AI Resources
Policies
Faculty Resources
Research
At 糖心Vlog视频, Bakersfield (糖心Vlog视频), we are committed to fostering a dynamic research environment that empowers both students and faculty to push the boundaries of innovation and discovery. As a proud participant in the National Research Platform, 糖心Vlog视频 provides cutting-edge resources and high-performance computing to support groundbreaking research across disciplines. Our faculty-led research spans fields such as Psychology and Computer Science, offering students unparalleled opportunities to engage in hands-on projects, collaborate with experts, and contribute to real-world solutions. By investing in advanced technology, interdisciplinary collaboration, and mentorship, 糖心Vlog视频 is shaping the future of research and preparing the next generation of innovators.
Security and Guidelines
糖心Vlog视频 does not have a contract or agreement for most AI tools or platforms. This means that standard 糖心Vlog视频 security, privacy, and compliance provisions need to be put in place when using these technologies. Therefore, like any other IT service or product without a 糖心Vlog视频 contract or agreement, AI tools should only be used with institutional data classified as Level 3 (General Data). The data owner must formally approve any exception to using sensitive data in public AI systems before any action can occur.
Understanding how the AI tool you wish to use will collect and maintain your data is crucial. You should never provide campus data that is not approved for use in the application, as this could compromise the security and privacy of the data.
Read our 糖心Vlog视频 AI Guidelines.
Efficiency
Generative AI has a wide range of practical applications that can significantly boost productivity across various domains. Here are some key examples of applications that demonstrate how generative AI can streamline workflows, enhance creativity, and improve overall productivity. (this list was created with the use of generative AI).
- : Generative AI can handle routine tasks such as scheduling meetings, managing emails, and organizing calendars, freeing up time for more strategic activities.
- : In marketing and advertising, generative AI can create high-quality content, including blog posts, social media updates, and ad copy, tailored to specific audiences.
- : AI-powered chatbots and virtual assistants can provide instant responses to customer inquiries, improving response times and customer satisfaction.
- : Generative AI can analyze large datasets to generate insights, create reports, and visualize data trends, aiding in decision-making processes.
- : AI can assist in coding by generating code snippets, debugging, and even suggesting improvements, thereby speeding up the development process.
- : For industries like entertainment and media, generative AI can help in brainstorming ideas, creating storylines, and even generating music or artwork.
- : AI tools can help in planning, tracking progress, and managing resources efficiently, ensuring projects stay on schedule and within budget.
Additional Resources
| Term | Definition |
|---|---|
| Artificial Intelligence (AI) | The ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. This includes learning from experience, reasoning, understanding language, recognizing patterns, and problem-solving. |
| Generative Models | These are a type of machine learning models that creates new content by learning from training data and then generating new output that are based on, or similar to, that specific training set. These generative models learn patterns, structures, and features from the training data and can create content with similar characteristics. |
| Generative Pre-trained Transformer (GPT) | A language model that uses deep learning to create realistic text. It is used in many applications, such as translation, question-answering, and text generation. This represents the "GPT" of ChatGPT. |
| Machine Learning (ML) | A subset of AI that involves the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world. |
| Deep Learning (DL) | A subset of machine learning that's based on artificial neural networks with representation learning. It can be supervised, semi-supervised, or unsupervised and aims to model high-level abstractions in data by using multiple processing layers. |
| Natural Language Processing (NLP) | A subfield of AI that focuses on the interaction between computers and humans through natural language. The ultimate objective of NLP is to read, decipher, understand, and make sense of human language in a valuable way. |
| Language Model (LM) | A type of model in NLP that predicts the next word or character in a sequence. These models are used in speech recognition, text generation, and other NLP tasks. |
| Token | In the context of NLP, a token is a single unit that is a building block for a sentence or document, such as a word, a character, or a subword. |
| Fine-Tuning | A process in machine learning where a pre-trained model (like GPT) is further trained on a new dataset with a smaller amount of data. The purpose of fine-tuning is to adopt the general knowledge of the pre-trained model to a specific task. |
| Text Classification | This involves assigning categories or labels to text. For example, sorting emails into "spam" and "not spam" is a form of text classification. |
| Transfer Learning | The application of knowledge gained while solving one problem to a different but related problem. For example, knowledge gained while learning to recognize cars could apply when trying to recognize trucks. |
| Prompt | In the context of AI, a prompt is an input given to a language model that it uses to generate a response or output. |
| Training | This is the process of feeding data into AI software so that it begins the machine learning process. |
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- This study examines the role of generative AI in enhancing or replacing traditional team dynamics using a randomized controlled experiment. It highlights that teams augmented with generative AI significantly outperformed those relying solely on human collaboration.
- The Impact of Generative AI on Labor Market Matching
- by Justin Kaashoek, Manish Raghavan, and John J. Horton
- Published: Mar 27, 2024
- Imagine applying for jobs by simply asking your artificial intelligence (AI) assistant to 鈥減lease put together a resume and cover letter based on my experiences and submit the application to senior management positions at clean-energy start-ups with fewer...
- The Productivity Effects of Generative AI: Evidence from a Field Experiment with GitHub
Copilot
- by Kevin Zheyuan Cui, Mert Demirer, Sonia Jaffe, Leon Musolff, Sida Peng, and Tobias Salz
- Published: Mar 27, 2024
- We are providing a preview of a project that analyzes two field experiments with 1,974 software developers at Microsoft and Accenture to evaluate the productivity impact of Generative AI. As part of our study, a random subset of developers was given...
- Bringing Worker Voice into Generative AI
- by Thomas A. Kochan, Ben Armstrong, Julie Shah, Emilio J. Castilla, Ben Likis, and Martha E. Mangelsdorf
- Published: Mar 27, 2024
- After conducting more than fifty interviews about generative AI with experts in business, academia, labor, government, and the AI development community, the authors summarize their findings and include recommendations for incorporating employees鈥� perspectives into AI development.