When we cut through the marketing hype, there is a lot to be said for Artificial Intelligence (AI). But what exactly is AI? And will it really help advance the viewing experience for broadcast audiences the world over?
AI is a generic term that covers four different disciplines; reasoning, natural language processing (NLP), planning, and machine learning (ML). Split into four more categories, ML includes supervised learning, unsupervised learning, reinforcement learning, and deep learning (or neural learning).
When referring to AI, especially in marketing, people are often talking about ML without realizing it. ML is a method of analyzing data in real-time to establish significant correlations between seemingly unrelated data-sets. Although AI research reaches back to the 1940’s, it’s only recently, with the proliferation of high-performance computing (HPC), has ML really started to make its mark.
AI is More Than ML
For ML to be truly successful it must have some form of feedback. Teaching an ML algorithm requires the algorithm to understand whether the outcome of a function was successful or not. Explicitly determining the outcome allows the ML algorithm to make a better guess at the next iteration of the function, thus developing more accurate predictions as time progresses and more data becomes available. This is the concept of learning.
Some parts of television are better suited to ML than others. For example, using ML to discover the viewing habits of OTT delivery to mobile phones is ideally suited to ML as there is a definite outcome; the viewer either watches the program or they do not. Feedback from the mobile device’s viewing software will provide this.
However, other aspects are less suited to ML, such as human language recognition. And for me, this is where the process moves to AI.
Human Communication is Difficult
Misunderstandings in human communication and language are a regular consequence of our interaction. This leads onto the suggestion that language processing isn’t deterministic, that is the same language doesn’t always have the same interpretation.
The following quote demonstrates this, “Always remember that you’re unique. Just like everyone else”. Is this humour? Satire? Or meaningless rubbish? Many people reading the quote will have their own interpretation and may well have another answer to the three examples I have given.
Now contrast this to the Peano axioms of mathematical logic. Under these rules, 1+1 is always 2. This isn’t open to interpretation. It’s a statement of fact (based on the axioms). Similarly, a disc drive either fails or not (based on its parity errors), and the temperature either increases to 28C or it doesn’t. Therefore, a machine can easily judge the outcome of many objective measurements and correlate other deterministic data-sets to achieve greater predictive accuracy.
Truth Varies for Humans
From my research, the non-determinism of human communication makes it challenging to train statistical NLP models. It’s difficult to measure the truth as it’s based on human subjective judgements, which vary and are dependent on many environmental, social, and economic factors.
And for me, this is where the whole concept of AI becomes fascinating. The accuracy of ML is limited by the data-sets available, so for subjective systems, such as human communication, we must have an external adjudicator deciding the rules. For NLP systems, who decides who the rule-maker should be? Democratically elected representatives who will form committees of committees? Scholars who agree? Or tyrants who benefit from the manipulation of the masses? We soon disappear down a philosophical rabbit hole and this is before we consider how languages throughout the ages change over time as cultures develop.
Understand AI Categories
NLP does have a strong foot in the ML camp as much of the recognition is ML based, but not enough to allow ML algorithms to work without significant assistance (at the moment). It is inevitable that this will change as our research into AI continues to improve and deliver better and better results.
We are just starting our journey of using AI in broadcast television. And we should be careful not to group all disciplines into the ML category. But to fully appreciate the major role it will play in all aspects of broadcasting, we must understand and differentiate between the categories in AI and how it will influence every aspect of our lives as television continues to embrace this new technology.
What’s your experience with AI? And where are the lines for you?
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