Human-in-the-loop: the unchangeable fuel for your AI engine
In today’s era, automation and mechanization are contributing significantly to our life by taking care of all the redundant tasks that require people to repeat it over and over. That leads to many concerns about automated machines will eventually replace the human workforce and turn millions of people unemployed. This thought consequently became one of the barriers to the improvement of Artificial Intelligence (AI) because the line between them is quite blurred for nonprofessional people.
Firstly, let’s be clear as to the differences between AI and automation. Automation just follows pre-programmed rules to run processes and is usually applied to repetitive tasks. AI is much bigger than that, it includes automation and is projected to be the future of the fourth industrial revolution. It is a machine or system that is programmed to seek patterns, self-learn by adapting through experience and self-select a choice from different options. Basically, AI is about mimicking human ability to think and do. In other words, there is no way that AI could work accurately without human integration.
So we could relax a bit as we are not going to lose our jobs. But we do need to start thinking about how we could collaborate with AI because the way we work, however, will change.
Human-in-the-Loop?
There is already an approach to design an AI system with the concept of “Human-in-the-Loop’ (HITL) which basically is the process of leveraging machine power and human intelligence to create machine learning-based AI models. HITL illustrated the picture when the machine or computer system is struggling with a problem, requiring human intervention in both building and testing the algorithm, providing a continuous loop of feedback so that the algorithm can consistently deliver better results.
Human plays an unchangeable role in developing a machine learning algorithm by annotating and data labeling. Basically, we teach the machine to take decisions from its predictions and then improve its accuracy. Finally, we test and validate the outputs by assigning different confidence scores when the machine is unable to make the right decisions or not.
HITL is right at the corner
HITL presents in several things around your daily life, machines learn from every of your interaction. For example: when a chess expert works with a computer to develop the best possible move, when the ATM requires you to tell the exact amount of money that you have just deposited, when Facebook automatically labels friends in your photos and still asks you to re-label it yourself. But It will be a mistake not to mention the self-driving system when discussing the top significant improvements of AI in this decade.
Tesla’s most discussed technology offers a fully automated and secured journey to drivers by gathering and analyzing data from several cameras and sensors on the vehicles. But after three upgrades in six years, since its first released version in 2014, Elon Musk — the CEO is still “optimistically” saying that the completed autonomy would happen soon. That means after years, the major improvement of AI in this decade still needs human intervention to be perfected, to be able to work more accurately.
Another saying, to deliver outstanding solutions or to achieve the most positive performance improvement, businesses should create more opportunities for their employees/users to work with AI as it will improve not just the productivity and the skillset of employees but also the effectiveness and the accuracy of AI systems.
Benefits of Human-in-the-Loop
Scale with no limit: Poor scalability is usually the primary obstacle to improvement. There are different processes that depend on a small number of human labor with minimal machine assistance. For instance, in HR departments of businesses that have a major amount of job applications, Reducing hundreds or even thousand to a second smaller batch of candidates may feel nearly impossible. Therefore, programming AI to help recruiters narrow down their initial selection can save them a lot of stress. Scaling will no longer be an issue as the time spent on reviewing applications has fallen by 75% approximately according to Unilever’s case.
Do more with less time: For some business activities, the premium is on processing speed. In some particular sectors such as financial ones, AI reduces the processing time and improves the accuracy of fraud detection, client onboarding, wealth management, license application, mortgage lending, and the list goes on… With less time spent on each case, it is obvious that any organization could have the capability to approach and win more clients in order to improve the overall business results.
Optimize resources: With the help of AI, businesses are offered opportunities to optimize and minimize the human workforce. Not just that, working with AI will eventually improve their productivity and skillsets since it took care of all the redundant tasks and allowed humans to focus on what matters the most — the core business. In addition, with human validation and decision-making, data resources could be reduced significantly as AI just needs an amount to learn from and improve upon itself.
How can humans collaborate with AI?
Reimagining your business: To achieve the most value from AI, operations need to be redesigned. Businesses must first define and point out an operation that can be enhanced. It might be the hiring process, the client onboarding, or even an intractable problem that can now be addressed using AI. Then, the implementation and customization of a framework where humans and AI can work together are highly needed. It should have a human-machine interface in which both humans and machines can easily understand what they have to do and quickly keep track of different processes.
Defining new roles and talent: Working with an innovative technology requires businesses to improve their employees’ skillset specifically their “fusion skills” which allow them to work effectively and sufficiently in the human-machine interface. Employees should have the ability to combine their human distinct with the intelligence of a machine to train and maintain it better, to get greater output than either could achieve alone. Then businesses should form a new specific team to ensure that their Ai systems are used responsibly and not for illegal or unethical purposes.
