AI英语
英语学习
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Demystifying Higher Education with AI
By Paul Dorney, Chief Technology Officer at Gravyt
Higher education is at a crossroads. Budgets are tightening. Student needs are growing more complex. And the pressure to demonstrate measurable outcomes—graduation rates, job placement, lifelong value—has never been higher.
As institutions grapple with these demands, artificial intelligence isn’t some futuristic buzzword anymore—it’s a practical, proven tool that’s helping colleges and universities rise to the challenge. It’s doing the real work: powering personalized support, enabling timely intervention, and helping leaders make better decisions faster.
This shift reflects a broader evolution in how we think about higher education. Students today expect their college experience to be as responsive and seamless as every other part of their lives. If a streaming service can recommend the right show, or a bank can alert you before you overdraft, why shouldn’t your university know when you might be struggling—and help before it’s too late?
Institutions that embrace AI aren’t chasing hype—they’re stepping up to meet a new standard. And if higher education is serious about delivering on its promise to help students succeed, then AI can’t just be an afterthought. It has to be core to the strategy.
One of the biggest challenges on campuses today is capacity. Student services teams are being asked to do more with fewer resources. Advisors, financial aid officers, and support staff want to offer high-quality, human-centered help, but they’re underwater. At the same time, students expect (and deserve) immediate, personalized guidance. They don’t want to wait days for a reply to a simple question. They need answers in real time, and they want to feel like someone is paying attention. That’s where AI can make an immediate impact.
With tools like intelligent chatbots and workflow automation, institutions can free up staff from repetitive, low-impact tasks. AI can triage student questions—whether it’s about FAFSA deadlines, transfer credits, or how to drop a class—24/7. It can route more complex issues to the right person or flag high-priority cases for intervention. This doesn’t replace human connection—it makes it more possible. Staff gain back time to focus on what matters most: nuanced, high-touch conversations that build trust and drive outcomes.
AI also increases the consistency of support. When responses are automated, they don’t vary based on who’s working that day or what time the question comes in. And for students who are first-generation, working full-time, or balancing caregiving responsibilities, that kind of accessibility can be the difference between persistence and giving up.
It’s not just about convenience—it’s about equity. AI helps ensure that every student, regardless of their schedule or background, has access to the timely help they need to succeed.
Most institutions know that improving retention is both a financial imperative and a moral one. But in practice, schools still rely on reactive approaches: midterm grade checks, end-of-semester surveys, or waiting for students to raise their hands. AI enables something better: early, proactive support driven by data.
By analyzing behaviors like LMS logins, assignment submissions, attendance, and GPA fluctuations, AI can help surface subtle signals that a student might be struggling, before they’re at risk of dropping out. These models aren’t about replacing advisors with dashboards. They’re about giving staff more insight and more time to act. Even simple nudges—a reminder to complete a form, encouragement to meet with a tutor, a check-in from an advisor—can have a big impact. When timed well, these messages show students that someone is paying attention. That sense of being seen and supported helps students stay engaged and on track.
And these moments matter. In an era where more students are questioning the value of higher education, institutions have to earn student trust and demonstrate tangible value at every turn. AI helps colleges shift from triaging problems to anticipating and solving them—one student, one moment at a time.
Perhaps the most exciting promise of AI is that it enables colleges to support students not just during enrollment or in the classroom, but throughout their entire journey. With AI, we can become proactive instead of reactive. The tools coming to market today will transform the student lifecycle experience—from the first moment a prospect starts researching schools, to the day they graduate, and well beyond. This is about more than retention. It’s about long-term engagement, continuous improvement, and mission alignment.
Imagine being able to understand how your alumni are doing years after graduation—not just through an annual survey, but through real-time feedback loops. Or being able to track which outreach messages drove the most enrollment conversions and act in real time. These aren’t one-time wins. They’re ongoing feedback mechanisms that help institutions deliver more value and stay aligned with student needs.
These tools don’t just benefit institutions—they benefit students. When things work more smoothly, when support is easier to access, when guidance feels personal and relevant, students are more likely to succeed. They’re more likely to feel like they belong.
Too often, AI is still treated as an add-on—a flashy tool reserved for innovation teams or short-term pilots. But to unlock real value, institutions need to treat AI the way they treat their learning management system or financial aid platform: as foundational infrastructure.
AI isn’t just a tool for chatbots or analytics. It’s a layer that can enhance nearly every touchpoint in the student lifecycle, from marketing and enrollment to advising and alumni engagement. Think about the full journey: A prospective student lands on a university website and gets dynamic, personalized content based on their interests. They’re guided through the application process with tailored messages. Once enrolled, they get just-in-time nudges to register for classes or apply for internships. Years later, they’re prompted to complete a graduate survey or participate in alumni mentoring.
That’s not a future scenario—it’s what’s possible today, when institutions treat AI as a strategic enabler rather than a side project. Of course, with that power comes responsibility. Institutions must be clear with students about how AI is used, where automation begins and ends, and how data is collected and safeguarded. AI systems should be trained on diverse data to avoid reinforcing existing biases. And students should always have a way to escalate to a human when they need one. Equity, transparency, and human oversight aren’t nice-to-haves—they’re non-negotiables. These principles must be embedded from the start, not bolted on later.
At its core, higher education is about helping people reach their potential. It’s about creating opportunity, fostering growth, and unlocking talent. Those goals haven’t changed—but the tools to achieve them have. AI, done right, doesn’t replace the human experience of learning. It enhances it. It removes barriers, extends capacity, and gives every student a better shot at success. The most meaningful impact of AI won’t come from major product launches or shiny demos. It will come from the small ways it makes life better—for staff, for faculty, and most of all, for students.
For institutions navigating change, facing pressure, and looking to do more with less, AI offers a way forward. A way to stay true to their mission while building for the future. Now is the time to stop asking whether AI belongs in higher ed—and start asking how we can use it to serve students better at every step of the journey.
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Deepseek告诉你过了CET6级后如何提高英语水平？
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transformer（deepseek)
Thinking…
Transformers are a type of neural network architecture introduced in the paper “Attention Is All You Need” by Vaswani et al. in 2017. They revolutionized NLP tasks. Unlike previous models like RNNs or LSTMs, Transformers use self-attention mechanisms to process all words in a sequence simultaneously, allowing for more parallelization and handling long-range dependencies better.
I should explain the key components: self-attention, multi-head attention, positional encoding, encoder-decoder structure. Also, mention the impact of Transformers on models like BERT, GPT, T5, etc. It’s important to highlight their advantages over RNNs and why they became so popular.
Wait, the user might be looking for both a basic explanation and maybe some details on how they work. Let me structure this logically. Start with an overview, then key components, how they work, advantages, applications, and popular models. That should cover the essentials.
I need to make sure the explanation of self-attention is clear. Maybe use an example, like how each word relates to others in a sentence. Also, touch on positional encoding since Transformers don’t have inherent sequence order understanding like RNNs.
Oh, and the user might be interested in knowing that Transformers are used beyond NLP now, in areas like computer vision with Vision Transformers. Applications are important to mention too—machine translation, text generation, etc.
I should check if there are any common confusions to address. For example, the difference between the Transformer architecture and the models built on it, like BERT vs. the original Transformer. Also, maybe mention the computational aspects, like the need for large datasets and resources.
Make sure the answer is comprehensive but not too technical. Use bullet points for clarity. Alright, time to put it all together in a structured way.
Thinking…Transformers in Deep Learning
Transformers are a groundbreaking neural network architecture introduced in the 2017 paper Attention Is All You Need. They revolutionized natural language processing (NLP) and are now widely used in tasks like translation, text generation, and more. Here’s a breakdown:
Key Components
- Self-Attention Mechanism
- Allows the model to weigh the importance of different words in a sequence relative to each other.
- Example: In “The cat sat on the mat,” the word “cat” relates strongly to “sat” and “mat.”
- Multi-Head Attention
- Runs multiple self-attention operations in parallel, capturing diverse relationships (e.g., syntax, semantics).
- Positional Encoding
- Injects information about word order into the model (since Transformers process sequences in parallel, unlike RNNs).
- Feed-Forward Networks
- Applies non-linear transformations to individual positions after attention.
- Encoder-Decoder Architecture
- Encoder: Processes input and builds contextual representations.
- Decoder: Generates output (e.g., translated text) using encoder outputs and previous decoder states.
How Transformers Work
- Input Embedding: Convert input tokens (words/subwords) into vectors.
- Add Positional Encoding: Embed positional information into vectors.
- Self-Attention Layer: Compute relationships between all tokens.
- Feed-Forward Layer: Refine representations.
- Repeat: Stack multiple layers to capture deeper patterns.
Advantages Over RNNs/CNNs
- Parallelization: Processes entire sequences at once (faster training).
- Long-Range Dependencies: Handles relationships between distant tokens better (no sequential decay).
- Scalability: Efficiently trained on massive datasets (e.g., GPT-3 with 175B parameters).
Applications
- Language Models: GPT, BERT, T5.
- Machine Translation: Google Translate.
- Text Generation: ChatGPT, Claude.
- Vision: Vision Transformers (ViT) for image classification.
- Multimodal Tasks: Models like DALL·E (text-to-image).
Popular Transformer Models
Model Type Use Case BERT Encoder-only Text classification, QA GPT Decoder-only Text generation T5 Encoder-Decoder Text-to-text tasks ViT Vision Image classification
Challenges
- Computational Cost: Requires massive compute/resources (e.g., GPT-4).
- Context Length: Limited by attention’s quadratic complexity (fixed in variants like Flash Attention).
Transformers have become the backbone of modern AI, pushing the boundaries of what’s possible in NLP, vision, and beyond. Let me know if you’d like a deeper dive into any aspect! 🤖
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Deepseek告诉你过了CET6级后如何提高英语水平？
通过英语六级后，可以采取以下方法进一步提升英语水平：
1. 扩展词汇量
- 阅读英文原版书籍：选择小说、专业书籍或杂志，如《纽约时报》、《经济学人》。
- 使用词汇应用：如Anki、Quizlet等工具帮助记忆新单词。
2. 提升听力
- 收听英语播客和新闻：如BBC、NPR、TED Talks等。
- 观看英文影视剧：尽量不看字幕，或使用英文字幕。
3. 加强口语
- 参加英语角或语言交换：与母语者或其他学习者练习。
- 模仿和跟读：模仿母语者的发音和语调，使用TED Talks或YouTube视频。
4. 提高写作
- 定期写作：写日记、博客或文章，并请他人修改。
- 学习写作技巧：阅读写作指南，分析优秀范文。
5. 深化语法和句型
- 学习高级语法：通过语法书或在线课程提升语法水平。
- 分析复杂句型：阅读时注意长难句的结构。
6. 参加英语考试或课程
- 准备更高级考试：如雅思、托福、GRE等。
- 参加专业英语课程：如商务英语、学术英语等。
7. 沉浸式学习
- 出国留学或游学：在英语国家学习或生活。
- 创造英语环境：日常尽量使用英语，如手机、电脑设置为英文。
8. 保持学习习惯
- 制定学习计划：每天固定时间学习英语。
- 定期评估进展：通过测试或自我评估调整学习策略。
9. 使用科技工具
- 语言学习应用：如Duolingo、Rosetta Stone等。
- 在线课程：Coursera、edX等平台提供优质英语课程。
10. 参与英语社群
- 加入英语学习社群：如Reddit的英语学习板块或Facebook群组。
- 参加英语活动：如演讲比赛、辩论赛等。
坚持学习和实践，逐步提升英语水平。
