Artificial intelligence at the edge could revolutionize your business, but what do you need to prevent unintended consequences?
With the growing demand for faster results and real-time insights, businesses are turning to state-of-the-art artificial intelligence. Edge AI is a type of artificial intelligence that uses data collected from sensors and devices at the edge of the network to provide actionable insights in near real-time. Although this technology offers many advantages, there are also risks associated with its use.
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Edge AI use cases
There are many potential use cases for AI at the edge. Some possible applications include:
- Autonomous vehicles: AI at the edge processes data collected from sensors in real time to decide when and how to brake or accelerate.
- Smart Factories: Edge AI monitors industrial machines in real time to detect anomalies or errors. The cameras also detect defects on the production line.
- Healthcare: Wearable devices can detect heart irregularities or monitor patients after surgery.
- Retail: Store sensors that track customer movement and behavior.
- Video Analysis: AI analyzes video footage in real time to identify potential security threats.
- Face recognition: Edge AI can be used to identify people by their facial features.
- Voice recognition: AI at the edge is already being used to recognize and transcribe spoken words in real time.
- Sensor data processing: Edge AI can process data collected from sensors to decide when and how to brake or accelerate.
Edge AI risks
Edge AI risks include data that may be lost or discarded after processing. One of the advantages of edge AI is that systems can delete data after processing, which saves money. The AI determines that the data is no longer useful and deletes it.
The problem with this setup is that the data may not necessarily be useless. For example, an autonomous vehicle might be driving down an empty road in the remote countryside. AI can consider most of the collected information useless and discard it.
However, data from an empty road in a remote area can be useful depending on who you ask. In addition, the collected data may contain information that may be useful if it reaches the cloud data center for storage and further analysis. For example, it can reveal patterns in animal migration or changes in the environment that would otherwise go unnoticed.
Increasing social inequalities
Another huge risk of AI is that it could exacerbate social inequalities. This is because edge AI requires data to function. The problem is that not everyone has access to the same data.
For example, if you want to use edge AI for face recognition, you need a database of face photos. If the only source of this data is from social media, then the only people who will be accurately recognized are those who are active on social media. This creates a two-tiered system where edge AI correctly recognizes some people while not others.
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Also, only certain groups have access to devices with sensors or processors that can collect and transmit data for processing by cutting-edge AI algorithms. This could lead to a situation where social inequality increases: Those who cannot afford the devices or live in rural areas where local networks do not exist will be left out of the AI revolution at the edge. A vicious cycle can result as edge networks are not easy to build and can be expensive, meaning the digital divide can widen and disadvantaged communities, regions and countries can fall further behind in their ability to take advantage of edge AI.
Bad data quality
If the sensor data is of poor quality, then the results generated by the final AI algorithm may also be of poor quality. This can lead to false positives or false negatives, which can have disastrous consequences. For example, if a security camera using edge AI to identify potential threats returns a false positive, it could lead to innocent people being detained or questioned.
On the other hand, if the data is of poor quality due to sensors that are not well maintained, this can lead to missed opportunities. For example, if an autonomous vehicle is equipped with edge AI, which is used to process data from sensors to make decisions about when and how to brake or accelerate, poor quality data can cause the vehicle to take bad decisions that could lead to an accident.
Poor accuracy due to limited computing power
In typical edge computing settings, the edge devices are not as powerful as the data center servers they are connected to. This limited computing power can lead to finite AI algorithms that are less efficient because they must run on smaller devices with less memory and processing power.
Edge AI applications are subject to various security threats such as data privacy disclosure, race attacks and privacy attacks.
One of the most significant risks of edge AI is the disclosure of data privacy. Edge clouds store and process large amounts of data, including sensitive personal data, making them attractive targets for attackers.
Another risk inherent in ultimate AI is enemy attacks. In this attack, an attacker interrupts the input of an AI system to cause the system to make a wrong decision or produce a wrong result. This can have serious consequences, such as causing a self-driving car to crash.
Finally, edge AI systems are also vulnerable to privacy or inference attacks. In this attack, the attacker tries to reveal the details of the algorithm and reverse engineer it. Once the correct conclusion is made about the training data or algorithm, the attacker can make predictions about future inputs. Edge AI systems are also vulnerable to a variety of other risks, such as viruses and malware, insider threats, and denial-of-service attacks.
Balancing risk and reward
Edge AI comes with benefits and risks; however, you can mitigate these risks through careful planning and execution. When deciding whether to use edge AI in your business, you must weigh the potential benefits against the threats to determine what is right for your specific needs and goals.