ARE YOU AFTER MORE REAL-TIME TRAFFIC DIRECTLY TO YOUR SITE?
ARE YOU AFTER BETTER ENGAGEMENT WITH THE AID OF ARTIFICIAL INTELLIGENCE?
DO YOU WANT TO PERSONALIZE CONTENT, TAILOR IT TO USER PREFERENCES AND TASTES?
IS THERE A NEED FOR NATURAL LANGUAGE GENERATION IN MARKETING CAMPAIGNS
What is GPT-3 and the need for it in digital marketing? These are all important questions that are not clear-cut. There are no definite answers or specific factors that point out one way or another but there are definitely some clues into what marketers can do differently when they implement this tool. More on this later... Let's start by asking ourselves, why do we care about intelligent machines at all? What is driving us towards these advancements in technology? The answer is simple: we need them.
Why do we care about intelligent machines at all? What is driving us towards these advancements in technology? The answer is simple: we need them.
As digital marketers, our work has been cluttered by a lot of manual processes for years now. From the beginning of content planning to the promotion and distribution activities, everything was mostly manual and it gave birth to a lot of problems. If you think about this from a UX perspective, human creativity cannot be replicated nor replaced by machines because that's where humans excel much more than any other living organism created thus far, but what about content creation and its optimization for quality traffic generation? That requires a lot of time and research as well as trial & error methods which may not always produce the desired results. Is it possible to automate some of these research and creation tasks? And if so, how can that be done? The answer lies in neural networks and machine learning that has evolved into different mechanisms over the years.
Neural networks are a subset of machine learning which is itself another subset of artificial intelligence. They are called "neural" because they are inspired by the structure of neurons within biological brains. Such networks consist of an input layer where data goes through multiple layers to emerge as altered images at the output layer. These many hidden layers give way to more complex processing which then gives humans an opportunity to use them for predictive analytics, pattern recognition, forecasting, or statistical modeling without having to follow every step manually. The only caveat here is, as with all machine learning techniques, the more data you have for your test cases, the better it will perform.
GPT-3 is a neural model that has been able to produce content with the most human-like language ever since its inception in 2016. Its recent improvement of generating human indistinguishable text from scratch has not only given it more power but also opened up new possibilities for digital marketers who are trying to work their way around non-readable online content (and let's face it, most of our clients do not want us reading their emails and offering product recommendations based on our own intuition).
GPT-3 stands for Generative Pre-trained Transformer with 3 layers. It is a type of language model that uses neural networks to predict or generate human language content where there are no annotations present. For the past few decades, NLP has been trying to solve this problem by using statistical models with manually created rules but all those efforts have failed to produce results that are as good as GPT-3. While it is still not perfect in all domains, its performance across different use cases is outstanding: sentiment analysis, image captioning, translation between two languages, etc. Just like other machine learning techniques, GPT-3 is trained with a huge dataset of annotated text data.
GPT-3 is one of the most accurate tools for AI-ready language generation. The biggest advantage it has over other tools is that it does not require clean or tagged training data. It can take any type of text and generate human-readable content after analyzing its structure. That means you do not need to bother yourself about cleaning up your training data before putting it through this tool. You can even feed it non-formatted HTML or excel files and get the desired output without losing any information along the way GPT-3 gives way to more complex processing which then gives humans an opportunity to use them for predictive analytics, pattern recognition forecasting, or statistical modeling without having to follow every step manually.
GPT-3 works with any kind of text. As long as it is encoded in UTF8, there is no need for cleaning up your data before you run it through this tool. Here is one example of how you can use this software: manually write out sentences with keywords in them using the correct syntax, run the sentence through GPT-3 for auto-completion and then add the desired keywords manually to optimize it.
GPT-3 can process a large dataset in a very short time because of its high processing speed. It takes the input data and divides it into chunks for parallel processing. This way, each chunk of data can be processed by different cores so you get the desired result sooner than ever before. The original GPT-2 model was built to work on 64 chunks at a time but the latest one has been able to scale this number up tenfold to 640 chunks at a time i.e. over half a million words per second which is astounding given that every word needs independent attention from the model during processing.
GPT-3 can also separate out sentences from raw text with relative accuracy, thanks to its ability to automatically learn how sentences are structured in any language. The same is not true for other NLP tools which rely on manually defined rules to create separate sentences from random text input.
Some people think that all we need is just a perfect predictive model for generating text and we are done with it. However, this is not true and we can never achieve that level of accuracy even if we keep trying. The end result is always going to be different from what you expect because the model cannot understand the actual meaning behind every concept.
For example: If you train a data set for generating news articles related to marketing, GPT-3 may start spitting out content that looks like phrase-based machine translations because it has learned concepts such as "marketing" or "sales" independently without understanding their context in the given data set. Moreover, these concepts have been repeated throughout relevant training samples so it might think that they are related and mark them as separate entities instead of connecting them together into logical sequences.
In conclusion, it can learn new concepts from related contexts and extract knowledge to give you better output with every use. For example, if you train the tool with news articles related to marketing campaigns, it will automatically learn all relevant information for generating metadata, writing SEO content, and publishing it on various media channels to extend your reach. However, it cannot be used to replace human writers because of its inability to understand the meaning behind concepts. As technology continues to evolve, we can expect these tools to get better at mimicking humans in due time.
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