In a previous blog, I argued that the best way to look into AI was not from a machine versus human perspective but more from a human PLUS machine paradigm. That is, the goal of AI should not be replacement but augmentation. Artificial Intelligence should be about enhancing human flourishing rather than simply automating human activities. Hence, I was intrigued to learn about the concept of HI (Hybrid Intelligence). HI is basically a manifestation of augmentation when human intelligence works together with machine intelligence towards a common goal.
As usual, the business world leads in innovation, and in this case, it is no different. Hence, I was intrigued to learn about Cindicator, a startup that combines the collective intelligence of human analysts with machine learning models to make investment decisions. Colin Harper puts it this way:
Cindicator fuses together machine learning and market analysis for asset management and financial analytics. The Cindicator team dubs this human/machine predictive model Hybrid Intelligence, as it combines artificial intelligence with the opinions of human analysts “for the efficient management of investors’ capital in traditional financial and cryptomarkets.”
This is probably the first enterprise to approach investment management from an explicitly hybrid approach. You may find other examples in which investment decisions are driven by analysts and others that rely mostly on algorithms. This approach seeks to combine the two for improved results.
How Does Hybrid Intelligence Work?
One could argue that any example of machine learning is at its core hybrid intelligence. There is some truth to that. Every exercise in machine learning requires human intelligence to set it up and tune the parameters. Even as some of these tasks are now being automated, one could still argue that the human imprint of intelligence is still there.
Yet, this is different. In the Cindicator example, I see a deliberate effort to harness the best of both machines and humans.
On the human side, the company is harnessing the wisdom of crowds by aggregating analysts’ insights. The reason why this is important is that machine learning can only learn from data and not all information is data. Analysts may have inside information that is not visible in the data world and can therefore bridge that gap. Moreover, human intuition is not (yet) present in machine learning systems. Certain signals require a sixth sense that only humans have. For example, a human analyst may catch deceptive comments from company executives that would pass unnoticed by algorithms.
On the machine side, the company developed multiple models to uncover predictive patterns from the data available. This is important because humans can only consider a limited amount of scenarios. That is one reason why AI has beaten humans in games where it could consider millions of scenarios in seconds. Their human counterparts had to rely on experience and hunches. Moreover, machine learning models are superior tools for finding significant trends in vast data, which humans would often overlook.
Can Hybrid Intelligence Lead to Human Flourishing?
HI holds much promise in augmenting rather than replacing human intelligence. At its core, it starts from the principle that humans can work harmoniously with intelligent machines. The potential for its uses is limitless. An AI aided approach can supercharge research for the cure of diseases, offer innovative solutions to environmental problems and even tackle intractable social ills with humane solutions.
This is the future of work: collective human intelligence partnering with high-performing Artificial Intelligence to solve difficult problems, create new possibilities and beautify the world.
Much is said about how many jobs AI will replace. What is less discussed is the emergence of new industries made possible by the partnership between intelligent machines and collective human wisdom. A focus on job losses assumes an economy of scarcity where a fixed amount of work is available to be filled by either humans or machines. An abundance perspective looks at the same situation and sees the empowerment of humans to reach new heights. Think about how many problems remain to be solved, how many endeavors are yet to be pursued, and how much innovation is yet to be unleashed.
Is this optimist future scenario inevitable? Not by a long shot. The move from AI to HI will take time, effort and many failures. Yet, looking at AI as an enabler rather than a threat is a good start. In fact, I would say that the best response to the AI threat is not returning to a past of dumb machines but lies in the partnership between machine and human entities steering innovation for the flourishing of our planet. Only HI can steer AI towards sustainable flourishing.
There is work to do, folks. Let’s get on with the business of creating HI for a better world!
