CS795 - day1- 2025 Jan 17 Friday

Zoom, start recording

Datacamp: registration

HPC survey (5 minutes)

 The Research & Cloud Computing group (RCC) recently launched a survey regarding the need for training for research computing users. We would like to ask you to promote this survey among your students in classes and research groups as well as your colleagues, postdocs and other staff. The survey link is:

 

https://odu.co1.qualtrics.com/jfe/form/SV_9zCyC5peVHeQgl0

 

Please encourage them to submit responses by the end of January so we can use the findings to adjust offerings for this semester. Your help will be greatly appreciated!

CoLab

syllabus, 

SoCrative ice break, anonymous

Github

Let students introduct each other in breakout room. Then student A introduce student B. 

skipp self-introduction video. 

project team, 

ChatGPT, anthropic, 

all of us account

AI101

A primer on deep learning in genetics, classification model

https://colab.research.google.com/github/hongqin/Python-CoLab-bootcamp/blob/master/A_Primer_on_Deep_Learning_in_Genomics_Public.ipynb

Rsync

 

rsync

https://www.cisecurity.org/advisory/multiple-vulnerabilities-in-rsync-could-allow-for-remote-code-execution_2025-007

Profiling the transcriptomic age of single-cells in humans

 https://zenodo.org/records/10405106

Data for "Profiling the transcriptomic age of single-cells in humans"

Creators

• Zakar-Polyák, Enikő (Researcher)1
 
• Kerepesi, Csaba (Supervisor)1

CVE-2024-27322 Should Never Have Been Assigned And R Data Files Are Still Super Risky Even In R 4.4.0

CVE-2024-27322 Should Never Have Been Assigned And R Data Files Are Still Super Risky Even In R 4.4.0

 

https://rud.is/b/2024/05/03/cve-2024-27322-should-never-have-been-assigned-and-r-data-files-are-still-super-risky-even-in-r-4-4-0/

Spring 2025 course schedule

 CS 795/895 DASC, AI for health and life sciences. 

Scheduled Meeting Times

Type	Time	Days	Where	Date Range	Schedule Type	Instructors
Scheduled In-Class Meetings	4:30 pm - 7:10 pm	F	ENGINEERING & COMP SCI BLDG 2120	Jan 11, 2025 - Apr 28, 2025	LECTURE	HONG QIN (P)

human scRNA aging data

 There are some human single cell aging data,

 

https://www.nature.com/articles/s41586-024-07606-7

 

https://www.nature.com/articles/s43587-024-00640-0

 

https://www.nature.com/articles/s41467-020-17876-0?fromPaywallRec=false

 

https://pmc.ncbi.nlm.nih.gov/articles/PMC10306289/#_ad93_

Human PBMC scRNA-seq–based aging clocks reveal ribosome to inflammation balance as a single-cell aging hallmark and super longevity

Hongming Zhu ^1,†, Jiawei Chen ^2,†, Kangping Liu ^2,†, Lei Gao ^1,3, Haiyan Wu ¹, Liangliang Ma ⁴, Jieru Zhou ⁴, Zhongmin Liu ^1,*, Jing-Dong J Han ^2,*

 

https://arxiv.org/abs/2412.14135

[Submitted on 18 Dec 2024]

Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective

Zhiyuan Zeng, Qinyuan Cheng, Zhangyue Yin, Bo Wang, Shimin Li, Yunhua Zhou, Qipeng Guo, Xuanjing Huang, Xipeng Qiu

OpenAI o1 represents a significant milestone in Artificial Inteiligence, which achieves expert-level performances on many challanging tasks that require strong reasoning this http URL has claimed that the main techinique behinds o1 is the reinforcement learining. Recent works use alternative approaches like knowledge distillation to imitate o1's reasoning style, but their effectiveness is limited by the capability ceiling of the teacher model. Therefore, this paper analyzes the roadmap to achieving o1 from the perspective of reinforcement learning, focusing on four key components: policy initialization, reward design, search, and learning. Policy initialization enables models to develop human-like reasoning behaviors, equipping them with the ability to effectively explore solution spaces for complex problems. Reward design provides dense and effective signals via reward shaping or reward modeling, which is the guidance for both search and learning. Search plays a crucial role in generating high-quality solutions during both training and testing phases, which can produce better solutions with more computation. Learning utilizes the data generated by search for improving policy, which can achieve the better performance with more parameters and more searched data. Existing open-source projects that attempt to reproduce o1 can be seem as a part or a variant of our roadmap. Collectively, these components underscore how learning and search drive o1's advancement, making meaningful contributions to the development of LLM.

AI model editing techniques

 chatgpt output, un-edited. Some wrong links and erorrs are apparent. 

---

1. Fine-Tuning

• Definition: Adjusting pre-trained models by retraining them on a specific dataset to tailor them to a particular task or domain.
• References:
  • Fine-Tuning Language Models
  • Full and Parameter-Efficient Fine-Tuning

---

2. Prompt Engineering

• Definition: Crafting specific inputs (prompts) to guide the behavior of large language models without altering their parameters.
• References:
  • Prompting for NLP
  • Chain-of-Thought Prompting

---

3. Model Editing via Retrieval-Augmented Generation (RAG)

• Definition: Integrating external databases or retrieval systems to improve or adapt the model's outputs without direct parameter changes.
• References:
  • RAG Explained
  • Dynamic Knowledge Updates

---

4. Knowledge Injection

• Definition: Incorporating domain-specific knowledge into a model post-training.
• References:
  • Knowledge-Integrated Language Models
  • Knowledge Graph Integration

---

5. Soft Prompt Tuning

• Definition: Learning a set of prompt tokens that adjust the behavior of pre-trained models without altering core weights.
• References:
  • Soft Prompt Tuning Explained
  • Prompt-Based Learning

---

6. Modular Transfer Learning

• Definition: Dividing models into modules (e.g., embeddings, encoders, decoders) and only updating or replacing specific components.
• References:
  • Modular Learning for NLP
  • Parameter-Efficient Transfer Learning

---

7. Dynamic Reweighting

• Definition: Adjusting the influence of certain parts of the model during inference based on specific tasks or inputs.
• References:
  • Sparsity-Based Optimization
  • Attention Mechanisms

---

8. Model Surgery

• Definition: Directly modifying neural network weights, layers, or architectures post-training.
• References:
  • MEMIT: Model Surgery Techniques
  • Layerwise Editing

---

9. Continual Learning

• Definition: Allowing a model to learn new information over time without forgetting prior knowledge.
• References:
  • Elastic Weight Consolidation
  • Continual Learning Overview

---

10. Gradient Editing

• Definition: Directly modifying gradients during training to induce specific behaviors or rectify known issues.
• References:
  • Gradient Editing Explained
  • Debugging Neural Models

---

11. Reinforcement Learning from Human Feedback (RLHF)

• Definition: Using human evaluations to fine-tune models, particularly for aligning AI with desired ethical or stylistic outcomes.
• References:
  • RLHF Overview
  • OpenAI's Use of RLHF

---

12. Model Patching

• Definition: Adding or replacing specific components in a model with updated or improved modules.
• References:
  • Knowledge Patch
  • Error Correction Patching

---

13. Parameter-Free Updating

• Definition: Techniques like black-box optimization or external decision systems that modify behavior without changing core parameters.
• References:
  • Black-Box Optimization
  • Decision-Based Editing

---

14. Memory Editing

• Definition: Directly modifying or updating specific "memories" in a model, allowing it to adjust responses to certain inputs or queries without retraining.
• Techniques:
  • MEMIT (Model Editing Made Informed by Targeting)
  • ROME (Rank-One Model Editing)
• References:
  • MEMIT
  • ROME
  • Direct Model Memory Editing

---

15. Multi-Modal Model Editing

• Definition: Modifying models trained on multi-modal data (e.g., text and images) for domain-specific applications.
• References:
  • Multi-Modal Learning
  • Vision-Language Consistency

---

16. Federated Learning Adjustments

• Definition: Decentralized learning where updates are based on data from multiple users without directly sharing datasets.
• References:
  • Federated Learning Overview
  • Privacy-Preserving Federated Learning

---

17. Meta-Learning (Learning to Learn)

• Definition: Training models to quickly adapt to new tasks with minimal data by leveraging meta-learning algorithms.
• References:
  • MAML: Model-Agnostic Meta-Learning
  • Meta-Learning Overview

---

This version now includes Memory Editing with references to emerging techniques like MEMIT and ROME for direct manipulation of model-specific knowledge.

editable neural networks in health science

 Meng, K., Bau, D., Andonian, A. & Belinkov, Y. Locating and editing factual associations in GPT.

Adv. Neural Inf. Process. Syst. (2022). at

<https://proceedings.neurips.cc/paper_files/paper/2022/hash/6f1d43d5a82a37e89b0665b33bf3a182-

Abstract-Conference.html>

26. Meng, K., Sharma, A. S., Andonian, A., Belinkov, Y. & Bau, D. Mass-editing memory in a

transformer. in International Conference on Learning Representations (arxiv.org, 2023). at

<https://arxiv.org/abs/2210.07229>

27. Mitchell, E., Lin, C., Bosselut, A., Manning, C. D. & Finn, C. Memory-Based Model Editing at

Scale. in Proceedings of the 39th International Conference on Machine Learning (eds. Chaudhuri,

K., Jegelka, S., Song, L., Szepesvari, C., Niu, G. & Sabato, S.) 162, 15817–15831 (PMLR, 17--23

Jul 2022).

28. Hartvigsen, T., Sankaranarayanan, S., Palangi, H., Kim, Y. & Ghassemi, M. Aging with GRACE:

Lifelong Model Editing with Discrete Key-Value Adaptors. in Advances in Neural Information

Processing Systems (2023). at <https://arxiv.org/abs/2211.11031>

29. Mitchell, E., Lin, C., Bosselut, A., Finn, C. & Manning, C. Fast model editing at scale. in

International Conference on Learning Representations (arxiv.org, 2022). at

<https://arxiv.org/abs/2110.11309>

30. Sinitsin, A., Plokhotnyuk, V., Pyrkin, D., Popov, S. & Babenko, A. Editable Neural Networks. in

International Conference on Learning Representations (2020). at <http://arxiv.org/abs/2004.00345>

31. De Cao, N., Aziz, W. & Titov, I. Editing Factual Knowledge in Language Models. in Proceedings of

the 2021 Conference on Empirical Methods in Natural Language Processing 6491–6506

(Association for Computational Linguistics, 2021).

32. Zhong, Z., Wu, Z., Manning, C. D., Potts, C. & Chen, D. MQuAKE: Assessing Knowledge Editing

in Language Models via Multi-Hop Questions. arXiv [cs.CL] (2023). at

<http://arxiv.org/abs/2305.14795>

33. Cohen, R., Biran, E., Yoran, O., Globerson, A. & Geva, M. Evaluating the ripple effects of

knowledge editing in language models. Trans. Assoc. Comput. Linguist. 12, 283–298 (2023).

De Cao, N., Aziz, W. & Titov, I. Editing Factual Knowledge in Language Models. arXiv [cs.CL]

(2021). at <http://arxiv.org/abs/2104.08164>

35. Meng, K., Sharma, A. S., Andonian, A., Belinkov, Y. & Bau, D. Mass-Editing Memory in a

Transformer. arXiv [cs.CL] (2022). at <http://arxiv.org/abs/2210.07229>

36. Mitchell, E., Lin, C., Bosselut, A., Finn, C. & Manning, C. D. Fast Model Editing at Scale. arXiv

[cs.LG] (2021). at <http://arxiv.org/abs/2110.11309>

37. Hartvigsen, T., Sankaranarayanan, S., Palangi, H., Kim, Y. & Ghassemi, M. Aging with GRACE:

Lifelong Model Editing with Key-Value Adaptors. (2022). at

<https://openreview.net/pdf?id=ngCT1EelZk>

Language Models: Problems, Methods, and Opportunities. arXiv [cs.CL] (2023). at

<http://arxiv.org/abs/2305.13172>

41. Hase, P., Hofweber, T., Zhou, X., Stengel-Eskin, E. & Bansal, M. Fundamental problems with model

editing: How should rational belief revision work in LLMs? arXiv [cs.CL] (2024). at

<https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=FO90FgM

AAAAJ:M3ejUd6NZC8C>

42. Cheng, S., Tian, B., Liu, Q., Chen, X., Wang, Y., Chen, H. & Zhang, N. Can We Edit Multimodal

Large Language Models? in Proceedings of the 2023 Conference on Empirical Methods in Natural

Language Processing (eds. Bouamor, H., Pino, J. & Bali, K.) 13877–13888 (Association for

Computational Linguistics, 2023).

Here are the URLs for the specified papers:

1. **Locating and editing factual associations in GPT**  

   Meng, K., Bau, D., Andonian, A. & Belinkov, Y. (2022).  

   [Link to Paper](https://proceedings.neurips.cc/paper_files/paper/2022/hash/6f1d43d5a82a37e89b0665b33bf3a182-Abstract-Conference.html)

2. **Mass-editing memory in a transformer**  

   Meng, K., Sharma, A. S., Andonian, A., Belinkov, Y. & Bau, D. (2023).  

   [Link to Paper](https://arxiv.org/abs/2210.07229)

3. **Memory-Based Model Editing at Scale**  

   Mitchell, E., Lin, C., Bosselut, A., Manning, C. D. & Finn, C. (2022).  

   [Link to Paper](https://proceedings.mlr.press/v162/mitchell22a.html)

4. **Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors**  

   Hartvigsen, T., Sankaranarayanan, S., Palangi, H., Kim, Y. & Ghassemi, M. (2023).  

   [Link to Paper](https://arxiv.org/abs/2211.11031)

5. **Fast model editing at scale**  

   Mitchell, E., Lin, C., Bosselut, A., Finn, C. & Manning, C. D. (2022).  

   [Link to Paper](https://arxiv.org/abs/2110.11309)

6. **Editable Neural Networks**  

   Sinitsin, A., Plokhotnyuk, V., Pyrkin, D., Popov, S. & Babenko, A. (2020).  

   [Link to Paper](http://arxiv.org/abs/2004.00345)

7. **Editing Factual Knowledge in Language Models**  

   De Cao, N., Aziz, W. & Titov, I. (2021).  

   [Link to Paper](http://arxiv.org/abs/2104.08164)

8. **MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions**  

   Zhong, Z., Wu, Z., Manning, C. D., Potts, C. & Chen, D. (2023).  

   [Link to Paper](http://arxiv.org/abs/2305.14795)

9. **Evaluating the ripple effects of knowledge editing in language models**  

   Cohen, R., Biran, E., Yoran, O., Globerson, A. & Geva, M. (2023).  

   [Link to Paper](https://transacl.org/ojs/index.php/tacl/article/view/3736)

10. **Language Models: Problems, Methods, and Opportunities**  

    (2023).  

    [Link to Paper](http://arxiv.org/abs/2305.13172)

11. **Fundamental problems with model editing: How should rational belief revision work in LLMs?**  

    Hase, P., Hofweber, T., Zhou, X., Stengel-Eskin, E. & Bansal, M. (2024).  

    [Link to Paper](https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=FO90FgMAAAAAJ:M3ejUd6NZC8C)

12. **Can We Edit Multimodal Large Language Models?**  

    Cheng, S., Tian, B., Liu, Q., Chen, X., Wang, Y., Chen, H. & Zhang, N. (2023).  

    [Link to Paper](https://arxiv.org/abs/2305.14795)

Citations:

[1] https://proceedings.neurips.cc/paper_files/paper/2022/hash/6f1d43d5a82a37e89b

Autonomous AI-Driven Drug Discovery

 

A Framework for Autonomous AI-Driven Drug Discovery

Douglas W Selinger, Timothy R Wall, Eleni Stylianou, Ehab M Khalil, Jedidiah Gaetz, Oren Levy

doi: https://doi.org/10.1101/2024.12.17.629024

https://www.biorxiv.org/content/10.1101/2024.12.17.629024v2

Comment Published: 26 June 2024 Harnessing the deep learning power of foundation models in single-cell omics Qin Ma, Yi Jiang, Hao Cheng & Dong Xu Nature Reviews Molecular Cell Biology volume 25, pages593–594 (2024)Cite this article 2697 Accesses 1 Citations 5 Altmetric Metrics

 https://www.nature.com/articles/s41580-024-00756-6

• Comment
• Published: 26 June 2024

Harnessing the deep learning power of foundation models in single-cell omics

• Qin Ma, 
• Yi Jiang, 
• Hao Cheng & 
• Dong Xu 

Nature Reviews Molecular Cell Biology volume 25, pages593–594 (2024)Cite this article

• 2697 Accesses

• 1 Citations

• 5 Altmetric

• Metrics

Aphasia is a language disorder t

 ChatGPT 

Aphasia is a language disorder that affects a person's ability to communicate. It typically occurs when the areas of the brain responsible for language (often in the left hemisphere) are damaged, usually due to a stroke, traumatic brain injury, brain tumor, or other neurological conditions.

Key Features of Aphasia

1. Impairments in Language:

   • Speaking: Difficulty finding words, forming sentences, or articulating speech.
   • Understanding: Problems comprehending spoken or written language.
   • Reading and Writing: Challenges in reading comprehension and writing.
   • Gestural Communication: Even non-verbal communication like gestures may be affected.

2. Preserved Abilities:

   • Intellectual and cognitive functions outside language (e.g., memory, reasoning) are often intact.
   • Basic social skills and emotional expressions are usually preserved.

Types of Aphasia

Aphasia varies in severity and type, depending on the brain areas affected:

1. Broca's Aphasia (Non-Fluent Aphasia):

   • Difficulty producing speech; sentences are short and effortful.
   • Comprehension remains relatively intact.
   • Example: "Want... coffee... shop..."

2. Wernicke's Aphasia (Fluent Aphasia):

   • Speech is fluent but often nonsensical or filled with made-up words.
   • Comprehension is impaired.
   • Example: "The fish are on the cupboard with delight."

3. Global Aphasia:

   • Severe impairment in both understanding and producing language.
   • Often occurs after extensive brain damage.

4. Anomic Aphasia:

   • Difficulty finding specific words, especially nouns and verbs.
   • Speech is grammatically correct but lacks specific terms.

5. Primary Progressive Aphasia (PPA):

   • A form of dementia where language abilities decline gradually over time.
   • Not caused by acute events like a stroke.

Causes of Aphasia

• Stroke: The most common cause.
• Traumatic Brain Injury (TBI).
• Brain Tumors.
• Neurodegenerative Diseases: Such as Alzheimer's disease or other dementias.
• Infections: Rarely, brain infections can cause aphasia.

Diagnosis and Treatment

• Diagnosis:
  • Medical imaging (MRI or CT scans) to locate brain damage.
  • Language assessments to evaluate speaking, listening, reading, and writing skills.
• Treatment:
  • Speech and Language Therapy: Tailored exercises to rebuild language skills and find alternative ways of communicating.
  • Technology Aids: Apps or devices that assist in communication.
  • Family Support and Education: Teaching family members effective ways to communicate.

Prognosis

• Recovery depends on the cause and extent of brain damage, as well as the individual's age, overall health, and rehabilitation efforts.
• Some people experience significant improvement, while others may have long-term challenges.

Patients with aphasia often have difficulty producing correct phonemes, but the nature and extent of this difficulty depend on the type and severity of their aphasia. Here's a detailed breakdown:

---

Phoneme-Related Issues in Aphasia

1. Broca’s Aphasia (Non-Fluent Aphasia):

   • Phoneme Production: Patients often struggle with motor planning for speech (a condition called apraxia of speech, which can co-occur with Broca’s aphasia). This leads to distorted or incorrect phoneme articulation.
   • Characteristics:
     • Speech is effortful and halting.
     • Frequent phoneme omissions, substitutions, or distortions.
     • Example: "spoon" might be pronounced as "poon" or "foon."

2. Wernicke’s Aphasia (Fluent Aphasia):

   • Phoneme Selection: Errors are more related to selecting the correct phonemes rather than articulating them.
   • Phonemic Paraphasias: Substitutions, additions, or omissions of phonemes within words.
     • Example: "table" might be pronounced as "fable" or "gable."
   • Speech remains fluent but may be filled with neologisms (made-up words) and phonemic errors.

3. Global Aphasia:

   • Severe impairments across all language domains, including phoneme production.
   • Patients may have limited speech output and struggle to produce recognizable phonemes.

4. Anomic Aphasia:

   • Phoneme production is usually intact, but patients struggle to retrieve the right words.
   • Phoneme errors are rare unless the person is also under stress or experiencing secondary language difficulties.

5. Conduction Aphasia:

   • Marked by phonemic paraphasias (errors in the arrangement of phonemes) during speech, especially when attempting to repeat phrases.
   • Example: "butterfly" might be pronounced as "flutterby."

---

Underlying Causes of Phoneme Errors

1. Motor Speech Disorders:

   • Co-occurring apraxia of speech or dysarthria can impair phoneme articulation.

2. Linguistic Processing Errors:

   • Difficulty selecting or sequencing phonemes in the brain's language-processing regions.

3. Auditory Feedback Issues:

   • Difficulty monitoring and correcting phoneme production in real time, particularly in Wernicke’s and conduction aphasia.

---

Treatment and Rehabilitation

1. Speech Therapy:

   • Techniques like phoneme repetition, minimal pair contrast drills (e.g., "bat" vs. "pat"), and syllable shaping exercises.

2. Motor Training:

   • For apraxia, focus on motor planning and sequencing exercises.

3. Auditory-Phonological Training:

   • Use of tools and exercises to improve phoneme selection and self-monitoring.

4. Technology-Aided Interventions:

   • Speech-generating devices and software to aid communication.

---

Prognosis

• With consistent therapy, many patients can improve their phoneme production, though the degree of recovery depends on the type and severity of the aphasia, as well as associated conditions like apraxia.

overleaf to IEEE PDF express

 download overleaf working directory in zip file

expand the zip file and remove unnecssary files, and then create a new file. When working on github folders, make sure that hidden .git folder is removed. 

"Create a new title" on IEEE PDF Express

upload the new zip file. (this sometime stuck. make sure that session has been log-out.)

wait for pdf to be generated. (This sometime fail frequently). 

compare two pdf files for IEEE PDF Express conference submission

 chatgpt: To compare two PDF files for differences, you have several options:

1. Using a PDF Comparison Tool

There are tools specifically designed for PDF comparison, such as:

• Adobe Acrobat Pro DC: It has a built-in "Compare Files" feature.
• DiffPDF: A lightweight application to compare two PDFs visually or textually.
• Beyond Compare: A general comparison tool with PDF support via add-ons.
• PDF-XChange Editor: Offers PDF comparison as part of its suite.

2. Online PDF Comparison Tools

There are free and paid online services for comparing PDFs:

• Draftable: Offers a detailed side-by-side view of differences.
• PDF24 Tools: A straightforward online PDF comparison tool.

3. Python for PDF Comparison

If you'd like to script the comparison, you can use Python libraries like PyPDF2, pdfplumber, or pdfdiff. Here's an example:

Example Python Code

from PyPDF2 import PdfReader

# Function to extract text from a PDF file
def extract_text_from_pdf(pdf_path):
    reader = PdfReader(pdf_path)
    text = ""
    for page in reader.pages:
        text += page.extract_text()
    return text

# Compare two PDFs
pdf1_text = extract_text_from_pdf("file1.pdf")
pdf2_text = extract_text_from_pdf("file2.pdf")

if pdf1_text == pdf2_text:
    print("The PDF files are identical.")
else:
    print("The PDF files have differences.")

4. Using Command-Line Tools

• diffpdf: A command-line tool to compare PDFs.
• pdftotext: Convert both PDFs to text and then use a standard text comparison tool like diff or vimdiff.

hqin@Hong-MBP2 Downloads % diff-pdf -v file1.pdf file2.pdf 

page 0 has 3756 pixels that differ

page 0 differs

page 1 has 369491 pixels that differ

page 1 differs

page 2 has 0 pixels that differ

page 3 has 0 pixels that differ

page 4 has 0 pixels that differ

page 5 has 0 pixels that differ

page 6 has 0 pixels that differ

page 7 has 0 pixels that differ

page 8 has 0 pixels that differ

2 of 9 pages differ.

Visual comparison shows that IEEE PDF Express put an extract figure name in Figure 3. This png file name has extra space. So, I renamed this png file by replace white space with '_'s. This seems to fix the error. 

hqin@Hong-MBP2 Downloads % diff-pdf -v overleaf-MABEL_neuralnetwork_SHAP_IEEE2024Fall.pdf /Users/hqin/Downloads/2024402761.pdf 

page 0 has 3756 pixels that differ

page 0 differs

page 1 has 0 pixels that differ

page 2 has 0 pixels that differ

page 3 has 0 pixels that differ

page 4 has 0 pixels that differ

page 5 has 0 pixels that differ

page 6 has 0 pixels that differ

page 7 has 0 pixels that differ

page 8 has 0 pixels that differ

1 of 9 pages differ.

Visual check the first page to find they are consistent. 

Schmidt Sciences

 Schmidt Sciences offers several funding opportunities across various scientific disciplines. Here are some of their current programs:

【{"image_fetch": "AI2050 Fellowship Program"}】 AI2050 Fellowship Program
Supports researchers working on artificial intelligence projects aimed at benefiting humanity by 2050. The program funds both senior and early-career scholars for two years to address a wide range of global challenges in AI. citeturn0search0

【{"image_fetch": "Humanities and AI Virtual Institute"}】 Humanities and AI Virtual Institute (HAVI)
Launching on January 15, 2025, this program will support research at the intersection of artificial intelligence and the humanities. It aims to advance humanities scholarship through AI-based technology. Expressions of interest are due by March 15, 2025. citeturn0search4

【{"image_fetch": "Safety Science Program"}】 Safety Science Program
Focuses on advancing the science of AI safety by developing methods for testing and evaluating large language models. The program seeks to deepen understanding of safety properties in AI systems. citeturn0search5

【{"image_fetch": "Schmidt Science Polymath Program"}】 Schmidt Science Polymath Program
Empowers early-to-mid-career researchers to explore new research domains and methodologies. Each award provides $500,000 per year for up to five years to support innovative, cross-disciplinary research. citeturn0search7

【{"image_fetch": "BioFutures Program"}】 BioFutures Program
Aims to accelerate the translation of bioeconomy research into public and economic benefits. The program focuses on repurposing sustainable waste biomass, overcoming engineering constraints, and mobilizing talent for bioeconomy-related federal agencies. citeturn0search8

Please note that Schmidt Sciences often operates through proactive grantmaking and does not always accept unsolicited proposals. For the most accurate and up-to-date information on funding opportunities and application processes, it's advisable to visit their official website or contact them directly. citeturn0search2