Takeaways from ASIP2023

These were the keynote presenters during the Asian Society and Innovation Policy Conference (ASIP2023), held on 22 and 23 September 2023. Parking the notes here for posterity:

Dr Yutao Sun (Dalian University of Technology, China)

Involvement & State Contribution to innovation = national innovation system/ scheme, cooperation with govt agencies

Policy+ Funding + Talent + Organisations (4 pillars)

Attract returning talents

Book: The Political Economy of Science Technology & Innovation in China

Q&A tech transfer with uni, funded by industry or govt; also challenge in entrepreneurship culture in universities

Prof Jonathan Winterton (Leeds University UK)

The end of work (as we know it)?

The phoney war for talent/skills = forecasts of unavailable/lost jobs due to tech may not ne accurate

Disruptive tech e.g. AI

Future = automation assumed to displace people BUT tech also create jobs

Transformation in coal mining, clothing & aerospace job restructure

Job losses also caused by socio economic reasons

Tech increase need for upskilling & learning on the job

Highest risk in low skilled jobs & many held by women

Adrian Hia (Partner, Kairous Capital)

Digital Transformation within enterprises: A Case study in Corp Ventures & VC

Fear of making mistakes overwhelms drive to make a difference >> Enterprises move slower & with caution, if they have more to lose (Lifecycle of an Enterprise)

How to extend LC?

Leverage VC

VC skill set & expectation of projects they take on

Corp venture: new space, new solutions, spur innovation

Sorato Ijichi (Creww, Japan (founded 2012))

Open Innovation

Startup ecosystem in Japan

Platform for entrepreneurship + startup in Japan via open innovation

Startup collab with big corporation (win win), accelerator programme to match corporations to startup, 6 mth programme

20-30 prog a year, have produced more than 1000 partnerships

Kickstarters ÷ capital from big corp & eventually foreign investors + open innov tax deductions

Global sustainability accelerator prog w Google for startups

Juan Carlos (Nordic Apiary)

Nordic Global Innovation (Enterprise Sales & Investments)

Nordic country = innovation leaders

Most digital region in the world

Most energy efficient countries in the world

Innovation + increasing complexities + talent shortage

IT has become core biz & in company's DNA, no longer support function

Software as strategic asset but hyper competitive products/brands ( so how to stand out? )

Innovation

    - automation

    - new production (eg AI)

     - sustainability + ESG

Availability + Empowerment

Digital acceleration > Increased complexity > Need data + system integration > Higher digital threshold

SaaS & iPaaS = leverage on other technologies

Shortage of talents - upskilling & accessibility

Solutions 

    - validate skills

        - matching supply & demand

    - employment options e.g. not having on site employees

Noraminah Omar (Startlah Innovation)

How MSMEs can grow & thrive innovatively

Innovation = do things better

Case studies

•     Agak Agak Nyonya
•     Penternakan Sumber Unggas = animal feed
•     Nasi Lemak Saleha = central kitchen + food delivery + supply at LRT and Petronas Mesra

How they grew innovatively

    - Leadership

    - Technology Adoption

    - Collaboration & Partnership

10 types of innovation in MSMEs:

1. Network 
2. Process 
3. Product System
4. Channel
5. Customer Engagement
6. Profit model
7. Structure
8. Product Performance
9. Service 
10. Brand

Environment/Ecosystem of innovation

1. educator
2. biz service
3. politicians and policy makers
4. public

Prof Ho Chin Kuan (Asia Pacific University of Technology & Innovation)

Are machines fair?

Crime Prediction Tool

Bias in ID recognition

Google speech recognition bias less than 50% accuracy in transcription

CEO image search = male

Face recognition for darker skin Amazon

"Fairness" is intuitive to humans but challenging to define technically

Bias

• encoded in data
• minimise errors by fitting majority
• inability to explore alternatives

Mitigation

• pre process
• during
• after/post testing

AI has HIGH carbon footprint Forbes 21 Aug 2022

• Options:
• Use renewable energy
• Energy efficient chipsets
• Pretrained models