The A.I. Race Timeline
Summary of "A Look at the Latest Developments in Generative | AI-SwissCognitive"
“The race starts today,” Microsoft chief Satya Nadella said as the company announced their new A.I. integration
A few days ago, I wrote an article
Even after it generated a good response from the readers, somewhere I am not yet convinced by one thing. Events such as this "rivalry" didn't started overnight. Though many would suggest that both companies tried to show their new AI stuff rather quickly, it looks superficial when we see how quickly the "revolution" happened.
The fact is, it was not a single event. The AI “hype” began at the end of the previous year. Even when this "A.I." race was extremely hyped up by the media, there was no concise timeline on how this started, and how it turned out.
Many of us believe the timeline is something as such: OpenAI released ChatGPT, Microsoft saw the opportunity and collaborated, Google got worried and started their own AI search development, and here we are watching the "race" in anticipation.
But life would have been so simple if this had been the case. The truth is, there's much more to it.
So to bring in more clarity, I wrote an extensive timeline just the next day after I posted the above article on my newsletter. This timeline I published in form of an article on AI-SwissCognitive titled "A Look at the Latest Developments in Generative AI-SwissCognitive." SwissCognitive describes itself as the world's leading AI network and a global community of business leaders and AI experts. Since it has a wide media reach, I found that as a perfect place to publish my article.
Here is the summary of the post that I wrote there along with the key insights. Check the original article (here) for better context.
Introduction
Lets get the basics clear. Generative AI is a branch of artificial intelligence that can create new content such as text, images, music, etc. based on existing data and user input.
The article provided a timeline of the major events and developments in generative AI, focusing on the rivalry between Bing and Bard. Two powerful AI search engines, using generative AI, aspire to provide better and more personalized results to users.
Microsoft created Bing, which (now) uses a large-scale language model called BingAI to generate natural language responses, summaries, suggestions, and so on. Bing AI was launched in 2023 and had since added many features such as voice search, image search, video search, news summaries, etc. Bing claims to have over 2 billion users worldwide. Moreover it says that it ranks first in terms of accuracy and speed.
Bard was developed by Google and uses a decentralised network of nodes that can generate content using various modalities, such as text, image, audio, video, etc., based on user preferences. Bard is Googles response to the new Bing AI browser.
Key Points
November 30, 2022:
OpenAI releases ChatGPT, a natural language processing tool that can create content, images, and even code on demand via conversations with a chatbot. The AI-driven tool is built on OpenAI’s GPT-3 family of models, which are trained on a large corpus of text from the internet.
February 6, 2023:
Google announces Bard, an experimental conversational AI service that can access the entire Internet and answer natural language queries. Google claims that Bard is more advanced and responsible than ChatGPT and that it uses information taken from the internet to provide fresh, high-quality responses. Bard is also built on Google’s own AI models, such as BERT and T5.
February 7, 2023:
Microsoft unveils the new Bing AI, a chatbot powered by OpenAI’s ChatGPT that can also access massive data sets of online data for user queries and prompts. Microsoft also announces the integration of ChatGPT into its Office products, such as Word, PowerPoint, and Outlook. Microsoft says that Bing AI is faster and more accurate than Bard and that it can generate more diverse and creative content.
February 10, 2023:
TechRadar compares the features and performance of Microsoft Bing AI and Google Bard and declares Bing as the winner for its faster and more accurate responses. The article also praises ChatGPT for its versatility and innovation and suggests that users try all three services to find the best fit for their needs.
December 2020:
Google asks its employees to test possible competitors to ChatGPT, such as BERT and T5, two of its own AI models that can process natural language queries. Google also starts developing Bard, a new conversational AI service that can access the entire Internet and answer natural language queries.
January 2021:
Google tests Bard internally with a small group of employees and collects feedback and data to improve the service. Google also works on ensuring that Bard is ethical and responsible and that it does not generate harmful or misleading content.
February 2021:
Google announces Bard, an experimental conversational AI service that can access the entire Internet and answer natural language queries. Google claims that Bard is more advanced and responsible than ChatGPT and that it uses information taken from the internet to provide fresh, high-quality responses. Google also says that Bard is built on its own AI models, such as BERT and T5, which are trained on a larger and more diverse data set than ChatGPT.
Google CEO Sundar Pichai sends an email to employees, highlighting the testing and rollout timeline of Bard and emphasising the importance of AI ethics and responsibility. Pichai also says that Bard is a milestone for Google’s AI research and that it will enable new possibilities for users and society.
Closing Note
Generative AI has always been an interesting idea for me. It is a powerful and promising technology that can transform the way we search, radically change how we communicate, change the way we learn, and, best of all, create a new way to "create." Bing and Bard are two examples of how generative AI aspires to provide better, more personalized, and more human-like (they are trying their best) search results to users across various domains and modalities.
However, as we all know, generative AI presents some challenges and risks that must be addressed (very) carefully. Technical limitations such as scalability, reliability, security, etc. need to be overcome when improving and expanding generative AI capabilities. But apart from the technical problem, what lies ahead of us is the problem of how AI gives its response in the first place. AI has recently been shown to be far more capable of spreading hate speech, disinformation, false news, and other forms of misinformation. This seems impossible to solve now, so taking some precautions from our side is important.
The future of generative AI is bright and exciting, but also uncertain and complex. It depends on how we use it wisely and ethically for the benefit of humanity and society.
