Highlights from the Singapore Formula 1 Grand Prix to Get Your Heart Racing
By BrandwatchSep 18
Published April 20th 2023
We take a look at some of the exciting new AI features dropping at Brandwatch in beta in May and speak to Data Science Manager Peter Fairfax about what the developments in AI mean for the future of our industry.
Brandwatch has been at the forefront of applying GPT and similar AI models to solve some of the hardest challenges and unlock hidden opportunities in our industry for many years.
We were the first to provide AI search of online data, built in-house and trained using GPT (in 2020) and recently upgraded our topic analysis with the same technology.
With the rapid advancement in new models like ChatGPT we’ve been exploring new possibilities and are excited to start releasing some new, cutting-edge features.
Manually scanning through verbatim mentions is extremely time consuming but it’s something our users have to do a lot. GPT is excellent at summarizing themes from a dataset of raw mentions very quickly. By integrating it with Brandwatch Consumer Research users will be able to quickly get natural language summaries of data sets they are looking at so they can spend more time taking action and less time sifting through mentions.
Across our social media management products, we are building a set of AI-assisted features directly in the tools where users manage their social content and engagement. This will include spelling / grammar checking, shortening / expansion of text, and copy suggestions based on user inputs.
Users of our Benchmark and Measure products can get valuable quantitative insights about how their own or their competitors' content is performing. We are building an “AI summarization” feature that instantly generates a short textual summary of that content. This will help bring out better insights on topics, themes, and good/bad performers faster.
We spoke to Brandwatch Data Science Manager Peter Fairfax to understand more about ChatGPT and large language models (LLMs) that are behind these new developments and what Brandwatch, and our industry, can expect in the future.
“ChatGPT is a language model, and there are many others out there. In simple terms, a language model analyses the likelihood of words being written, based on existing texts the model has been shown. For example, imagine we have the sentence: Will Smith likes to eat [BLANK].
A language model can compare the likelihood of [BLANK] being lots of words, such as spaghetti or cats. Will Smith isn’t a cat-eating psychopath, and there has been a recent fascination online with crude, AI-generated videos of him eating spaghetti. A model trained on that data will probably think spaghetti has a greater likelihood than cats.
Predictive text on your phone is powered by a simple language model. Bigger models can be more complicated, but the important thing to remember is that language models are powered by word probabilities.”
“A few reasons. Firstly, it’s a particularly large and sophisticated one. As LLMs become bigger and are fed more data, they tend to be able to solve more complex tasks. For example, ChatGPT and GPT-4 can write code and answer complex questions. Bigger models with more training can do more abstract and complicated tasks, and help us unlock more features like the ones we are launching soon. For me, the capacity of the most recent LLMs show that we’re on the cusp of AI overtaking humans on certain language-based tasks – like the language equivalent of Deep Blue beating the best human chess champion in 1997.
Unfortunately for machines, this means from now on they’ll probably spend more time answering questions about lost parcels than having fun playing board games, but I’m fine with that.”
“ChatGPT is stunningly good at a lot of things, but even the best tech has weaknesses. In ChatGPT’s case, it doesn’t have up-to-date knowledge and sometimes struggles with numeracy.
As well as that, the internet can be a dark and strange place at times, with people sharing all sorts of views. This material can end up in the training data of LLMs, so they sometimes parrot views we find unsavoury or wrong.”
“As I said, these models aren’t perfect but they do complement our tech.
ChatGPT doesn’t have any idea what’s happening right now – its training data cutoff is September 2021. Brandwatch Consumer Research takes in up to 50,000 new documents per second and enriches each with sentiment, location, GPT powered entities, and all the other metadata within a few minutes.
ChatGPT can struggle with arithmetic, confidently giving plausible but wrong answers. By contrast, Brandwatch is built to analyze data at scale and create insights using AI, statistics, and complex aggregations. Our new features mesh the best of ChatGPT and Brandwatch, so you’ve got reliable live quantitative insights that are even easier for a user to understand.
One example is AI-powered conversation insights. This lets you click on any data point in our dashboard, such as peaks or segments within your data, and now with ChatGPT’s power of summarizing large amounts of text you’ll get a succinct, natural language overview of what is causing the trend.
More generally, I expect we’ll see a move towards more chatbot-like functionality for non-expert users, allowing people to interact with all our data without needing to know all the tricks for drawing insights from the noise. Behind the scenes, this will require explaining more information in text form, and I really hope one side effect of this would be to improve accessibility for blind and low vision people.”
“It’s impossible to really anticipate, but off the top of my head it would be: more modes of interaction, seamlessly combining numerical AI with language models, and promoting human creativity.
We should be able to make social listening available to more people, and build on technologies like AI-powered Search. It’s great that you don’t need to be an expert in Boolean to write queries anymore, but new LLMs will also make synthesizing insights and telling stories quicker and easier.
There will always be a key role for statistics, and the world of AI beyond language models. These will continue to be the backbone powering real-time analysis and monitoring as well as long term predictions, and these will mesh more closely with language models.
ChatGPT and other LLMs are able to produce human-sounding content extremely well, and will continue to improve. The companies with the brightest long-term future are those who listen carefully to their users and think creatively about the core problems which can be solved by AI, plus what parts of the job users want to spend less time doing.
There will be risks along the way, and we shouldn’t get so caught up in the hype that we forget our ethical and scientific standards. As data scientists, we have ethical obligations to accuracy and robustness. But there will also be huge serendipity too, and I’m genuinely excited to see what cool stuff we’re now able to build, and who we can help along the way.”