In this week’s edition of WiredWisdomWednesday, we delve into the forefront of tech evolution and the dynamic shifts impacting the digital landscape. Join us as we dissect insights from leading minds at McKinsey and IEEE to navigate the waves of technological change.
McKinsey
- Reshaping the Digital Landscape – a return to the decentralized internet is key to protecting businesses and consumers. Chris Dixon, in his book “Read Write Own: Building the Next Era of the Internet” chronicles the evolution of the internet and argues that a decentralized framework could promote innovation and growth.
With the top 5 big tech companies accounting for 50% of the Nasdaq market cap; users, creative people and startups end up inadvertently paying high “take rates”. While blockchains and tokens were supposed to counter this centralization, recent frauds have made them unreliable. Non Functional Tokens (NFTs) on the other hand, allow creative people to sell things and interact directly with their audiences, however they are too going through a downturn.
A lot of problems can be solved through technology and innovation, along with regulation. Regulation needs to be thought of in a balanced way that protects consumers but also encourages start-up innovation.
- The CIO’s four point guide to navigating technology trends – which trends are relevant to business and how does one evaluate them? The four trends are:
- Disruptive business value: Incremental is out, disruptive is it!
- Independence: reinforce modularity, reduce technical debt, adopt the product and platform operating model
- Connectivity: Stable and reusable interfaces between technologies
- Extensibility: Part of an ecosystem, with ready support processes.
Evaluate engagement approaches such as First Mover, Fast Follower, Slow Adopter, and Non Partaker for adoption of a trend, iteratively.
IEEE
- AI Prompt Engineering is Dead > Long live AI prompt engineering : Fascinating article on the quixotic quest to create the perfect prompt, only to find themselves in a room of mirrors. This research team from VMWare set out to systematically test how different prompt-engineering strategies impact an LLM’s ability to solve grade-school math questions – only to find a surprising lack of consistency in the response to the different prompt combinations. Their aha moment – “No human should manually optimize prompts again”.
A similar experience was observed while fine tuning prompts to create the perfect image; a machine tuned prompt was able to create more perfect pictures – almost like they read your mind.
A new job title will likely emerge – Large Language Model Operations, or LLMOps – its lifespan is yet to be determined!