Scaling Humans With AI

One of the most persistent problems within many fields is the lack of communication. We have access to vast amounts of data, but all that information is siloed. You have so many different systems and hundreds of ways to access various pieces of data, but nothing really communicates. AI could transform our ability to make decisions and innovate, but we’ll have to learn how to scale our own intelligence. Sean Lane, CEO of Olive, is here to tell us how we can begin scaling humans with AI.

[Related Article: AI Ethics: Avoiding Our Big Questions]

Scaling Humans with AI

What does scale actually mean? If we’re going to scale a task, our most common tactic is to add more humans. If we leverage AI, we could have a tool that allows us to scale our activities, improve our quality of life, and put us back in charge of what we’re good at.

Think about it. The printing press allowed us to scale our ideas. The automobile allowed us to scale our transportation. Email allowed us to scale our connections. Getting the most out of an activity without expending more energy is what we’re after.

One famous example was a ranking of animals and transportation. Which animal can go the farthest while expending the least amount of energy? In a routine test, the condor is at the top of the list while humans are pretty far down. However, if you hand a human even a simple tool such as a bike, we rocket up that list with little effort. AI is the bicycle we need to get further.

Building AI for Scale

We’ve spent the last 30 years building these systems to allow us to get more done with less effort. Software applications ease our labor. Every dashboard you have at your job makes your job more efficient. AI can be trained to use these tools to put humans back into tasks that don’t require rote automation.

Robotic Process Automation is the ability to interact with a human interface. Once robots master this task, they can move around our tools the way we do. Global Awareness is another element of this. Robots can learn how to see information across silos and tie it into the interface so that you can manipulate that information and share it with other robots. 

For example, in healthcare, a robot could learn to recognize different patients across different hospital systems. They could share this information across the board, streamlining the process and making it safer.

Machine Intelligence is the final piece, and it allows robots to make decisions that only humans made in the past. For example, we know how humans have handled denied claims in health insurance, but now with neural networks, we can train machines to follow the same process. 

These three elements create an AI solution with the potential to work the way humans do. Humans could be freed up to perform higher-level tasks. Lane Calls them SCILBOTS or Superconnected Intelligent Learning Bots. They’re connected across a new bot layer, and they’re intelligent enough to deal with the data. They understand the entities they’re working with, and they can learn over time.

Olive SCILBOTS

Olive was built to handle all the crappy software in healthcare. Other solutions were just shiny new interfaces, but Olive decided to build a robot that could use existing dashboards. They’re super connected, reducing the need for task switching and breaking down silos for better care. It continues to expand across interfaces as the bots speak to each other, helping humans accomplish more in a smaller amount of time and without employing more human labor. It’s the future of AI.

One organization that’s done an excellent job of organizing data the way Lane envisions is Google. Google can query publicly available data, but that doesn’t necessarily translate to something like healthcare because of privacy issues. Instead, these robots help query the medical system without putting information out to the public. 

Sometimes, the most significant revolution isn’t the most powerful. In the ways personal computers enabled us to access the power of the computer, the next revolution for AI is the democratization of its use. Right now, AI exists only for enterprise solutions, but soon, individuals will be able to access the use of AI to scale effort. Anyone could create Scilbots, and that network could bring together information across enterprises.

[Related Article: OS for AI: How Serverless Computing Enables the Next Gen of ML]

The Future of AI is Networked

We talk a lot about the future of AI, but giving this power to individuals could help us scale our own abilities using AI as your most modern tool. Once scaling humans with ai happens, our world will be very different.