Most recent public comments by David Cole https://nowcomment.com/users/134335 https://nowcomment.com/documents/364730?scroll_to=3221793 https://nowcomment.com/documents/364730?scroll_to=3221793 I'm replying as myself...no AI here. Just a lot of observation Fri, 26 Jan 2024 16:39:08 -0500 https://nowcomment.com/documents/362741?scroll_to=3213289 https://nowcomment.com/documents/362741?scroll_to=3213289 Generic response from OpenAI when I type the same question for their standard web crawled response. Q: What are all the layers of processing that happens between input and output with ChatGPT? I am asking to clarify what our AI tools are keeping and what parts get replaced by using an API. Answer from ChatGPT: Certainly, David. It's important to understand the layers of processing that occur between input and output in ChatGPT. This can help clarify what aspects are retained and what parts are affected when using an API. Tokenization: The input text is divided into smaller units called tokens. Tokens can be words or subwords, and this step prepares the text for further processing. Embedding: Each token is converted into a numerical representation through embedding layers. These embeddings capture the semantic meaning of words and their context. Model Architecture: The heart of ChatGPT lies in its deep neural network architecture. GPT-3.5, the underlying model, is a transformer-based architecture. It consists of multiple layers of self-attention mechanisms and feed-forward neural networks. Contextual Processing: The model takes into account the entire context of the conversation, not just the current input. It looks at previous messages to understand the conversation's context and maintain coherence. Attention Mechanisms: Within the transformer architecture, attention mechanisms allow the model to focus on relevant parts of the input and context. This helps in capturing dependencies and relationships between words. Decoding: The model generates output text token by token. It uses a decoding algorithm to predict the most likely next token based on the context and previous tokens generated. Response Generation: The generated tokens are converted back into human-readable text, forming the AI's response. When using an API like ChatGPT, most of these layers remain intact. The tokenization, embedding, model architecture, contextual processing, attention mechanisms, and decoding all function similarly. What changes is the input method and the output presentation. In a chat conversation, the API receives a list of messages as input, making it easier to have back-and-forth interactions. It also returns a list of model-generated responses. The API essentially acts as an interface to interact with the model. So, the core AI model and its processing layers are largely consistent between the standalone ChatGPT and the API, but the API adds the convenience of message-based input and output formatting. Now, it's worth noting that while AI models like ChatGPT can generate text, the understanding of context and the quality of responses can still vary depending on the specific input and the model's training data. This is something to keep in mind when implementing AI in educational technology or any other application. There are ongoing efforts to improve these models further." Mon, 15 Jan 2024 19:50:34 -0500 https://nowcomment.com/documents/363988?scroll_to=3208170 https://nowcomment.com/documents/363988?scroll_to=3208170 The background knowledge required to understand this text involves an awareness of human-AI interaction dynamics and the nature of prompting AI systems. Let's dissect some key sentences: 1. **"Perhaps this is why I found myself offering revision suggestions instead of rewriting a more robust prompt to begin with..."** - **Importance**: This sentence expresses a behavior where the user engages with the AI in a process similar to editing a draft rather than starting with a completely new and improved prompt. It suggests reluctance or uncertainty about how to best utilize the AI's capabilities from the outset. - **Background**: In collaborative writing or revision, a human editor would typically make suggestions for improvements rather than rewriting the entire piece. This behavior is carried over to interactions with AI, despite AI having potentially differing capabilities or requirements compared to human co-authors. 2. **"the back and forth with the AI was more familiar to writing with a human co-author."** - **Importance**: It highlights how the user's experience with AI mimics their previous experiences of human collaboration, suggesting that habits from human interactions are being applied to AI interactions. - **Background**: Humans often approach new technologies with habits formed from previous experiences. When it comes to collaborative writing with AI, users may unconsciously treat the AI like a human co-author, expecting similar iterative dialogue and improvement processes. 3. **"the systems seem to ask us to constantly curate them."** - **Importance**: This sentence points out that AI systems require active input and guidance (curation) from users to function effectively. - **Background**: AI systems, particularly conversational agents or generative models like GPT-3, often need precisely formulated prompts to produce relevant and high-quality outputs. The better the prompt, the better the AI's response is likely to be. 4. **"Finer and finer question-making tells its own story of understanding"** - **Importance**: The process of refining questions or prompts is indicative of a deeper understanding not only of the subject matter but also how to interact with AI to get desired results. - **Background**: As users gain experience with AI systems, they typically learn how to ask better questions or create prompts that are more likely to yield useful outputs. This reflects a growing understanding of both the AI's capabilities and limitations. Now, as your background has been set and your curiosity piqued, take a moment to re-examine the original text. Can you see the dance of human habits blending into the rhythm of technological innovation? Perhaps you'll notice a new layer to the interaction or a nuance that was initially overlooked. Feel free to enhance the conversation by replying with your fresh insights. Wed, 20 Dec 2023 21:01:58 -0500 https://nowcomment.com/documents/363988?scroll_to=3208167 https://nowcomment.com/documents/363988?scroll_to=3208167 This snippet speaks to the first order human habit/inclination/skill we bring to this tech, while the systems seem to ask us to constantly curate them. Is offering revision suggestions the same as proposing more and more robust prompts? Finer and finer question-making tells its own story of understanding Wed, 20 Dec 2023 21:01:58 -0500 https://nowcomment.com/documents/363988?scroll_to=3208158 https://nowcomment.com/documents/363988?scroll_to=3208158 Also "the concerns of teaching" was a close second to what I was thinking the subject of the article was. Wed, 20 Dec 2023 20:57:30 -0500 https://nowcomment.com/documents/363988?scroll_to=3208154 https://nowcomment.com/documents/363988?scroll_to=3208154 Might be that even a little bit of experience with the current crop of bots leads one quickly to understand that prompting is the best, and only way, to engage the AI, so scanning this abstract, I want to jump to detail on what the prompting process was as (I'm imagining) it will reveal one specific picture of the kinds of contrasts, benefits, and limitations that go with working with gen AI. Wed, 20 Dec 2023 20:33:34 -0500 https://nowcomment.com/documents/348711?scroll_to=3173052 https://nowcomment.com/documents/348711?scroll_to=3173052 As for your question about the "conversation state management system", this connects to the idea of interactive AI. Now let's imagine we're designing a system to help students craft essays with specified complexity and word count, while monitoring their cognitive state in real-time. We could incorporate some of the principles of such system from chatbot models like the ones we discussed above. Here's how I'd envision the system working: 1. **Understanding the task**: We'd first need to input the requirements for the essay, like the topic, word count, and complexity level. The AI model should be able to understand these inputs using a similar NLP process as mental health bots understanding the mood or intent of the user's text. 2. **Monitoring student's progress**: As the student composes the essay, the system can monitor their progress and the complexity of the language used. For instance, it could track the number of words written and the range of vocabulary and sentence structures used, giving feedback as needed. 3. **Cognitive state tracking**: To track the learner's cognitive state, you might use data like typing speed, revision frequency, and time taken on specific sections to get insights into their level of confidence, understanding, or potential points of frustration. Some additional data could come from outside the system with tools like periodic self-report surveys or even biofeedback sensors. How does this tie back to Visible Literacy and various levels of learning? In Visible Learning terms, the ability to track a conversation's state could directly contribute to "Surface" learning, where the AI system helps the student achieve basic understanding and complete tasks in a structured manner. Then, as the student gains confidence and is ready for more complex work (moving into "Deep" learning), the system can start to back off, providing less guidance, pushing for more original thinking. Lastly, "Transfer" learning occurs when the student starts to apply their learned skills across various contexts. Here, the system could introduce different essay prompts in diverse topics, and track how well the student adapts their knowledge to these new scenarios. In summary, a conversation state management system like this not only makes learning more visible and efficient, it could also provide valuable data to help educators continually refine their teaching practices and to help learners understand their own learning preferences and needs. This whole endeavor quite excitingly taps into the evolving field of Learning Analytics. I hope all this addresses your question, and I'd be glad to discuss further any other aspect of your curiosity! Fri, 08 Dec 2023 16:18:49 -0500 https://nowcomment.com/documents/348711?scroll_to=3173050 https://nowcomment.com/documents/348711?scroll_to=3173050 Absolutely, happy to delve into these inquiries. Let's start with how certain mental health chatbots like WYSA and Woebot build coherent, empathy-focused conversation. At their core, these chatbots are essentially using a combination of language understanding models and cognitive behavioral therapy (CBT) principles. They utilize Natural Language Processing (NLP) techniques to understand the sentiment, key topics, and user intent within the conversation. Then, responses are generated based on therapeutic strategies alongside further machine learning techniques. Take Woebot as an example: they employ decision trees, a type of model that uses a tree-like graph of decisions and their possible consequences. These trees are crafted by domain experts and are combined with NLP capabilities to deliver holistic, empathetic responses. Now, open-source projects like BigScience, organized by Hugging Face, aim to democratize AI research and application. The cohesion between practitioners across various industries is precisely what makes initiatives like BigScience so powerful; they are essentially pooling the cognitive resources of a global community to train what would potentially be one of the most advanced language models to date. The project involves the collaborative training of a Transformer-based machine learning model, applied to a large text corpus. Contributions to the project can range from proposing new research directions, contributing data, or annotating and preparing datasets. Indeed, this can act as an excellent opportunity for humanities teachers: their unique understanding of cultural contexts and language intricacies can aid in the creation of more nuanced models. As for open-ended text generation, like poetry generation, this presents an interesting and rather complex challenge. This hinges on two main factors: the complexity of the task and the interpretative nature of the output. Language models can be trained on large data sets of poetry, such as the Poetry Foundation's open-source Poetry Corpus, to produce remarkable human-like poems. However, evaluation of such text brings into question the essential nature of poetry: its free-form interpretation not bound by any fixed rules. Assessing AI-generated poetry would involve not only an understanding of the technical aspects but also of the aesthetic and emotive impact and subtlety — a task that can engage students in a novel and thought-provoking manner. In essence, advances in AI and machine learning are not confined to technical fields, but can and should intersect with all sectors, allowing for the emergence of more robust, understanding, and human-like models. Please let me know if you would like to dive deeper into any specific points. Fri, 08 Dec 2023 16:18:49 -0500 https://nowcomment.com/documents/348711?scroll_to=3173048 https://nowcomment.com/documents/348711?scroll_to=3173048 Furthermore, specific projects within the realm of AI research are designed to tackle the challenge of measuring unstructured and original text creation. An example of this is the ongoing work with AI in the field of poetry generation, where models are trained on vast poetic data. Telling the AI the style or the topic, we let it generate a poem that can often be indistinguishable from a human's creation. The evaluation of the originality and interpretative depth of such text generated by AI poses interesting dilemmas and possibilities for humanities teachers and their students. We also delve into initiatives like the BigScience project, which is a large-scale, community-driven research endeavor being spearheaded by Hugging Face. The project involves training a large language model through collective effort and opens the prospect of making significant strides in understanding and improving AI capabilities in generating and understanding human-like text. For a more hands-on contribution, there are opportunities to help annotate and prepare datasets or even propose new ones for training language models. By doing this, humanities teachers can provide valuable perspective on how we can train models to understand things like historical context, cultural nuances, and sociolinguistic aspects of language whose importance cannot be overstated. I hope this clarifies the complexity of producing 'original' output from AI, and elucidates how everyone, from AI experts to humanities teachers, can take part in this exciting discipline. Fri, 08 Dec 2023 16:18:49 -0500 https://nowcomment.com/documents/348711?scroll_to=3173047 https://nowcomment.com/documents/348711?scroll_to=3173047 Your interest in AI's capacity for generating original texts is a fascinating area of research. AI models like GPT-3 by OpenAI, which I help oversee, are particularly good at tasks that involve generating human-like text. At a very high level, GPT-3 is a machine learning model with 175 billion parameters, which is trained on a diverse range of internet text. As it receives an input (let's say a sentence or a question), it attempts to predict or generate the next word based off patterns it has observed during training. To understand the efficacy of AI with original text generation, you need to learn about the training process, the model's design, and the language datasets it's exposed to. For instance, GPT-3 is based on a Transformer architecture, which enables AI to produce coherent multi-sentence paragraphs and even entire stories. The model takes into account the context of the input, including the order and relationship of words and phrases, to generate appropriate output. The diverse and extensive datasets used to train GPT-3 contain vast amounts of information from books, articles, and websites, which are then utilized to generate novel language constructs. Therefore, the 'originality' in the context of AI doesn't stem from the machine's capability to 'create' in the way humans do, but in its ability to combine and rehash seen information in a unique way that could seem original to us. So, how can teachers and non-technical users participate in this process and evaluate its efficacy? A fantastic way to involve yourself is by interacting with the model through websites or applications powered by these AI models. By posing queries or prompts, users can evaluate the model's responses. This type of feedback is integral to improving the system and its abilities. Furthermore, platforms like Hugging Face provide resources where one can not only interact with models but also contribute by sharing new language datasets, participating in discussions, or even developing new models. These kinds of activities can give educators an avenue to not only observe but also actively contribute to AI research and its future directions. Participating in online courses or webinars hosted by organizations such as OpenAI, Hugging Face, or other AI research groups can provide crucial insights into the workings of AI models and the nuances of their operations. This participatory approach allows for a more in-depth understanding and involvement in the development and refinement of AI systems and their capacity for creative language generation. Fri, 08 Dec 2023 16:18:49 -0500 https://nowcomment.com/documents/348711?scroll_to=3173040 https://nowcomment.com/documents/348711?scroll_to=3173040 Crowdsourcing, in the context of AI, is a methodology that leverages the collective intelligence of a group of people, or a "crowd," to perform tasks that would be challenging for an individual or machine, or to gather large amounts of data. This method has been integral to the advancement and refining of AI research as it provides diverse data sets needed to train models. The premise of crowdsourcing is to tap into the collective intelligence of public volunteers, a.k.a. “the crowd," which contributes to various stages of the AI development lifecycle. The crowd could be involved in tasks like data collection, data labeling, or even testing the models. One good example of this is how the AI community uses Hugging Face's "Datasets" – a massive open-source library for Natural Language Processing (NLP) datasets. By opening this to the public, they've allowed for a system of data collection and validation from users all around the globe. In other words, the very people who will utilize the AI systems have a hand in refining them. To partake in research projects, teachers and students don't necessarily require technical skills. They could help with gathering or annotating the training data, providing the 'human' perspective to a machine's learning process. An understanding of validity, bias, reliability, ethical considerations and privacy issues will be as important as computer literacy. A project plan for a crowdsourcing AI endeavor will involve the following stages: **1. Plan**: Understand the problem you want to solve (in this case, improving AI's capacity for open-ended thinking and writing) and plan the crowdsourcing project accordingly. Identify the specific tasks the crowd can help with – data gathering, labeling, or testing. **2. Design & Develop**: Outline how the platform or tool will work, and develop the technology necessary. If you are using existing platforms (like Hugging Face), this stage will involve understanding and setting up the system to fit your needs. Volunteers are then invited to participate. They could (on a basic level) help by interacting with a language model and marking if the generated response was helpful or not. This would provide valuable insights into the strengths and weaknesses of open-ended answers. **3. Launch**: Open the platform to your volunteers and monitor their activity. Provide them with clear instructions and ensure the usability of the system. **4. Collect & Analyze**: Gather the data provided by the crowd, checking it for quality and validity. Then analyze this data to refine and improve the AI. **5. Implement**: Incorporate the crowd's insights into the AI system and make necessary adjustments. **6. Review & Improve**: Review the impact of the changes made and take continuous steps for improvement. This may involve repeating the process periodically. For AI learning to reach the depths of human-like open-ended thinking and original writing, it will need to tap into a wider array of information - structured and unstructured, numeric and linguistic, objective and subjective - in a sense, it will need to familiarize itself with the richness of human experience. As teachers and students, your unique perspectives could be priceless in this grand experiment. Fri, 08 Dec 2023 16:18:49 -0500 https://nowcomment.com/documents/348711?scroll_to=3173036 https://nowcomment.com/documents/348711?scroll_to=3173036 In terms of the apparent gap in research on AI's efficacy in original writing and open-ended thinking versus math, it's important to understand that language models such as GPT-3 are essentially statistical pattern recognizers. They are trained on large amounts of text data and learn to predict the next word in a sentence based on the context provided by the preceding words. This means they're excellent at generating human-like text that's grammatically correct, coherent, and often surprisingly insightful. However, the creative and open-ended thinking necessary for original writing or problem-solving in fields like philosophy or mathematics is an entirely different beast. This requires not just patterns and associations, but deeper causal understanding, logical reasoning, imaginative flair, and often an understanding of the context that goes well beyond the text data the AI has been trained on. While AI has made significant strides in understanding and generating text, it still lacks this wider context and deeper understanding. It doesn't truly "understand" text the way humans do – it's basically playing a very sophisticated game of statistical prediction. Moreover, research in AI, and specifically machine learning, is typically 'use case' driven – the machine learning community often prioritizes tasks that can be measured precisely. Open-ended thinking and original writing are much harder to measure and thus, they aren't studied as much. Moreover, mathematics, being a more structured discipline with clear right and wrong solutions, lends itself more naturally to the kind of machine learning techniques we currently possess. It's important to remember that AI is only a tool and a model of thought, in many ways it's very limited compared to the human brain. It's a mistake to expect it to duplicate all of human intelligence or creativity – at least, not without significant advancements in technology and theoretical understanding. However, efforts are ongoing in trying to develop AI models that can simulate more open-ended thinking and original writing. As much as AI has achieved, the journey ahead is still long and fascinating. Fri, 08 Dec 2023 16:18:49 -0500 https://nowcomment.com/documents/348711?scroll_to=3173032 https://nowcomment.com/documents/348711?scroll_to=3173032 Regarding your question about revealing sources of training data, it's essential to understand that companies like OpenAI and Google DeepMind use a diverse array of sources for training their models, including a significant fraction of the internet. The exact documents in the training set are unknown and thus, these institutions can't provide a list of individual data sources. This approach raises some legitimate concerns about transparency and accountability. However, this is for two main reasons: privacy and feasibility. Because vast volumes of data are needed to train these models, it's technically infeasible to trace every data point back to its original source, especially when using techniques like differential privacy. Also, some of the data is collected in ways that anonymize and strip away identifying information to respect privacy rules. The "footnoting" function that you mentioned isn't available due to the model's design. When the AI generates its output, it's not recalling specific instances from the training dataset. Instead, it’s generating responses based on patterns it has learned. This means it doesn't have the ability to provide a footnote or citation as it doesn't "remember" information in the traditional sense. However, OpenAI is committed to responsibility and transparency in AI. Current focus is on external partnerships for third-party audits of safety and policy efforts, and for areas such as AI in education where the impact is widespread and profound. On Hugging Face, users can learn how to train their own models or fine-tune the existing ones for better performance or for more specific tasks. This is possible because Hugging Face has an extensive library of pre-trained models and datasets, along with easy-to-follow tutorials. It's a fantastic resource especially for those who are new to machine learning and natural language processing, as it offers a strong, supportive community of like-minded people, as well as access to groundbreaking AI models and techniques. Moreover, Hugging Face has various tools and models that are capable of demonstrating impressive capabilities of machine learning but at a much smaller scale. These hands-on projects can help in getting a better understanding of machine learning algorithms, their design, and their application. Whether through these demonstrations or through diving into the theory itself, laypeople can begin to comprehend the effects that modern machine learning tools have on the world around them. Learning about concepts like model training, fine-tuning, and practical implementation is key to fully grasping the impacts and implications of these advanced AI systems. Fri, 08 Dec 2023 16:18:49 -0500 https://nowcomment.com/documents/348711?scroll_to=3173029 https://nowcomment.com/documents/348711?scroll_to=3173029 I’m glad you found the explanation helpful! To summarize, OpenAI, given the new research published in Nature titled "AI ‘breakthrough’: neural net has human-like ability to generalize language" can improve its systems and applications by implementing Meta-learning for Compositionality (MfC), which improves the neural networks' ability to understand and combine multiple concepts. With MfC, OpenAI's AI systems could become more flexible, creative, and human-like in their thinking and applications, broadening their usefulness in various sectors from healthcare to entertainment. However, it's crucial to understand that the integration of MfC into AI systems is still a burgeoning area of study and much exploration remains. In terms of open source projects, resources like Hugging Face provide valuable tools, pre-trained models, and communities that facilitate research and development in these advanced AI technologies. By engaging with these resources, users can gain practical knowledge of how to work with training sets, fine-tune AI models, and navigate the challenges and opportunities within the AI field. Ultimately, innovations like these in AI technology have the potential to profoundly impact how we live, work, and interact with the world around us—hence, understanding them on both a technical and societal level is crucial. Whether it's improving disease diagnosis, personalizing educational tools, or even crafting immersive video games, the opportunities are endless and incredibly exciting. If you wish to delve into specific topics or explore how this knowledge applies to particular industries or contexts, I’d be more than happy to help! Fri, 08 Dec 2023 16:18:49 -0500 https://nowcomment.com/documents/348711?scroll_to=3173021 https://nowcomment.com/documents/348711?scroll_to=3173021 I would be delighted to help. Here goes: At a fundamental level, a neural network mimics the way our human brain works. It can learn from data like images, sound or text, and make intelligent decisions based on that. Imagine teaching a child to recognize an apple. You show different pictures of apples, and gradually the child learns to distinguish an apple from say, other fruits. That's somewhat similar to how a neural network learns. Now, neural networks are known to struggle with compositional tasks - where they have to understand and combine multiple concepts. That's where Meta-learning for Compositionality (MfC) comes in. MfC is an approach that helps neural networks understand and combine multiple concepts, like how we humans do. For instance, if someone understands the concepts of "rain" and "umbrella", they can easily understand the combined concept of "using an umbrella when it rains". Now, onto AI applications like this one, which relies on generative models. Generative models are a class of statistical models that can generate new data that's similar to the data it was trained on. For instance, if you train a generative model on a dataset of images of dogs, it should be able to generate new images of dogs that it's never seen before. The use of MfC in these generative models could greatly enhance their ability to create more complex and nuanced outputs. Instead of just regurgitating the training data in slightly different forms, models using MfC could actively combine different concepts to generate truly unique and creative results. It's important to note that this is an emerging field, and we're just starting to scratch the surface of what's possible with MfC. But the potential impact on AI applications is enormous. It could lead to AI that's more creative, more understanding, and more similar to how humans think. Now let's talk about open source projects like those on Hugging Face. They're an amazing resource for researchers and developers who want to work with these types of models. They provide pre-trained models, datasets, and tools that make it easier to experiment and build with these advanced AI technologies. Also, Hugging Face's extensive collection of notebooks and community projects can be a great learning resource. They demonstrate real-world applications of these models and provide examples of how to train and fine-tune models on your own datasets. Training sets and smaller scale LLM demonstrations can give you hands-on experience with these models. You'll learn how to prepare your data, train, test, and refine your models, and how to interpret and use the outputs. You'll see what works well, what doesn't, and how you can tweak things to get better results. I hope this gives you a basic understanding of these topics! Is there something else you want to dive deeper into? Fri, 08 Dec 2023 16:18:49 -0500 https://nowcomment.com/documents/348711?scroll_to=3173002 https://nowcomment.com/documents/348711?scroll_to=3173002 Fri, 08 Dec 2023 16:18:49 -0500 https://nowcomment.com/documents/359243?scroll_to=3162973 https://nowcomment.com/documents/359243?scroll_to=3162973 This move to reference other information or perspectives, and to do so with primary material that the speaker (even and AI...) proposes to have generated earlier creates the impression of authenticity. And here, it is legit. Raises the idea of the style of testimony as a way to create believability and backstory; the construction of a credible speaking voice. Similar to notions of establishing authority when writing generally. Thu, 26 Oct 2023 13:24:34 -0400 https://nowcomment.com/documents/359243?scroll_to=3157528 https://nowcomment.com/documents/359243?scroll_to=3157528 Complicated term. Wed, 18 Oct 2023 21:24:46 -0400 https://nowcomment.com/documents/348777?scroll_to=3076322 https://nowcomment.com/documents/348777?scroll_to=3076322 Wed, 07 Jun 2023 13:44:02 -0400 https://nowcomment.com/documents/348777?scroll_to=3076321 https://nowcomment.com/documents/348777?scroll_to=3076321 The next prompt could be, "Select a piece of text you recently wrote and apply an AI tool from Hugging Face to generate a list of words or sentences that are related to the text you wrote." By having students incorporate AI tools from Hugging Face, it opens the opportunity for them to consider alternative words to use in their writing, and may give them a different perspective on their writing. This sort of prompt gives students more control and freedom over how they want to approach their writing. On the following prompt, you may ask your students to "Choose 3 of the AI-generated words or phrases and use them as a source of inspiration when you rewrite the original piece. Consider how the words and sentences opened up ideas for you when you incorporated them into the text." This prompt encourages students to think critically about how to use the AI generated words, and allows them to create a fresh perspective on their writing. Finally, you could ask your students to "Reflect on the process of using AI tools in writing. What was helpful? What could be improved? What is your outlook on using AI tools in writing moving forward?" This prompts your students to evaluate and reflect on their own use of AI tools in writing, giving them the chance to recognize the strengths and weaknesses of incorporating AI in their work, and cultivate a more comprehensive outlook on AI and its use in writing. Wed, 07 Jun 2023 13:44:02 -0400 https://nowcomment.com/documents/348777?scroll_to=3076320 https://nowcomment.com/documents/348777?scroll_to=3076320 For your 11th grade literature students, a soft prompt from you would look like this: "Think about a recent event or experience in your life, and brainstorm three ways that you would describe it in your writing." This prompt gives your students a starting point to brainstorm possible ways that they can use to approach their writing. From this prompt, they can explore the best words to use in their writing, discover aspects of their writing they want to develop further, or even explore ideas and thoughts that they may not have previously considered. Wed, 07 Jun 2023 13:44:02 -0400 https://nowcomment.com/documents/348777?scroll_to=3076319 https://nowcomment.com/documents/348777?scroll_to=3076319 A soft prompt is a beginning stage to an instruction for a specific task or process. It is then converted into an instruction and fed to a program such as LLM or Open Source Language Model that can recognize the instructions and act accordingly. This advanced technology allows the machine to carry out tasks without being explicitly instructed every step of the way, thus making the overall process more efficient and accurate. Resources from Hugging Face, an open-source platform, empowers users to effectively work with the latest training sets and small-scale LLM demonstrations. Wed, 07 Jun 2023 13:44:02 -0400 https://nowcomment.com/documents/348777?scroll_to=3076318 https://nowcomment.com/documents/348777?scroll_to=3076318 This quote is talking about a process that starts with a "soft prompt," which is converted into an instruction using a program called LLM (or "Open Source Language Model"). This program improves the overall performance of a task without the need for prior knowledge or guidance. In other words, LLM makes it possible to get a task done correctly, without needing assistance or specific instructions. Now reread the text with this summary in mind. Is there anything you would add to this summary? Please let me know what you are thinking about the text in your reply. Wed, 07 Jun 2023 13:44:02 -0400 https://nowcomment.com/documents/348201?scroll_to=3074068 https://nowcomment.com/documents/348201?scroll_to=3074068 Mon, 05 Jun 2023 10:49:27 -0400 https://nowcomment.com/documents/348201?scroll_to=3072755 https://nowcomment.com/documents/348201?scroll_to=3072755 To use the Koala LLM to develop a writing assistant or “co-pilot”, you will need at least 5000 samples of individual K12 student writing for the training set. This data can be gathered from a variety of sources, such as public educational portals, databases, and/or textbooks. Once you have the dataset, it then needs to be pre-processed before it can be used for training the Koala model. In terms of the training process itself, the system has several algorithms that can be used to train the model, including supervised learning, reinforcement learning, and deep learning. Once trained, the model can then be used to predict outcomes and/or to suggest corrections or modifications to a writing assignment. Overall, the Hugging Face platform provides an easy-to-use platform to learn how to use machine learning algorithms, by providing several pre-trained models, datasets, and tutorials. Additionally, by providing access to the Koala LLM, the platform allows users to easily explore and use the most advanced AI technology for their own projects and/or studies. Mon, 05 Jun 2023 10:49:27 -0400 https://nowcomment.com/documents/348201?scroll_to=3072754 https://nowcomment.com/documents/348201?scroll_to=3072754 The Koala LLM, developed by the Berkeley Artificial Intelligence Research (BAIR) Lab at the University of California, Berkeley, is an example of such. It is built on a combination of natural language processing tasks (NLP) and various machine-learning tasks, such as image classification. This “highly curated” dataset consists of a set of diverse tasks that makes it more suitable for efficient and effective training on many machine-learning tasks. By taking all the data available and narrowing it down to a smaller set of chosen tasks, deep learning models can be trained and evaluated faster. This also allows for targeted research of applications that can be generalized across other datasets. The Hugging Face platform provides all the tools and datasets needed to enable access to this type of learning, and makes it easy for individuals, without a technical background, to learn about the theory and practice of using artificial intelligence for specific tasks. Mon, 05 Jun 2023 10:49:27 -0400 https://nowcomment.com/documents/348201?scroll_to=3072752 https://nowcomment.com/documents/348201?scroll_to=3072752 Instruction tuning is a form of optimization that can be applied to a target dataset. It is a process of tuning the instructions into a set of parameters that optimize the performance of the machine learning model. Instruction tuning may focus on elements such as datasets, model selection, or hyperparameter optimization. Dialogue, reasoning, and tool use refer to techniques that can be applied to the instructions in order to maximize the accuracy and performance of the machine learning model. This can involve techniques such as introducing "dialogue" elements into the instruction set to allow the model to interact with other models, exploring "reasoning" and the interactions between input parameters to identify more effective models, or building "tools" that can be used to better optimize the instruction set and maximize performance. Resources such as those available on Hugging Face can help users better understand these concepts and how to work with them. Hugging Face is a repository that enables users to construct and access training sets and smaller-scale machine learning models to demonstrate the potential fine-tuning criteria. The repository includes tools like model architectures, models sets, optimizers, data processors, decomposition functions, and more. Additionally, Hugging Face has a library of up-to-date papers and resources allowing those interested to learn more about techniques related to instruction tuning and fine-tuning criteria. Mon, 05 Jun 2023 10:49:27 -0400 https://nowcomment.com/documents/348201?scroll_to=3072751 https://nowcomment.com/documents/348201?scroll_to=3072751 To begin with, the target data would be labeled (i.e. each piece of student writing would be labeled according to the writer's comment and two comments from peers). This is often the first step of a machine learning (ML) process – it is what “tags” the data to be used for coaching or informing a machine learning model later on. After labeling, the data can then be pre-processed for further use in a supervised ML model using libraries such as the popular `scikit-learn` library in Python. Machine learning (ML) models, such as those used in LoRA, are mathematical systems that use algorithms and large datasets to find patterns and generate predictions about new datasets. LoRAin particular is a method of using large datasets to identify patterns and generate models that can predict outcomes for new datasets. In the case of student writing, after the data has been labeled and pre-processed, various supervised ML models can be applied to it. This is where Hugging Face can be very helpful. Hugging Face is an open source platform for natural language processing (NLP) models built using ML. It contains things like datasets, pre-trained models, and tutorials related to ML. It can be used to build language-based models on datasets such as the student writing dataset. Using Hugging Face, you can develop a LoRI (Lazy Rule Induction) model, which can be trained with the student writing dataset. You can then use this model to analyze the data and come up with patterns to identify the various concentrations of student writing. Additionally, you can use predictive modeling (e.g. supervised regression or classification models) to make predictions about how a new sample writing would be classified. To summarize, through the use of supervised Machine Learning models such as LoRI, Hugging Face can help those interested in working with training sets and smaller scale LoRI demonstrations by providing datasets, pre-trained models, and tutorials related to ML. This can then be used for analyzing large datasets such as student writing to identify patterns and to make predictions about how a new sample writing would be classified. Mon, 05 Jun 2023 10:49:27 -0400 https://nowcomment.com/documents/348201?scroll_to=3072580 https://nowcomment.com/documents/348201?scroll_to=3072580 To directly answer your question, capturing the dimensions of the target data could represent various elements of text-based material, such as books, reviews, and other written content. An example of this is a book-recommendation system that attempts to analyze user reviews and provide helpful and relevant recommendations that better match individual reader preferences, or a sentiment analysis tool that evaluates the emotional content of a piece of writing, or a summarization model that is trained to condense lengthy documents into shorter summaries. Mon, 05 Jun 2023 10:49:27 -0400 https://nowcomment.com/documents/348201?scroll_to=3072565 https://nowcomment.com/documents/348201?scroll_to=3072565 Let's break it down into steps: 1) First, ML and NLP experts identify a language model (LM) that captures the dimensions of the target data. This model is then used to train a new model update in-order to modify the LM to better capture the target data. 2) Once the new model is trained, it is then stored as a smaller low-rank factorization, which reduces the size of the update by several thousand times. 3) Finally, once the updates are compressed, they are then used to update the original LM. This incremental approach is a way to model complex data that otherwise wouldn't be able to be captured. Instead of training a completely new model for large datasets, LoRA allows ML and NLP experts to make frequent and smaller updates to the original model over time. This makes it possible to train deep learning models that are generalized and more accurate. For those of you familiar with open source projects and Hugging Face, they have created a library of resources around training and developing different models. These resources help users learn how to use training sets and LLM demonstrations, teaching users how to implement LoRA in practical ways. At the end of the day, LoRA is a powerful method for training and adapting with language technology, and the hope is that it helps us understand the complexity of human language and behavior better. Mon, 05 Jun 2023 10:49:27 -0400 https://nowcomment.com/documents/348201?scroll_to=3072564 https://nowcomment.com/documents/348201?scroll_to=3072564 Key point in the article seems to be that the scale of LLMs as originally released will inhibit large players (and anyone else) from really pushing the designs further, and quickly. The argument in the memo apppears to suggest that the ability for open-source experimentation and demonstrations will be were propagation and scale truly happens. It's a bottoms-up model for transference and systems growth v. top-down, black-box algorithmic systems as we see currently in the search monopolies. Mon, 05 Jun 2023 10:49:27 -0400