When the industrial revolution brought sweeping changes in both economic and social organizations, workers thought that their jobs would be taken over by machines. However, it became clear that these machines needed people to operate, maintain, assemble, among other things.

The same applies to Artificial Intelligence (AI); most people thought that AI would take over our lives and many of our jobs would no longer need humans. However, this is not the case. These AI systems are not all-knowing. They need human experts to feed them valid data.

The AIWORK project is an open network for Artificial Intelligence powered by a crowdsourced network of human experts.

The Important Role of Humans in AI

The intelligence and creativity possessed by humans is required by AI at all times as this is what directs and corrects its assumptions. AIWORK built this into their core. They created a blockchain network that facilitates a marketplace of crowdsourced AI Human Experts to help create, verify, and validate AI data sets that make AI smarter, not only the AI power.

These human experts play a critical role in creating the data sets the AI will abide by, verifying these sets, and validating them. The platform can only get smarter with the human experts onboard; simply put, AI would not perform its tasks without them, and certainly not continuously improve. Humans with expertise in tagging, editing, and translation can participate in the AIWORK shared economy to help with the verification, validation, and/or creation of video metadata.

This need is clear in other uses of AI. For instance, you might have noticed apps — or even your phone — suggesting tags of certain people. That’s because once the AI understands what a face is, a human can further guide the AI by teaching it to recognize specific faces (e.g. to associate different characteristics and details of each face with a specific tag, such as balding, or even a person’s name).

Humans to train the AI, and help fine tune its tagging and recognition, though, are essential. And, regardless of how advanced the field of technology is, we are yet to attain the level of complexity AI needs to mimic the human mind. Human inputs are critical to a computer system as it is only through them that AI can govern ethical considerations.

The community of human experts ensures that the AIWORK platform does not leave the major decisions to the bits and bytes inside a computer algorithm.

The Added Advantages Human Intelligence Brings to Artificial Intelligence

One advantage of human intelligence is that it brings in an air of creativity and compassion to Artificial Intelligence. The AI field is desperately in need of continuous innovation to create useful applications and improve its predictive capability and deep learning.

This is especially needed in the AIWORK project because developers can build Decentralized Applications (dApps) on top of the platform’s protocol. The challenge for AIWORK is to decentralize operation of the AI and human expert community on top of a consensus protocol, so that all sorts of dApps, whether free or commercial, can reap the benefits of the AIWORK protocol with much better metadata and their ContentGraph. ContentGraph is the project’s trademark content safety index.

Developers can then drive the creativity and compassion needed to use AI outside the video content landscape to create other artforms, like music or poetry.

Another major advantage of human intelligence is that these people provide governance. Within, and even outside of, the digital world, planning consists of inputs, outputs, and processes. Without a well-designed structure, AI can do very little on its own. AIWORK’s community of human experts provides the human intervention that maps out how machines should work together.

Simply put, tasks performed by AI need to be monitored as closely, if not more closely, than those performed by human employees. This is what governance is all about. And, currently, still requires a human touch.

The Bottom Line

As the project itself insists, AI is not perfect and still requires human intelligence for verification, validation, and correction, as well as training to teach it patterns, like faces, objects, scenes, etc. All the same, AI brings measurable benefits, allowing a much larger scale of processing data.

The bottom line is that a hybrid approach of combining AI with human verification and correction is the most optimal way of achieving AI computer vision at scale and accuracy.