Hey everybody, so welcome to another edition of Reading Fun in the Sun. And I am going to be watching the boats come and go. And if you want to join me for watching that while we talk about the new Google AI Lambda Functions, make sure you stick around because this is going to be, I really think, revolutionizing the way that we interact with virtual assistants, doing question and answer in search, all kinds of things that involve human language and how tricky it is to really understand what we're all doing most of the time with our own language. So if this sounds interesting to you, I'm watching. Alright, so the first thing we have to get out of the way is, there is something else in the world called Lambda and that is from AWS. This is not that. This is a new neural network feature in machine learning from Google AI. And it was originally derived from something in a paper they published in 2020. This model has since become a little notorious because there is an ex-Google-er who in 2022 claimed that they felt that this model was sentient. Now I use model specifically here because yes, it is just a model. It's just a very clever one. It is not sentient. So let's put that to rest. So let's get into what is Lambda and why all the hype. So Lambda is short for a language model for dialogue applications. And it is a neural network and it was originally called MENA. And I have a bunch of links down below if you want to go check out more about the history and more of the technical details around this. So this model is focused on conversational dialogue and it is analyzing sequence data to predict the next word in a conversation. To accomplish this, it has to be trained on huge amounts of data to derive parameters. In this case, growing from 2.6 billion parameters in 2021 to now 137 billion parameters in 2022. So hold up for those that are not as familiar with neural networks of machine learning. A parameter in this sense is a sort of like a common word co-occurrence vector. And these vectors are coming from training sets from the open web. And those are then extracted into parameters that are used to predict what word likely comes next in a sentence. So this sort of model is not novel. There are a lot of others out there and I'm going to have another video comparing all of these different models later. But the most notable is OpenAI's GTP3 and that was also released in 2020. Now what sets land apart from some of these other models is the folks at Google AI are trying to drill into the specificity of sense in a conversation. So essentially trying to avoid the dreaded, I don't know. Or that's nice vanilla responses that many chatbots present when they are stumped. And training on a much larger data set is of course going to be giving more data to support the pathways that a normal conversation can move into based on human dialogue where we do happen to bounce around in conversation to multiple topics. These models are trying to achieve a semblance of that. So the way Lambda is doing this is through a new metric based on sense, specificity and interest called SSI, which is generated by crowdsourced human evaluators. Google AI says SSI is similar to an automated metric already used in neural networks called perplexity, which is basically measuring how many choices the model has to choose from when predicting the most logical next word in a conversation. So think of it like this, the lower the complexity, the higher the confidence of the predicted next word in a conversation or text. And all this is based on common ML methodologies that are around the transformer models and in this case it's sec to sec or sequence to sequence modeling, which is sort of like a lot of other two vec type models, except it's comparing sequences of words instead of a word all on its own. So the model is trained on this text or speech data, again in Lambda's case is from the open web, where sequences of words with their contacts are extracted as vectors. So think Java code versus Java the island. And you're both talking about Java, but you understand the context of each because it is not one word by itself. So let's take an example here. So a conversation stating, I am streaming the finale of Game of Thrones. What are you doing tonight? The model may extract these sequences based on those phrases. Now you can see here these are not incredibly complex, but this allows for a conversation about any of these sequences to work properly if enough training has been added for the model to be able to predict on these parameters. So if I am talking about the Game of Thrones finale, I can then have Lambda respond, I love that show. So it knows it's a show. I am streaming the umbrella academy, again another show of similar ilk. And then the follow up could be, let me know how you like the finale. So the model here is jumping off of the TV show mentioned in the dialogue. And that is where the special sauce of Lambda really kicks in. These jumps in conversation are supported by how Lambda tests its model parameters. Using the SSI metric, human reviewers assess if the Lambda dialogue makes sense in context and without contradictions, is it specific in that same context and is it interesting which is measured as a response that is unexpected or witty? Basically a way to keep conversation going is what is helping make the Lambda slash Mina model mimic human dialogue more naturally. Lambda can make the jump between Game of Thrones and umbrella academy because they are both set in fantasy like worlds, both have an actor that they share which is Thomas Hopper, both have similar motifs and themes and both were very popular shows in 2019, the year of Game of Thrones finale. This is a great example by the way of where neural networks can benefit from a knowledge graph because a knowledge graph would be connecting all of those things together for the model to be able to use in the next predicted piece of dialogue. And finally in this example, the model can also add more of a human touch, humans care for one another and show this through asking how the other is doing, asking for details about the other person to bring them into the conversation more, in this case asking how the person liked the season finale of Game of Thrones. Not only does this add in a feedback loop that is practical to help the chatbot learn from the user's likes and dislikes, it also adds more of a human and pathetic touch, asking for a follow up and mimicking how humans reach out and try to learn and build a relationship with the other person in their day to day conversations. This is likely why there has been some misgivings as to Lambda's sentience and the ethics of what this model and models of similar nature are trying to do. Now Google AI has stressed they are taking ethical precautions with the data going into the model and how the model is used. Google AI has put their money where their mouth is by introducing two additional metrics to help address these ethical concerns and those are safety and groundedness. So safety is a measured against a Google AI generated data set of things like slurs, violent content, gore, profanity, stereotypes, and a lot of other problematic elements that the model then can use to proactively avoid proposing conversational topics that may cause harm or reinforce bias. Grounding assertions, which are not facts, are identified as real world assertions in existing Lambda to human chats because this was only introduced recently. This is then followed by assessing how many external data sources agree with the assertion. This gives a level of confidence that while the model cannot guarantee something is factual, it can give a metric for those using the model to create a threshold of accepting the risk associated with each assertion generated by the model and thereby discarding anything that falls below that threshold. Both safety and grounded metrics are a continuous effort that will always need adjustments. Google is not alone in this. Safety and grounding assertions with triangulation methods will be true for anyone working in these types of models where uncharited human generated data sets in mass are used as training sets. So at the end of the day, Lambda is a very clever facsimile of human conversation that shows promise of making conversational assistance less generic and more helpful. But it is no replacement for regular old human interaction with our laughs, our cries, and our funny faces that we can all experience in real life. So with that, I hope this has helped you understand a little bit more about the new Google AI Lambda and how this might be pertinent for any of the work that you are doing. So with that, I want to thank you very much and I'll catch you next time.