According to research by McKinsey, approximately 51 percent of all job-related activities in the U.S. can be replaced by robots. Another recent research conducted by PwC shows that 40 percent of jobs in the U.S., 31 percent in the UK, 35 percent in Germany and 21 percent of the jobs in Tokyo are vulnerable to replacement by robotics or other artificial intelligence (AI).

An imagined image of an all-robot workforce (Source: Digital News Asia)

The figures are daunting, but in practice, companies have yet to realize a fully-automated system that matches human labor. The truth is, humans need robots to take over mundane and physically demanding tasks, while people take on creative and innovative roles in the workplace. What is more likely is a shift in workplace dynamics, not a decrease in the overall number of jobs available. Today, most companies have automated their systems to varying degrees, and robots are continually being upgraded.

The Human-Robot Collaboration

Last year, the International Organization for Standardization (ISO) published ISO/TS 15066, the safety standards for collaborative robots, or cobots that work in the same workplace as humans simultaneously. Before, it was common to find robots restrained in cages, or shut off while human workers loaded equipment onto the robots. Now, with enhanced sensors and diverse power settings, the safety risks of working with cobots are low. To add, many industries are embracing cobots over traditional industrial robots that work alone, because cobots are less expensive and require much less energy to operate.     

A welder gets help from a cobot

Not only are they a sustainable solution for companies, they increase workers’ productivity. According to Business World, Julie Shah and her team at the Massachusetts Institute of Technology conducted a study looking at human-robot collaborations at a BMW plant. She found higher levels of productivity, efficiency and performance when humans and robots collaborate in comparison to all-human or all-robot systems. Collaboration also reduced human idle time by 85 percent. Many argue that this increase in productivity will lead to more business and eventually create more jobs that revolve around working with cobots.

The International Federation of Robotics (IFR) found that robot-driven productivity accounted for 10 percent of total GDP growth in OECD countries from 1993 to 2016, and that number will continue to grow in the coming years. The IFR predicts worldwide usage of industrial robots to reach 2.6 million by 2019. That’s one million more robots than in 2015.

Cobots are already making their way into factories and warehouses, and human workers are learning to work with their new counterparts.

Auto Industry

The automotive industry is one of the most common places to find cobots as major automakers were early movers in incorporating robotics into the workforce. That’s why it’s the perfect place to observe the changing dynamics of the workplace with cobots. At Ford’s new production facility, cobots work shoulder to shoulder with the production team in what they call a blended solution. Mostly, cobots have taken over repetitive tasks, physically demanding or dangerous tasks and freed up workers to focus on new innovations and creative projects. The facility runs 21 hours a day, 6 days a week thanks to around 550 cobots and robots.

Take a look at the new working dynamics at Ford below:

Food Industry

The food industry is another sector ripe for human-cobot collaboration, especially in the packaging sector. However, pizza restaurant Zume is changing the delivery pizza experience with automated technology. Cobots have taken over tasks such as spreading the sauce and lifting the pies into the oven. They will be implementing more robot technology to free up kitchen staff to work in their offices for business expansion. Another addition to come is a delivery truck equipped with 50 ovens that will start cooking the pizzas based on exactly calculated arrival times.

Watch the video below to find out more about what Zume is working on:

E-Commerce Industry   

Amazon is another early mover that introduced 15,000 cobots named Kiva to their warehouses in 2014. Two years prior in 2012, they bought Kiva Systems, a robotics company, for USD 775 million. Since then, the number of cobot employees has tripled.

Here’s how it works. Human employees scan items and place them into the shelf-like pods. Instantly, the scanned item is available to purchase online. Kiva’s software keeps track of where each item is so that it can easily be found at the picking station, where a human employee retrieves the product for shipping. Amazon managed to rapidly increase efficiency and speed up delivery times with Kiva. They continue to incorporate AI into their services, including their drone delivery system.

Take a look at Kiva in action in the video below:

Healthcare Industry

In the healthcare industry, cobots are being incorporated to assist doctors in surgery and lighten the burden of caregivers by taking over mundane tasks. Pairing cobots and their software technology with doctors has several benefits including minimized invasiveness, exactness and reduced mistakes in surgical procedures. To date, AI and cobots have allowed doctors to access new parts of the human body for surgery, and achieve breakthrough feats the way they examine, monitor and operate on patients.

Take a look at the most innovative cobots to date in the healthcare industry:

Steel Industry

POSCO is a leader in applying AI to the steel production systems. In 2015, they developed an exoskeleton cobot designed to enable workers strength beyond their own. The cobot is made of carbon fiber, aluminum and steel, and was first tested with Daewoo Shipbuilding and Marine Engineering, at their facility in Okpo-dong. The cobot enabled shipyard workers to easily lift heavy pieces of metal weighing around 70 pounds, and researchers are working to allow workers to lift up to 200 pounds effortlessly.

Watch the video to find out more:

Aside from cobots, POSCO also incorporates AI and IoT into their smart factory, the first smart steel factory in the world, and continues to research and develop smart solutions to optimize the production processes.

Continued automation and the incorporation of cobots in the workplace is inevitable, and governments and institutions will have to provide resources for people to fill jobs tailored to working with cobots or those that cannot be replaced in any way by robots and AI. What’s ahead is figuring out how to think of cobots as coworkers that will only enhance one’s work. Also, companies have to figure out how to fully realize the potential for increased economic productivity through this type of cobot co-work that will lead to the development of more innovations, technology and ultimately, new business ventures.

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POSCO’s smart safety features

As a steelmaking company, POSCO has been working towards becoming a “Smart POSCO”, or incorporating the implementation of AI into their production practices. In 2016, POSCO established a Smart Solution Council to research AI, big data and IoT applications. To date, the company has achieved a cost reduction of USD 14 million with the new technology. Even as one of the first steel producers in the world to implement a smart factory for production, the road ahead is a long one. Members of the discussion panel at the 2017 Smart POSCO Forum offered their expert opinions on how companies should move forward with AI research and applications.

Problem Mining

“One of the biggest difficulties in applying AI to manufacturing companies is problem mining. The only way to solve a problem is to find it and define it.” Kim Byung-In, a professor at the POSTECH Department of Industrial and Management Engineering, started off discussions and added, “Along with problem mining, people can learn from the past with reinforcement learning. Reinforcement learning means learning through similar tasks from the past to apply to new assignments. Defining problems and solutions more precisely and systematically managing related keywords can be useful for future tasks.”

Professor Zhang Byoung Tak, an AI professor at Seoul National University and a distinguished professor for POSCO added, “Traditionally, people solve problems, but in the generation of AI, AI will be able to solve problems for us… People now have to focus on defining problems, implementing systems and getting the right kind of data.”

Cooperative Research and Investment

Along with defining the exact problems that AI can provide solutions for, the implementation of AI will also call for a new working culture for companies and industries.

Tech company VUNO CTO Jung Gyuhwan stated, “Each company has its own production or operation method, and different data formats and protocols, but product inspection and screen UI (User Interface) are common areas. If we can find such common areas that can be commercialized and continue to research ways to expand AI applications to different sectors, we can achieve meaningful results together.”

Researchers developing programming and coding technologies

Jung went on to highlight that most AI personnel are unfamiliar with manufacturing. In particular, steel production terms are so difficult that it takes a considerable amount of time to share and understand information. There are also numerous obstacles and obligations for startups entering an unfamiliar development environment full of regulations and restrictions on things like the use of codes.

“In other words, task efficiency is a trade off with information security. I believe that if there is such a process in POSCO where the risk of technology leakage is not high, it will speed up results through collaboration with start-ups. POSCO ICT in Pangyo is a good place for startup employees to work. If we can take advantage of the POSCO ICT databases, collaboration with start-up companies will be easier. As of now, there are not many AI companies in Korea that specialize in manufacturing. Most people don’t even know that there is a lot of data available in the manufacturing industry. That’s why fostering start-up companies specializing in manufacturing now, is a good idea.”

Google’s Fei-Fei Li announces Google’s acquisition of Kaggle. (Source: Venture Beat)

According to CB Insights, M&A (mergers and acquisitions) of AI startups have increased 700 percent since 2011, because they are more efficient and often more insightful when it comes to AI research. Besides investing in existing startups, members of the panel agreed that collaborative competitions such as Kaggle, a contest platform where data scientists from around the world participate in solving problems in industry and society, are a great place to find talent and foster them as start-up companies.

Tackling New Challenges

The flurry of activity around AI research shows just how new the sector really is. People are still finding ways to apply AI across different sectors and industries. In such a volatile environment, how do companies and researchers navigate their way forward?

According to Choi Heeyoul, a professor of computer science at Handong Global University, companies need to “start where the people, problem definition and data are ready. Whether it is in production, sales, distribution, etc., if there is the will to solve problems, then results will follow. Especially when collecting data, you need the help of data analysts. For example, classification data for different types of steel can only be analyzed if the history of the classification criteria is recorded. Speech recognition is not any better for AI application than other fields, yet the most active and productive research has been completed in this sector. This is attributable to the will to apply AI, the abundance of accurate data and close cooperation with data analysts.”

Obtaining accurate data is vital for AI research

Professor Zhang Byung-Tak had another approach to offer. “I hope people will boldly take on challenges. The dramatic increase in computing power can now enable attempts that were previously considered reckless…No matter how much data we gather, it will never be enough. We need to approach problems as experiments and just start. One way to go about it is to work in reverse instead of waiting for data to be accumulated. You can start the task to define the data that is needed and determine the coherence of the data to the task.”

Lee Jong-Seok, a Professor at Sungkyunkwan University added, “When we are defining problems, we need to separate them into ones that can be solved in the short term and ones that require long term solutions. Some problems can be solved with minor improvements, while other problems will persist. For the latter, it is important to have patience and invest in a solution over a long term.”

That’s exactly what manufacturing companies are doing. They are collecting data every stage of production all the way to sales and administration. Big data is now available to study and apply to boost companies’ efficiency, sustainability and performance. Those who come up with the optimal software and modeling systems for data analytics will win the competitive edge early on.

UNIST professor Choi Jaesik echoed the panel’s sentiment, in closing, saying, “It’s very uncommon for a manufacturing company to hold AI courses for all their employees. I think POSCO can take pride that…POSCO is a great place to apply AI and achieve results. There is already plenty of data on site and employees are continuously working to obtain more accurate data. I look forward to seeing what POSCO will bring to the advancement of AI.”

POSCO CEO Kwon Ohjoon plans on doing exactly that, by increasing investment in research and implementing AI technology where applicable. Read more on POSCO’s smartization efforts here.

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