Chatbots are gaining popularity as a result of advancements in AI and NLP, making them useful in a variety of contexts. An example of such a chatbot that works in tandem with Google Search is Bard AI. Like other conversational AI chatbots like ChatGPT and Bing Chat, Bard is powered on Google’s LaMDA language model. Yet, Bard stands out from its rivals in a number of significant ways.
Bard’s LaMDA language model sets it apart in a number of ways, the most significant being its ability to create completely original text. This means it can participate in natural-sounding discussions on a wide range of topics, as well as assist with creative projects, explain complicated concepts, and synthesize data from a wide range of online sources. For instance, Bard can assist you in finding recipes that use the components you already have on hand, or it can explain the latest findings from NASA’s James Webb Space Telescope to a child of nine.
It’s not only the easy questions that Bard can answer; he’s also good at the more in-depth ones, such “Which instrument is easier to master, the piano or the guitar, and how much practice does each require These are the kinds of queries that can be challenging to answer, even for humans, and usually need some digging. Google, however, claims that Bard can summarize hundreds of webpages into several paragraphs, which may then be displayed at the very top of search results.
The language model is another major distinction between Bard and ChatGPT. Both rely on huge language models as their foundation and boast improved performance in free-form discussions, although they employ different models. Bard employs Google’s LaMDA model, whereas ChatGPT utilizes GPT-3.5. The effectiveness of chatbots may be impacted by this variation, as the quality of replies varies greatly depending on the training data used to construct the language model.
For instance, the year 2021 is the end-of-knowledge for ChatGPT. After that point, it can give you entirely fabricated details regarding incidents you inquire about. Similarly, if there are any biases in the data used to train the model, the results will be inaccurate. The delay in Google releasing Bard to the public may be attributable to these restrictions. The propagation of false information might be facilitated by Bard’s ability to provide replies that seem authoritative but are actually incorrect.
In order to solve this problem, Google has been hard at work developing a language model based on reliability, security, and veracity. Google used “public conversation data and other public online papers” to create a 1.56 trillion-word dataset for LaMDA’s training. The business has tailored LaMDA training for conversational activities with the intention of producing replies that are both logical and engaging when placed in the context of a prompt. LaMDA may actively seek information from external sources in real-time to enrich its replies, hence increasing their factual correctness.
Google has not yet said when Bard will be made available to the public, although it is widely anticipated that this will happen within the next several weeks. Google may first limit access to a select group of people, much as the Bing Chat waiting, due to the high demand for conversational chatbots. Bard, like other chatbots built on machine learning, will use up a lot of Google’s computer resources. It has been speculated that the cost to the business for each chatbot answer will be 10 times that of a standard search. The corporation may be able to scale these expenses over time by limiting access to and use of Bard to a select group of users.
As compared to ChatGPT and other chatbots, Bard stands out due to its capacity to acquire real-time internet data. Because of this, it has the potential to become a useful resource for resolving intricate issues and facilitating the discovery of specific data. While this has many benefits, it also creates new difficulties in policing false information.
