Tag Archive for: Vectorisation

A recent trip to CCW 2023, Berlin

By Björn Gülsdorff

CCW is the “international conference and trade show for innovative customer dialogue”, which is a bit bulky a title, but much better than the original ”CallCenterWorld” which is no longer a good fit. Unfortunately, this is now easily confused with ContactCenterWorld, also called CCW. But I managed to get to the right event to support our German partner SOGEDES in matters related to Conversational AI.

One very non-technical but nonetheless important takeaway from CCW is that Real life ain’t dead. CCW sported a hybrid concept, with all talks and product presentations available through live streams (and recordings after the event).  And still, many people came to Berlin, and I think that nearly everyone who had come was ready for business. The quality of the conversations at the SOGEDES booth was outstanding.

Unsurprisingly, ChatGPT was a big topic. However, although it was hot in talks and booth side chat, it was evident that few real applications had been built yet, except for the obvious use cases of summarising texts. This was not only due to the short timeline between the launch of CHATGPT and CCW, but what I heard many people saying was that ChatGPT is not ready for customer dialogue. This is correct of course (as GPT4 was not out then) but I was still very surprised how educated and relaxed people were about it.

I had expected a buzz similar to the machine learning hype some years back. At the same time, ‘AI’ has become a commodity. Everyone has it – because everything is now called AI. Similarly, but to a lesser extent, Conversational AI has lost leading edge appeal and simply become something that’s used for automated conversations i.e., chat bots – but those, as a word, have fallen from grace.

The top two top – Agent Assist and Voice Bots

After years (decades?) of promoting Digital Self Service, voice is still a strong channel and companies are now looking to automatise calls and are no longer avoiding this. The handover/routing of calls from bots to agents is an important ingredient in the requirements.

In a way, organisations are now looking for something that Creative Virtual has been suggesting and recommending since its founding. That is, virtual agents are a part of an overall conversational strategy and there should be collaboration between human and silicon agents.

We have been delivering solutions that enable human/digital agent collaboration for decades.  In fact, “agent assist” is the name of some of the internal projects we deliver to our customers that, well, assist agents, so they can provide a better service. Our recent work with smart and audio-codes is also testament to our credentials as leaders in conversational AI, and the best choice for customers.

It is worth saying, however, that this new focus on voice brings with it very simplified conversations, or at least very rigidly structured conversations, something that is no longer prevalent in text.  You can’t get very complex in a phone conversation, you can’t show images to let users choose the right product, and you can’t play a video to explain something.  To deliver the experiences customers expect, organisations do need to ensure there is integration, with seamless interfaces for these simple flows.

The Overlooked Feature of GPT : Vectorisation

By Olaf Voß, Lead Application Designer

These days everyone is stunned by the generative power of the GPT models, including myself. However, today I want to discuss a GPT feature that is largely overlooked in media coverage: the embeddings endpoint in the OpenAI API. This feature ‘translates’ any text into a 1536-dimensional numerical vector. Personally, I prefer to use the term ‘vectorisation’ instead of ’embedding’.

The idea to turn individual words into vectors is about 10 years old now. These word vectorisations were trained on large corpora – or what we thought were large corpora 10 years ago. They are useful because the way words are distributed in the vector space represents relationships between them. The most famous expression of this concept is probably the equation king – man = queen – woman, which holds approximately true when creating word vectors with tools like GloVe or Word2Vec. These word vectorisations have been in use ever since, forming the basis for much of the progress in machine learning for language-related tasks.

Now, GPT offers the same level of analysis for entire texts. It’s not the first model that can do this beyond single words, but its high quality and affordability make it highly attractive. If you experiment with it, you can quickly see how useful it may be. For example, a question and its answer are matched to fairly similar vectors. Additionally, a text in one language and its translation into another will be mapped to close-by vectors.

This vectorisation can be used for anything that can be done with vectors. Text comparison and thereby text search is one obvious use case. We at Creative Virtual will be using it this way in our upcoming Gluon release for intent matching – still keeping rules as the fallback option if and when needed. Another way we are already using it is for text clustering. Finally, you could use the text vectors as the input layer for a neural network and train it for whatever task you want, thereby ‘inheriting’ many of GPT’s text understanding capabilities.

So, if you have access to the OpenAI API and if you are running out of ideas for what to do with the chat endpoint, give the embeddings endpoint a chance. Vectorise away!