Facebook
Twitter
LinkedIn
Whatsapp
Google Gemini Vs. OpenAI ChatGPT - The World War
By - Kaustubh Katdare • 10 months ago • 11.1k views
The entire world got introduced to a new term a few months ago; and it's already changed the way we work. The term is LLM aka Large Language Models.
OpenAI, with its ChatGPT is already dominating the game and it's threatened the search monopoly that Google's held for large part of the 21st century.
But not many know that the core technology that ChatGPT uses was actually developed by Google and open sourced a few years ago. Now Google wants to reclaim its dominance in AI world and has tasked Demis Hassabis (CEO, Google DeepMind) to accelerate Google's effort in that direction.
Back in 2016, Google's AI program called AlphaGo, developed by DeepMind's engineers made history by defeating a world-champion player of the board game "Go". Hassabis, who founded DeepMind says that their upcoming AI system Gemini will be more capable than OpenAI's ChatGPT.
The Gemini is still under development and expected to be ready over the next few months. Hassabis is confident that Gemini will combine the techniques that AlphaGo used combined with LLM training to build a system better than ChatGPT.
Both OpenAI (with investment from Microsoft) and Google have deep pockets to train their algorithms on latest hardware. These projects cost hundreds of millions of dollars.
Google's being extra cautious this time because it rushed out its own Chatbot called 'Bard' and it failed miserably.
It'd be interesting to see how these programs develop. A few influential AI insiders have called for a pause on the development of more powerful algorithms to avoid creating a monster that ultimately threatens human race.
I'd like to know from our fellow CEans what they think about the AI race. Is this going to benefit humans or destroy them?
Whatever is the outcome, which company will lead the race? Let me know in comments below.
Replies
Note: Only logged-in members of CrazyEngineers can add replies.
Other questions
I'm considering switching to a mechanical keyboard like NuPhy Air 75 or 96, but wondering if the keys are harder to press.
Been watching a lot of videos on YouTube about how easy and comfortable it is to type on a Mechanical Keyboard.
I'm currently used to typing on Apple Magic Keyboard. If anyone of you have made a switch from Apple Magic Keyboard to Mechanical Keyboard, please let me know your experience. Thank you!
Can an engineer become a doctor? If you got forced into studying engineering and want to go back to your first love - medicine (MBBS), what are your options? Let's discuss.
Yes, an engineer can become a doctor. But it won't be easy. Engineering degrees worldwide are mostly 4-year degree courses and medicine too requires an average of 4-5 years followed by mandatory internship. Not everyone has the patience, money and time to dedicate a decade only to learning.
If you are an engineer or engineering student who wants to...
I was recently asked if a data engineer can become a data science. How difficult is it for data engineers to switch career to data scientist and what type of training is involved. Looking for responses from data engineers and data scientists.
A data engineer can become a machine learning engineer with proper training. Keep in mind that being a data engineer is not a prerequisite for becoming a machine learning engineer.
I find this question very similar to Can data engineer become a data scientist?. Do take a look at that discussion as well.
Data engineering is an emerging field in the big-data domain and there's a growing demand for data engineers.
A typical data engineer role involves collecting data, creating data pipelines, verifying the data, correcting it and...
Can data engineer work from home? Or do they have to be physically present at the work-site to get their data engineering job done? Let me answer this commonly asked question.
A data engineer can definitely work from home. The role of data engineer does not require the engineer to be physically present on-site. Why? because 99% of the data engineering work happens in the cloud. All you'll need is a strong Internet connection and credentials to login to your cloud accounts to build data pipelines.