Comparing Bing Chat , Open AI chat GPt and Google bard: Chatting with BOB

First half of 2023 has been exciting month in terms of further developments in the AI ecosystem, while Meta launched new Llama version. MS integrated Open AIs chat GPT capabilities into its Azure stack of services as well as power apps, flows and AI generator. Microsoft also launched Bing Chat public and Enterprise version rapidly scaling GPT 4 based chat searches to an enterprise grade tool with many additional capabilities. Google Bard which works on PaLM - 2  is also improving gradually , I decided to compare Bing , Open AI chat GPT and Bard (BOB) form an end user perspective with publicly available versions (Not Enterprise versions). For the purpose of this comparison I have ignored any open AI chat GPT plug ins which may extend its functionalities and allow it to perform some more tasks.   GUI:- While Open AI GPT has still been running on same GUI from last 6 months or so its other counterparts have not only done some improvements but also used their ecosystems to integrate their search Bots with it Open AI CHAT GPT has prompt input, output area as well as historical view. Whereas if we look at Bing Chat it has added image and voice inputs with text for prompt input . It provides latest web indexed up to date information post searching from internet and citations as well as follow up prompt suggestions related to topic. One of the key drawback of Bing chat is that its currently locked in with MS edge and doesn’t work with any other browser. Bard is also well integrated with google search. Its prompt input supports both text and voice (No image yet) but you can provide an image of video for it to analyse via URL for example you can directly give it a you tube video link and ask Bard to explain same .Once the output is provided there is option to convert response text to voice as well as share or google and provide feedback. The output of each prompt is provided in 3 draft variants and any one of those can be further explored.  Up to date information: Both Bing chat and Google Bard use indexed internet UpToDate information to provide prompt response whereas open AI has still been restricted to 2021 data making Bing and Bard immediate first choice for users to get their answers based on live information.   Citations: One of the key struggle for organizations and users developing AI use cases is to get the clarity and source of information behind the output provided and Bing and Bard has now taken next step by providing link to citations of their source information whereas in Open AI chat users are still left wondering form which exact source the information has been provided.   Custom doc training: Bing and Bard have provided easy way to provide response on custom user docs , Bing can access links to a web page or a document which Is opened in MS edge  and provide summarization , answers on those document which is also possible for Bard but same is not an off the shelf functionality with Open AI chat GPT.   Image and Speech handling: While Open AI GPT is still a text-based interface even after having a sibling Dall E available for image generation its synergies have not been used. Microsoft Bing has combined both images and text and not only it can recognize images but also use Dall-E to generate images for various prompts. Bing is also able to understand speech prompts. Google bard has also extended its functionalities to speech where it is able to understand speech prompt and also read out the outcome to users. These functionalities represent true inclusiveness for AI which will enable Bing and Bard to reach larger section of users.   speed & Temperature control: All 3 chats support temperature control helping response to be either more factual or more creative. Since Bing and Bard are actually using internet search as well they seem a bit slower then Open AI GPT which starts generating response as soon as prompt is submitted.   Summary: Overall Microsoft has leaped ahead in using its mighty ecosystem and integrating GPT4 based bing chat with its various platforms thus creating a perfect package for organizations which can be adopted rapidly without much work for their custom data training. Its key to highlight while GPT 4 has been found to be better then GPT 3.5 , recent study by UC Berkley and Stanford which compared two lates versions of GPT 4 released between March and June have raised concern about GPT 4s performance and accuracy for mathematical calculations . Google Bard has also been catching up rapidly and available for enterprise use by Google workspace. Open AI GPT has been released for commercial use with various plans and is open for enterprise use either directly or via API integration.

 Keeping an Eye on AI

Recent announcements and developments in the field of Generative AI has triggered a race to “AI first” systems. Within 2 months over 100 million users rushed to experiment with chat GPT and all large product organizations have been making announcements about new products, use cases and LLMs. This creates a ripple effect for business and operations for faster adoption and especially fear of missing out. What also need to evolve at same speed and urgency is local and global governance for these AI systems and use cases at both technological as well as ethical level. While entire concept of AI is being independent in creating, learning, and taking decision, we have sufficient and significant examples to indicate that we need to keep an eye on AI systems and design them in such a way that enables monitoring of various key parameters like efficacy, bias, usage of data and adherence to various legal frameworks.

Recent announcements and developments in the field of Generative AI has triggered a race to “AI first” systems. Within 2 months over 100 million users rushed to experiment with chat GPT and all large product organizations have been making announcements about new products, use cases and LLMs. This creates a ripple effect for business and operations for faster adoption and especially fear of missing out. What also need to evolve at same speed and urgency is local and global governance for these AI systems and use cases at both technological as well as ethical level. While entire concept of AI is being independent in creating, learning, and taking decision, we have sufficient and significant examples to indicate that we need to keep an eye on AI systems and design them in such a way that enables monitoring of various key parameters like efficacy, bias, usage of data and adherence to various legal frameworks.   •             Hallucinations handling :  Its not unlikely for humans to speculate or make statements based on assumptions. With human like creativity its obvious that AI has also learnt to provide made up answers when it doesn’t have needed facts and data on the topic, this is called hallucination. In near future there will be more development to handle hallucination, but two things are key here first of all to train AI model with as much data as possible and second to provide users a feedback options so that they can report an hallucination of the system to owners for corrective actions and interventions.   •             Watermarking:  With AI creating human like artifacts its becoming increasingly important to differentiate what’s generated by AI and what’s not hence watermarking the content created by AI should be one of the basic and standard principles.     •             Sustainability:  In an interview in Q1 2023 Sam Altman, Open AI CEO had mentioned that cost of cloud infrastructure training and running chat GPT is eye watering. Open AI and all such organizations are using thousands of chips and other hardware (directly or indirectly) as well as energy which have significant and probably equally eye watering impact to environment ranging from carbon footprint to e waste generated with out of use hardware. This makes its critical that all AI use cases in business and IT consider possible impact on sustainability vs the benefit such AI systems will generate. A technological eco system need to be evolved which can help us in better review and decision making of environmental impact of any It system.   •             Performance Monitoring:  At the current fast paced AI adoption into business at times the ultimate business functionality is of paramount importance but with that we also need a comprehensive approach for performance monitoring and benchmarking tailored to each system. While its good to rely on product vendors promises, we need to embed our own performance review and benchmarking which will lead to overall efficacy improvement for system and models.   •             Human in loop: At the early stage of AI adoption for any system and process we must keep human in loop to accept or reject the outcome generated by AI. This will help in setting up accountability on the team or organization for the decisions taken by AI .   •             Transparency: We discussed watermarking in this document but that’s invisible to human eyes and mostly detected by system whereas its critical that all users know and understand what part of their decision or outcome they have been handed over is coming from AI hence systems need to be designed for giving clear warnings and disclaimer to business users and also how has that been arrived

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