At the edge of innovation: What can edge AI do for you?

(Credit: Unsplash)

This article is brought to you thanks to the collaboration of The European Sting with the World Economic Forum.

Author: Kay Firth-Butterfield, Head of Artificial Intelligence and Machine Learning; Member of the Executive Committee, World Economic Forum & Beena Ammanath, Executive Director, Global Deloitte AI Institute and Trustworthy AI/Ethical Tech Leader, Deloitte

  • artificial intelligence (AI) is decentralized computing that allows data-led decisions to be made by a device at the closest point of interaction with the user.
  • The benefits of this kind of technology include improved privacy and cost savings, but data is typically discarded after being processed.
  • Upcoming advancements, including 5G technology and less costly processing chips, will make edge AI increasingly useful for certain applications – from smart home devices to medical technology.

Imagine you want your new smart thermostat to quickly turn up the heat so that your house will be warm after your get home from work on an unusually cold day. You connect from your smartphone and ask it to act. You won’t know it, but that action may take several seconds as it moves your request to the cloud and receives instructions back.

Now imagine the self-driving car you’re in suddenly senses a dog running into the road in front of you. The car needs to react in milliseconds to avoid a disaster. That kind of reaction requires edge artificial intelligence (AI) – technology that can make a decision at the closest point of interaction with the user, in this case, the car’s sensors. It’s the definition of a split-second decision.

Data in motion

With today’s Internet of Things (IoT), data is always in motion. It flows from legacy systems to the cloud, all the way to edge devices and beyond an organization’s systems to partners and customers. Answers need to be delivered in real-time and so it’s not always effective to use centralized computing power when data can be processed via edge devices. A self-driving car doesn’t have time to wait for a decision to be made in the cloud when it has mere seconds to react.

Vast amounts of data can be fed into AI algorithms on the edge wherever the device happens to be – and the benefits are plenty. Data in motion can deliver critical patient information to doctors, shorten lines in amusement parks, alert power companies of a potential outage, and make a self-driving car react in time to prevent a tragedy.

Edge AI allows a device to make these decisions on its own, at the device level. It doesn’t necessarily have to be connected to the internet to process the data. Consider a watch that can monitor your sleep patterns, but instead of pushing the data into the cloud for storage and processing, it records the data for processing on the watch itself.

Edge-enabled AI devices also include video games, smart speakers, drones, and robots. Security cameras can also be edge-enabled – a camera on a factory floor that looks for product defects during manufacturing can quickly identify which products to immediately pull. Edge AI can also be used to analyze images for emergency medical care, when speed can save lives. The closer the processing capabilities, the quicker the response time.

Although edge technology will not replace the cloud, user data that belongs only to you – your sleep patterns or gaming data, for example – can be processed in an edge-enabled device. This decentralization of data addresses the issue of privacy, a significant concern in the IoT market. Edge AI can provide convenience without compromising privacy. And, in some cases, it can be much cheaper – one company is currently developing voice-activated home appliances such as washing machines and dishwashers using tiny microprocessors that cost a few dollars apiece.

“When it comes to gadgets that share my house, I’d actually prefer they be less smart.”—Clive Thompson, Wired

The trade-off is a less ambitious bot: A voice recognition AI for a coffee maker only needs to recognize about 200 words, all related to the task of brewing coffee only. Think of it this way, says Wired reporter Clive Thompson: “I don’t need light switches that tell bad jokes or achieve self-awareness. They just need to recognize ‘on’ and ‘off’ and maybe ‘dim’. When it comes to gadgets that share my house, I’d actually prefer they be less smart.”

In addition to quicker and less expensive processing, edge AI doesn’t require an ever-expanding internet. With the rapid growth of IoT, there is now a vast amount of data being sensed and produced at the edge – Statista estimates the figure will reach almost 80 zettabytes by 2025.

This is so enormous that it is not technically feasible to use the bandwidth of today’s internet to transfer the entirety of this data from edge devices to cloud servers for storage and processing. Even if the bandwidth was available, there would need to be enough data center resources available to handle all of the data. Less required bandwidth translates into cost savings. Around 10% of enterprise-generated data is created and processed outside a traditional centralized data centre or cloud. Gartner predicts this figure will reach 75% by 2025.

Balancing risk and reward

One of the most vexing problems in the IoT world is the fact that large numbers of people that can’t afford the devices or that live in rural areas where local networks don’t exist may be unable to participate in this remaking of our everyday world. A history of limited network capacity can become a vicious cycle. Edge networks are not simple to build and can be expensive. Developing countries may fall further behind in their ability to process data through edge devices that need updates. The growth of edge computing, then, is another way in which structural inequality could increase, particularly as it relates to the accessibility of life-changing AI and IoT devices.

Another risk with edge AI is that data may be discarded after being processed – by its very nature “at the edge” means it may not make it to the cloud for storage. The device may be directed to discard information to save costs. While there are certainly disadvantages with central processing and storage, the advantage is that the data is there if and when needed.

AI, machine learning, technology

How is the World Economic Forum ensuring that artificial intelligence is developed to benefit all stakeholders?

Artificial intelligence (AI) is impacting all aspects of society — homes, businesses, schools and even public spaces. But as the technology rapidly advances, multistakeholder collaboration is required to optimize accountability, transparency, privacy and impartiality.

The World Economic Forum’s Platform for Shaping the Future of Technology Governance: Artificial Intelligence and Machine Learning is bringing together diverse perspectives to drive innovation and create trust.

  • One area of work that is well-positioned to take advantage of AI is Human Resources — including hiring, retaining talent, training, benefits and employee satisfaction. The Forum has created a toolkit Human-Centred Artificial Intelligence for Human Resources to promote positive and ethical human-centred use of AI for organizations, workers and society.
  • Children and young people today grow up in an increasingly digital age in which technology pervades every aspect of their lives. From robotic toys and social media to the classroom and home, AI is part of life. By developing AI standards for children, the Forum is working with a range of stakeholders to create actionable guidelines to educate, empower and protect children and youth in the age of AI.
  • The potential dangers of AI could also impact wider society. To mitigate the risks, the Forum is bringing together over 100 companies, governments, civil society organizations and academic institutions in the Global AI Action Alliance to accelerate the adoption of responsible AI in the global public interest.

  • AI is one of the most important technologies for business. To ensure C-suite executives understand its possibilities and risks, the Forum created the Empowering AI Leadership: AI C-Suite Toolkit, which provides practical tools to help them comprehend AI’s impact on their roles and make informed decisions on AI strategy, projects and implementations.
  • Shaping the way AI is integrated into procurement processes in the public sector will help define best practice which can be applied throughout the private sector. The Forum has created a set of recommendations designed to encourage wide adoption, which will evolve with insights from a range of trials.
  • The Centre for the Fourth Industrial Revolution Rwanda worked with the Ministry of Information, Communication Technology and Innovation to promote the adoption of new technologies in the country, driving innovation on data policy and AI – particularly in healthcare.

Contact us for more information on how to get involved.

A huge stream of data about an empty road may not seem important if it’s just you and your autonomous vehicle on it but think again. Much can be learned from data about that empty road, including information on road conditions and how the vehicle, and others like it, behave under those conditions. Finally, a clear business case must be closely scrutinized when it comes to edge computing to ensure the costs of the network balance with the value created.

Still, despite inequalities or lost data, and with the coming advancements in 5G technology and less costly processing chips, it’s easy to see how being “on the edge” could be here to stay – whether it’s your self-driving car or your coffeemaker that gets you ready for your commute.


  1. […] sourceConnect with Chris Hood, a digital strategist that can help you with AI. […]

Speak your Mind Here

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

%d bloggers like this: