AI may have reached a pivotal turning point, much like the historic Sputnik moment. DeepSeek, a Chinese company, is changing AI development. They focus on cost-effectiveness, energy efficiency, and making AI more accessible to everyone. If their innovation is as groundbreaking as expected, it could change how we build and use AI models. This would make advanced technology easier to access and use fewer resources.
Famous tech investor Marc Andreessen praised DeepSeek. He called it a groundbreaking innovation and one of the best advancements he has seen. He calls it a transformative breakthrough. He highlights its importance as an open-source contribution. This technology could shape the future of AI. It also makes advanced tools more accessible to everyone.
Open-source AI is artificial intelligence technology that anyone can access for free. Developers can study, change, and improve its features. Its importance is in boosting innovation, teamwork, and openness. This method lets more users contribute to its improvement. It also reduces dependence on proprietary systems.
What is DeepSeek?
DeepSeek is a new AI model from a Chinese startup. It has gained a lot of attention. Its performance rivals or even beats the latest models from OpenAI. What makes it special is its low cost and high efficiency. It needs much fewer resources to develop. DeepSeek stands out from many proprietary AI systems. It shares its methods without reservation. It also offers its models for free to researchers everywhere. This promotes collaboration and sparks innovation in the AI community.
DeepSeek stands out because it is open source. OpenAIโs ChatGPT and Anthropicโs Claude are proprietary models. They keep their data and algorithms private. Its code and technical details are open to everyone. This lets developers and teams worldwide download, edit, and build on its foundation.
Meta and Google share their models with the public. Yet, they do not give full access to their source code. Licensing rules restrict their use, and the training datasets are not shared.
Cheaper and more energy efficient
DeepSeek is notable for its low development cost of about $5.6 million. Thatโs a small part of what Meta spent on Llama. This raises questions about the big investments in the U.S. AI industry. Nvidiaโs stock experienced a significant decline in late January. The impact was obvious.
DeepSeekโs low-energy development is a great step to cut AIโs environmental impact. AI’s rising electricity needs are increasing carbon emissions. This approach may lead to more sustainable growth for AI.
Is DeepSeek safe to use?
Notre Dame users searching for approved AI tools should go to the Approved AI Tools page. You can find details about Google Gemini there. It’s now available for faculty and staff. If you have advanced expertise, contact AI Enablement. They can help you access different AI models via Amazon Web Services. The AI Enablement Team works with Information Security and General Counsel. They check the technology and legal aspects. This ensures the tools follow the university’s data security standards.
With growing interest in DeepSeek, many are eager to explore its capabilities. But the key question remainsโcan someone use it without risk?
Understanding the difference between DeepSeek’s services and its open-source models is key. Accessing the models matters. They are free and usually hosted by local providers. Think of the AI model as an engine, while the chatbot is the vehicle built around it. The goal is to ensure users can explore the potential of DeepSeek without risk. OIT Information Security created this guidance. It helps you understand the best practices for secure use.
There are three basic ways of interacting with DeepSeek:
- ๐ซNot Approved: DeepSeek-Controlled Access Methods
- Web: Users can access DeepSeek on its website. Yet, a recent security issue revealed user chats and other data. This flaw lies in the platform itself, not the underlying AI model. To ensure a safer experience, we recommend alternative access methods.
- Mobile: The app is not recommended due to reports of excessive data access requests. Safer options exist for programmers and everyday users to explore DeepSeek without risks.
- DeepSeek API. The DeepSeek API is made for programmers. It is not approved for campus use and is not the best choice compared to other options.
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Safe to Use: Chat Through US-Based Providers (Public Data Only)
- Perplexity, based in San Francisco, offers DeepSeek as a search option. Other chat services do too. They likely host it on their own servers. While this can be used safely, it should be limited to public data.
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Safe to Use: Programmer Options
- Local Open Source Model Use
- You can download DeepSeek models and their variations from Hugging Face. Itโs a top platform for AI and machine learning models. You can run these locally using tools like Ollama. For better security, use these on devices with limited internet access. Also, avoid using them in services that end users can access.
- API Access through AWS Bedrock
- DeepSeek is now available on Amazon Web Services’ Bedrock. This platform focuses on data privacy. As a trusted partner of OIT and Notre Dame, AWS ensures the secure usage of its models. Programmers and researchers interested in this option can contact AI Enablement for access.
- Local Open Source Model Use
Right now, non-programmers can’t use DeepSeek with sensitive or internal data. There are no approved methods for this.
How is DeepSeek so much more efficient than previous models?
AI model costs have two main parts: training and inference. Training is a one-time expense, while inference is an ongoing cost for running the model. DeepSeek has cut training costs to under $6 million. This is much lower than the estimated $100 million for ChatGPT-4. Its inference costs are very low. They are a small part of what it costs to run Anthropicโs Claude 3.5 Sonnet model.
The impressive performance of DeepSeek at such a low cost remains a topic of debate. The restrictions limit China’s access to high-end AI chips, like the NVIDIA H100. Still, DeepSeek says it trained its model on older, less advanced hardware. Yet, some remain skeptical of this assertion. The reported costs only cover the final training phase. This raises questions about the possible expenses from earlier research and development.
DeepSeek has made a significant breakthrough in training efficiency. Yet, there are still questions about its total cost and the hardware used in its development. Researchers can download and check the open-source models. They find that these models need much less computing power than similar AI systems.
DeepSeek’s chat-time efficiency comes from its “mixture of experts” design. This design uses several specialized models rather than a single large system. This method lets it create responses using less processing power. This cuts down on both computation and energy costs.
DeepSeek’s efficiency gains come from new algorithm techniques. It’s not about using more data or scaling up.
Did DeepSeek steal data to build its models? (or a win for synthetic training data)
OpenAI has accused DeepSeek of using data from its models for training. This claim is somewhat ironic. DeepSeek admitted in its research paper that it used data from OpenAIโs O1 reasoning model. They made this clear from the beginning.
DeepSeek shows how synthetic training data can boost AI models. DeepSeek shows a different way. Traditional thinking says models like ChatGPT need lots of human text to get better. But DeepSeek proves that’s not the only option.
ChatGPTโs o1 model has pauses before it responds. This mimics a thinking process. DeepSeek trained on o1โs reasoning scripts instead of using internet data. This way, it makes full use of ChatGPTโs refined output as its training material.
It’s still unclear if this method will be sustainable long term. Yet, it has shown promise in creating an efficient, high-performing model. Yet, this also raises questions about DeepSeek’s low-cost reputation. Its success depends on OpenAI’s large investment and past efforts.
Positive Developments for Open-Source AI
DeepSeek stands out for its commitment to transparency. It shares its methods without reservation. It also makes its models available to the global open-source community. Some people are concerned that a Chinese company will take the lead in AI and its global effects. Its open approach lets researchers and businesses worldwide use and enhance its advancements. Anyone can explore all aspects of DeepSeekโs models. They can analyze how they work or create new ideas using this technology.
Others are already replicating DeepSeekโs efficient, cost-effective training approach. A team in Hong Kong on GitHub improved Alibaba Cloud’s Qwen model. They improved their math skills and used less data and computing power than before. DeepSeek is improving optimization and reducing costs in AI development even more.
How does this affect U.S. companies and AI investments?
DeepSeek’s efficiency and performance have shaken the AI industry. Major players are feeling the impact. NVIDIA’s stock fell by 17% on Monday. This drop raises questions about the impact of DeepSeek’s progress.
DeepSeek launched immediately after the announcement of Project Stargate. This project is a huge investment of $500 billion in AI infrastructure. OpenAI, Oracle, SoftBank, and MGX are behind it, with help from Microsoft and NVIDIA. DeepSeek delivers high performance at a lower cost. This makes us question the need for such big spending. Is it worth investing so much if developers can build advanced AI with less effort?
The drop cut about $21 billion from CEO Jensen Huangโs wealth. Yet, NVIDIAโs stock is now back at its October 2023 levels. This shows the rapid surge in AI investments.
A Win for Efficiency
Worries about AI’s energy use and environmental effects make DeepSeek’s efficiency exciting. It could help more people adopt AI while lowering its impact. Some experts mention the Jevons Paradox. Better efficiency could increase demand rather than lower resource use.
A setback in the fight against AI bias.
AI models can have biases from their training data. This is why researchers focus on “AI alignment.” They want to fix these problems so responses fit human intent. DeepSeek has built-in limits. It skips sensitive topics like Tiananmen Square. It also avoids issues with the Chinese government.
AI developers often add safeguards to their models. For example, Google Gemini avoids talking about U.S. political figures. Yet, DeepSeekโs selective restrictions reveal a deliberate intent. Though its open-source, future models will still carry these biases.
What does this mean for the AI industry at large?
AI is increasingly becoming a commodity, with foundational models now widely accessible. More data doesnโt always mean smarter models. “Reasoning” models have gone further, but they need more time and energy to work. Despite this, AI-driven applications and services will continue to expand. Wharton professor Ethan Mollick says that if AI stopped improving now, we would need ten years to fully use its current potential.
DeepSeek shows a shift in AI. Now, value comes from how well it’s used, not just from the size of the model. The real advantage now lies not in the models, but in the innovative AI-driven apps built around them.
How could open-source AI change the market?
By January 2025, DeepSeek had surpassed ChatGPT as the most downloaded free app on the U.S. Apple App Store. Its quick adoption is a big step in making AI more accessible. Startups, small businesses, and independent developers can now use advanced technology more easily.
An inclusive AI landscape can bring about new ideas in regions with little access to advanced technology. It will also speed up progress. Developers can handle real-world challenges better. They should shift from making new models to enhancing AI-powered applications.
The World Economic Forum’s AI Transformation of Industries initiative makes this vision real. It explores the challenges and opportunities that come with AI-driven innovation. The latest white paper shows how AI can change industries and create real progress.
The tech community believes DeepSeekโs open-source model will boost teamwork. This, in turn, should speed up AI progress. Open access builds trust. Users can check the training data, which boosts transparency.
The hidden training data used in top AI models has caused legal issues for major companies.
DeepSeek faces criticism for supposed censorship in its replies and training data. This has caused some governments to ban it due to privacy issues. To build trust and gain wider acceptance, we should be more transparent. We also need to boost security and reduce content restrictions.
Critics say open-source AI poses serious risks. These include misuse in bioweapons and the spread of misinformation and disinformation.
DeepSeek is reshaping how the AI industry views competitiveness. Its low-cost, open-source model challenges old ways. This makes countries rethink what leads to success in the changing AI world.
DeepSeekโs open-source model supports inclusivity and transparency. But, it also raises questions about data privacy, security, and geopolitical risks. Addressing these challenges will be crucial for responsible adoption and maximizing its potential.
FAQs
What is DeepSeek, and how does it work?
DeepSeek is a smart AI model. It offers great performance while using less computing power. It uses a unique “mixture of experts” approach. This makes it more efficient and scalable than traditional models.
Is it safe to use DeepSeek?
DeepSeek is open-source, offering transparency. Still, there are concerns about data privacy and the risk of censorship. Many researchers and developers feel itโs safe. Still, users need to be careful with sensitive information.
Why do some AI models, like DeepSeek, have unusual-looking architectures?
DeepSeek and other models use special structures like expert networks. This helps them work with high efficiency and save power. These designs let the model use the parts needed for each task. This boosts response times and cuts costs.
Why does AI take so much power?
AI requires significant computational power due to complex training processes and inference tasks. DeepSeek aims to reduce energy use. It does this by optimizing its training methods. This approach could make AI more sustainable.
What is the derivative of acceleration?
Jerk is the derivative of acceleration. It measures the rate of change of acceleration over time. This idea is common in physics and engineering. It often appears when studying motion.
Conclusion
DeepSeek marks a big change in AI. It offers a cost-effective, open-source alternative to traditional models. Its transparency helps collaboration and accessibility. Yet there are still concerns about data privacy, security, and possible biases. Its new method cuts energy use and boosts efficiency. This challenges the usual expensive AI development model. As the industry evolves, itโs vital to navigate these complexities. This will help ensure the responsible and ethical use of AI. DeepSeek will change AI democratization. It may bring new challenges too. Its effect on the future of artificial intelligence is clear.
