All About Google Bard: The New Disruptor in Conversational AI

Conversational Artificial Intelligence (AI) has been making waves in the technology industry, especially with the introduction of Microsoft’s ChatGPT. However, this chatbot has now found stiff competition with the announcement of a new rival that has been developed by Google, known as Google Bard. The next few months will witness how Bard will fare against ChatGPT in producing optimized, relevant results to all kinds of user queries.

The announcement of Google’s Bard comes close at the heels and in direct response to ChatGPT, developed by Microsoft in association with OpenAI, an AI research and deployment foundation. While the underlying technologies may seem similar to the untrained eye, there are subtle differences between the two in terms of potential use and computational power.

In this article, we will be focusing on Google’s Bard AI chatbot and how test runs with a limited group of experienced testers have revealed its exciting capabilities. We will also dive deep into what went into making this chatbot and how it may change the way we scour the internet for information.

What Is Google Bard?

In its bid to reinvent web search, Google announced today that it has been developing a chatbot for text generation in response to the most random user queries, utilizing generative AI technologies. The chatbot, Google Bard, is expected to revitalize research activities in education, business as well as our daily lives by integrating conversational AI with knowledge generated from real-time web-crawled data.

Google is strategizing the enhancement of its search capabilities by integrating the conversational AI features of Google Bard. The expected result of this is that billions of bytes of data representing multiple perspectives will be synthesized in real time and users will be served with the most precise and distilled information in formats that are easy to digest.

About six years ago, Google repurposed its organizational strategy by focusing on gathering information from disparate sources and making it easily accessible through AI applications. Similar to ChatGPT, Google Bard has also been aimed at producing profound technological opportunities to unlock the potential of people, communities, and businesses.

Customer Success Story: Daffodil develops voice-first super application for American tech firm.

Bard utilizes Google’s breakthrough conversational AI technology known as LaMDA, or Language Model for Dialogue Applications (LaMDA). LaMDA is essentially a family of conversational neural language models and has been developed under Google’s AI research-based sister company, DeepMind.

How Is Google Bard Different from ChatGPT?

The main difference between Google Bard and ChatGPT lies in the sources of data that these chatbots have been trained on and garner their information from. While ChatGPT has been trained on all of the world’s data ending in 2021, Bard pulls information from the web and provides high-quality responses leveraging fresh, real-time data.

Bard has presented another opportunity for Google and other leading technology providers to deepen our understanding of information synthesis and make our engagement with data more efficient. The end goal of developing conversational AI products such as ChatGPT and Google Bard is to optimize the generation of the most useful information that is at the same time actionable as well.

Image: Google Bard generates an AI response and the most relevant search results parallelly

How Can You Access Google’s Bard?

For the time being, Google Bard will be launched as an experimental conversational AI service and at some point will be available to be consumed as an Application Programming Interface (API). It will be rolled out as part of the existing services under Google Cloud Conversational AI, a collection of tools, solutions, and APIs.

Using the Google Bard API, enterprising individuals and businesses will be able to build highly intelligent conversational AI platforms and applications. These applications will also be able to perform advanced analytics to produce highly concise insights from web-crawled information.

While a time frame for general availability has not been explicitly defined by Google, Bard will be integrated into its search engine as well. For broad searches where there are multiple potential answers to a question, the search engine will be able to combine several perspectives to come up with a distilled answer.

In its announcement, Google described the scenario wherein one could ask the question: “ How to explain new discoveries from NASA’s James Webb Space Telescope (JWT) to a 9-year-old?”. As a response to this question, Google Bard’s underlying neural language models could combine multiple subjective opinions with web-crawled results to provide creative answers. It could come back with responses such as:

• “In 2023, the JWT helped discover a number of round, green-colored galaxies nicknamed ‘green peas’.”
• “The telescope captured images of galaxies 13 billion years old, meaning that the light from these galaxies may have taken 13 billion years to reach us.”

All of the above applications of Google Bard have been made available to a limited group of trusted testers to identify potential issues that users could face. Following fine-tuning, regression testing, and removal of various biases and kinks, the chatbot will be released to the public via the existing Google search engine and as an API.

What Is The Primary Technology On Which Google Bard Is Built?

Google developed the large language model LaMDA two years ago with next-generation language synthesis and conversation abilities, which served as the underlying technology for the development of Google Bard. However, it used a lightweight language model version of LaMDA for the same, which will ensure a faster deployment for its release to the general public.

At the center of innovation under the LaMDA research project are three tenets: safety, groundedness, and high-quality conversations. While processing data in real-time from various sources across the internet, Google Bard has also been trained to filter out biases that might produce less-than-ideal results.

LaMDA has been trained to exceed the computational capabilities of any existing pre-trained model in the market. It has been trained in multiple dimensions and across all model sizes for automated fine-tuning of results. With further fine-tuning, the gaps in intelligence with human capabilities in terms of safety and groundedness will also be bridged.

What AI Models Is Google Bard Trained On?

Google’s conversational AI chatbot Google Bard is primarily trained on four major models which include Large Language Models, Transformers Models, Generative adversarial networks (GANs), and Diffusion Models.

Large language models: It has emerged as a key technology in the field of generative AI that allows users to generate realistic and coherent text responses. These models are trained on massive amounts of text datasets and are capable of producing high-quality outputs that can range from simple text responses to writing articles, stories, and even computer code.

Transformer Models: Transformer models, such as BERT (Bidirectional Encoder Representations from Transformers) and MUM (Masked Unit Model), are a subset of deep learning models. These pre-trained models are designed to process and understand natural language text. These transformers have been trained on a corpus of text datasets that allows them to generate robust representations of the correlation between words and their context in a sentence with unprecedented accuracy. Furthermore, these representations can be fine-tuned for specific NLP tasks such as sentiment analysis, question answering, and language translation.

Generative adversarial networks (GANs): It is used to generate visual and multimedia content from images and texts. It consists of two neural networks namely a generator and a discriminator. The generator network produces new content similar to the original training data whereas the discriminator network evaluates the quality of output produced by the generator to the actual training dataset. During this adversarial training, both algorithms help train the other, the generator learns which results are more accurate while the discriminator learns which results are most likely to reflect realism.

Diffusion models: These models are based on predicting the adoption of new products or ideas in a market as well as simulating the spread of information in a network. By Integrating these models into generative AI systems, the systems can produce solutions that are more aligned with real-world trends and patterns.

For example, in the context of text generation, diffusion models can be leveraged to model the diffusion of topics, ideas, and opinions through a network of individuals. As a result, the generative AI system will be able to produce text that reflects the current state of the network and is more grounded in reality.

How Are Other Tech Giants Planning To Compete With ChatGPT?

Within months of ChatGPT’s debut in November 2022, it had become a global sensation. It was utilized by millions of students, technology industry insiders, and general enthusiasts to write essays, and poetry, solve coding challenges, test code, develop travel itineraries as well as conduct guerilla therapy.

The launch of ChatGPT has triggered a worldwide AI arms race, wherein technology leaders have been rushing their AI research to develop tools like ChatGPT. Various alternatives to ChatGPT are only in their nascent stages or have biases, and produce mixed results at best. Companies like Alphabet, Amazon, and Meta as well as independent establishments such as Haptik and Morph.ai have joined the race:

IBM’s Watson Assistant: Watson was developed by IBM and can understand natural language with considerable accuracy and was one of the first publicly available chatbots. It was mainly implemented for evaluating the performance of other virtual assistants by examining their conversational traffic.

Meta’s Galactica and BlenderBot: While Meta’s BlenderBot was claimed to be a powerful chatbot that can produce results for nearly any topic, the reality was far from what was promised. The technology flopped and efforts were shifted towards Galactica, a new large language model meant to assist scientists in their research efforts.

Haptik’s Commerce Assistants: Haptik was developed as a conversational commerce product to utilize Natural Language Understanding (NLU) to provide personalized recommendations and enhance customer satisfaction. It can be deployed as a WhatsApp chatbot that can be leveraged to respond to customer queries and in turn, generate sales.

Morph.ai for CRM/CX: Morph.ai builds chatbots for Customer Relationship Management (CRM) that are aimed toward customer onboarding and Customer Experience (CX) enhancement. It enables the development of conversational AI chat-flows for lead generation, customer support, booking of services, and various e-commerce use cases.

Amazon Lex: The Amazon Lex chatbot leverages NLU and Automatic Speech Recognition (ASR) and is designed for a continuous streaming conversation. It understands the user’s goal during the conversation and keeps adjusting the responses until that goal is met.

Baidu’s Melody: The Melody chatbot developed by the Chinese search engine provider Baidu integrates with the Baidu Doctor App. It helps patients book appointments, ask doctors questions and search for appropriate healthcare centers. Additionally, it can be utilized by doctors to get suggestions related to patient histories and get feedback.

ALSO READ: 10 Uses of Artificial Intelligence in Day to Day Life

What Does The Future Of Conversational AI Look Like?

The immediate future of conversational AI technology is expected to witness several chatbots with increasingly more advanced capabilities. Its implementation across industries such as healthcare, e-commerce, messaging, social media, research, and so on will keep expanding in popularity and degree of adoption.

The launch of ChatGPT and now Google Bard, which are currently at the bleeding edge of the conversational AI domain has further pushed tech conglomerates around the world to expedite their AI research efforts.

More companies are pushing the production of market-ready text-generation applications in pursuit of the AI arms race. If you are looking to build AI-enabled voice assistants for your specific business requirements, you can book a free consultation with us to know how.

Originally published at https://insights.daffodilsw.com.