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Have you prepared for the rise of the robots?

By Ken Wong - 24 Dec 2020

Have you prepared for the rise of the robots?

We’ve always been fascinated with artificial lifeforms. Whether a robot, cybernetic life form or some futuristic hybrid, artificial lifeforms we envision interacting with them in a multitude of ways.

Sometimes they’re the loyal family help, caring and protecting the elderly and young. In others they’re the enemy bent on wiping out mankind. But either way, they’re an intricate part of our lives.

But that is entertainment.

This is reality.

Image courtesy of Panasonic.

We now have them delivering files in hospitals, helping maintain social distancing in malls, and even helping to drive our cars for us. 

But are these considered robots? How smart are they really? Have we reached levels of artificial intelligence that will allow us to have natural conversations with chatbots and robots? Will they rise and kill us? Are we going to lose our jobs? The robot conversation seems to consist of questions we need answering.

Takehiko Ryu, Regional Head for Southeast Asia and Oceania and Managing Director of Panasonic Asia Pacific. (Image courtesy of Panasonic.)

We spoke to Takehiko Ryu, Regional Head for Southeast Asia and Oceania and Managing Director of Panasonic Asia Pacific to find out how Panasonic is envisioning a future where humans and robots live and work together.

 

How smart is the current AI? Are we looking at a smart dog, dolphin or Skynet equivalent?

AI has advanced rapidly over the years, with some aspects such as computer vision, text analysis and conversational AI coming close to human ability. While many consumers tend to draw references to AI based on what they see in popular culture, in reality, AI is already prevalent in our daily lives and its applications are wide-ranging.

If you take a quick look around you – the voice assistants on our smartphones, the virtual customer service officers that we interact with online and the facial recognition technology at institutes of higher learning for attendance-taking of staff and students – all of these are AI at work in our everyday lives.

In recent months, we’ve also seen AI being widely deployed in the form of autonomous robots at parks to assist with the enforcement of social distancing requirements, in malls to assist with cleaning and disinfecting procedures, at roadsides to aid traffic flow management, and at hospitals to deliver medicine, specimens and patient notes to healthcare staff.

Beyond its current applications, we continue to see unlimited potential for AI. To enable the continued development of AI in a sustainable and responsible manner, we believe that we have a duty to develop the best possible human resources to be at the forefront of innovations that will change the lives of our customers for the better. By putting the development of our people before our products, we can then pave the way for AI to play a bigger role in enabling safer and easier lives.

Image courtesy of Pexels.

Where will AI likely to be used? What forms will AI take?

As the applications of AI continue to expand, we will see rapid growth in the use of AI in homes, buildings, vehicles, healthcare scenarios and more. We believe that the Internet of Things (IoT) and AI will also play key roles in shaping a different future for Panasonic, and help us shift from a product-oriented mindset towards a people-oriented mindset to become a “Lifestyle Updates” company that grows with our customers.

 

How do we tell the difference between machine learning, deep learning and AI?

In recent years, the availability of massive volumes of data has accelerated the development of Artificial Intelligence (AI). While AI, Machine Learning and Deep Learning can be used interchangeably in some settings, they do not quite refer to the same thing. In fact, Machine Learning can be considered as a subset of AI, and Deep Learning a subset of Machine Learning.

To put it simply, AI refers to the use of intelligent machines to mimic human action and thought. Not only can AI-powered machines sense and learn from the information they receive, but they can also reason, act and adapt independently to solve problems. For example, AI-powered refrigerators have the ability to sense how frequently the door is opened to understand a family’s needs to automatically switch between the power saving mode and other power modes.

Machine Learning on the other hand refers to empowering systems with the ability to automatically learn and improve from experience. These systems improve their performance over time as they are exposed to more data. Take video streaming services as an example. When integrated with a Machine Learning algorithm, the service can look at an individual’s viewing preferences and compare it with other users habits, to make recommendations on what other videos the individual would like.

Deep Learning represents the next evolution of Machine Learning, using multi-layered neural networks to analyse different factors from vast amounts of data. Deep Learning machines act very much like the human brain and can analyse data with a logic structure similar to how humans draw conclusions.

Video Courtsy of Tesla

Self-driving cars aren’t far off, remote medical diagnosis is now available and we talk about smart security these days. Remembering movies like War Games, Total Recall, The Terminator etc. how close are we to that? Do we need the 3 laws of robotics?

While the possibilities for AI are limitless, responsible AI requires us to put careful thought and consideration into how it is designed and implemented. Biases need to be taken into account in the design of AI. One source of bias can happen through flawed data sampling, in which certain groups are over- or under-represented in training data. It is important to be aware of such risks and work to reduce them during the research and development stage.

At Panasonic, we are conscious of the risks of biases during the development of our solutions, and we handle such data imbalances through data augmentation and algorithm improvement. We also ensure that training data are universally collected from all aspects of the global population to ensure fair representation and avoid biasness as much as possible.

When it comes to AI implementation, it is important to understand that not every problem requires an AI solution. We first need to assess whether AI is feasible and suitable to be implemented for the project requirements, then consider how the AI solution can be integrated with the existing system, how it can be customised to meet the organisation’s needs, and how it will affect current operations. Another aspect to consider in AI implementation is the provision of “explainable AI”, to ensure greater transparency in the decision-making process and that decisions made by AI can be accounted for.

 

We talk about smart cities, buildings etc. is that AI? Are we using any AI now? How will AI fit into the house?

More than ever before, AI is featuring in a big way in smart homes, smart buildings and smart cities. In today’s homes, we can find AI-powered home appliances, personal assistants and even smart care solutions such as walk-training robots that empower the elderly with the ability to lead more independent lives.

In commercial and institutional buildings, facial recognition technology is being used at institutes of higher learning to assist with attendance-taking of staff and students, and integrated within buildings and office management systems to enhance overall safety and security.

On a larger scale, AI is being used in smart city development to enable a better and safer living environment for everyone. For example, Panasonic’s Smart Street solution uses AI to analyse pedestrian traffic, track speed and volume of the traffic flow to help reduce accidents and improve road operations; while AI in driverless parking systems helps to reduce vehicle accidents caused by drivers' mistakes.

 

IT vendors and the government have been talking about digital transformation, how close to that is AI? What is needed past that to create AI?

Digital transformation, or the process of leveraging technology to reinvent existing business processes and organisational culture to improve performance, is rapidly changing the business landscape. Today, companies are harnessing the power of technology to streamline their business processes, develop new products and services, improve productivity and the customer experience.

With massive volumes of data being more readily available now than ever before, businesses can leverage AI more effectively to accelerate the digital transformation process. For instance, Panasonic’s automated facial recognition gates at the Tokyo Narita and Haneda airports in Japan use AI to speed up the immigration processes. By comparing photographic data of the individual’s face in the IC chip embedded in the passport with an image taken at the facial recognition gate to verify his or her identity, this removes the need for prior registration of biometric data and improves productivity and the efficiency of immigration clearance at the airports.

We are also partnering with FamilyMart in a pilot project to utilise IoT and AI-powered facial recognition technology at convenience store chains. Not only does this help speed up payment verification processes, it also improves the overall customer experience at the convenience stores.

Image courtesy of Unsplash.

How much control or information is safe for us to turn over to vendors like yourself for AI development and use?

While the sharing of data can enable the creation of products and services that better meet the needs of the public, this data needs to be used responsibly and within the boundaries of the rules and regulations set by the prevailing authorities.

Panasonic has clear data privacy guidelines in place to protect the security and privacy of individuals and businesses that use our technology. We take great care in the design of our systems, and the way we handle data, to ensure that we comply with the prevailing data protection standards of the countries that we serve.  

To find a common ground for the sharing and use of data within boundaries, businesses and technology companies can set up shared responsibility models to clearly define the control boundaries and ensure that there are no gaps. This way, a good balance can be struck between safeguarding the public’s interest, while harnessing the power of AI to enable a better society for everyone.

Image courtesy of Unsplash.

What about jobs - will we and what are we likely to lose to robots? During the industrial revolution in the 1800s, machines replaced much simple human labour, are we going to see that again?

According to a World Economic Forum report, 75 million jobs globally are expected to be displaced by robots by 2022, but at the same time, more than 133 million new job roles will be created. From this, we can be optimistic about the continued growth of job opportunities for people in a digital future.

However, as the rate of technological change accelerates, the jobs landscape is also undergoing a massive transformation. Manual and repetitive tasks will increasingly be replaced by technology and robots, while new jobs that require higher-order skills – such as app development, piloting drones, remote monitoring of patient health and more — will be created at the same time, opening up opportunities for an entirely new range of livelihoods for workers.

As such, there is a need for workers to future-proof themselves by equipping new skills. According to a Microsoft-IDC study, business leaders in Asia Pacific found quantitative and analytical skills, digital skills, adaptability and a continuous learning mindset to be the top three skills needed for a digital future, where demand exceeds existing supply. By actively acquiring new skills, while keeping an open and continuously learning mind, workers can be well-positioned to thrive in a digital future.

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