No, AI won't change our world. Not until you find it boring🥱
Welcome to a new edition! This newsletter will take you about 5 minutes to read.
I. Spotlight: The emoji hype cycle - Why AI won’t change our world until you find it boring🥱
How do the public expectations from technology such as Artificial Intelligence change over time? How does its perceived value change as the technology matures? Most people expect AI’s value to increase rapidly over time. (see picture)
This is not true.
The Gartner hype cycle shows how business and consumer expectations from a technology innovation change over time.
It all starts with flashes of brilliance that trigger the journey. The technology catches the attention of early adopters who toy with the idea and build cool pilots.
Excited by the early results, expectations shoot up. As it gets mass-market appeal, the perceived value hits the peak of inflated expectations.
There’s hype everywhere.
And, people expect the technology to change the world too soon. This leads to disappointment and pulls down expectations. The downward journey hits rock bottom as it reaches the trough of disillusionment. (see picture)
(Picture: Technology Hype Cycle from Gartner)
At this stage, those innovations that hold real promise AND retain a loyal support base rise up from the ashes. Just that this time it doesn’t receive a hero’s welcome.
It gently inches up the slope of enlightenment as people begin to understand its real value. Then it goes mainstream and stays on the plateau of productivity.
The adoption is seamless and the value very obvious. In hindsight, people wonder why the innovation ever went through all the ups and downs.
Let’s look at an example.
How did radio move through the hype cycle?
What’s a good proxy to understand the chatter around an innovation? Books published over time. I turn to Google Books Ngram Viewer to look for mentions of ‘radio’.
We must keep in mind that these are search results. They would bring in all mentions of ‘radio’. Yet, it serves as a good approximation.
The radio was invented around 1900. It was heralded as a game-changing technology with applications across broadcasting, navigation, military, and more. It hit the peak of expectations in the 1940s. (see picture)
(Picture: Radio mentions from Google Ngram Viewer)
Then, ‘radio’ saw a steady decline before seeing some resurgence around the 1990s. In this phase, it birthed entirely new fields such as wireless networking, voice and data communications, and unmanned aerial vehicles.
It continues to stay relevant and change the world, almost a century after its invention. Do we find it exciting now?
No, it’s boring.
We’d rather talk about AI than radio! That raises the question, how do public emotions change during an innovation’s journey through the hype cycle?
Presenting the emoji hype cycle 🤩
Here’s the range of emotions an innovation elicits as it goes through the adoption journey: interest - excitement - hype - disappointment - disillusionment - hope - boredom (see picture).
(Picture: The Emoji Hype Cycle - Public emotions during an innovation’s lifecycle)
Today, AI is at the peak of its hype and is slowly entering the phase of disappointment. Yet again.
AI has been through a couple of such peaks/troughs over the past 50 years. This time, it appears that it would finally slope up the enlightenment curve, and offer hope.
If you’re an adopter or a practitioner, you must buckle up and prepare for the roller coaster ride ahead. When AI causes disillusionment, it will need your support to rise again.
And yes, the goal would be to make AI boring. Only then would it have gone mainstream, delivered steady gains, and transformed our world!
II. Industry Roundup:
1. How AI helped Moderna in the race for the COVID-19 vaccine
AI helped Moderna fast-track the development of its COVID vaccine by crashing timelines from 10 years to 10 months. This article shows how Moderna’s digital-first approach helped it collect data and bring algorithms into drug discovery.
2. Top AI predictions for 2021 and how to navigate your business issues
It’s that time of the year again. Here are predictions for 2021 along with five recommendations on how your company can make the most of AI. Find out how you can build your AI flywheel to create a virtuous cycle in your organization.
3. Why your struggle with data literacy is good news
Data literacy is critical for your teams to make sense of data. Why is a struggle with this skill good for you? Data literacy is a 2nd order problem. It manifests itself as a challenge only after you solve some first order problems. Read on to learn more.
III. From my Desk:
1. Talk: Applications of AI in Supply Chain: Hype versus Reality
In this session for the APICS Massachusetts Minuteman chapter, Mark Chockalingam and I covered the current state of AI in supply chain applications. We showed how SCM professionals can equip themselves with data science skills.
2. Whiteboard video: Why do you need a data science strategy
McKinsey found that only 30% of organizations aligned their corporate strategy with the analytics strategy. This video uses examples to show why you need it. It presents a simple framework to help you create one for your business.
-> Watch the Video (5 min)
3. Whiteboard video: What are the 5 biggest challenges in data science?
There are 5 common challenges reported by leaders in the Gartner Chief Data Officer survey. This whiteboard video shows how you can tackle these challenges by planning ahead for five critical phases in the data science lifecycle.
-> Watch the Video (5 min)
As 2020 winds down to a close (phew!), here’s a 3-min video from Google’s Year in Search.
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