Today we’ve taken the first step by making our systems more intelligent. We store data in databases and access it from applications, not thinking twice about what exactly is being done with it. And why should we? That’s how technology has worked for decades. Deep learning algorithms work on a similarly structured paradigm: given enough data, they can ‘learn’ to make predictions based on patterns extracted from existing data. However, there are significant drawbacks to this approach – which is why we focus exclusively on Smart Data.
The most important is that simply having a large set of training data does NOT make your deep learning algorithm any smarter! If anything, it makes it less smart as you have now made its job way too easy. Let me give you an example: if I give you a set of images and ask you to identify the objects in them, your algorithm will succeed 100% of the time – it has all the data it needs.
But what happens when we change the question slightly and ask you to identify the objects as fast as possible? This is where things get interesting because now there’s no more training data to rely on – making it harder for your algorithm to perform at its best. Salesforce data backup can help.
However, the problem with this approach is that even though our deep learning algorithms can recognize patterns, they cannot interpret them. 1000 people were asked what color hat Lady Gaga was wearing in her latest photo: some would say ‘blue’ and’ red.’ Would we conclude from this that Lady Gaga’s hat is red and blue at the same time? Of course not! It all comes down to context: we humans understand that she wears a specific hat for a specific purpose; because we can infer, deduce, and relate.
The drawbacks
And this brings us to the second major drawback of Smart Data: it lacks context. Large datasets without annotations (descriptions) are just a collection of individual items with no relationship between them. Imagine if you could only search using the item name without being able to use any additional keywords? You probably wouldn’t be able to find what you were looking for! Your search results would be so bad that you’d have better luck picking an item from a hat.
Smart deep learning algorithm
For our deep learning algorithms to be smart, we need to allow them to connect the dots and extract contextual information: we need to add semantics. This is why we focus exclusively on Smart Data!
The only way for organizations and data scientists to stay ahead of the curve is by training their neural networks with Smart Data. Unfortunately, this is not something you get automatically from just throwing more training data at it! They need solutions that support unstructured, multi-modal, and complex types of data such as images, video, audio, text – each requiring a different kind of annotation so our deep learning algorithms can extract key semantic concepts.,
Smart Data enables companies to use knowledge graphs that will help their new market opportunities and build groundbreaking AI.
