As Eric Schmidt, former CEO of Google, said in his 2017 book, How Google Works, "A.I. is imprecise, which means it can be unreliable as a partner. But, on the other hand, it's dynamic in the sense that it's changing all the time. It's emergent and does things that you don't expect. And, most importantly, it's capable of learning."
It is becoming increasingly clear that human-in-the-loop (HITL) systems are critical for competitive advantage and a necessary part of any socially responsible AI/ML system.
While it has always been essential to have a real person involved in natural language programming (NLP), content moderation, and other systems where humans interact with the machine, HITL is becoming critical as regulatory pressure builds on the industry. However, beyond the regulatory pressures, HITL is essential to the social responsibility of creating systems that people can understand.
Data powers intelligent systems, and in many critical cases, human insight is a critical data input. As such, human participation is vital to the success of semantic inferencing in intelligent systems.
How does an enterprise fortify the deployment of these systems with humans? The editorial team at Inside Big Data recently wrote about the collaboration of human experts and machine reasoning which can best be summarized as:
First, humans have an important role in creating and maintaining knowledge graphs based on semantic inferencing. Knowledge graphs are a source of the structured data that powers artificial intelligence, and the knowledge they contain is often provided and maintained by humans.
Second, humans contribute to this process by being the subject matter experts, enriching the knowledge graph, populating a knowledge graph with the most relevant, curated knowledge, and making this information available to intelligent systems. Another way humans fortify deployments of semantic inferencing is by assembling the vocabularies, taxonomies, thesauri, and rules on which these intelligent systems reason for applications like text analytics.
Overall, these implementations of human knowledge augmentation are examples of how intelligent systems are making the most of knowledge graphs. With humans in the loop, applications can adapt to changes in their environment with greater agility and accuracy.
Human-in-the-loop systems are a critical part of any competitive advantage and a necessary part of any socially responsible AI/ML system. It is also important to have a philosophy of operational excellence and continuous improvement of our products, as well as a competitive advantage for our clients and ourselves.
Katana Graph has adopted Kaizen as an instrument of operational excellence, and human ingenuity is deeply embedded in the intelligent systems we create. The Katana Graph culture is one of empowering innovation and creativity while pursuing perfection through continuous improvement.
With the advent of HITL, the role of humans in AI/ML systems is changing fundamentally. This is both good news and bad news for organizations, but we believe it presents a huge opportunity for companies that get it right.
We're also convinced that this shift will require more than just adopting HITL technology—it will require adopting a philosophy of operational excellence.
Whether structured or unstructured data, Katana Graph can greatly improve the insights and opportunities uncovered from your organization’s data. Click to schedule a meeting.
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Katana Graph was born of cutting-edge research and scientific rigor, and these beginnings have a powerful effect on who we are to this day. We’re devoted to problem solving, and are relentless in our pursuit of more effective and more efficient solutions to real-world challenges. Continuous improvement is the very foundation of our success.
