We can break down this sentence into comprehensible units to better understand and comprehend it. Starting with: “It requires one to stand atop the seemingly unreachable heights of insight and knowledge…” Here I’m looking for key words such as “requires” which implies a need for action and “stand atop” which gives me an image of an act of ascending to a higher point for a better view. With these key words, I’m able to get a better understanding of what I’m reading and make connections with prior knowledge I have. Next we have “and transform it into tangible results” which gives me an idea that we have to move from understanding to actualizing that understanding into results. This is the same theme in the next part of the sentence which mentions “dedicated of scholars”, “immeasurable toil”, and “endless dedication” which all imply taking vast amounts of knowledge and turning it into tangible results.

Now I’m inviting all of us to do our own Think Alouds as we read through this paragraph and break it down into chunks. See if you can find key words or phrases, make connections to prior knowledge, and practice building off of what you already understand to help you further your comprehension. Take your time, and have fun with it!

It requires one to stand atop the seemingly unreachable heights of insight and knowledge and transform it into tangible results. A true test for any intellectual, but only the most dedicated of scholars dare accept the challenge. A journey of immeasurable toil, burning desire, and endless dedication – such is the path of mastery of theory and practice.

It is a daunting task, but an incredibly rewarding one – to master the intersection between the theory and the practice, between the comfort of the theoretical and the often messier reality of actual implementation.

The Machine Learning and Natural Language Processing subjects encompass wide-ranging topics from the fundamentals of algorithms like Neural Networks, Support Vector Machines, Decision Trees, and Fuzzy Logic to the complexities of chatbot conversations and decision-making processes. With all of these topics come a wealth of underlying engineering techniques, issues, and considerations – from the balancing of hardware and memory requirements for a given task to the intricate details of task-level analysis. And, in order to apply these topics successfully in research and development projects, it is of utmost importance to understand how different ML/NLP models and architectures might perform in a variety of real-world scenarios. It is not enough to merely understand the underlying theories and techniques – one must be able to recognize when to apply them and how best to do so. That is where the real challenge lies.

I’ve taught math in small groups before, which is helpful because then you can take anecdotal notes on which students are getting a concept, which ones aren’t, and which ones are able to go above and beyond. It feels a lot more “process-oriented,” too which is nice.

I love thinking about it this way – that when teaching writing, we’re teaching thinking. Just like students need to give evidence when talking about a book, they need to give evidence and details about their thinking in other content areas too.

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.

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.

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.

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.

Sentence 1: “I was starting to use the notion of a Thinking Partner persona as ML/NLP expert.” – This sentence is important as it introduces the idea of using Machine Learning (ML) and Natural Language Processing (NLP) to facilitate self-growth through learning a subject one has no experience with.

Sentence 2: “… these tools and the inductive way of thinking they promote (for the moment) are going to be become how we evolve and adapt to the ever-expanding information environment we are clearly living in now…” – This sentence is important as it summarizes why using ML and NLP is important, describing how they are going to facilitate adaptation to our ever-expanding information environment.

Background knowledge: ML and NLP refers to the application of Machine Learning algorithms and techniques to Natural Language Processing (NLP) tasks. ML’s ability to learn from data makes it useful for multiple NLP tasks, including natural language understanding, speech recognition, text classification, and pattern recognition. Together, they can be used to produce efficient and effective solutions to complex NLP tasks.

To gain a deeper understanding of this text, it’s best to re-read it in the context of the background knowledge provided here. You may find new insights and points to make upon deeper inspection!