LaMDA Software

 LaMDA Software

Introduction:

·   LaMDA is a family of conversational large language models or AI-driven chatbot developed by Google. Originally developed and introduced as Meena in 2020, the first-generation LaMDA was announced during the 2021 Google I/O keynote, while the second generation was announced the following year. it's basically an advanced form of a chatbot.         

·        The acronym stands for "Language Model for Dialogue Applications". Built on the seq2seq architecture, transformer-based neural networks developed by Google Research in 2017, LaMDA was trained on human dialogue and stories, allowing it to engage in open-ended conversations.

·        Google will release LaMDA to small groups of people in batches, test the app, and work on feedback to make it better, before making it available to the general public.

·        Chatbots are virtual machines that act as advisors, consultants or assistants who talk to internet users in real-time. Usually, a human is behind the scenes controlling the chatbot but nowadays they need little to no human intervention. They come equipped with specialised algorithms that are tailored to enable conversations with users as per their requirements.

Why We Use This Technology/Purposes of LaMDA

We Can Use This for Following Purposes:

• §  Content marketing – providing knowledge and information from various fields.
• §  Customer service
• §  Notifications – personalized reminders
• §  Location of sites
• §  Purchase and ordering of products (e.g. food)
• § Product consulting – recommendations based on preferences
• §  Competitions – Receipt of applications
• §  Entertainment

Latest Updates/Updated Versions

Ø At Google I/O in 2022, Google revealed "LaMDA 2," a more advanced version of the conversational AI. This time, Google allowed "thousands of Googlers" to test it — partly to reduce instances of problematic or offensive answers. LaMDA 2, by all appearances, has much of the same features and functionalities as the original, operating as a sophisticated general purpose chatbot.

Ø However, Google's demonstration at the time was more focused, pointing out technical demos like keeping a conversation on the topic, generating lists tied to a subject, or imagining being in a specific place.

Ø LaMDA's abilities certainly aren't limited to these workflows, they're just one avenue that Google wants to take to test and refine how LaMDA works. Google reportedly plans to expand testing to larger groups over time through an AI Test Kitchen.

Google LaMDA: A Conversational Intelligence Revolution

v Pivoting on conversational technology, many tech giants unite day and night to pull back the drawbacks and limitations that still exist in voice assistants and robotic chatbots.

v The pre-coded set of options and responses delivered by them over one-on-one user conversations are yet not 100% efficient and that ultimately, allows us to find them useless at most times.

LaMDA: The Internal of the Technology

v LaMDA is built on Transformer which is a neural network architecture and open-sourced in 2017. The recent algorithms of BERT and GPT-3 also work on Transformer.

v The architecture uses an attention mechanism and allows the language model to predict text(s) following the previous words/conversations related to each other.

v As human conversations are distinctive and chaotic, LaMDA is prepared to handle such unexpected situations where a conversation initiates with one topic and in a few moments ends with a different one.

v With LaMDA tackling communications with a range of connecting topics, it will bring revolutionary changes within Chatbot Technology, making chatbots more humanly- intelligent, interactive, and engaging with people than ever before.

v Till then a lot of developments and testing cycles are being conducted so that LaMDA does full justice to its invention and leaves no stone unturned to augment the conventional language and conversational models.

v Demonstrations on LaMDA’s competence are being conducted and in the recent one, LaMDA was seen conversing with its human counterparts being the planet Pluto and a paper plane.

OpenAI’s ChatGPT Vs Google’s LaMDA

Ø LaMDA and ChatGPT are the two main competitors in the field of AI chatbots. The Language Model for Dialog Applications, or LaMDA, is an acronym for supervised learning and is trained using dialogue data. ChatGPT is a pre-trained model developed on the GPT-3.5 architecture and optimized for web documents.

Ø ChatGPT, a chatbot that was recently released, has won all the praise in the field of conversational technology. However, another market player was released before OpenAI’s chatbot project. This one deserves a lot of attention. LaMDA, Google’s conversational chatbot, was praised for its ability to be called “sentient” even by its engineer.

Ø The Language Model for Dialog Applications (LaMDA), a Transformer-based neural-language model, has up to 137B parameters. It is pre-trained using 1.56 Trillion words of publicly accessible dialogue data and web documents. The model can be fine-tuned using three metrics: Safety, Quality, and Groundedness.

Ø LaMDA’s progress can be quantified by collecting responses from the pre-trained model, fine-tuned models, and human raters (i.e., human-generated replies) to multi-turn, two-author dialogues. The responses are then evaluated on a series of questions against the above-described metrics by other human raters.

Ø ChatGPT, on the other hand, is built upon the GPT-3.5 architecture and has 175B parameters. GPT-3.5 consists of three models. Code-DaVinci 002, the base model, which is used for code completion tasks. 

Ø Both LaMDA and ChatGPT are highly capable language models, but LaMDA takes things a step further in terms of responsible and relevant responses. Its built-in mechanisms for Safety, Quality, and Groundedness ensure that LaMDA outputs only meaningful responses, free of nonsense. ChatGPT, on the other hand, benefits from GPT-3’s massive training dataset and sophisticated architecture, allowing it to generate more nuanced and diverse responses and perform better on more complex tasks such as content creation, translation, and code generation.

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