«We can get lost in politics, when what we should be discussing is policy.»

In this seasons final episode, we’re thrilled to have Ingrid Aukrust Rones, a policy expert with a rich background in the European Commission and Nordheim Digital, shed light on the role of the global geopolitical landscape in shaping digital policies.

Explore with us the dominant influence of big tech from the US to China, and how the EU's regulatory approach aims to harmonize its single market while safeguarding privacy and democracy. Ingrid breaks down the contrasting digital policies of these regions and discusses how the EU's legislative actions are often driven by member states' initiatives to ensure market cohesion. We also chart the historical shifts in digital policy and market regulations from the 1980s to the present, highlighting key moments like China's WTO entry and the introduction of GDPR.

Lastly, we delve into the future landscape of digital societies and the challenges nation-states face within the context of Web3. Ingrid emphasizes the concentration of power in big tech and its potential threat to democracy, while also lauding the EU’s robust regulatory measures like the Digital Markets Act and the Digital Services Act.

Here are my key takeaways:
Geopolitics

• our security, economy, the national and international system relies on data.
• How data is collected, stored, protected, used, transferred, retained.. happens as much across boarders as within.
• Data Strategy on this geopolitical level is about creating a digital autonomy, not being reliant on big international enterprises, but for our political system to stay sovereign
• US is based on a liberal, free market model that is very innovation friendly.
• China is based on a very controlled environment, with limited access to their domestic market. Incubation of local companies, shield from global competition.
• The EU is setting the regulatory standard. Freedom is balanced with other values, like fairness or democracy.
• We need to talk about the role that big tech has on the global scene.
• Geopolitical impact on digital policies.
• Ingrid has a role between policy and business, coordinating and finding opportunities between both.
• EU has set the global standard in how we could deal with data and AI from a regulatory perspective.
• Politics are the decisions we make to set the direction for society.
• «Policy is the plan and implementation of what is decided through politics.»
• Cultural differences influence how we perceive, utilize and establish global policies, but also how we work with data in a global market.
• We have an issue if we only think in 4-5 year election cycles for tackling long term issues.

The EU

• Regulation is the biggest tool the EU has.
• «We are always in competition with technology, because technology develops so fast, and legislation develops so slowly.»
• You can see a change in responsibility for enforcement of EU rules and regulations, where implementation is moved from national responsibility to EU responsibility.
• The EU system is not any easy system to understand from the outside.

The rise of Big Tech

• We can go back to the anti-trust laws from the 1980s that opened for much more monopolistic behavior.
• The rise of the internet had a large influence on big tech.
• The liability shield was a prerequisite for social media platforms to gain traction.
• Big tech has created dependency for other organizations due to eg. their infrastructure offerings.
• We need to be aware of that concentration of power in the market.
• Big Tech is not just leading but also regulating the development of the market.
• Bigger companies that are competing with Big Tech, feel their influence and size the most.

«Hva er mulig å gjøre med disse teknologiene når de blir 10 ganger så bra som de er idag? / What might be possible to do with these technologies when they become 10 times as good as they are today?»

Can moonshot innovation really be the key to solving challenges that traditional methods fail to address? Today, we're thrilled to welcome Yngvar Ugland from DNB's New Tech Lab, who will unravel the complexities of digital transformation and share his unique insights from both corporate and startup ecosystems. From breaking the mold of the classic "people, process, technology" framework to stressing the importance of customer-centric approaches, Yngvar’s perspective offers a refreshing and profound look into fostering genuine innovation within established enterprises.

Technological innovation isn't always smooth sailing, and Yngvar helps us understand the friction between traditional mindsets and innovative approaches. Balancing high-trust societies against the urgency-driven dynamics of capitalism, we discuss the complex landscape of AI hype and explore technologies like GPT-3 and GPT-4. With an optimistic outlook, Yngvar encourages us to embrace the transformative potential of generative AI, highlighting the unprecedented opportunities that lie ahead. Tune in to gain a deeper understanding of the ever-evolving world of technology and digital transformation.

Here are my key takeaways:

• Yngvar has build and is leading the as he calls it «Moon-shoot unit at DNB».
• What do we need to do to actually implement and adopt to new technology and ways of working?
• How do we think tech for people in tech?
• We can identify three needed dimensions for change:
  • a data / tech component,
  • a business component and
  • a change component.
• There is a difference between necessary and sufficient - just because a change is necessary, doesn’t mean that the proposed solution is sufficient.
• You need to find ways to navigate uncertainty, be active beyond concrete hypothesis testing, or tech-evaluation.
• For organizations to be successful, you need to coordinate both maintenance, improvement and innovation - it’s not one of those, but all there in concert that can ensure success over time.

• Innovation and digital transformation is not a streamlined process.
• Uncertainty offers a space for opportunity.
• We use the term agile without grasping its true meaning - an inspect-and-adapt mindset is key to agile.
• The development from GPT-1 through GPT-2 to GPT-3 is an example for the exponential development of technology.
• The digital infrastructure in Norway, that can utilize data and technology for value creation across public and private sectors is a reason for our success.
• The difference to the US market is that there are large cooperations that take on societal challenges.
• How our society is structure has an influence on how we perceive the need for innovation.
• It is natural to meet resistance in change and innovation.
• To iterate effectively you really need to live a mindset build around FAIL - First Attempt in Learning.
• We overestimate the effect of technology in the short term and significantly underestimate the long term.

"Det var jo veldig urealistisk å tenke kanskje at en haug med folk som har matematisk eller Computer Science bakgrunn, skal komme inn og skjønne forretningen. / It was very unrealistic to think that maybe a bunch of people with a mathematical or computer science background would come in and understand the business."

Join us on Metadama as we welcome Erlend Aune, an accomplished data science expert with a rich background in both academia and industry. Through real-world examples from the Norwegian industry, we illustrate how successful research collaborations and technology transfers can stimulate innovation and create value. Despite the promising advances, we also candidly address the cultural and operational challenges businesses encounter when integrating AI research into their workflows.

What practical steps can bridge the gap between theoretical education and real-world application? Our conversation further explores the intersection of business development and the practical application of machine learning and data science. We emphasize the need for environments that foster hands-on experience for students, such as hackathons and industry-linked thesis projects. Additionally, we discuss the importance of tailored training development within organizations, focusing on understanding trainee characteristics to achieve meaningful training outcomes. Tune in to gain valuable insights and actionable advice on nurturing the next generation of data scientists and enhancing organizational capabilities.

Here are my key takeaways:

Data Science and Business Development

• Data science needs a strong connection to business development
• You need to embed Data Science in a cross-functional environment
• Business acumen needs to be ingrained in the work with data
• Data Science needs to start from a Business side - ensure that you work on the problems that generate value for your organization.
• Data Science works with probability, not certainty - this notion is not yet understood by everyone in business.
• Data organizations are often build on an engineering mindset, that can be contradictive to an exploratory mindset.
• Even when designing Data Warehouse, you need to understand the business impact, have a business development mindset.

Norway & AI

• Norway has a great AI and ML research community.
• The public discourse on AI portraits a quite narrow view, that doesn’t reflect the broad application and research done in the field.

Research & Business

• Responsible AI is not a one-size fits all. Different organizations have different needs, for either certainty, security, reliability of outcome, etc. So a rAI approach needs ton be tailored to the business need.
• Startups and companies that have products related to the AI research environment, have the advantage that products are improved in tact with research development.
• In addition to in-house R&D, organizations can collaborate directly with research environments at universities.
• You cannot do R&D just as a pocket of excellence, if you want to operationalize results in your organization.
• We need to shorten the distance between R&D and operations.

For the Data Science Student

• If you apply knowledge on different challenges, you will get an intuition on how to solve a broad variety of challenges.
• When selecting a task within an organization as a Master thesis, make sure the task is delimited.
• Traits to succeed as a student working in industry:
  • Interest in your discipline
  • Interest in the organization and its sector
  • Problemsolving
  • Creativity