China’s Firms Escapes US AI Chip Ban
Plus: Alibaba releases Qwen 2 AI models, SAP and NVIDIA are collaborating to develop next-gen application using generative AI.
Hello Engineering Leaders and AI Enthusiasts!
Welcome to the 293rd edition of The AI Edge newsletter. This edition features how Chinese tech companies are exploiting Nvidia AI chip loophole.
And a huge shoutout to our amazing readers. We appreciate you😊
In today’s edition:
🚀 Alibaba's Qwen2 AI models outperform GPT-4 & Llama-3
and
🧠 SAP & Nvidia are developing applications with AI & digital twins
🕵️♂️ Chinese tech giants exploit Nvidia AI chip loophole
🧠 Knowledge Nugget: AI is breadth-first, not depth-first by
Let’s go!
Alibaba's Qwen2 AI models outperform GPT-4 & Llama-3
Alibaba launched Qwen2 with five sizes ranging from 0.5B to 72B parameters. These models are trained in 27 additional languages besides English and Chinese, showcasing state-of-the-art performance in benchmarks. The models deliver significantly improved performance in coding and mathematics and extended context length support up to 128K tokens. Despite having fewer parameters, qwen2-72 B outperforms leading models like Llama-3-70B and its predecessor Qwen1.5-110B.
Qwen2-72B-Instruct performs comparably to GPT-4 in terms of safety and significantly outperforms Mistral-8x22B. The models are released under Apache 2.0 and Qianwen License on Hugging Face and ModelScope.
Why does it matter?
Qwen2 beats Meta’s model despite being trained on relatively fewer tokens. The researchers attribute it to more efforts put into data cleaning and training, implying innovative approaches on their end.
However, it also signals the slow shift in how LLMs are developed– from solely relying on quantity of data to prioritizing the quality of data and training techniques.
SAP & Nvidia are developing applications with AI & digital twins
At SAP's Sapphire event in Orlando, Florida, SAP and NVIDIA announced their collaboration to enhance SAP's generative AI copilot, Joule, with two new capabilities: SAP Consulting and ABAP Developer. These new features are powered by NVIDIA AI Enterprise software.
Additionally, SAP is integrating NVIDIA Omniverse Cloud APIs into its Intelligent Product Recommendation solution to simplify the buying and selling process for complex products. This integration will allow salespeople to visualize 3D product digital twins directly within the SAP Intelligent Product Recommendation interface, making it easier to understand the products.
Why does it matter?
Using NVIDIA Omniverse Cloud APIs in SAP's Intelligent Product Recommendation solution accelerates the quote generation process and increases sales and customer satisfaction by enabling sales representatives to provide more accurate, tailored recommendations.
Chinese tech giants exploit Nvidia AI chip loophole
The U.S. government prohibits Nvidia from selling A.I. chips directly to Chinese companies due to national security concerns. Still, ByteDance is accessing Nvidia's A.I. chips for its U.S. operations by leasing them from Oracle, as the current U.S. rules do not explicitly prohibit Chinese companies from accessing the chips if used within the U.S.
Other Chinese tech giants like Alibaba, Tencent, and China Telecom seek similar arrangements with U.S. cloud providers. The U.S. Commerce Department proposed a rule to tighten controls, but it faced opposition from cloud providers and remains in limbo.
Why does it matter?
Even if the loophole is closed, Alibaba and Tencent have discussed obtaining Nvidia chips for their U.S.-based data centers. It could further escalate the AI "arms race" and rivalry between the USA and China as both nations seek to outpace each other in developing advanced AI systems for economic and military advantages.
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Knowledge Nugget: AI is breadth-first, not depth-first
In their thought-provoking article,
and challenge the "AI for X" analogy, arguing that it fails to capture the nuances of AI's capabilities. They point out that while AI, particularly LLMs, excel at a broad range of tasks spanning traditional job functions, they lack the depth and finesse that professionals accumulate over years of experience.As a result, AI applications shouldn't be limited by traditional job boundaries and instead blur the lines between adjacent functions to unlock new opportunities for innovation. They also emphasize the importance of data as a moat and advise product builders to design AI applications for experts who expect the basics to be covered while seeking the next level of insight.
Why does it matter?
By recognizing AI's breadth-first nature, product builders can design applications that integrate perspectives from multiple areas. This approach meets customer demands more effectively and helps establish a stronger market position in an increasingly competitive landscape.
What Else Is Happening❗
🍎 Apple will launch "Apple Intelligence" at WWDC 2024 for iPhone, iPad, and Mac
Leaks suggest Apple will reveal “Apple Intelligence”, aka AI, at the WWDC event this week. These AI features will focus on broad appeal and privacy, with opt-in not mandatory. Apple will use its own tech and OpenAI tools to power the new AI features. (Link)
🚀 TCS launches TCS AI WisdomNext™, an industry-first GenAI aggregation platform
The platform allows organizations to compare and experiment with GenAI models across cloud services in a single interface. It offers ready-to-deploy business solution blueprints with built-in guardrails for quick adoption. (Link)
🚨 A study by Harvard, MIT, and Wharton reveals junior staff is not reliable for AI training
Junior consultants who participated in a GPT-4 experiment struggled with AI risk mitigation, with their tactics lacking a deep understanding of the technology and focusing on changing human behavior rather than AI system design. The findings highlight the need for top-down AI governance, expert input, and upskilling across all levels of the organization. (Link)
🤝 Human Native AI is building a marketplace for AI training licensing deals
The platform helps AI companies find data to train their models while ensuring rights holders are compensated. Rights holders upload content for free and connect with AI companies for revenue share or subscription deals. Human Native AI helps prepare and price content, monitors for copyright infringements, and takes a cut of each deal. (Link)
🤖 Hugging Face and Pollen Robotics launched an open-source robot for household chores
The humanoid Reachy2 was initially controlled by a human wearing a VR headset. Then, a machine learning algorithm studied the teleoperation sessions to learn how to perform the tasks independently. The dataset and trained model used for the demo are open-sourced on Hugging Face, allowing anyone to replicate the process on smaller robots at home. (Link)
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