In today's world of artificial intelligence tools, ChatGPT and Google BARD are two prominent models that keep pushing the boundaries of AI. Although both models are designed to perform in terms of processing textual data, there are some key differences between the 2 tools. Let's take a closer look at these differences and discover how they differ.

1. Developer and Training Data: ChatGPT was developed by OpenAI, a leading artificial intelligence research and development company. The model has been trained on a wide range of Internet resources and texts, giving it a good understanding of different contexts and topics. Google BARD, on the other hand, was developed by Google, a company known for its expertise in search algorithms and language processing. Although the exact details of BARD's training data are not publicly known, it is likely that Google BARD benefits from Google's vast collection of textual data, including web pages, documents and other sources.

2. Model architecture: Both ChatGPT and Google BARD use transformer architectures, which are known for their ability to understand complex language patterns. ChatGPT is based on OpenAI's GPT-3.5 model, which consists of multiple layers of transformers and self-supervision to understand the context of the text. Google BARD uses a custom transformer architecture specifically designed for bi-directional encoding of text. This means that BARD can look at both the left and right context of a word while understanding the text, which can lead to a deeper understanding of the meaning.

3. Accessibility: ChatGPT is available as an API, allowing developers to access its functionality and integrate it into their own applications. With the ChatGPT API, developers can create chatbots, develop question-answer systems and even perform text generation tasks. Google BARD is not publicly available at the moment. It is possible that Google BARD will be made available through Google's Cloud AI platform or other services in the future, but limited information is currently available on BARD's accessibility to developers.

4. Applications: Both ChatGPT and Google BARD can be used for a wide range of applications in natural language processing. They can be used for text generation, chatbot implementations, question-answer systems, language translation and more.

Using ChatGPT can be useful when a developer is looking for an instantly accessible API that enables robust and versatile language processing. With ChatGPT's broad training and contextual knowledge, it can be a valuable tool for various language-related tasks.

Given Google's expertise in search algorithms and language processing, BARD could excel in understanding complex searches, semantic search applications and advanced language interpretation.

Conclusion: ChatGPT and Google BARD are both advanced natural language processing models developed by reputable organisations. Although they have similar goals, such as understanding textual data, they differ in developer, training data, model architecture and accessibility. ChatGPT is currently available as an API, while Google BARD has only just become publicly available. However, both models have potential to be valuable in different applications within the language processing space.

Prompt A/B test ChatGPT VS Google BARD

Let's put it to the test with an A/B test. What will be the difference in response from different AI tools? As an example, let's take a much-discussed topic in the online marketing world: Attribution. Which platform will come up with the best explanation?

Start question: What exactly is an attribution model?

ChatGPT:

An attribution model is an analytical approach used to track and allocate the value or contribution of different marketing channels or touchpoints to achieving a conversion or goal. The attribution model provides insight into the sequence and impact of the interactions a customer has with different marketing channels before taking a desired action, such as a purchase, a sign-up or a download.

In the digital age where consumers use multiple devices and channels before completing a conversion, it can be difficult to accurately determine the effectiveness of individual marketing channels or campaigns. Attribution models help marketers understand the relative value of each touchpoint and better optimise marketing budgets.

There are several attribution models marketers can use, each with their own approach to assigning value to touchpoints. Some common attribution models are:

1. Last-click attribution: This model attributes the full value of the conversion to the last touchpoint that took place before the conversion was completed. It ignores the impact of other touchpoints experienced by the customer.

2. First-click attribution: Unlike the last-click model, this model attributes the full value of the conversion to the first touchpoint experienced by the customer. It ignores the impact of subsequent touchpoints.

3. Linear attribution: This model distributes the value of the conversion evenly across all touchpoints experienced by the customer. Each touchpoint gets an equal share of the value, regardless of its position in the customer journey.

4. Time decay attribution: This model gives more value to touchpoints closer to conversion and less value to touchpoints further away in time.

It assumes that touchpoints that take place closer to the conversion have a greater impact.

5. U-shaped attribution (also known as the Bathtub Curve): This model assigns a higher value to the first and last touchpoint and distributes the remaining value evenly across the intermediate touchpoints. It recognises the impact of both the initial contact and the last contact for conversion.

Choosing an attribution model depends on a company's specific needs and objectives. Some companies may choose to use multiple attribution models to get a broader picture of the effectiveness of their marketing channels and campaigns. The goal is to gain insights that can help optimise marketing strategies and improve overall ROI (return on investment).

Google BARD:

An attribution model is a statistical method that measures the influence of different marketing channels on a conversion. This is done by attributing the contribution of each channel to the conversion.

There are different types of attribution models, each with its own advantages and disadvantages. The most common attribution models are:

- Last-click: this model attributes the entire contribution to the last marketing channel a visitor used before converting.

- First-click: this model allocates the entire contribution to the first marketing channel a visitor used before converting.

- Evenly weighted: this model gives each marketing channel an equal contribution.

- Time-decay: this model allocates the contribution of each marketing channel based on the time the visitor has used the channel.

The best attribution model for a company depends on a number of factors, such as the marketing channels the company uses, the company's objectives and the available data.

Attribution models can help companies spend their marketing budgets more efficiently. By knowing which channel contributes the most to conversions, companies can focus their budget on the channels that are most effective.

Follow-up question: Describe the above in 1 paragraph

ChatGPT:

An attribution model is an analytical approach used to allocate the value and impact of different marketing channels or touchpoints to achieve conversions or goals. It helps marketers understand the sequence and contribution of customer interactions with various channels, and enables marketing budgets to be optimised more effectively. Different attribution models, such as last-click, first-click,

linear, time decay and U-shaped attribution, offer different ways to assign value to touchpoints in the customer journey and help gain insights for improving marketing strategies and ROI.

Google BARD:

An attribution model is a statistical method that measures the influence of different marketing channels on a conversion. This is done by attributing the contribution of each channel to the conversion. There are different types of attribution models, each with its own advantages and disadvantages. The best attribution model for a company depends on a number of factors, such as the marketing channels the company uses, the company's objectives and the available data.

Conclusion Alona:

The answers from both AI tools give a solid answer to the question asked and also to the follow-up question asked. They both seem to be well capable of the same functionalities. ChatGPT, however, generally seems to give more comprehensive answers to the initial question asked. Working with bullet points, a factor by which you can often recognise an AI-generated text, seems to be common to both tools.

However, the answers still differ from each other. For example, there are attribution models in one answer which are not reflected in the other AI's answer. In our view, this does not mean that one answer is wrong, or better than the other. However, it again labels the conclusion that AI tools know a lot, but do not always provide everything or the answer you are looking for. A human eye remains irreplaceable for that reason!