Reviews
LLM Models
We reviewed AI software tools beginning with outlining a criteria for the performance of large language models (LLMs) such as Gemini, BARD, Bing, BERT, ChatGPT, Claude, Dalai, DeepMind, Dojo, Lambda, Nemo, Palm 2, and Picasso:
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Data size. The size of the dataset that the LLM was trained on is a good indicator of its potential performance. Larger datasets typically lead to better performance, as the LLM has more data to learn from.
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Architecture. The architecture of the LLM is also important, as it determines how the LLM processes and learns from data. Some architectures are better suited for certain tasks than others.
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Training methodology. The way the LLM was trained also affects its performance. Certain training methods can lead to better performance than others.
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Evaluation metrics. The performance of an LLM is typically evaluated using a variety of metrics, such as accuracy, fluency, and coherence. The metrics used can affect the relative performance of different LLMs.
Gemini
Hands-on with Gemini: Interacting with multimodal AI
The video begins with a demonstration of Gemini's ability to understand and respond to visual input. The user shows Gemini a piece of paper with a squiggly line on it, and Gemini correctly identifies it as a bird. The user then shows Gemini a picture of a duck, and Gemini provides information about ducks, including their color, habitat, and relation to other birds.
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The video then showcases Gemini's ability to translate languages. The user asks Gemini to teach them how to say "duck" in Mandarin, and Gemini provides the pronunciation and tone information. This demonstrates Gemini's potential for language learning and communication across cultures.
Next, the video explores Gemini's ability to generate creative text formats. The user asks Gemini for a game idea, and Gemini suggests a game called "Guess the Country." The user then asks Gemini to write a poem, and Gemini generates a poem about a duck in the ocean. This demonstrates Gemini's potential for creative writing and storytelling.
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The video also highlights Gemini's ability to understand and respond to gestures. The user plays a game of rock-paper-scissors with Gemini, and Gemini correctly identifies the user's hand gesture. This demonstrates Gemini's potential for interactive gaming and entertainment.
Finally, the video showcases Gemini's ability to generate images based on text descriptions. The user asks Gemini to draw a dragon fruit, and Gemini generates a realistic image of the fruit. This demonstrates Gemini's potential for design and illustration.
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Overall, the video provides a comprehensive overview of Gemini's capabilities and potential applications. Gemini is a powerful multimodal AI system that can understand and respond to a variety of inputs, including visual, textual, and gestural. It has the potential to revolutionize the way we interact with computers and technology.
Google's DeepMind is a British artificial intelligence research company that is part of Alphabet Inc. It is known for its work on reinforcement learning, which is a type of machine learning that allows agents to learn how to behave in an environment by trial and error. DeepMind has also developed a number of large language models (LLMs), which are neural networks that are trained on massive datasets of text and code.
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DeepMind's LLM has 175 billion parameters, which is significant and is built on a transformer architecture using a combination of supervised and reinforcement learning. Supervised learning involves training the model on a dataset of labeled data, such as pairs of questions and answers. Reinforcement learning involves training the model to generate text that is similar to text that has been generated by humans in the past.
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DeepMind's LLMs have been shown to be more accurate, fluent, and coherent than previous LLMs. This is likely due to the large number of parameters that they have, as well as their training methodology, which combines supervised and reinforcement learning. DeepMind's LLMs are still under development, but they have the potential to be used for a variety of NLP tasks, such as machine translation, text summarization, and question answering.
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Google Gemini is a large language model (LLM) that was developed by Google AI. It is trained on 1.56 trillion parameters on a transformer architecture using a combination of supervised and reinforcement learning.
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Google's BARD is a large language model (LLM) that has been trained on a massive dataset of text and code. It can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way.
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BARD uses 1.56 trillion parameters, which is a measure of the size and complexity of the model. More parameters typically means that the model can learn more complex relationships between words and phrases. It is built on a transformer architecture, which is a type of neural network that is well-suited for processing natural language. Transformers have been shown to be effective for a variety of NLP tasks, such as machine translation, text summarization, and question answering.
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BARD was trained using a combination of supervised and unsupervised learning. Supervised learning involves training the model on a dataset of labeled data, such as pairs of questions and answers. Unsupervised learning involves training the model on a dataset of unlabeled data, such as a large corpus of text.
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The performance of BARD has been evaluated using a variety of metrics, such as accuracy, fluency, and coherence. Accuracy measures the percentage of times that BARD's output is correct. Fluency measures the extent to which BARD's output is grammatical and natural-sounding. Coherence measures the extent to which BARD's output is relevant and well-organized.
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BARD's large parameter size, transformer architecture, and combination of supervised and unsupervised learning methods all contribute to its high performance on a variety of NLP tasks. BARD has been shown to be more accurate, fluent, and coherent than previous LLMs, and it is likely to continue to improve as it is trained on more data.
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Google's BERT (Bidirectional Encoder Representations from Transformers) is a large language model (LLM) that was introduced in 2018. It was trained on a massive dataset of text and code, and it can be used for a variety of natural language processing (NLP) tasks, such as question answering, text classification, and sentiment analysis.
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While BERT has 110 million parameters, it is a measure of the size and complexity of the model. More parameters typically means that the model can learn more complex relationships between words and phrases.
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BERT is built on a transformer architecture. Transformers have been shown to be effective for a variety of NLP tasks, such as machine translation, text summarization, and question answering.
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BERT was trained using a combination of supervised and unsupervised learning. Supervised learning involves training the model on a dataset of labeled data, such as pairs of questions and answers.
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BERT has been shown to be more accurate, fluent, and coherent than many LLMs, and it is likely to continue to improve as it is trained on more data.
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Bing is a search engine developed by Microsoft. It uses a variety of technologies to index and rank websites, including a large language model (LLM).
Bing's LLM has 1.6 billion parameters and is built on a transformer architecture. It is trained using a combination of supervised and unsupervised learning.
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The performance of Bing shows a high performance on a variety of NLP tasks. Bing's LLM has been shown to be more accurate, fluent, and coherent than early versions of LLMs, and it is likely to continue to improve as it is trained on more data.
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ChatGPT is a large language model (LLM) that was developed by OpenAI. It was trained on a massive dataset of text and code, and it can be used for a variety of natural language processing (NLP) tasks. It has 1.37 billion parameters, which is a measure of the size and complexity of the model. More parameters typically means that the model can learn more complex relationships between words and phrases.
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ChatGPT is built on a transformer architecture, which is a type of neural network that is well-suited for processing natural language. It was trained using a combination of supervised and reinforcement learning. Supervised learning involves training the model on a dataset of labeled data, such as pairs of questions and answers. Reinforcement learning involves training the model to generate text that is similar to text that has been generated by humans in the past.
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The performance of ChatGPT has been shown to be more accurate, fluent, and coherent than previous LLMs, and it is likely to continue to improve as it is trained on more data. ChatGPT may be the most accurate current LLM, but it is also less fluent and has a training cutoff of Sept. 2021.
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Google/Anthropic's Claude 2 is a large language model (LLM) that was developed by Anthropic. It is trained on a massive 1.56 trillion parameters dataset of text and code, and it can be used for a variety of natural language processing (NLP) tasks, such as question answering, text generation, and translation.
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Claude 2 is built on a transformer architecture using a combination of supervised and unsupervised learning. Supervised learning involves training the model on a dataset of labeled data, such as pairs of questions and answers. Unsupervised learning involves training the model on a dataset of unlabeled data, such as a large corpus of text.
Claude 2's output is grammatical and natural-soundingand as well as relevant and well-organized.
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Google's Dalai is still under development. It was trained on a dataset of 1.56 trillion words, which is much larger than the datasets that were used to train previous LLMs. Dalai is trained using a combination of supervised and unsupervised learning. This large dataset allowed Dalai to learn more complex relationships between words and phrases, which resulted in its improved performance.
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Google's Lamda is excellent at translating languages, but it is not as good at other tasks.
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Google's Palm 2 is excellent at advanced reasoning tasks, including code and math, classification and question answering,
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Nividia's Picasso is the best with image generations of different kinds of creative content, but it is not as good at other tasks.
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Tesla's Dojo is an AI tool which is best at video interpretation, but it is not as good at other tasks.
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Nvidia's Nemo is the best at following your instructions and completing your requests thoughtfully, but it is not as good at other tasks.