CNET Japan Live - ビジネスにAgilityとFlexibilityを与える Data x IoT x AI 最前線Daiyu Hatakeyama
迅速さと柔軟性。これが新規事業を成功させるシステムに求められる重要なキーワードとなっています。
企業・組織にとって「情報」のデジタル化と、作成され、増え続ける「データ」の活用は、重要な課題になっています。他社との差別化たるエンジンとなりうる AI の開発も実用段階に入っています。各種 IT 技術の中でも、AI の進化は特に早く、数か月前の情報が古いという事も増えてきました。
このセッションでは、幾つかの事例を見ていきながら、ビジネスを支える技術に焦点を充てて次の一手を考えるヒントをご提示します。
CNET Japan Live - ビジネスにAgilityとFlexibilityを与える Data x IoT x AI 最前線Daiyu Hatakeyama
迅速さと柔軟性。これが新規事業を成功させるシステムに求められる重要なキーワードとなっています。
企業・組織にとって「情報」のデジタル化と、作成され、増え続ける「データ」の活用は、重要な課題になっています。他社との差別化たるエンジンとなりうる AI の開発も実用段階に入っています。各種 IT 技術の中でも、AI の進化は特に早く、数か月前の情報が古いという事も増えてきました。
このセッションでは、幾つかの事例を見ていきながら、ビジネスを支える技術に焦点を充てて次の一手を考えるヒントをご提示します。
Smart data integration to hybrid data analysis infrastructureDataWorks Summit
To improve customer value and corporate competitiveness, it is necessary to deal with advanced analysis using big data, including data of core systems, and digital transformation.
At the same time, examples of hybrid construction of on-premise clouds are also spreading.
In this session, we will introduce the technology and the latest case examples of applying real-time replication utilized in the backbone system (RDBMS) to the Hadoop data analysis infrastructure (Hadoop Data Lake) of the hybrid configuration.
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.
What do you think about Rules and Virtues when running small companies. Of course, we need laws and regulations when managing big corporates, where a legal department, auditors, R&D, sales & marketing departments, and a personnel department are functioning. Here, let me point out a drawback derived from rules (i.e., internal laws and regulations in a big corporate) . Those hinders creativities of individuals, because they are confined to the area bounded by the rules. Instead, I highly recommend to introduce "virtues," in other words, disciplines or manners when running start-ups. Those define a center of individuals around which they share a strong culture.
Please refrain introducing more rules into a small company. I love a small company where all workers share a strong culture which fosters fruitful endeavours.
Rules protect you in a status-quo situation, and those spoil you in a mediocre business.
Virtues may harsh you, those may force you work so hard, and those will bring you strength.
46. IF/ETL
IF/ETL
IF/ETL
Hot data
Warm data
Cold data
BI
BI
BI
Real
Business
バッチ処理とストレージのレイヤ
Data Analysis / Scienceのレイヤ
OLTP，サービス提供DB，
キューブのレイヤ
• データは常に新しく，更新が激しい
• 即応を要求され，⾼いAvailability，⾼性能
王道なら・・・
46
Data analysis platform
62. データ X ASP X DevOps(内製化） を同時に⼿⼊れたい．
https://yooniqimages.blob.core.windows.net/yooniqimages-data-storage-resizedimagefilerepository/
Detail/20777/41b2413f-a0e6-496a-97fa-0334c5b38499/YooniqImages_207779841.jpg
技術がビジネスのリソースに
なっちゃいけないんですよ．
村上⾂(2014)
62
