The battle of chatbots and other AI-powered assistants rages on. ChatGPT came but it could not conquer the frontier it was supposed to. With numerous shortcomings, data infringements, plagiarism issues, and many more associated with using AI; it landed in a zone it was supposed to avoid.
Google also entered the race to create AI models and software. It has been working hard to displace OpenAI’s ChatGPT and did so with the introduction of Bard. Now Google has launched another AI model which is more advanced than ChatGPT.
Presenting Google Gemini – promised to be more advanced and much better than ChatGPT
Google’s Deepmind recently announced Gemini as its new AI model. It will compete with OpenAI’s ChatGPT. Both Deepmind and GPT are good examples of generative AI. They learn to look for patterns of training information given to them. They generate new data (like pictures, words, text, or other media-based outcomes).
A thing worth noting is that ChatGPT is a large language model (LLM) that has a core focus on text-based outcomes. The software works as a web app for conversations. They are based on the neural network known as GPT. That network is trained on monumental amounts of text-based data.
Google has also made a conversational web app known as Bard. It differs from ChatGPT as it is based on the model LaMDA (dialogue trained). Now the tech giant is upgrading it based on its newest product Gemini.
What makes Gemini different from previous models of generative AI?
Gemini is different from earlier models of generative AI (especially LaMDA). It stands out because it is a multi-modal model. The model works directly with multiple input and output methods and modes. It also supports both text-based input and output, plus audio, images, and video as well.
A new abbreviation has emerged i.e. LMM (meaning large multimodal model). It is different from LLM (large language model). In September last year, OpenAI announced the launching of a model known as GPT-4Vision. That model can easily work with images, audio, and text. Yet, it is not a complete multimodal model as it differs from Gemini.
ChatGPT-4 can work with audio inputs to give speech-based outcomes (Thanks to GPT-4V). However, OpenAI confirmed the modus operandi involved the conversion of speech input into text first via Whisper. It used another deep learning model for converting speech into text, and then text to speech.
ChatGPT-4 can also convert text to speech through a different model. However, GPT-4v works solely with text. ChatGPT-4 can also produce images but only through text prompts which are passed on to a separate deep learning model Dall-E 2. It converts descriptions in text into images.
In comparison, Google designed Gemini as a truly multimodal AI software. It means that its core model can directly manage a wide array of inputs of various kinds, especially audio, images, text, and video. It can also output them directly easily.
What is the verdict of Experts?
Experts of custom mobile app development in Dallas find a theoretical distinction between both approaches. Yet it should be noted. The conclusion given by Google’s technical report plus other updated tests shows Gemini’s current version (1.0 pro) lags behind GPT-4 It resembles the capabilities of GPT 3.5.
Did Google announce a more robust version of Gemini?
Marketers from a custom website development company have followed both Google and Gemini. Google recently announced Gemini’s most powerful version known as Gemini 1.0 Ultra. It did present some good results, and it proved itself to be stronger than GPT-4. However, it is not easy to assess that yet. There are two reasons behind it:
· Google hasn’t released Gemini 1.0 Ultra yet. Results need to be validated independently.
· Google chose to release a deceptive video demonstrating Gemini’s capabilities.
Bloomberg initially reported that the demonstration video was not conducted in real-time situations. The model had to learn certain tasks earlier. It also had to be given a sequence of still images for image and movement recognition.
Does it have a promising future?
Despite these issues, Gemini along with other large multimodal AI models are not boring. These models take generative AI steps ahead to make strides. Gemini has modern capabilities and can amplify the competitive AI tools’ landscape.
It can get ahead of GPT-4 because the latter was trained on 500 billion words. It was trained on top-notch and publicly available text.
Deep learning models’ performance runs on two key factors:
1) The model’s complexity
2) Amount of training data. It has led many to question the way further improvements can be made.
This is because a lot of companies have almost run out of data to use for training large language models.
Yet multimodal systems have opened up new avenues of training data. Such kind of data is present as audio clips, images, and videos.
Why is Gemini at an advantage over others?
AI models like Gemini can be trained directly on all this data. They are also likely to have more capabilities in the long run. Models that are trained on videos can develop more sophisticated internal representations of many things. Even physics concepts can be easily explained thanks to these models.
Animals and humans also have a basic understanding of movement and physics, especially in terms of casualty, gravity, and other physical phenomena. Although they may not know the definitions but they are aware of what is happening around them.
Is the competitive AI landscape going to improve?
A lot of technology enthusiasts, experts, and professionals are excited. This means a lot for Competitive AI.
In the past year, numerous generative AI models emerged. OpenAI’s GPT models are the ones leading the race for AI. They were able to demonstrate performance levels that outpaced that of other models. Other models could not approach such performance levels.
The entrance of Google’s Gemini signals the entrance of a major competitive player that can help drive the field ahead. Open Ai is indeed working on GPT-5. Everyone can expect it to be multimodal and showcase some exceptionally remarkable new capabilities.
Over to You
A lot is being said and showcased. Both consumers and tech experts are keen to see the emergence of large multimodal models that are open-source and non-commercial in use. This can complement the release of Web 3.0 and improve the internet in the coming years.
Technology professionals also like some features of Gemini to be implemented in real time. For instance, Google announced a version of it known as Gemini Nano. It does sound lightweight as it is a lighter version. But it is mobile-capable and can easily run on smartphones and tablets.
The creation of lightweight models of AI software helps reduce not just the environmental impact but also saves resources on mobile devices. These models are also privacy-friendly which will lead competing developers to develop similar models on a similar footing.
The creation of AI models and software is becoming more competitive. It is also becoming interesting and problematic. We all hope that the race for the development of AI models is ethical. If not, the world as we know it will be at the mercy of unethical crooked technologies that can harm our way of life.