The Augmented Intelligence pipeline is built specifically to support humans. It’s designed to “enjoy” and learn from its feedback from humans.
We have 3 main actors in this process:
- Historical and new data
- The human factor
- Artificial Intelligence
The symbiosis begins with the AI’s initial training. We feed the AI structured and unstructured historical data from human operations.
The more data, the merrier.
Once the AI module becomes relatively accurate, we move to testing and deployment. Then we can take a look at the direct impact of the AI solution. How effective will it be for the team or person using it?
And here come those humans again.
The human team constantly interacts with both the data and the Augmented Intelligence. When the AI works correctly, the human team receives insights to help them work better and faster. Everyone contributes by inputting passive and active feedback into the system.
What is passive feedback? All humans need to do is use the system in their own way. When we make a decision to use, or not use, a certain recommendation, the AI knows what it needs to improve.
What is active feedback? In some systems, humans can give feedback by directly telling the AI how it’s doing. Does the AI’s score or answer make sense? How much? Even a thumbs up or thumbs down can give a lot of actionable input.
Here are some common examples of Augmented Intelligence. You’ve probably used it before, even if you weren’t aware at the time — most people have.
Gmail — Fast Reply Recommendations
You might already be familiar with this example. Google launched “Smart Reply” in their Inbox app, and it became a huge success. The AI suggests email replies based on your past activity, and you can choose with just one click. This feature is fast, easy, and intuitive.
- The data — an unimaginable amount of historical emails
- The AI — Trained Neural Network
- Human — well, you
Jenna —Candidate Screening & Scoring
The second example, Jenna.ai, is one of our daughter ventures at Spartans. So yes, I am definitely biased, but that doesn’t mean it’s not a great system.
The platform interacts textually or vocally by using NLP/U with potential job candidates. It gathers the relevant information about each one and then predicts if they’re a good fit. Jenna also conducts fully autonomous decisions to filter irrelevant candidates.
Jenna provides the human staff with ML-based insights and data. It gives them the tools to make the decision or action that is best for the company. With a simple thumbs up and thumbs down, recruiters can give Jenna feedback on what to improve.
- The data: Pre-gen berated data set + newly generated data loop
- The AI: Series of ML modules and NLP/U engine
- Human: The recruiter team and the relevant candidates
Gong — Candidate Screening & Scoring
Sales intelligence platform “Gong” recently raised $200 million (show.me.the money!). The platform offers AI-driven insights for sales teams around the world with key clients such as Linkedin, Pinterest and others.
The platform provides a series of critical insights to and about the sales process with a clear goal to increase win % and ROI.
No replacement of the human sales reps (at least for now) but an ongoing human-AI relationship which includes analysis of calls and textual communication to generate actionable insights for the sales cycle.
You made it to the bottom line. As promised, you deserve the TL;DR version, so here it goes:
- Augmented Intelligence is an approach and point of view, not a specific technology.
- It doesn’t want to replace humans, but help them get better at certain tasks.
- Most AI implementations today are Augmentations.
- Some examples include Gmail’s reply suggestions and Jenna.ai’s employee screening process.
- There are three components in the product chain → Data, Humans, AI
- Humans and AI working well together → Big success. I like! (Read aloud like Borat)
Augmented Intelligence, in many ways, is the healthiest way to look at our relationship with AI. It’s collaborative tech, and it doesn’t have the same unrealistic goal of simply replacing humans. We can acknowledge the strengths and weaknesses of both AI and humans, and use that to our advantage. Disruptive innovation can start right here.
The base assumption for Artificial or Augmented Intelligence is just that: Intelligence. So use it wisely.
Not too bad of a keynote is it?