#] #] ********************* #] "$d_web"'Software programming & code/Large Language Models/0_Large Language Model notes.txt' - ??? # www.BillHowell.ca 11Feb2023 initial # view in text editor, using constant-width font (eg courier), tabWidth = 3 https://developer.nvidia.com/blog/tag/transformers/ Vaswani etal 2017 "... We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. ..." #48************************************************48 #24************************24 # Table of Contents, generate with : # $ grep "^#]" "$d_web"'Software programming & code/Large Language Models/0_Large Language Model notes.txt' | sed "s/^#\]/ /" # ********************* "$d_web"'Software programming & code/Large Language Models/0_Large Language Model notes.txt' - ??? 11Feb2023 search 'transformer-based generative language model' Rick Merritt 25Mar2022 What Is a Transformer Model?, Google 2017 NVIDIA Jensen Huang - Transformers made self-supervised learning possible, and AI jumped to warp speed >> Howell- does this relate to Generative Adversarial Networks (GANs), Hinton PhD student 11Feb2023 search 'Google equivalent to chatGPT' Here comes Bard, Google’s version of ChatGPT Google makes a $300 million investment in Anthropic in a race against ChatGPT 11Feb2023 search 'How do Large Language Models work?' Tyler Bryden 02Dec2022, How Do Large Language Models Work?? most commonly used type of LM is the “recurrent neural network” (RNN) How do Large Language Models Work? Advantages of Large Language Models Disadvantages of Large Language Models most common applications : Mathew Emmanuel Pineda 09Feb2023, Large Language Models Explained, Purpose and Apps uses deep learning algorithms OpenAI - autoregressive language model called generative pre-trained transformer or GPT LLM R&D - Turing Natural Language Generation transformer-based generative language model Google - Bidirectional Encoder Representations from Transformers or BERT beginning 11Feb2023 search 'ChatGPT alternatives' Samanyou Garg 02Feb2023, Top 14 ChatGPT alternatives that will blow your mind in 2023 ChatSonic - Writesonic's multi-turn conversations, https://writesonic.com/chat Chinchilla - Alphabet's Deepmind Bloom multilingual language model - open-source platform, one of the best ChatGPT alternatives Jasper Chat - by Jasper LaMDA - Google's "Language Model for Dialog Applications" Elsa Speak language-learning DialoGPT - Microsoft for multi-turn conversations Perplexity - OpenAI API for conversational AI Character AI - ChatSonic’s conversational experiences via AI characters OpenAI playground Megatron-Turing Natural Language Generation - NVIDIA and Microsoft >> I didn't list the rest of the 14 LLMs Maxwell Timothy 06Jan2023, The 3 Best Alternatives to ChatGPT every ChatGPT alternative on our list, is powered by OpenAI's GPT-3.5 AI model chatSonic https://writesonic.com/chat GPT-3 Playground https://beta.openai.com/playground YouChat https://you.com/search?q=who+are+you&tbm=youchat #24************************24 # Setup, ToDos, https://writesonic.com/blog/chatgpt-alternatives/ Top 14 ChatGPT alternatives that will blow your mind in 2023 (Free & Paid) Feb 2, 2023 Samanyou Garg ChatSonic Chinchilla Bloom Replika Jasper Chat by Jasper LaMDA (Language Model for Dialog Applications) Elsa Speak DialoGPT YouChat Perplexity Character AI OpenAI playground Megatron-Turing Natural Language Generation Socratic by Google 29Dec2023 TrNNs_ART- transferred from '0_ToDos_priority.ods' : Grossberg images multiple images [reader-adaptable script for browser, create several] Grossberg TblOfContents of sections Grossberg Why is ART unknown? TrNN-LLM use chatGPT [as-is, Grossberg’s book] TrNN-LLM Sejnowski’s humanIntelligence - use chatGPT, can chatGPT do better than almost all research experts? TrNN-LLM [bash, chatGPT] search of webSite content TrNN-LLM emto Ilya Sutskever opinions TrNN-LLM Grossberg’s [CLEAR, ART, etc] - already a reason for LLM success? TrNN-LLM BARD, chatGPT, DALL-E, etc Transformer Models self-attention callerID-SNNs QNial fireL_synSeqA_advance_test MindCode create ndf for [step-wise, procedural] program sequence MindCode draft webPage Mind2023 #08********08 #] ??Feb2023 #08********08 #] ??Feb2023 #08********08 #] ??Feb2023 #08********08 #] ??Feb2023 #08********08 #] ??Feb2023 #08********08 #] ??Feb2023 #08********08 #] 11Feb2023 NVIDIA images - https://blogs.nvidia.com/blog/2022/03/25/what-is-a-transformer-model/ "$d_web"'Projects - mini/Large Language Models/...' : Adam Gomez NVIDA 2017 transformer models of tagged large encoder-decoder blocks.jpg NVIDIA 2021 encoder for the Switch Transformer, the first model to have up to a trillion parameters.jpg NVIDIA 2022 computational requirements for training transformers.jpg NVIDIA 2022 Transformer training and inference will get significantly accelerated with the NVIDIA H100 GPU.jpg NEMO - modular architecture NVIDIA Video - building conversational models Keu take-aways : develop conversational AI models in 3 lines of code highly interoperable with PyTorch framework and PyTorch lightning trainer API easily modify the behavior of models using Hydra config enable mixed precision and distributed training like the flip of a switch >> Howell - PyTorch is from Google? https://developer.nvidia.com/nvidia-nemo https://github.com/NVIDIA/NeMo #08********08 #] 11Feb2023 search 'transformer-based generative language model' https://developer.nvidia.com/blog/tag/transformers/ https://blogs.nvidia.com/blog/2022/03/25/what-is-a-transformer-model/ 2017 paper : [Submitted on 12 Jun 2017 (v1), last revised 6 Dec 2017 (this version, v5)] Attention Is All You Need Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. Cite as: arXiv:1706.03762 [cs.CL] (or arXiv:1706.03762v5 [cs.CL] for this version) https://doi.org/10.48550/arXiv.1706.03762 Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin 12Jun2017 "Attention Is All You Need" [v5] Wed, 6 Dec 2017 03:30:32 UTC https://arxiv.org/abs/1706.03762 /home/bill/web/References/Neural Nets/Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser, Polosukhin 12Jun2017 Attention Is All You Need.pdf Ashish Vaswani∗ Google Brain avaswani@google.com Noam Shazeer∗ Google Brain noam@google.com Niki Parmar∗ Google Research nikip@google.com Jakob Uszkoreit∗ Google Research usz@google.com Llion Jones∗ Google Research llion@google.com Aidan N. Gomez∗ † University of Toronto aidan@cs.toronto.edu Łukasz Kaiser∗ Google Brain lukaszkaiser@google.com Illia Polosukhin∗ ‡ illia.polosukhin@gmail.com +-----+ https://blogs.nvidia.com/blog/2022/03/25/what-is-a-transformer-model/ #] Rick Merritt 25Mar2022 What Is a Transformer Model?, Google 2017 A transformer model is a neural network that learns context and thus meaning by tracking relationships in sequential data like the words in this sentence. So, What’s a Transformer Model? A transformer model is a neural network that learns context and thus meaning by tracking relationships in sequential data like the words in this sentence. Transformer models apply an evolving set of mathematical techniques, called attention or self-attention, to detect subtle ways even distant data elements in a series influence and depend on each other. First described in a 2017 paper from Google, transformers are among the newest and one of the most powerful classes of models invented to date. They’re driving a wave of advances in machine learning some have dubbed transformer AI. Stanford researchers called transformers “foundation models” in an August 2021 paper because they see them driving a paradigm shift in AI. The “sheer scale and scope of foundation models over the last few years have stretched our imagination of what is possible,” they wrote. What Can Transformer Models Do? Transformers are translating text and speech in near real-time, opening meetings and classrooms to diverse and hearing-impaired attendees. They’re helping researchers understand the chains of genes in DNA and amino acids in proteins in ways that can speed drug design. The Virtuous Cycle of Transformer AI Any application using sequential text, image or video data is a candidate for transformer models. That enables these models to ride a virtuous cycle in transformer AI. Created with large datasets, transformers make accurate predictions that drive their wider use, generating more data that can be used to create even better models. #] NVIDIA Jensen Huang - Transformers made self-supervised learning possible, #] and AI jumped to warp speed “Transformers made self-supervised learning possible, and AI jumped to warp speed,” said NVIDIA founder and CEO Jensen Huang in his keynote address this week at GTC. #] >> Howell- does this relate to Generative Adversarial Networks (GANs), Hinton PhD student #] >> clearly related to autoencoders (eg Hinton, also Johannes Suykens RBM-kernel dual) #] great diagram!!! No Labels, More Performance Before transformers arrived, users had to train neural networks with large, labeled datasets that were costly and time-consuming to produce. By finding patterns between elements mathematically, transformers eliminate that need, making available the trillions of images and petabytes of text data on the web and in corporate databases. In addition, the math that transformers use lends itself to parallel processing, so these models can run fast. Transformers now dominate popular performance leaderboards like SuperGLUE, a benchmark developed in 2019 for language-processing systems. How Transformers Pay Attention Like most neural networks, transformer models are basically large encoder/decoder blocks that process data. Small but strategic additions to these blocks (shown in the diagram below) make transformers uniquely powerful. Transformers use positional encoders to tag data elements coming in and out of the network. Attention units follow these tags, calculating a kind of algebraic map of how each element relates to the others. Attention queries are typically executed in parallel by calculating a matrix of equations in what’s called multi-headed attention. With these tools, computers can see the same patterns humans see. Self-Attention Finds Meaning For example, in the sentence: She poured water from the pitcher to the cup until it was full. We know “it” refers to the cup, while in the sentence: She poured water from the pitcher to the cup until it was empty. We know “it” refers to the pitcher. #] Ashish Vaswani, led work on the seminal 2017 paper #] a former senior staff research scientist at Google Brain “Meaning is a result of relationships between things, and self-attention is a general way of learning relationships,” said Ashish Vaswani, a former senior staff research scientist at Google Brain who led work on the seminal 2017 paper. “Machine translation was a good vehicle to validate self-attention because you needed short- and long-distance relationships among words,” said Vaswani. “Now we see self-attention is a powerful, flexible tool for learning,” he added. How Transformers Got Their Name Attention is so key to transformers the Google researchers almost used the term as the name for their 2017 model. Almost. “Attention Net didn’t sound very exciting,” said Vaswani, who started working with neural nets in 2011. Jakob Uszkoreit, a senior software engineer on the team, came up with the name Transformer. “I argued we were transforming representations, but that was just playing semantics,” Vaswani said. The Birth of Transformers In the paper for the 2017 NeurIPS conference, the Google team described their transformer and the accuracy records it set for machine translation. Thanks to a basket of techniques, they trained their model in just 3.5 days on eight NVIDIA GPUs, a small fraction of the time and cost of training prior models. They trained it on datasets with up to a billion pairs of words. “It was an intense three-month sprint to the paper submission date,” recalled Aidan Gomez, a Google intern in 2017 who contributed to the work. “The night we were submitting, Ashish and I pulled an all-nighter at Google,” he said. “I caught a couple hours sleep in one of the small conference rooms, and I woke up just in time for the submission when someone coming in early to work opened the door and hit my head.” It was a wakeup call in more ways than one. “Ashish told me that night he was convinced this was going to be a huge deal, something game changing. I wasn’t convinced, I thought it would be a modest gain on a benchmark, but it turned out he was very right,” said Gomez, now CEO of startup Cohere that’s providing a language processing service based on transformers. A Moment for Machine Learning Vaswani recalls the excitement of seeing the results surpass similar work published by a Facebook team using CNNs. “I could see this would likely be an important moment in machine learning,” he said. A year later, another Google team tried processing text sequences both forward and backward with a transformer. That helped capture more relationships among words, improving the model’s ability to understand the meaning of a sentence. Their Bidirectional Encoder Representations from Transformers (BERT) model set 11 new records and became part of the algorithm behind Google search. Within weeks, researchers around the world were adapting BERT for use cases across many languages and industries “because text is one of the most common data types companies have,” said Anders Arpteg, a 20-year veteran of machine learning research. Putting Transformers to Work Soon transformer models were being adapted for science and healthcare. DeepMind, in London, advanced the understanding of proteins, the building blocks of life, using a transformer called AlphaFold2, described in a recent Nature article. It processed amino acid chains like text strings to set a new watermark for describing how proteins fold, work that could speed drug discovery. AstraZeneca and NVIDIA developed MegaMolBART, a transformer tailored for drug discovery. It’s a version of the pharmaceutical company’s MolBART transformer, trained on a large, unlabeled database of chemical compounds using the NVIDIA Megatron framework for building large-scale transformer models. Reading Molecules, Medical Records “Just as AI language models can learn the relationships between words in a sentence, our aim is that neural networks trained on molecular structure data will be able to learn the relationships between atoms in real-world molecules,” said Ola Engkvist, head of molecular AI, discovery sciences and R&D at AstraZeneca, when the work was announced last year. #] Uof Florida & NVIDIA create GatorTron - clinical data to accelerate medical research. Separately, the University of Florida’s academic health center collaborated with NVIDIA researchers to create GatorTron. The transformer model aims to extract insights from massive volumes of clinical data to accelerate medical research. Transformers Grow Up Along the way, researchers found larger transformers performed better. #] Technical University of Munich - natural-language processing to understand proteins For example, researchers from the Rostlab at the Technical University of Munich, which helped pioneer work at the intersection of AI and biology, used natural-language processing to understand proteins. In 18 months, they graduated from using RNNs with 90 million parameters to transformer models with 567 million parameters. The OpenAI lab showed bigger is better with its Generative Pretrained Transformer (GPT). The latest version, GPT-3, has 175 billion parameters, up from 1.5 billion for GPT-2. With the extra heft, GPT-3 can respond to a user’s query even on tasks it was not specifically trained to handle. It’s already being used by companies including Cisco, IBM and Salesforce. Tale of a Mega Transformer #] NVIDIA and Microsoft Megatron-Turing Natural Language Generation model (MT-NLG) #] NVIDIA NeMo Megatron - custom chatbots, personal assistants, etc NVIDIA and Microsoft hit a high watermark in November, announcing the Megatron-Turing Natural Language Generation model (MT-NLG) with 530 billion parameters. It debuted along with a new framework, NVIDIA NeMo Megatron, that aims to let any business create its own billion- or trillion-parameter transformers to power custom chatbots, personal assistants and other AI applications that understand language. MT-NLG had its public debut as the brain for TJ, the Toy Jensen avatar that gave part of the keynote at NVIDIA’s November 2021 GTC. “When we saw TJ answer questions — the power of our work demonstrated by our CEO — that was exciting,” said Mostofa Patwary, who led the NVIDIA team that trained the model. Creating such models is not for the faint of heart. MT-NLG was trained using hundreds of billions of data elements, a process that required thousands of GPUs running for weeks. “Training large transformer models is expensive and time-consuming, so if you’re not successful the first or second time, projects might be canceled,” said Patwary. Trillion-Parameter Transformers Today, many AI engineers are working on trillion-parameter transformers and applications for them. “We’re constantly exploring how these big models can deliver better applications. We also investigate in what aspects they fail, so we can build even better and bigger ones,” Patwary said. To provide the computing muscle those models need, our latest accelerator — the NVIDIA H100 Tensor Core GPU — packs a Transformer Engine and supports a new FP8 format. That speeds training while preserving accuracy. With those and other advances, “transformer model training can be reduced from weeks to days” said Huang at GTC. MoE Means More for Transformers Last year, Google researchers described the Switch Transformer, one of the first trillion-parameter models. It uses AI sparsity, a complex mixture-of experts (MoE) architecture and other advances to drive performance gains in language processing and up to 7x increases in pre-training speed. For its part, Microsoft Azure worked with NVIDIA to implement an MoE transformer for its Translator service. Tackling Transformers’ Challenges Now some researchers aim to develop simpler transformers with fewer parameters that deliver performance similar to the largest models. “I see promise in retrieval-based models that I’m super excited about because they could bend the curve,” said Gomez, of Cohere, noting the Retro model from DeepMind as an example. Retrieval-based models learn by submitting queries to a database. “It’s cool because you can be choosy about what you put in that knowledge base,” he said. The ultimate goal is to “make these models learn like humans do from context in the real world with very little data,” said Vaswani, now co-founder of a stealth AI startup. He imagines future models that do more computation upfront so they need less data and sport better ways users can give them feedback. “Our goal is to build models that will help people in their everyday lives,” he said of his new venture. Safe, Responsible Models Other researchers are studying ways to eliminate bias or toxicity if models amplify wrong or harmful language. For example, Stanford created the Center for Research on Foundation Models to explore these issues. “These are important problems that need to be solved for safe deployment of models,” said Shrimai Prabhumoye, a research scientist at NVIDIA who’s among many across the industry working in the area. “Today, most models look for certain words or phrases, but in real life these issues may come out subtly, so we have to consider the whole context,” added Prabhumoye. “That’s a primary concern for Cohere, too,” said Gomez. “No one is going to use these models if they hurt people, so it’s table stakes to make the safest and most responsible models.” Beyond the Horizon Vaswani imagines a future where self-learning, attention-powered transformers approach the holy grail of AI. “We have a chance of achieving some of the goals people talked about when they coined the term ‘general artificial intelligence’ and I find that north star very inspiring,” he said. “We are in a time where simple methods like neural networks are giving us an explosion of new capabilities.” 08********08 #] 11Feb2023 search 'Google equivalent to chatGPT' see +-----+ https://www.vox.com/recode/2023/2/6/23588308/google-bard-chatbot-chatgpt-ai-testing-public #] Here comes Bard, Google’s version of ChatGPT The new AI chat bot is available to “trusted testers” for now and will be released to the public in the “coming weeks.” By Shirin Ghaffary Feb 6, 2023, 4:10pm EST +-----+ https://www.businessinsider.in/tech/news/chatgpt-will-soon-be-rivalled-by-a-similar-ai-engine-from-google/articleshow/97637277.cms Google might announce ChatGPT rival in its Search and AI event on February 8th Rahul Verma Feb 6, 2023, 11:16 IST #] Google makes a $300 million investment in Anthropic in a race against ChatGPT According to the Financial Times, Google has invested $300 million in Anthropic, a prominent rival to OpenAI. Anthropic's recently launched generative AI model, Claude, is deemed a worthy competitor to ChatGPT. The investment, which gives Google a 10% stake, values the San Francisco-based company at approximately $5 billion. This development follows Microsoft's recent $10 billion investment in OpenAI, indicating the intensifying competition among big tech companies in the generative AI sector. However, Google never felt compelled to make its AI products readily available to the general public in their early stages. The launch of ChatGPT changed this, as the longer Google takes to launch competitive alternatives, the more widespread ChatGPT could become due to the absence of other options. +-----+ https://speakai.co/how-do-large-language-models-work/ #] 11Feb2023 search 'How do Large Language Models work?' #] Tyler Bryden 02Dec2022, How Do Large Language Models Work?? What are Large Language Models? Large language models (LLMs) are a type of deep learning model that is used to learn and make predictions about natural language. These models are trained on large amounts of text data, such as books, news articles, and social media posts, and are designed to capture complex relationships between words and phrases. #] most commonly used type of LM is the “recurrent neural network” (RNN) LLMs are typically trained using a technique known as “transfer learning” where a pre-trained model is adapted to a specific task by fine-tuning the weights of the model. This allows the model to better capture the nuances of the task at hand. The most commonly used type of LM is the “recurrent neural network” (RNN) which is a type of artificial neural network that is designed to process sequential data. #] How do Large Language Models Work? Large language models work by taking in large amounts of text data and using that data to learn the relationships between words and phrases. These models are trained using a technique known as “transfer learning” where a pre-trained model is adapted to a specific task. The training process begins with a “corpus” of text data which is a collection of documents that contain the language that the model will be trained on. The model is then trained on this corpus of text data in order to learn the relationships between words and phrases. Once the model has been trained, it can be used to make predictions about new text data. This is done by feeding the model a new sentence or phrase and having the model predict the most likely words that come next in the sequence. This process can be used to generate new text or to analyze existing text for sentiment and meaning. #] Advantages of Large Language Models Large language models offer several advantages over traditional NLP models. For instance, they are capable of capturing complex relationships between words and phrases which can lead to more accurate predictions. Additionally, these models can be trained on large amounts of data which allows them to learn the nuances of a language quickly and accurately. #] Disadvantages of Large Language Models However, there are also some drawbacks to using large language models. These models require a large amount of computing power which can be expensive and time-consuming. Additionally, these models can be difficult to interpret which can lead to unexpected results. Most of us who are interested in large language models have seen crazy outputs that are being shared on Twitter, Reddit forums and other social media platforms. For example, Microsoft famously had a chatbot on Twitter that became racist in less than a day. Examples of Large Language Model Applications Large language models are being used in a variety of ways to improve the accuracy and efficiency of NLP tasks. Some of the most common applications include: #] most common applications : [Text, Image] Generation, Text sumarization, Question answering, Sentiment analysis, Image recognition Text Generation We’ve seen explosions of text generation functions within large language models from companies like OpenAI, Jasper, and Copy Ai. Image Generation We’ve also seen a rampant increase in the application of text-to-image generation from companies like Stability Ai, Midjourney, OpenAI and more. Text sumarization Large language models can be used to generate summaries of text documents or articles. These summaries can be used to quickly read and understand large amounts of text. Question answering Large language models can be used to generate accurate answers to questions posed in natural language. This can be used to create chatbots and other AI-driven customer service systems. Sentiment analysis Large language models can be used to analyze text data and accurately determine the sentiment of the text. This can be used to understand customer feedback and improve customer experience. Image recognition Large language models can be used to generate captions for images. This can be used to improve the accuracy of image recognition systems. +--+ Conclusion Large language models are powerful tools for natural language processing tasks. These models are capable of capturing complex relationships between words and are being used in a variety of ways to improve the accuracy and efficiency of NLP tasks. In this article, we’ve explained what large language models are and how they work. We’ve also discussed the advantages and disadvantages of using these models and provided some examples of their applications. If you are interested in learning more about large language models, you can also check out our article on the best large language models. +-----+ https://www.profolus.com/topics/large-language-models-explained-purpose-applications-llms/ #] Mathew Emmanuel Pineda 09Feb2023, Large Language Models Explained, Purpose and Apps Posted on February 9, 2023 by Mathew Emmanuel Pineda Explaining Language Models and How It Relates to Artificial Intelligence A language model is a probability distribution over sequences of words. It is specifically a statistical model that is trained on a huge corpus of text data to predict the likelihood of a sequence of words in a specific language. It specifically works by assigning a probability to the whole sequence of words. #] uses deep learning algorithms This model is considered a type of artificial intelligence and one of the positioned solutions to problems involving computational linguistics. It uses machine learning techniques, specifically deep learning algorithms, to make predictions based on learned patterns and relationships in a given training dataset made of texts. Note that there are two types of language models. These are generative models and discriminative models. Generative language models generate text and other related content based on the learned language patterns and with language input. Discriminative language models analyze and sort a particular text into pre-defined categories. Key Organizations Involved in Developing Large Language Models #] OpenAI - autoregressive language model called generative pre-trained transformer or GPT The most notable organization involved in advancing LLMs and NLP research is the American AI research lab OpenAI. This company has been credited for deploying one of the largest large language models in the world: the autoregressive language model called generative pre-trained transformer or GPT and the GPT-3 model. #] LLM R&D - Turing Natural Language Generation transformer-based generative language model Other tech companies have also been involved in LLM research and development. Its Turing Natural Language Generation or Turing-NLG is a transformer-based generative language model with a 17-billion parameter language that has demonstrated capabilities to outperform different LLMs and NLP models. It can generate words to complete open-ended textual tasks. #] Google - Bidirectional Encoder Representations from Transformers or BERT beginning Google has also introduced its LLM based on Bidirectional Encoder Representations from Transformers or BERT beginning in 2019. The company has integrated this model into its search engine technology to increase the capabilities of its Google Search service to understand human language and improve the results of search queries. 08********08 #] 11Feb2023 search 'ChatGPT alternatives' #] Samanyou Garg 02Feb2023, Top 14 ChatGPT alternatives that will blow your mind in 2023 https://writesonic.com/blog/chatgpt-alternatives/ ChatGPT giving outdated replies? Or have you been finding ChatGPT down now and then? Let's take your conversational AI game to the next level. It's now or never! In 2023, don't settle for the basics when it comes to conversational AI. ChatGPT is amazing and generates astonishing results for writing codes, building stories, creating beautiful poems, songs, articles, and whatnot. But if you are looking for platforms that offer a similar experience with some more features, then you are at the right place. As technology advances, many other AI tools similar to ChatGPT have emerged in the market, confusing users to choose which is right. So are you ready to be amazed? We've scoured the web for the best ChatGPT alternative out there and can now confidently say that the future of AI-based conversational technologies looks a whole lot brighter! From attentive personas to incredible features, these AI like ChatGPT will take your conversations to an unprecedented level. Buckle up for a wild ride—as we explore these 14 ChatGPT alternatives that are set to blow your mind in 2023! ChatSonic Chinchilla Bloom Replika Jasper Chat by Jasper LaMDA (Language Model for Dialog Applications) Elsa Speak DialoGPT YouChat Perplexity Character AI OpenAI playground Megatron-Turing Natural Language Generation Socratic by Google >> great article! Why go for a ChatGPT alternative? ChatGPT might be the top dog in the AI conversational world, but there is a rising tide of alternatives worth checking out! The main benefit of a ChatGPT alternative is that it comes with an incredibly diverse set of features to meet the needs of multiple use cases. So, despite being the talk of the town and an incredible tool, ChatGPT has its own limitations: ChatGPT can not generate real-time data because it has been trained on data from and before 2021. It can't generate visuals or AI art. On top of that, it can not take voice commands or generate voice responses. ChatGPT doesn't provide an API, so you can't integrate it on your apps or any other platform. Due to heavy traffic, most of the time, when you try to access ChatGPT, it may show an error message saying ChatGPT is at capacity or down. ChatGPT Plus costs $20/month, which not everyone can subscribe to. Learn more about ChatGPT Plus and its features. +--+ #] ChatSonic - Writesonic's multi-turn conversations, https://writesonic.com/chat Writesonic's ChatSonic is the latest and greatest in large-scale pre-trained dialogue response generation models, specifically built for multi-turn conversations. It is the best ChatGPT alternative out there as it is integrated with Google and gives results on the latest topics. It brings the knowledge of a sage, the conversation skills of a therapist, the wit of a stand-up comedian, a data scientist's problem-solving abilities, and a novelist's creativity. Plus, it never gets tired, forgets the conversation, or invites awkward silence. It is an advanced AI chatbot by Writesonic that helps with real-time data, images, and voice searches. It can also quickly create content requirements based on user input, from Facebook ad copy to long-form articles and blogs. The Chatsonic model is also trained to provide conversational answers, making it a great tool for customer service operations. ChatSonic is like a professor, best friend, brainstorming partner, and barista all rolled into one. It listens to what you have to say and suggests relevant topics to delve into. It can help you find the right words to communicate your ideas or generate compelling stories and projects. The possibilities with ChatSonic are endless. +--+ #] Chinchilla - Alphabet's Deepmind Chinchilla is also one of the ChatGPT alternatives offering various features and advantages. It is a project with Deepmind and is regarded as the GPT-3 killer. It is a compute-optimal model with 70 billion parameters but four times more data than Gopher. Chinchilla is based on transformer models, similar to GPT-3 and BERT, and has been known to outperform ChatGPT on the mathematical MMLU dataset. This makes Chinchilla an ideal choice for those who want to use a language model for reasoning or who need to create more sophisticated AI art, search engine, and writing tasks. Let’s have a look at Chinchilla's main features: When compared against Gopher (280B), GPT-3 (175B), Jurassic-1 (178B), and Megatron-Turing NLG (530B) on a variety of downstream tasks, Chinchilla comes out on top in a major way. It requires significantly less computing for fine-tuning and use in downstream applications, making it much easier to implement. This model boasts a pretty impressive 67.5% accuracy rate, which is about 7% better than the widely used Gopher language model. It looks like people are increasingly picking this model over the other options out there. Chinchilla is 3x the size of OpenAI’s GPT-3. +--+ #] Bloom multilingual language model - open-source platform, one of the best ChatGPT alternatives Developed with the assistance of a collective of over one thousand artificial intelligence specialists, Bloom is an open-source platform and is believed to be one of the best ChatGPT alternatives. Bloom is a cutting-edge multilingual language model which is widely regarded as one of the top alternatives to ChatGPT. Let’s have a look at Bloom's main features: Bloom is capable of generating text in a total of 46 languages and 13 programming languages which are incredibly similar to something a human would write. Bloom is capable of taking on text assignments that it has not been taught to do specifically by viewing them as opportunities to generate text. +--+ #] Jasper Chat LLM - companies that need to generate high-quality content Jasper is an AI writing software tool that is a decent alternative to ChatGPT (not literally). Formerly known as Jarvis, it is one of the most used AI writing tools available on the market besides Writesonic and is ideal for companies that need to generate high-quality content in a short amount of time. Jasper Chat launched by Jasper is a new chat interface that helps to create content in an efficient way. With the help of cutting-edge technology, it helps to come up with average outputs. +--+ #] LaMDA - Google's 'Language Model for Dialog Applications' Another great ChatGPT alternative is LaMDA, developed by Google. It is developed with 137 billion parameters and pre-trained on 1.56T words of publicly available web documents and dialog data. LaMDA is considered a revolution in the NLP (Natural Language Processing) world. The revolutionary model is fine-tuned on three metrics: Quality, Safety, and Groundedness. LaMDA, an AI-powered dialogue system from OpenAI. This tool can take natural language input and generate a response that is context-aware, coherent, and natural. LaMDA also has a unique ability to answer follow-up questions, which makes it a good ChatGPT alternative. Let’s have a look at LaMDA's main features: LaMDA has the ability to comprehend complex inquiries and discussions that span a wide range of subjects. This model has been honed with the use of 1.56 trillion words and a whopping 137 billion parameters. During the fine-tuning phase, LaMDA is trained to execute both generation and classification tasks, aiming to guarantee the most pertinent, excellent, and, most importantly, secure answer. +--+ #] Elsa Speak multi-language-learning It is an AI-based language-learning application. Utilizing AI, it runs a user speech examination and then formulates a set of tasks that the user can comprehend with ease. Hence, Elsa Speak is also one of the top ChatGPT alternative to discuss. Elsa is an English language speech assistant and helps you translate different languages into English. ELSA's AI technology was created utilizing voice recordings of people speaking English with many different accents. This enables ELSA to identify the vocal patterns of those who don't have a native level of proficiency, giving it an edge over most other voice recognition programs. Let’s have a look at Elsa's main features: Elsa provides bite-sized customized lessons to improve your English. Quick assessment to test your progress. Progress tracking board and graphs to show your accomplishments. AI coaching support to help you stay motivated and focused. Real-Time Speech Recognition Feedback +--+ #] DialoGPT - Microsoft for multi-turn conversations Microsoft's DialoGPT is a large-scale pre-trained dialogue response generation model specifically built for multi-turn conversations. It is one of the best alternative to ChatGPT out there. DialoGPT is a significant pre-trained system for producing replies that can be used in multiple dialogue exchanges. It has been trained with a vast number of 147 million multi-turn conversations taken from Reddit dialogue threads over a period of 2005-2017. Let’s have a look at DialoGPT's main features: The phrases that DialoGPT puts together are remarkably varied and include details that relate to the original prompt, much like the outputs of GPT-2. Microsoft points out that DialoGPT is more conversational, energetic, often light-hearted, and usually quite lively - which could suit the purpose you're considering. +--+ #] Perplexity - OpenAI API for conversational AI Perplexity is another great ChatGPT alternative recently launched in the conversational AI space. It offers ChatGPT-like features, including conversational responses and content generation. Perplexity AI is also powered by large language models (OpenAI API). You can see it collecting information from various popular platforms like Wikipedia, LinkedIn, and Amazon. However, it's still in the beta phase, so it sometimes can pick up the information as it is, leading to plagiarized content. Let’s have a look at Perplexity AI's main features: Generates short conversational responses similar to ChatGPT. Gathers information from sources like Wikipedia and sites them. It has a simple interface without many features to complicate. +--+ #] Character AI - ChatSonic’s conversational experiences via AI characters Similar to ChatSonic’s personas feature—Character AI also focuses on AI personalities, but completely. This Chatgpt-like platform is set to deliver conversational experiences via AI characters. This ChatGPT alternative gives you the option to choose from and converse with various personalities (like Sam Altman or Mario!). Built on neural language models, Character AI is more for having fun and playing with a conversational AI chatbot. Let’s have a look at Character AI's main features: Fun to use with the ability to carry out random chats. Offers a variety of characters to talk with: from real-life people to fictional characters. +--+ #] Playground - OpenAI chatGPT-like with different types of language models to choose from This ChatGPT alternative is much like a demo version of ChatGPT. OpenAI offers GPT3 and its other models, so users can easily experiment with different use cases. OpenAI Playground isn't intended for the everyday user, but it's a great way for those locked out of ChatGPT to get a taste of its advanced functions. On the web, it works like ChatGPT and allows users to experiment with various language models. The playground is technical to use as it has functions like setting temperature, frequency penalty, number of tokens, stop sequences, etc. Let’s have a look at Open AI Playground's main features: Great accuracy and speed. Different types of language models to choose from. Flexible so you can experiment effortlessly. +--+ #] Megatron-Turing Natural Language Generation - NVIDIA and Microsoft NVIDIA and Microsoft have produced one of the largest language models with 530 billion parameters—Megatron-Turing NLG. The highly accurate Megatron-Turing NLG, a transformer-based 105-layer LLM, was trained on the NVIDIA DGX SuperPOD-based Selene supercomputer. It's unbeatable at zero-, one-, and few-shot settings and has topped state-of-the-art models in terms of accuracy. Let’s have a look at Megatron Turing's main features: It can carry out tasks like reading comprehension, word sense disambiguation, completion prediction, natural language inferences, commonsense reasoning, etc. Specialized in English language models. +--+ #] >> I didn't list the rest of the 14 LLMs +-----+ https://www.makeuseof.com/best-alternatives-chatgpt/ #] Maxwell Timothy 06Jan2023, The 3 Best Alternatives to ChatGPT ChatGPT isn't the only text generator AI in town. #] every ChatGPT alternative on our list, is powered by OpenAI's GPT-3.5 AI model +--+ #] chatSonic https://writesonic.com/chat The underlying technology behind ChatGPT (which is GPT 3.5) is the same technology that powers Chatsonic, making it as interesting as ChatGPT itself. Rather than just being a clone of ChatGPT, Chatsonic goes a step further and builds on the abilities of ChatGPT while fixing some of ChatGPT's limitations. If you ask ChatGPT who won the 2022 World Cup, it wouldn't know. You'd expect a powerful AI model like ChatGPT to answer this straightforward question in a heartbeat. However, because ChatGPT's knowledge base has a cut-off date of 2021, the AI model can't answer questions about anything that happened after 2021. This is where Chatsonic outperforms ChatGPT. Chatsonic can access the internet and can pull information from Google's Knowledge Graph to create improved answers that are up-to-date and more consistent with recent events. Of course, we asked Chatsonic who won the 2022 World Cup and who got the best player award—it didn't disappoint. Another noticeable problem with ChatGPT is that it can't generate images. Because OpenAI is a heavyweight in AI arts, it's a bit confusing why its ChatGPT model can't create images. There are probably technical explanations for that, but it's a problem nonetheless. On the other hand, Chatsonic can create digital art from prompts. It uses both Stable Diffusion and DALL-E APIs to generate stunning AI art. Although ChatGPT has a simple and intuitive user interface, there are a few features that could make the user experience even better. Chatsonic adds some of those. If you're tired of the back-and-forth typing, Chatsonic can use voice commands and, if needed, get responses via voice as well, just as you'd do with Siri and Google Assistant. There's also a feature to help you share, edit and download your conversations with the AI chatbot. However, Chatsonic is not all rosy. Although you'll get freemium access when you sign up, unlike ChatGPT, Chatsonic is a paid service. You get allocated tokens, and once you're out of tokens, you'll have to stick with the barebones on offer. Also, compared to ChatGPT, Chatsonic isn't great with computer codes. We asked ChatGPT to solve different problems in PHP, JavaScript, and HTML. ChatGPT responses were more "complete" and properly formatted in all instances, although not necessarily more accurate. You'll find ChatGPT responses to be more detailed and longer than Chatsonic's. In several cases, Chatsonic tends to summarize its responses. That might work for some people, but we didn't find it useful when we needed a long-form response. However, those limitations aside, Chatsonic is exciting and one of the best ChatGPT alternatives you can find. +--+ #] GPT-3 Playground https://beta.openai.com/playground Even before ChatGPT went viral, there was GPT-3 Playground, a platform for the public to play with OpenAI's GPT-3 AI model. Unfortunately, the tool didn't create as much buzz as ChatGPT has. This is partly because of its rather technical user interface and lack of consumer-focused publicity. Ironically, although ChatGPT is getting much of the spotlight, GPT-3 is a much larger and significantly more powerful AI model. It is undoubtedly one of the most powerful AI language models around. ChatGPT is like an iteration of the GPT-3 model that has been streamlined and fine-tuned to be more conversational and human-like in its response. It can better understand human intent, provide context-specific answers and sustain coherent conversations. You can picture GPT-3 Playground as ChatGPT for power users. You can tweak it to do what ChatGPT does and even much more. There are more options and settings to customize the AI model to behave the way you want. There are also some differences in the nature of the response you'll get from both demonstration models. While ChatGPT will refuse to answer questions on some sensitive topics, the GPT-3 Playground tool is less likely to refuse to answer questions. If you'd love to get handy with the GPT-3 Playground, here's a guide on how to use the GPT-3 Playground. +--+ #] YouChat https://you.com/search?q=who+are+you&tbm=youchat YouChat, like every other ChatGPT alternative on our list, is powered by OpenAI's GPT-3.5 AI model. This gives it similar capabilities as ChatGPT. It has a sleek, colorful interface and is neatly interwoven into You.com's search engine. As a result, YouChat can act as a search engine that gives you a list of links to indexed web pages relevant to your query. Or, you can get the typical ChatGPT-styled conversational responses for questions. If you are looking for a search engine and a chatbot like ChatGPT rolled into one product, then YouChat is an excellent option. AI chatbot like ChatGPT and a search engine rolled into one product -YouChat Unfortunately, YouChat is plagued by the limitations of GPT-3.5, which is its base technology. Remember, GPT-3.5, and all related models can not provide accurate responses to events that occurred after 2021 (which is its knowledge base cut-off date). As a result, if you ask YouChat questions about recent events, prepare for unpredictable outcomes. While ChatGPT will refuse to answer questions related to events after 2021, YouChat might attempt to answer such questions with wrong answers. Nevertheless, YouChat still has the edge over ChatGPT when it comes to handling queries about recent events. We asked YouChat and ChatGPT to compare the iPhone 13 Pro and iPhone 14 Pro smartphones. Asking YouChat to compare iphone models YouChat made a decent effort to make a comparison. # enddoc