee training program.
There are many retired employees who are active after leaving the company.
There is an organization that integrates marketing, development, and operations.
There are no academic cliques.
日本ではディープテック・スタートアップが育つ環境がない。1. イノベーター人材育成が不十分、2.ベンチャーキャピタル側の人材欠如、3. 大企業側レセプターが未発達という課題を指摘した上で、今後どうすれば良いのかという未来志向の議論をしたい。巷間で言われる「AIは幻想だった」という評価を乗り越えるべく「AIはデジタルだ」の見地から、米国の非営利団体OpenAIが開発した巨大言語モデルGPT-3を例にして、これから見えるイノベーショントレンドを共有する。
Business Environment of Deep Tech AI Startups
There is no naturing environment for deep-tech startups to grow in Japan. I would like to point out the following issues: (1) insufficient development of innovator education system, (2) lack of human resources on the venture capital side, and (3) lack of development of receptors on the large enterprises side. In order to overcome the reputation of "AI was an illusion." from the viewpoint of "AI is digital.", we will share the innovation trends to be seen using the huge language model GPT -3 developed by the non-profit organization OpenAI in the United States as an example.
The most conservative part in a company is mediocre experts who love status quo. Top tier experts tend to climb up mountains from one peak to another peak, so as to explore new ideas and products. You must move also from one to another one. It is said,
“You can raise the bar or you can wait for others to raise it, but it’s getting raised regardless.” Raise your bar higher enough no to succeed now but in the future eventually. Your life is counted hoe many oh-shit moment you experienced. Gotta run.
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.
【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。