agentic jensen intelligence

[chris macrae]

agenticai.docx 

(more work needed - who's in dc arm office of jensen ceo prodigee rene .). doznes of engineers who have been at nvidia for 25 years doing life's work ...

notes on contents of Jensen guide to ai collaborations with productive human brainpower- file by year/month with additions to those in above doc labeled XX ;eg xx 2007.1..Jensen Huang interview with Taiwan's Maurice Chang (helps explain how much AI20s depends on Taiwanese people  http://www.youtube.com/watch?v=u-x7PdnvCyI

-references to 25X involve data sovereignty applications deploying 25 times energy more efficient supercompute  ..xx 24.9.1 http://www.youtube.com/watch?v=doJDuLMnaWc  jensen testimony to dc bipartisan committee - launch of 25x supercompute - ability to build one every 3 months with sovereign data regional partners national energy week

25.6.1 Macron (3rd leg king charles ai nov 2023 summit- modi to be 4th) Lecun Mistral Mensch- Macron invites top deep 10 french corporates eg schneider volia ... to design deep search llms and startup infrastructure with Mistral (meta fair llama 3 with Yann Lecun), edge ai (eg dell),- first time ever EU country supported youth ai startups/  XX25.6.2 jensen uk ai summit with PM Sharmer & 10 corporates; keynote-

typical q to grok june 2025- what intelligence uses and data sovereignty maps will be priritised by places with 25x supercomputers

REINTRODUCTION TO JENSEN HUANG VIEWING

His tips are often brutally honest; unconventional wisdom has its place in studying entrepreneurship- at least i feel so ,my dad made this argument in The Economist from 1976

2013.1 how nvidia started in 1993 

 XX25.6.2 http://www.youtube.com/watch?v=SsNS3Xm9ig4 london tech week ai summit jensen keynote included maps of euro countries nvidia open partners (bloomberg coverage hunag uk prime minister Starmer 

25.5.1 update taiwan 25x supercompute partners inluding 8 deepest corporates of taiwan as deep ai nation since 1987 national startegy of changs ai foundry and foudational maps of sme value chains of ai japan-korea--taiwan-Guo , H Li - (coming grok assessment of main applications of taiwan 25x compute

xx25.4.1 https://www.c-span.org/person/jensen-huang/141815/jensen and leading global ai industry friends collect solutions for and with president trump

xx 25.4.2 stephen witt author of thinking machines reviews first 25 years of jensen's 21st C https://www.c-span.org/program/book-tv/after-words-with-stephen-wit... 1 minute of jensens time is worth a million $ approx- at same time deep family kids now grown up jensen kids 28.30 come from louis vuitton and cocktail bar taiwan before now at nvidila- see also 2010.1 family launch stanford's deep learning lab- hugely supportive engineering wife lori- noth enegineers at orgeon state (big gift)                                                                                                       jargon check  min 5.5what is a gpu?  14.30 what is supercomputer's operating language  "platform" cuda (and how did this emerge from lifework inspirations inspirations like medical imaging, early material after big bang and weather forecasting ) 18 mins headhunt bill daly 2009 (science users of supercompute before ai) - what if i can help your science work be 1000 times faster (nvidia enters mamograms early 200s as well as coding pixels with steve jobs pixar) 44.00 what is omniverse - eg robot simulation dishwashing by breaking a million dishes in virtual world before. 48.30 pivoting whole cokpany on neural nets before even the ai expsrts understood!.. (2009 stnafird deep learning lab)

25.3.1 gtc 2025 keynote- often jensens main annual update in usa. on eoof big abbouncement is Netwon Robot lab as partnership between nvidia (deep mind hassabis and google) and diseny now owners of pixar. This brings full circle one of earliest pixel coding partnerships between nvidia and pixar then owned by steve jobs. Newton Lab is for robots to train on physics of touch 

25.3.2 At GTC jensen shares the stage with quantum companies to clarify their visions

25.1.1 At Consumer Electronics Show Jensen focused on advancing AI across industries, particularly through physical AI (AI that perceives, reasons, and acts in the real world) and NVIDIA’s new products. The keynote emphasized gaming, robotics, autonomous vehicles (AVs), and personal AI computing, powered by NVIDIA’s Blackwell architecture and Cosmos platform (this platform - an open-source world foundation model (WFM) platform  (on Github & Hugging Face) designed to train and deploy physical AI for robotics and AVs

24.11.1 In Tokyo Jensen Huang interviews Softbank Maya Son. Celebrates builinding one of world's first 25 time X supercomputes in Japan around Maya Son's frineds. Back before covid Maya Son owned Arm and valued Nvidia. Coviud firced sell off his most profiatbale investment ie both Arm and Nvida. But ceos on siftbank, nvidia, arm have mainatied same vision - supercomputes need best of arm's cpus as well as nvidia's gpus.  (If it hadnt been for Biden Justice departments mono0oly decision, nvidia and arm would be one company - see XX 24.9.1 http://www.youtube.com/watch?v=g5llbNt7_Ik

24.11.2 At CS24 annual (annual summit for high performsamnce computing developers) Buck and Jensen announce cuPyNumeric - this simplifies connectiosn python science programmers need between up to 450 python libraies nvidia offers

24.3.2 jensen brings together the 7 who broke through with transgormer model attemtion is all you need

24.3.1 Digital Twins was one of the interesting foci of jensens gtu 2024 keynote. Increasingly if you are building a factory  for soace with robots it makes sense to digitally architect this first. That way you can uoyodate or replicate design of human and robot interaction. Notably Jensen and Taiwan companies value digital twins in case they are asked to move supply chains etc. (taiwan has over 40 years of being world class leader at advanced manufacturing dssign - clarifying exactly how to move designs with hundreds of parts has become too detailed a digital infrastructure game not to use AI.

24.3.3 GTc24 sees updates to Jensen's t=vision to 3 core models in one needed for erath 2.0 not just to be best weather forecast model but to plug and plau various climate chamge games. Jensen has announced with Taiwan the intention to make Taiwan world chlass epicentre of earth 2.0 modeling- Taiwan's national investment arm and data from IBM's weather company are early partners. Both Taiwan and Jaoan currently have to import most of their energy needs- suggesting they have win-win needs to understand future of energy

xx24.11.1 Indonesia AI Day http://www.youtube.com/watch?v=w64JT0HwqHI  with Jensen Huang and Accenture's Julie Sweet  https://indonesiaaiday.com/#section-speakers  - see also numbers of accenture staff trained in AI

[chris macrae]

potentially related ai networks - health

 H25.4.1 army health surgeon - affordable health ai in society book launch harvard  https://www.youtube.com/watch?v=MPvgqfUZLJs

Booknotes+ Podcast: Dr. Hassan Tetteh, "Smarter Healthcare ...

YouTube · C-SPAN

670+ views · 2 months ago

1:05:57Dr. Hassan Tetteh, in his latest book, opens the introduction with a question: "How do we prepare for the future with AI?

9 key moments in this video

[chris macrae]

AI's unique advantage is structured data which is too much for human analysis as a whole or for real time operatio

Oddly with few exceptions ai was not designed around deep computer data analysis until  breakthroughs in early 21st C even though Neumann Eimstein Turing's mid 1950s legacy founded what has become 10**18 more tech 1965-2025

million fod multiplier of moores law

million fold multiplier of jensdens law

million fold mobilsation of communuty data to clouds

jensen's hi-yrust networks include

4th decade as silicon valley longest running ceo

connections of 20000 startups "inception"

at origin of coding computers on pizels instead og bismary from 2002 with setve jobs pizar (rendering) soon radiology ai, then all v models of surgery ai

connected stanfird alumni and 3 taiwanese american networks - the hunags, yangs, tsaia - critical to asian engineers impact on worldwide digital advances and taiwan as ep[icemytre of chip supply chains since 1987 changs chip foundry and earloiier with Fixcoon's guo and national leader Li

at staford quadrangle huang fa,ily started deep learningh lab 2009 after meeting hassbais and fei-fei li in meeting partly organsiaed by mooc founders ng and koller and their investment backers doerr and project leader lila ibrahim; this jas put nvidia ahead of each huge leap in valley popularity ai inckudeinh

imagenet breakthroigh 2012

first nvidia big chip 2015 open ai and musk

each of proven leaps of ai since 2015

[chris macrae]

2025: this decade is most exciting to be alive because technological choices will lock in opportunitoies and threats to humanity everywhere.That's because tech multipliers are converging at an unprecedented rate liftign off from more than 10**18 more tech since 1965. with 3 observable million-fold multipliers:

******moores law million fod more between 1965-1995

 since 1995 mobilising million timesmore data transmissions betwen communities and worldwide clouds (eg 1G to %g stanards

***Jensen's law which has acce;lerated ca[pacity of computers to both analyse very deep data and rspond to real-time queries based on multidimesional models beyond any human barins alone can integrate

***

Here are some guidelines to humanity win-winning from opportunities not collapsing mistrust in each other  ot in mother earth's resources. We build up from clues from partners in 33 years of work around jensen's law as gravitating both engineering and human coding. Fortunately Jensen engineers have at least 15 year presence sharing innovation comncerns on digital videos.

according to grok nvidia first started million fodl accelerated computing round cuda in 2006

Cuda is the operational language of all nvidia produce since 2006. Ian Buck is one of nvidia's great storytell eg this annoucement of Nvudia Washingtom DC Summit in 2019!

 To clarify nikolai tesla inspired both Jensen and from 2003 Elon Musk. They have not needed to quarrel over the brand name - clearly different uses. Actually they are close partners on the platform of autonomous driving as are most companies eg waymo offering computer driven car services.

2025 TOP TIPS

1 Integrate 25 times more energy efficient suoercomputers. Whatever your view on using carbon energy, if 25 yimes more energy efficient supercomputers are as affirdable as other supercomputers - why would anyone use less energy efficient computers.

2 Currently a few hundred engineers and their inversirs connecetd with 33 yeras of worekd by Jensen Hiang are pioneering super-efficient computer use. Its importamt to benchmark forst applications which may quickly link regions around the globe

Some of the ideas of Jensen we like most are as follows- which do you like? ( This is a question which freedom of learning needs to faciliate at every grade of human development and comnsiousness)

CODING IS MOVING FROM NUMERICA PROGRAMMING TO CATALOGUING SOLUTIONS TO ANY SYTSEMICALLY POSED CHALLENGE 

Jensen partners started going beyond coding o.1s in early 2000s. With setve jobs pixar, the fisrt choice of additional codes was pixels. This improved rendering of games and fillms. Fortiunatley expereiments showed it also improved the maps radiolists makes and then all papptern analyses which surgeons prepare operations around. We can call this the first time computational breakthrough scaled human intelligence. (More accurately Yann Lecun had succeeded coding zipcodes in early 1990s; Neumann-Einstein-Turing whop prepped 20th c quarter 2 tech liftoff had expected neural networking computation to be essentila- but largely speaking pscjolgists and other mind theorists had spent tyhen second half of the 20th century on o0ther stuff.

In 2009 jensen Hunag met at lesdst 2 newcomers to stanford _ hisd pots graduate alma mater since eraly 1980s - Demis Hassabis and Fei-Fei Li. Hassabis was convinced that paptern maths with deep computing could adrress all sorts of life science challemnges which had first been announced by Einstein's e=mcsquared in 1905! 

Fei-Fei li introiduce jensen to a scodnd type of coding need beyong pattern maths. What if machines could be taugh to use all 5 human senses. In Fei-Fei's mind this would be essential to safety before autonomous robots come out to the streets.

Taking these 2 ideas for new coding and the new computer design their data woulld need, Jensen bet the company nvidia on these future ai opportunities. The hunag family sponsored 2009 Deep Learning Lab in Stanfird's engineering quadrangle. Alongside an interdisciplinary tech lag the yang family had already built. Tigether with the tsia family, 3 taiwanese americans have connected valley brains to the bet Taiwan made as a country in 1987 - to be best at manufacturing advances in silicon chips. Mauric Chang started a chips founry in 1987 aligned to exisiting national investment made by Foxconn's Guo and tech father of i.

Its important to note that all of jensens favortite application partners of his million fold accerlation of deep copute are concerned with deep data sets - either tho9se like natires life sciences which are too big for individual human brains to map; or thoese like autonomous driving where to much real time data is generated for humans to real-time govern

AI CHANGING EVERY SKILL STUDIED OR TAUGHT

as studnets of von neumann my family has debated over half a centiry why and how (tech multipliers - curremtly AI mobileses at leasyt 10**18 - morres law, jensens law, satellite data transmission betwenn communities and clouds0 a changing any skill humans go to school to learn There is no need for AI to replace human jobs as long as education is redesihned around multiplying ecah others intelligence expereimetially focused.

No one person can define everything inteloigence does but we take time to map each new leap of partners around jensens 33 year long learning curve- 

as well as mapping hundred sof interconnected projects of million fold deeepr computer analu=ysis and data. we recommend ai game - if you are aware of jensens innovations, whoic might you track as win-wons 2ndld 3rdy 4th 5th 6h ...

over here we update our curent 6 :

we have chosen 3 wizards whose work is  hugely findedas mind expanding as jensens but depends on his supercompute

one place leader - you need to choose this to linkin regions you know most about - we explain why king carles has done a good enough job explaining ai potential to thosse who code in english

an academic who is mostyy trusted across regions [arty because he doesnt have as much funds as the 3 comercial wizards biut who may have even deeper maths unerstanding

a stroytellr on natures peculairatites

[chris macrae]

AI will destroy jobs unless people transform education

we first wrote a book in 1984 on need for education to chnage ahead of tech; dad was then 33 years into faciliating von neumann's queries with Economist readers; I had just started what became 40 yeras of opinion pol;ling in asia mainly commissio0ned by western global companies

\von neumann had expected  advanced nations would experiment  with 10**18 more tech by 2025; in 1984 we played out 3 million fold tech changes but we although we didnt know speeds of tech accleration

moores law which was already known - 100 fold each of 3 decade 1965-1995 ; engineers commiting to develop more and more computational and computing capacity out og silicon

DOD - deat of cost of distance in sharing life critical information once satelites were installed

what is now known as jensens law- the way you mix computational designs can offer million times more power to analyse deep data such as that einstein's 1905 paper e=mcsquared revelaved natire plays at nano scale

in 2025 we can see that roughly how tech accelerated is as folows

1965-75 100 fold moores law

1975-195 100 fold moores law

1985-1995 300 folc moores law starteing to declince - personal intermnetworking beginning

1995-2005 300 fold ditto

2005-2015 10000 fold mainly dod and jensens law

20015-25 100000 fold

in other words powers of ten tech has accelerates by per decade are approximately 

2.,2, 2,5,2,5,4,5  (summing 18)

its interesting to note that the first 1000 fold or moores law did not come to computers but microelectronics; while intel on us west xoast was main innovation Asias far east coast was main client - japan korea s taiwan hk singapore; microelectronics doubling product benefots every 2 years needed deming type enginersd and generared sme entrepreneur networks across countries main supercity ports

moores law slowly started to impact personal computer design in 1980s but the biggest deal was advanced by taowan - chip foundry designing chips for makoor cliennts opposite to itels model of one improved [rovessor every 2 yrasr- yaiwan had alteady become the world's most advanced electronics manufactirer see eg Foxxcoonn's Guo and the nation's tech startegy fathered by H Li

in these wayspacifiuc trades were leaping ahead at speed of tech; atlantic ones not so miuch

comtracsting east and westv coast usa expereinces of 200s is infomrative

west coast steve jobs host 2001 summit how asian women leapfrogging with tech (having missed out on electricity grids but now expereiemnts ing with silar and mobile)

2002 steve jonbns and jensen huang start coding pixels; steve jobs starts making commencement speeche s encouraing women engineers at stanfird and warning 4 yera paper degrees are suboptimal for valley venture capitaloism

other taiwan american families stanfird grads join huang's accelerated innovation spirit - eg the yangs invest in an interdiscipliary engoeering lab at stnaford; the dpoeers biy mooc from  coder andrew ng and biotech daphne koller; they assign lila ibrahim to project management

in 2009 fei-fei li is headhunetd; others like demis hassabis vist stanford from london; jensen huang hears why both of these young people need chips to be designed for deep learining- the huang family starts a deep lerarning lab at stanford; jensen bets the company nvidis on ai; eg he hores bill dialiy (a mentor of fei-fei's0 from stanfird comourer science prof

from mid 200s asians started reporting t5hat even ha;lf of grads found their education misaligned with livelihoods; by early 2010s leading countries in asia were requiring all tecahers from 1st grade up to be curious about ai future- how would these imoact a child pover 13 yaers of k-12

some conclusions aree simple

make sure your countru=y is good at high school maths

stop failing kinds by examining - fail education systems which do not interevene if a child is behing eg on age 6 with literacy -on age 8 with social intelligence; on age 10 with curiosity abot health of body; note how eg most tech requires expereintual learning even more than playing sporrts; - schools need to see their mission as multiplying intelligences - how teacher and students time is spent involeves total system redesign

[chris macrae]

Trust AI

20th c media has not been about valuing trust; 21st intelligence needed to be about valuing trust

yann lecun is one of 3 ai engineers i trust most - he's been to dc 3 times dyrinfgg stiudent yera of 24/25- his advice to ai students dont work on chat

mathjematically my ot5her 2 fav engineers are jensen huang and demis hassabis

ai breakthroughs from 2002 strated in coding poixels (steve jobs and jensen hiang0 and so the pattern maths of radiology, then all surgeons maps, then proteins and indeed all science patterns which eintsein 1905! pa[per described as needing deept science analyysis than newton and the human eye could see

the 200s spun very differemtluy out of us east caost wiyth 9/11 , wars and finacial meltdonw and jbs hunag and stanford enginners- women visoipns had started to be included by jobs, huang, yang, tsai, the doerrs koller, ibrahim; they helsped headhunt fei-fei li from princetion; fei-fei li convinvenced huang that before he designed humanoid robots it would ne best to code macines to use the 5 huamn senses - that wasnt just blind use of chat

today 12 peopl i would folow 

commercial engineers haunag and hassabis

acadmeic engineers lecun and li

ropyal family influencers of engineers king charles and japan emperor whose families started to isnpire each other from 1964

2 republical nleaders macron and modi- modi started to be interest ed in guang in 2019- both have advanced ai world wummit started by king charles with  behind scenes support from japan roral fami;y (and even qatars royal fiamily whose forst lady is interested in womens and refugee health and education (epicentred on bangladesh's sir fazle bed0 - wise and wish laureatses)

2 magical smaller islands taiwan and singapore

2 misubderstoor engineering geniis musk and zengfei

- the orginal net neumann eistin turing - their clues on whow to naviage ara of 10**18 deeper analysis

dads sork studeied economics from 1941 abd mental health from 1942 and practised medi from 1948 and exploreds inteluigence with von neumann from 1951

my own lesser work studied statistics from age 18, reserached data meia 4th qyarter 2ort c - see brand  ;leadershipreality  1999 triple special issue journal of marketing manage,ent, amatuerly followed intelligence i trust since 2001

[chris macrae]

“A Global Perspective on the Drug Discovery Ecosystem”

Kenichiro Watanabe, Director, National Healthcare Policy Secretariat, Cabinet Office
Rami Suzuki, Representative Director CEO, ARC Therapies Inc.
Shinichiro Fuse, TPG Life Sciences Innovations / Partner and Managing Director
Dan Kemp, CEO, Shinobi Therapeutics, Inc.
Mikkel Skovborg, Senior Vice President, Innovation, Novo Nordisk Foundation,

<Moderator>
Akihiko Soyama, CEO, Life Science Innovation Network Japan / Specially Appointed Professor, Tohoku University

10:50～11:00Break

11:00～11:50Special Session 1 Supported by Novartis Pharma KK

“Fueling the Future of Innovation”

John Paul Pullicino, President and Representative Director, Novartis Pharma KK
Fumiaki Ikeno, Researcher, Stanford University
Kazumasa Oguro, Professor, Faculty of Economics, Hosei University
Masato Iwasaki, Senior Executive Fellow, IGPI Group, Inc.

<Moderator>
Yasuko Shoji, Manager, Research Unit / Medical & Healthcare Institute, Nikkei BP Intelligence Group, Nikkei Business Publications, Inc.

11:50～13:30Panel Session 2

“Optimization of Institutional Design to Support Drug Discovery” (tentative)

Tadayoshi Mizutani,Director, Policy Planning Division for Pharmaceutical Industry Promotion and Medical Information Management, Health Policy Bureau, Ministry of Health, Labor and Welfare
Masaaki Miyakawa, Executive Board Member, Japan Medical Association
Hitoshi Kuboniwa, Japan Bioindustry Association,Chairman Steering Committee
Yukiko Nishimura, NPO ASrid, President
Yasuhiro Fujiwara, Chief Executive, Pharmaceuticals and Medical Devices Agency
Yuji Kashitani, Executive Director, Global Regulatory Policy & Innovation Japan, Takeda Development Center Japan
Masanobu Saito, Corporate Officer, Head of Value & Access, Japan, Novartis Pharma KK
Masahisa Jinushi, Executive Direcotr, Head of Medical Affairs, Gilead Sciences KK

<Moderator>
Shintaro Sengoku, Professor, School of Environment and Society, Institute of Science Tokyo

13:30～13:40Break

13:40～15:00Panel Session 3

“Challenges in fostering drug discovery startups from Japan: Creating a Virtuous Circle”

Hirokazu Shimoda, Director, Bio-Industry Division, Commerce and Service Industry Policy Group, Ministry of Economy, Trade and Industry
Yoshiki Sawa, President,Organization of Future Medicine　
Toshio Fujimoto, Chief Executive Officer, iPark Institute Co., Ltd.
Yusaku Katada, Restore Vision Inc. CEO
Morten Sogaard, Head, Innovation Lab, Astellas Pharma
Hiroo Igarashi, President & Representative Director, Pfizer Japan Inc.

<Moderator>
Masamitsu Harata, Chairman, CEO and Founder at Human Life CORD Japan Inc.

15:00～15:50Special Session 2 Supported by Novo Nordisk Pharma Ltd.

“Challenges and Value Assessment of Obesity: Exploring the Ecosystem”

Koutaro Yokote, President, Japan Society for the Study of Obesity/ President, Chiba University
Ataru Igarashi, Associate Professor, Dept. of Public Health and Health Policy, Graduate School of Pharmaceutical Sciences, The University of Tokyo

15:50～16:00Special Speech 1

Fumio Kishida, Member of the House of Representatives

16:00～16:50Special Session 3 Supported by Gilead Sciences, Inc.

Gaku Hashimoto, Professor, Faculty of Health and Welfare Services Administration, Kawasaki University of Medical Welfare (Former Member of the House of Representatives)
Wataru Sugiura, Director General, Center for Clinical Science, Japan Institute for Health Security
Hiroto Araki, Director, Infectious Diseases Control Division, Ministry of Health, Labor and Welfare
Toshio Fujimoto, Chief Executive Officer, iPark Institute Co., Ltd.
Kennet Brysting, President and Representative Director of Gilead Sciences KK


Toshihiko Takeda, Senior Advisor, Boston Consulting Group

16:50～17:00Break

17:00～18:00NIKKEI Drug Discovery Startup Pitch
Finalist Presentation DAY1

<Commentator>
Masamitsu Harata, Chairman, CEO and Founder at Human Life CORD Japan Inc.
Toshio Fujimoto, Chief Executive Officer, iPark Institute Co., Ltd.　
Tsuyoshi Tsujimura, Investment Principal, Investment Department, Kyoto University Innovation Capital Co., Ltd.
Masakazu Komahashi, General Manager, Investment Department Ⅲ, SMBC VENTURE CAPITAL CO., LTD.

【Day2】 Wednesday, June 25

9:00～9:20Special Speech 2

Akihisa Shiozaki, Member of the House of Representatives

9:20～10:40Panel Session 4

“Drug Discovery Frontiers from Japan: Connecting with the Global Community”

Kanae Kurata, Director, Life Sciences Division, Research Promotion Bureau, Ministry of Education, Culture, Sports, Science and Technology (MEXT)
Shin Kaneko, Professor, Center for iPS Cell and Research Application, Kyoto University
Professor, Faculty of Medicine, University of Tsukuba
Kiyohumi Kaneshiro, Chief Financial Officer, PeptiDream Inc.
Takeyuki Akiyama, Director, Japan Alliance for Lysosomal Disease Patient Organizations
Shinichiro Komoto, Eight Roads Ventures Japan・Partner
Hiroshi Miyake, Chief Executive Officer, Chordia Therapeutics

<Moderator>
Aya Kubota, Editor in Chief, Nikkei Biotechnology & Business, Nikkei Business Publications, Inc.

10:40～11:30Special Session 4 Supported by Japan Pharmaceutical Manufacturers Association (JPMA)

“Co-creation and Innovation from the Patient's Perspective”

Jin Shiomura, Founder, Managing Director & CEO of Nobelpharma
Sumito Nishidate, Chairman of the GIST & Sarcoma Patients and Families Association “NPO GISTERS”
Kazuhiko Mori, Senior Managing Director of Japan Pharmaceutical Manufacturers Association
Masami Sakoi, Chief Medical and Global Health Officer of Ministry of Health, Labor and Welfare

<Moderator>
Asuka Miyabashira – President of Japan Pharmaceutical Manufacturers Association

11:30～11:40Break

11:40～12:10Special Speech 3

Kazuto Ihara, Vice-Minister of Health, Labor and Welfare

12:10～13:30Panel Session 5

“Innovation in Drug Discovery Driven by AI and DX”

Yasushi Okuno, Professor, Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University
Akira Izumi, CEO & Founder, RyuWell Co.,LTD
Hidenobu Ishizaki, Executive director, Center for Development of Advanced Cancer Therapy, Cancer Institute Hospital, Japanese Foundation for Cancer Research (JFCR)
Hiroyoshi Toyoshiba, Director/ Chief Technology Officer
Hiroyuki Tsunoda, Deputy Head of Research Division, Chugai Pharmaceutical Co., Ltd.

<moderator＞
Masanori Shindo, Deputy General Manager, Life Intelligence Consortium general incorporated association</moderator＞

13:30～13:40Break

13:40～14:00Special Session 5 Supported by Johnson & Johnson Innovative Medicine

“Leading where medicine is going”

Sarah Brennan, Company Group Chairman, Global Commercial Strategy Organization, for Johnson & Johnson Innovative Medicine

14:00～15:30Panel Session 6

“Toward a Japanese Drug Discovery Ecosystem”

Shintaro Sengoku, Professor, School of Environment and Society, Institute of Science Tokyo
Asuka Miyabashira, President of Japan Pharmaceutical Manufacturers Association
Takahiko Iwaya, Chair of European Federation of Pharmaceutical Industries and Associations, Japan (EFPIA Japan)
Hans Klemm, Japan Representative, Pharmaceutical Research and Manufacturers of America (PhRMA)
Hitoshi Nakagama, Japan Agency for Medical Research and Development/President
Kazuto Yamada, President and CEO, Japan Tissue Engineering Co., Ltd.

<Moderator>
Kiyoshi Ando, ​​Senior Staff Writer, Nikkei Inc.

15:30～15:40Break

15:40～16:40NIKKEI Drug Discovery Startup Pitch
Finalist Presentation DAY2

<Commentator>
Masamitsu Harata, Chairman, CEO and Founder at Human Life CORD Japan Inc.
Toshio Fujimoto, Chief Executive Officer, iPark Institute Co., Ltd.　
Tsuyoshi Tsujimura, Investment Principal, Investment Department, Kyoto University Innovation Capital Co., Ltd.
Masakazu Komahashi, General Manager, Investment Department Ⅲ, SMBC VENTURE CAPITAL CO., LTD.

*Please note that the program and contents of the presentations may be changed without notice.

Speaker

Fumio Kishida

Member of the House of Representatives

Hideki Murai

Member of the House of Representatives

Akihisa Shiozaki

Member of the House of Representatives

Kazuto Ihara

Vice-Minister of Health, Labor and Welfare

Kenichiro Watanabe

Director, National Healthcare Policy Secretariat, Cabinet Office

Rami Suzuki

Representative Director CEO, ARC Therapies Inc.

Shinichiro Fuse

TPG Life Sciences Innovations
Partner and Managing Director

Dan Kemp

CEO, Shinobi Therapeutics, Inc.

Mikkel Skovborg

Senior Vice President, Innovation, Novo Nordisk Foundation,

Akihiko Soyama

CEO, Life Science Innovation Network Japan / Specially Appointed Professor, Tohoku University

John Paul Pullicino

President and Representative Director, Novartis Pharma KK

Fumiaki Ikeno

Researcher, Stanford University

Kazumasa Oguro

Professor, Faculty of Economics, Hosei University

Masato Iwasaki

Senior Executive Fellow, IGPI Group, Inc.

Yasuko Shoji

Manager, Research Unit
Medical & Healthcare Institute, Nikkei BP Intelligence Group, Nikkei Business Publications, Inc.

Tadayoshi Mizutani

Director, Policy Planning Division for Pharmaceutical Industry Promotion and Medical Information Management, Health Policy Bureau, Ministry of Health, Labor and Welfare

Masaaki Miyakawa

Executive Board Member, Japan Medical Association

Hitoshi Kuboniwa

Japan Bioindustry Association,Chairman Steering Committee

Yukiko Nishimura

NPO ASrid, President

Yasuhiro Fujiwara

Chief Executive, Pharmaceuticals and Medical Devices Agency

Yuji Kashitani

Executive Director, Global Regulatory Policy & Innovation Japan, Takeda Development Center Japan

Masanobu Saito

Corporate Officer, Head of Value & Access, Japan, Novartis Pharma KK

Masahisa Jinushi

Executive Director, Head of Medical Affairs, Gilead Sciences KK

Shintaro Sengoku

Professor, School of Environment and Society, Institute of Science Tokyo

Hirokazu Shimoda

Director, Bio-Industry Division, Commerce and Service Industry Policy Group, Ministry of Economy, Trade and Industry

Yoshiki Sawa

President,Organization of Future Medicine

Toshio Fujimoto

Chief Executive Officer, iPark Institute Co., Ltd.

Yusaku Katada

CEO, Restore Vision Inc.

Morten Sogaard

Head, Innovation Lab, Astellas Pharma

Hiroo Igarashi

President & Representative Director, Pfizer Japan Inc.

Masamitsu Harata

Chairman, CEO and Founder at Human Life CORD Japan Inc.

Kotaro Yokote

President, Japan Society for the Study of Obesity/ President, Chiba University

Ataru Igarashi

Associate Professor, Dept. of Public Health and Health Policy, Graduate School of Pharmaceutical Sciences, The University of Tokyo

Gaku Hashimoto

Professor, Faculty of Health and Welfare Services Administration, Kawasaki University of Medical Welfare (Former Member of the House of Representatives)

Wataru Sugiura

Director General, Center for Clinical Science, Japan Institute for Health Security

Hiroto Araki

Director, Infectious Diseases Control Division, Ministry of Health, Labor and Welfare

Kennet Brysting

President and Representative Director of Gilead Sciences KK

Toshihiko Takeda

Senior Advisor, Boston Consulting Group

Kanae Kurata

Director, Life Sciences Division, Research Promotion Bureau, Ministry of Education, Culture, Sports, Science and Technology (MEXT)

Shin Kaneko

Professor, Center for iPS Cell and Research Application, Kyoto University

Kaneshiro Kiyohumi

Chief Financial Officer, PeptiDream Inc.

Takeyuki Akiyama

Director, Japan Alliance for Lysosomal Disease Patient Organizations

Shinichiro Komoto

Eight Roads Ventures Japan・Partner

Hiroshi Miyake

Chief Executive Officer, Chordia Therapeutics

Aya Kubota

Editor in Chief, Nikkei Biotechnology & Business, Nikkei Business Publications, Inc.

Jin Shiomura

Founder, Managing Director & CEO of Nobelpharma

Sumito Nishidate

Chairman of the GIST & Sarcoma Patients and Families Association "NPO GISTERS"

Kazuhiko Mori

Senior Managing Director of Japan Pharmaceutical Manufacturers Association

Masami Sakoi

Chief Medical and Global Health Officer of Ministry of Health, Labor and Welfaresuka Miyabashira

President of Japan Pharmaceutical Manufacturers Association

Yasushi Okuno

Professor, Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University

Akira Izumi

CEO & Founder, RyuWell Co.,LTD

Hidenobu Ishizaki

Executive director, Center for Development of Advanced Cancer Therapy, Cancer Institute Hospital, Japanese Foundation for Cancer Research (JFCR)

Hiroyoshi Toyoshiba

Director/ Chief Technology Officer

Hiroyuki Tsunoda

Deputy Head of Research Division, Chugai Pharmaceutical Co., Ltd.

Masanori Shindo

Deputy General Manager, Life Intelligence Consortium general incorporated association

Sarah Brennan

Company Group Chairman, Global Commercial Strategy Organization, for Johnson & Johnson Innovative Medicine

Takahiko Iwaya

Chair of European Federation of Pharmaceutical Industries and Associations, Japan (EFPIA Japan)

Hans Klemm

Japan Representative, Pharmaceutical Research and Manufacturers of America (PhRMA)

Hitoshi Nakagama

Japan Agency for Medical Research and Development/President

Kazuto Yamada

President and CEO, Japan Tissue Engineering Co., Ltd.

Kiyoshi Ando

Senior Staff Writer, Nikkei Inc.

[chris macrae]

Chartering is Q&A format we first publiished nearly 30 years ago - we were designing a process needed by organsiations aiming to serve f y64 352e a purpose that would be uniquely missed if they did not exist

Today, it feels as if intelligence could make the whole world better or worse. Which handful of intelligence designers do you most need to be influenced by. Please note in chartering we aim to avoid listing 2 people with very similar impacts. If you think we have missed someone out with an essential impact please tell us [e-mail]

AI Corporate Wizards 1 Jensen Huang -over 30 years now jensen and coworkers have redesigned computing to be a million + times more efficient for deep learning (and multidimensional) analysis which 21st C breakthroughs in AI depend on. WE can catalogue50 deepest partners of nvidia in different ways- countries: data sovereigntly; digital platforms eg self-drving cars in one of about 10 platforms that most majors connect through nvidia; supplying differentiated models as eg us 6 major digital comp[anoies (meta apple microsoft amazon google ,,,) become digital ai companies; full stack partnership eg from dell supplying devices at the edge to various digital twin services; links to ecosystems supporting 2000 startups in 100 countries; deep data partnerships eg with snowflake for clients of ai data warehousibg to service plus to safes force

There are 2 particula engineering circles which huang appears to be in middle of. 1 building 25X more energy efficient supercomputers. Which will be first 10 countries with these and what will each supercomputer specialise in. Ai foundriies - ed desiging ai for clients. Most of the world’s best engineers for doing this are in Taiwan. Whilst digital twinning will likely show what skillsare needed if a franchise is to move its supply cains to another hemisphere o0f the world, 50 years of advanced manufacturing skills (eg demin quaslity), accelerating value leaps every few months) involes webs of hundered of just in time suppliers. Its not obvious how many engineers beyomd jensen have all the truts relationships needed to do this.

AI Corperate Wizard 2 Demis was effectively the first to map pattern ai relevant to most of natural science challenges Einstein first -published E=mcsquared in 1905.With neumann einstein turing dyting suddenly in mod 1950s its almsot as if most of their wos forgotten untl hassabis rediscovered this early in 21st c. Back in 2012 google acquired hassabis deep mind when it was clear his training of mnachine learning ihn gmes lile go was going to achieve leaps like open sourcing 250 million plroteins. Thhis work continuens mainly out of cambridge and london offiecs whilst demi is als key to google own leaps into ai. One partnership google deep mind, nvidia, and disney todays owners of pixar is newton robotics. Codibg pixels with pixar when owned by steve jobs circa 2002 was arguably where nvidia first saw ai as the nain ourpose of the company. Today nvidia not ponly codes pixels but increasingly beleives ai will soon blurv line between programmking and simply defining what problem you want help withy in normal human convrerssation

Yann Lecun is the most connected academic in ai and what were once the famous 3 of algorith wplrd (ie Hinton and bengio). Hinton has retired; bengio and lecun do summer camps. But lecun who first proved ai can do zipcodes in early 90s connects his chair pout of nyu courant maths lab wityh meta models from west coast to east coast tro france and india - egde o[pen ai models like llama and mistral are While the sacling iof llama by meta and mistral by mistral - the valiadtion as open academic models came from Lecun. The timing was good as the ai world series summit started by king charles has through France Macron and Modi india ended the drag onAI caused by EU bureaucracy.

Country sponsor -if you speak english then King Charles support of AI as the most important innovation was timely; french modi; india - we are talking about ai agenting youth brains and productivie life not just big corporate ai nor gov ai by a few superpowers

[chris macrae]

Deep%20Compute%20Resources%20Comparison.docx Axios AI+

By Ina Fried · Jun 24, 2025

Apologies for missing that yesterday was National Typewriter Day, an embarrassing oversight for someone who has three actual and two Lego typewriters. Today's AI+ is 1,090 words, a 4-minute read.

1 big thing: Musk's thumb on history's scale

By Ina Fried

Illustration: Aïda Amer/Axios

Elon Musk still isn't happy with how his AI platform answers divisive questions, pledging in recent days to retrain Grok so it will answer in ways more to his liking. Why it matters: Efforts to steer AI in particular directions could exacerbate the danger of a technology already known for its convincing but inaccurate hallucinations. The big picture: Expect more of Musk's thumb-on-the-scale approach, as governments and businesses build and embrace AI models with preferred responses on hot-button topics from LGBTQ rights to territorial disputes. Driving the news: In a series of tweets over the past week, Musk has expressed frustration at the ways Grok was answering questions and suggested an extensive effort to manipulate its output. "We will use Grok 3.5 (maybe we should call it 4), which has advanced reasoning, to rewrite the entire corpus of human knowledge, adding missing information and deleting errors," he wrote on Saturday. "Then retrain on that. Far too much garbage in any foundation model trained on uncorrected data." Musk also put out a call for people to suggest things that are "divisive facts," adding that he meant things that are "politically incorrect, but nonetheless factually true." The suggestions, though, included examples of Holocaust denialism and other conspiracy theories. An xAI representative did not respond to a request for comment. Reality check: AI models are already hallucinating in ways that suggest failed attempts by company staff to manipulate outputs. Last month, Grok started injecting references to "white genocide" in South Africa to unrelated conversations, which the company later attributed to an "unauthorized change" to its system. At the other end of the political spectrum, Google and Meta appeared to make an effort to correct for a lack of diversity in image training data, which resulted in AI generated images of Black founding fathers and racially diverse Nazis. Between the lines: These early stumbles highlight the challenges of tweaking large language models, but researchers say there are more sophisticated ways to inject preferences that could be both more pervasive and harder to detect. The most obvious way is to change the data that models are trained on, focusing on data sources that align with one's goals. "That would be fairly expensive but I wouldn't put them past them to try," says AI researcher and Humane Intelligence CEO Rumman Chowdhury, who worked at Twitter until Musk dismissed her in November 2022. AI makers can also adjust models after training them, using human feedback to reward answers that reflect the desired output. A third way is through distillation, a popular process for creating smaller models based on larger ones. Creators can take the knowledge of one model and create a smaller one that aims to offer an ideological twist on the larger one. What they're saying: AI ethicists say the problem extends well beyond Musk and Grok. Many companies have been exploring how they can tweak answers to appeal to users, regulators and other constituencies. "These conversations are already happening," Chowdhury tells Axios. "Elon is just dumb enough to say the quiet part out loud." Chowdhury says his comments should be a wakeup call that AI models are in the hands of a few companies with their own set of incentives that may differ from those of the people using their services. "There's no neutral economic structure," Chowdhury says, suggesting that instead of asking companies to "do good" or "be good," perhaps powerful AI models should be treated similar to utilities. Yes, but: Efforts to scour all bias from generative AI are doomed because the human data AI trains on is full of bias. The training data reflects biases based on whose perspectives are over or underrepresented. There's also a host of decisions large and small made by model creators as well as other variables. Meta, for example, recently said it wants to remove bias from its large language models, but experts say the move is more about catering to conservatives than achieving some breakthrough in model neutrality. Bottom line: Ultimately — as we reported over a year ago — it boils down to a battle over what values powerful AI systems will hold.