Intuitive
Decisions
Can AI machines start being more observant and act towards more advanced objectives? Context-Aware AI allows devices to make intuitive decisions, and given the particular case, can enable the execution of nonstop ubiquitous interactions and provide cyber immunity.
Context-Aware AI
We, as human beings, are contextually aware. Imagine computers doing that. With all the diverse and multi-level data available through device interactions, context-aware AI can help to infer the meaning of a communication, a situation, and respond accordingly.
The context-aware computing market was valued at USD 32.76 billion in 2018, and is expected to reach a value of USD 158 billion by 2024, at a CAGR of 30.0% over 2019-2024.
Workflow
Takeaway:
Currently, we feed the algorithms contextual data according to a set goal we want to achieve. We should create algorithms capable of assessing social reasoning, as well as behavioral cues and their implications. It should be able to analyze unstructured and low-level data from interconnected devices, and let the data interact and cooperate among themselves. The algorithms must autonomously react to an unprepared situation and provide a contextual output.
Ubiquitous
Interactions
The combination of sensors, voice assistants, personal devices, and context-aware AI is enhancing the Smarter Assistance ecosystem to be seamlessly ubiquitous in interactions.
Smarter Assistance
Technologies like speech and image recognition, chatbots, and context-aware AI are working in unison. They will integrate all the devices to create a smarter assistance ecosystem filling our daily lives with ubiquitous interactions.
In the statistic: The market size of voice assistant applications will grow from USD 1.3 billion in 2019 to USD 5.2 billion by 2024, a Compound Annual Growth Rate (CAGR) of 31.9% during the forecast period.
The computer vision market is expected to grow from USD 10.9 billion in 2019 to USD 17.4 billion by 2024-growing at a CAGR of 7.8% during the forecast period.
Workflow
Takeaway:
Let's start with enhancing the capability of voice recognition devices, sensors, and personal devices that users either put in everyday places or carry with them. They should be interconnected with each other as well as with the Smarter Assistance app, powered by context-aware AI. The devices can then work collectively and collaboratively as a team to understand the user better and work for the user instead.
Cyber
Immunity
While advancing the interactions and decisions, organizations should also consider promoting the security of their customers' digital assets. Leveraging on context-aware AI, security applications can identify unknown threats and eliminate them before they act.
Self-Adaptive Security
Self-adaptive security dynamically learns about the behavior, method, and technique of a new threat and eliminates it, during its constant monitoring against threats. It is a closed-loop and intuitive security shield.
The adaptive security market was valued at USD 5.49 billion, in 2019 and is anticipated to register a CAGR of over 15% during the forecast period (2020 - 2025)
Workflow
Takeaway:
Companies who want to provide immunity from cyber attacks should start gathering contextual information both from within the system and from the environment. It can monitor different states of exposure the systems are in and analyze the context from the data. Simultaneously, it can dynamically change the structure or adjust parameters of the security systems in run time, and learn from the entirety of the actions.
Continuous Authentication
Each of us has a signature way of handling our devices. Just as your digital device handling behavior is unique to you, so it is with an attacker. It’s nearly impossible for them to replicate your same typing or maneuvering actions, and get access to an application on your device.
The behavioral biometrics market projects to grow to nearly USD $2.6 billion by 2023, up from USD $675.6 million last year,according to a forecast from MarketsAndMarkets.
Workflow
Takeaway:
Continuous authentication works on an AI model that verifies a combination of attributes and user behaviors, like finger pressure, movement patterns, and speed on a device screen. It can monitor a user activity from the task they started until they finish on their devices. Hence, at any time, if the user behavior is inconsistent, it can interrupt the execution of the task.
