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Author Topic: Dealseek [sic.]  (Read 775 times)

Offline Chip (OP)

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Dealseek [sic.]
« on: January 28, 2025, 08:53:56 PM »
https://acrobat.adobe.com/id/urn:aaid:sc:AP:b7420ad4-e236-48f4-8462-d8d54f54637b

DeepSeek is a Chinese artificial intelligence (AI) company founded in 2023 by entrepreneur Liang Wenfeng.

The company has garnered significant attention for its rapid advancements in AI, particularly with the release of its model, DeepSeek-R1, on January 20, 2025.

This model is noted for its reasoning capabilities comparable to OpenAI's o1 model but developed at a fraction of the cost.
Key Highlights:

• Cost-Effective Development: DeepSeek's R1 model was developed with significantly lower resources compared to its Western counterparts, challenging the prevailing notion that advanced AI development necessitates substantial financial and computational investments.

• Open-Source Commitment: Unlike many proprietary models, DeepSeek has made its AI models open source, allowing developers and researchers worldwide to access, modify, and build upon their technology.

• Market Impact: Following the release of DeepSeek-R1, the company's AI assistant quickly became the top free app on Apple's App Store, surpassing competitors like ChatGPT. This success has prompted discussions about the future of AI development and the competitive landscape between Chinese and Western tech companies.

Considerations:

While DeepSeek's advancements are impressive, it's important to note that the R1 model has certain limitations, particularly concerning sensitive political topics in China.

For instance, when queried about the status of Taiwan, the chatbot redirects the conversation to topics like math, coding, and logic problems, or suggests that Taiwan has been an integral part of China for centuries.

In summary, DeepSeek represents a significant development in the AI industry, demonstrating that high-level AI capabilities can be achieved with more accessible resources.

Its open-source approach and rapid market acceptance highlight its potential to influence the future direction of AI research and application.

You bring up a nuanced and important point about the possible origins and methods behind models like DeepSeek.

If the claim is that DeepSeek's apparent capabilities come from leveraging a subset of already post-trained datasets from existing models like OpenAI's or others, there are several dimensions to unpack.

1. Post-Training Knowledge Leakage

• Possible Scenario: If DeepSeek or similar models were trained using datasets or techniques derived from other AI models’ outputs (like GPT-based systems), their rapid success might not entirely stem from ground-up innovation. This could mean that the "open-source" claim doesn’t fully reflect the source of its core intelligence.

• Challenge to Prove: Identifying whether such models re-use intellectual property from other systems is You bring up a nuanced and important point about the possible origins and methods behind models like DeepSeek. If the claim is that DeepSeek's apparent capabilities come from leveraging a subset of already post-trained datasets from existing models like OpenAI's or others, there are several dimensions to unpack.

1. Post-Training Knowledge Leakage
Possible Scenario: If DeepSeek or similar models were trained using datasets or techniques derived from other AI models’ outputs (like GPT-based systems), their rapid success might not entirely stem from ground-up innovation.

This could mean that the "open-source" claim doesn’t fully reflect the source of its core intelligence.

Challenge to Prove: Identifying whether such models re-use intellectual property from other systems is challenging unless the original training data or architecture reveals overlaps (unlikely to be disclosed).

2. Code Transparency Doesn't Equal Data Transparency

Open-sourcing code is not the same as open-sourcing the training data.

The real "magic" of AI often lies in the dataset and the methodologies used for fine-tuning.
If DeepSeek trained on datasets "contaminated" with post-training outputs from pre-existing LLMs (including responses generated by models like ChatGPT), the cleverness might reflect derivative knowledge rather than pure innovation.

3. Ethical Concerns

Data Scraping: If the training datasets incorporated responses or behaviors from established AI models without explicit permission, this could raise ethical (and potentially legal) concerns.

Transparency Issues: Even if open-sourced, the training process may obscure critical steps that helped achieve the model’s performance, making it impossible for third parties to evaluate whether the AI is genuinely groundbreaking.

4. Implications for the AI Ecosystem

Global Competition: If countries or companies prioritize speed and cost over ethical training practices, we may see a rise in models leveraging "shortcut" methods that don't respect original creators' intellectual property.

Trust in Open Source: Claims of openness lose weight if key parts of the development process—like training datasets—are inaccessible or possibly derived from unauthorized sources.

In short, while DeepSeek’s performance and market adoption are impressive, questions around its origins and dataset transparency remain critical.

* If its success is indeed built on a foundation of post-trained knowledge from other systems, it highlights the urgent need for clearer guidelines and ethical practices in AI development. What do you think about such practices?

2. Code Transparency Doesn't Equal Data Transparency

• Open-sourcing code is not the same as open-sourcing the training data. The real "magic" of AI often lies in the dataset and the methodologies used for fine-tuning.

• If DeepSeek trained on datasets "contaminated" with post-training outputs from pre-existing LLMs (including responses generated by models like ChatGPT), the cleverness might reflect derivative knowledge rather than pure innovation.

3. Ethical Concerns

• Data Scraping: If the training datasets incorporated responses or behaviors from established AI models without explicit permission, this could raise ethical (and potentially legal) concerns.

• Transparency Issues: Even if open-sourced, the training process may obscure critical steps that helped achieve the model’s performance, making it impossible for third parties to evaluate whether the AI is genuinely groundbreaking.

4. Implications for the AI Ecosystem

• Global Competition: If countries or companies prioritize speed and cost over ethical training practices, we may see a rise in models leveraging "shortcut" methods that don't respect original creators' intellectual property.

• Trust in Open Source: Claims of openness lose weight if key parts of the development process—like training datasets—are inaccessible or possibly derived from unauthorized sources.

In short, while DeepSeek’s performance and market adoption are impressive, questions around its origins and dataset transparency remain critical.

If its success is indeed built on a foundation of post-trained knowledge from other systems, it highlights the urgent need for clearer guidelines and ethical practices in AI development.

What do you think about such practices?

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Offline Chip (OP)

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