Guest Post from George Krasadakis
How is AI impacting our world? What are the risks and how can we get prepared?
Artificial Intelligence. One of the most popular technology terms of our time— and very frequently, overused or even, misused.
Media love both success stories of AI and ‘dystopias driven by Artificial Intelligence: machines replacing human workers, AI exceeding human intelligence, robots taking control and so on.
But, if you look beyond this hype, you will realize that there is a real revolution in progress. To understand the potential of AI, just examine the recent advances in fields like Deep Learning and their applications in domains such as Computer Vision and Natural Language Processing.
There is a massive disruption in progress?—?powered by a combination of technologies, enabling machines to make sense of massive volumes of data and perform cognitive functions.
AI is changing our world and the impact to come is massive: on the way we work, we live, collaborate, decide and act as a society.
Artificial Intelligence can be defined as ‘the technology enabling systems to encapsulate cognitive functions along with adaptive and learning capabilities?—?leading to self-improvement’.
AI-powered systems can capture and ‘understand’ their environment and make optimal, real-time decisions towards specific objectives.
As a characteristic example of AI, ‘Computer Vision’ enables systems to ‘see’?—?via sophisticated algorithms, which are trained to identify a wide range of entities such as landscapes, persons and objects in a picture or video.
In another example of applied AI, ‘Natural Language Processing’ technologies, enable interaction with a machine based on free-form, natural, speech: NLP and related technologies can ‘understand’ natural speech and respond in a meaningful way: as soon as the machine extracts the context of the ‘natural speech’ request, it synthesizes the right response which is also served back to the user as ‘natural speech’.
The rapid progress of AI is empowered by streams of data on major human activities?—?online communication, social interaction, device usage, searches, content consumption and IoT data streams- to name a few.
To make sense of these vast amounts of complex data, AI systems leverage the power of cloud computing and specialized machine learning algorithms. World-scale data centres, with huge, labelled data sets are being used for training AI algorithms in performing certain cognitive functions.
The State of the Art
Algorithms can now ‘see’
The ability for a computer to ‘see’ is an astonishing achievement. AI-powered systems can ‘understand’ the context of an image or a video in impressive level of detail: they can identify an expanding set of entities?—?such as persons, named individuals, cars, houses, streets, trees and more?—?with increasing levels of success.
Given an image or video, algorithms can estimate additional properties such as the number of persons in the picture, their gender, age or even their emotional state.
You can simply submit a family photo to one of the commercially available cognitive services, and get in milliseconds a response with the persons identified, their gender, age and the dominant emotions. An object in a photo can also be identified?—?for example, AI can recognize a car and also its maker and model; then tag it for improved searching, grouping and discoverability.
In the close future, algorithms will be able to infer even the situation implied?—?such as a kids party, a sports event, a business conference or a random arrangement of people in a park.
The possible applications of computer vision are impressive: from autonomous cars which can ‘see’ in 360 and understand their environment and its dynamics in real-time, to special applications like the Seeing AI by Microsoft?—?a prototype system helping people who are visually impaired or blind to understand their environment!
Computer vision is making huge steps, with massive applications in autonomous cars, navigation, robotics, pattern recognition, medical diagnosis and more. AI systems keep learning and they learn fast.