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Mining the AI Potential


Mining the AI Potential

Once upon a time, there was an industry that called programmatic code software and not artificial intelligence .

In media storage, you've probably heard AI used in the context of image searching your media archive to find a matching actor's face

Because AI has become an almost ubiquitously used phrase, and therefore somewhat grating, it would be easy to dismiss it from our thoughts but is there anything behind the term that we should be taking notice of?

Search: The Final Frontier

Google processes a lot of data. Some estimates say that Google handle 1.2 trillion searches per annum. I don't know, but I'd imagine 99.999% of those searches are based around language. However, in a world where an estimated 80% of data is unstructured, and the vast majority of that unstructured data is video data, it is clear that search has a long way to go.

The next stage is to be able to search video and, for reasons we will briefly touch on, often the techniques used to analyse video and to allow meaningful search of video is described as AI.

Why is it called AI? A few years ago we'd have called it: image searching; video searching or software algorithms , but since the success of Apple Siri, Amazon Echo etc., the general public psyche and lexicon has begun to consider any such technology artificial intelligence . This is even if it doesn't have a self-learning feedback loop or some other mechanism of analysis enhancement over time. So, though it irks me, let's join the bandwagon and call it AI video analysis and search as well.

What cannot be ignored is that this is exciting. Video archives that previously sat dormant can now be queried for all types of things:

What footage of Princess Diana do we have?

Have we ever shot a news item on that street before?

Who has ever played number 7 for England?

Those image searching algorithms are nascent but are quickly being realised. On the other hand, semantic video analysis is more basic to date but will answer more and more questions such as:

This part of the movie is a car chase

Alert! Look at this CCTV stream because a street fight has broken out

This movie is a modern day remake of the magnificent seven

Cars on this road have gone from 100% driven to 30% self-driven in the past 5 years

Unstructured data (or unstructured information) is information that either does not have a pre-defined data model or is not organized in a pre-defined manner. Unstructured information is typically text-heavy, but may contain data such as dates, numbers, and facts as well.

So AI video analysis and search is beginning to be able to unlock the value in media archives, and this importantly allows better reuse of media assets for all types of analysis, documentary making, sports fan lookups, and countless other usages. This will only go from strength to strength, but, is that really what AI in media archiving is all about?

AI: Beyond Image Searching

Although the trend is to think about AI in the context of search that would certainly limit our definition.

One of the key challenges with storage today is data gravity . Or, to put it simply, having the video data where it needs to be processed and when it needs to be processed, is a mixture of moving data to where it needs to be seen or processed, or, moving to process to where the data is. Ingenious storage systems are needed to manage this. Take for instance a production that is to be post-produced by teams in different countries. Assets should be available to those teams as and when they are ready to work on those assets. That's where artificial intelligence can come in: moving assets around for us.

Furthermore, artificial intelligence can manage this in a manner that also protects data against any kind of loss and with security in mind. AI storage becomes your 24/7 super-administrator so that you don't need to spend your days making data transfers or worrying about data security.

The term AI in media storage then should not be limited to purely AI searching; it is also about AI data management.

Can Video Search Change the Industry Dynamics for Video Owners?

All in all the media industry should be hugely excited about the new video search techniques coming on to the market for the simple reason that they allow the holders of media archives to better monetise and re-use content.

But could it be that this is just the beginning? Will AI production mixed with video search be able to cut new content? Will video search allow a whole new analytics industry to develop new Googles new Facebooks? Could it be that the owners of video archives will gain more of their revenue from that archive than they do from new content?

The future is uncertain, but the foundations of that future are here and now.

Data gravity is an analogy of the nature of data and its ability to attract additional applications and services. The Law of Gravity states that the attraction between objects is directly proportional to their weight (or mass). Dave McCrory coined the term data gravity to describe the phenomenon in which the number or quantity and the speed at which services, applications, and even customers are attracted to data increases as the mass of the data also increases.

The Object Matrix View

Our conclusion for AI, in general, is that the term will quickly become redundant (again) since it is being used too ubiquitously! However

For AI Search our conclusions are:

Includes image search, object searching, semantic analysis and speech to text

It will revolutionise how and why we hold video archives

It will monetise archives in new ways: keeping archives offline will no longer be viable

It will move from niche to mainstream over the coming years

For AI data management our conclusions are:

It will move data and data processing algorithms
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