This week, I had a chance to dig deep into the report published by the German Federal Office for Information Security, "Generative AI Models - Opportunities and Risks for Industry and Authorities." This report has covered a few areas, such as the planning, development, and operation phases of generative AI models, where a systematic risk analysis should be conducted.
For those of us involved in organizational projects that employ Large language models, it's crucial to be aware of the potential risks associated with such projects. Fortunately, an excellent resource is available that outlines 28 risks associated with large language models.
By familiarizing ourselves with these risks, we can better plan and execute these projects with greater efficiency and safety.
The report has categorized the LLM risk into 3 areas:
Risks in the context of proper use of LLMs
R2. Lack of Quality, Factuality, and Hallucinating
R3. Lack of Up-to-dateness
R4. Lack of Reproducibility and Explainability
R5. Lack of Security of Generated Code
R6. Incorrect Response to Specific Inputs
R7. Automation Bias
R8. Susceptibility to Interpreting Text as an Instruction
R9. Lack of Confidentiality of the Input Data
R10. Self-reinforcing Effects and Model Collapse
R11. Dependency on the Developer/ Operator of the Model
Risks due to misuse of LLMs
R13. Social Engineering
R14. Re-identification of Individuals from Anonymised Data
R15. Knowledge Gathering and Processing in the Context of Cyberattacks
R16. Generation and Improvement of Malware
R17. Placement of Malware
R18. Remote Code Execution (RCE) Attacks
Risks resulting from attacks on LLMs
R20. Embedding Inversion
R21. Model Theft
R22. Extraction of Communication Data and Stored Information
R23. Manipulation through Perturbation
R24. Manipulation through Prompt Injections
R25. Manipulation through Indirect Prompt Injections
R26. Training Data Poisoning
R27. Model Poisoning
R28. Evaluation Model Poisoning
Below is the representation of different Risks across the typical life cycle of an LLM project.
Weekly News & Updates...
This week's AI breakthroughs mark another leap forward in the tech revolution.
OpenELM from Apple: open-source training and inference framework
Phi-3 - SLM (Small language models) from Microsoft is available in two context-length variants, 4K and 128K tokens.
Snowflake Arctic: Largne Language models under the Apache 2.0 license provide ungated access to weights and code.
PyTorch/XLA 2.3: Distributed training, dev improvements, and GPUsfrom Google; XLA is a specialized compiler designed to optimize linear algebra computations for the foundation of deep learning models.
NVIDIA to acquire GPU Orchestration Software Provider Run:ai, a Kubernetes-based workload management and orchestration software.
Cohere Toolkit: This collection of prebuilt components enables users to build and deploy RAG applications quickly.
Potential of AI
GPT-Author: It utilizes a chain of GPT-4, Stable Diffusion, and Anthropic API calls to generate an original fantasy novel. Users can provide an initial prompt and enter how many chapters they'd like it to be, and the AI then generates an entire book, outputting an EPUB file compatible with e-book readers
Things to Know
Tracking new Gen AI models is challenging every week, and here you can find all the details from Stanford University. They are tracking them (along with datasets and applications) in the ecosystem graphs
The Opportunity...
Podcast:
This week's Open Tech Talks episode 133 is "The Rise of AI in Creative Writing: Its Impact and Potential with Alex Shvartsman". He’s the author of Kakistocracy (2023), The Middling Affliction (2022), and Eridani’s Crown (2019) fantasy novels. Over 120 of his short stories have appeared in Analog, Nature, Strange Horizons, and many other venues.
CoreNet from Apple: is a deep neural network toolkit that allows training of standard and novel small and large-scale models for various tasks, including foundation models (e.g., CLIP and LLM), object classification, object detection, and semantic segmentation.
llamafile: Enables to distribute and run LLMs with a single file
IDM-VTON: Improving Diffusion Models for Authentic Virtual Try-on
Data Sets...
IMF Datasets: Several datasets from the International Monetary Fund
Hit reply and let me know what you found most helpful this week - I'd love to hear from you!
Until next week,
Kashif Manzoor
The opinions expressed here are solely my conjecture based on experience, practice, and observation. They do not represent the thoughts, intentions, plans, or strategies of my current or previous employers or their clients/customers. The objective of this newsletter is to share and learn with the community.
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Kashif Manzoor
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