NVIDIA's Nemotron-4 Beats 4x Larger Multilingual AI Models
Plus: GitHub launches Copilot Enterprise, Slack's survey on AI adoption
Hello Engineering Leaders and AI Enthusiasts!
Welcome to the 220th edition of The AI Edge newsletter. This edition brings you NVIDIA’s new model and how it is outperforming other models.
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In today’s edition:
🏆NVIDIA's Nemotron-4 beats 4x larger multilingual AI models
👩💻 GitHub launches Copilot Enterprise for customized AI coding
⏱️ Slack study shows AI frees up 41% of time spent on low-value work
📚 Knowledge Nugget: Where Are the Good AI Products? by
Let’s go!
NVIDIA's Nemotron-4 beats 4x larger multilingual AI models
Nvidia has announced Nemotron-4 15B, a 15-billion parameter multilingual language model trained on 8 trillion text tokens. Nemotron-4 shows exceptional performance in English, coding, and multilingual datasets. It outperforms all other open models of similar size on 4 out of 7 benchmarks. It has the best multilingual capabilities among comparable models, even better than larger multilingual models.
The researchers highlight how Nemotron-4 scales model training data in line with parameters instead of just increasing model size. As a result, inferences are computed faster, and latency is reduced. Due to its ability to fit on a single GPU, Nemotron-4 aims to be the best general-purpose model given practical constraints. It achieves better accuracy than the 34-billion parameter LLaMA model for all tasks and remains competitive with state-of-the-art models like QWEN 14B.
Why does this matter?
Just as past computing innovations improved technology access, Nemotron's lean GPU deployment profile can expand multilingual NLP adoption. Since Nemotron fits on a single cloud graphics card, it dramatically reduces costs for document, query, and application NLP compared to alternatives requiring supercomputers. These models can help every company become fluent with customers and operations across countless languages.
GitHub launches Copilot Enterprise for customized AI coding
GitHub has launched Copilot Enterprise, an AI assistant for developers at large companies. The tool provides customized code suggestions and other programming support based on an organization's codebase and best practices. Experts say Copilot Enterprise signals a significant shift in software engineering, with AI essentially working alongside each developer.
Copilot Enterprise integrates across the coding workflow to boost productivity. Early testing by partners like Accenture found major efficiency gains, with a 50% increase in builds from autocomplete alone. However, GitHub acknowledges skepticism around AI originality and bugs. The company plans substantial investments in responsible AI development, noting that Copilot is designed to augment human developers rather than replace them.
Why does this matter?
The entire software team could soon have an AI partner for programming. However, concerns about responsible AI development persist. Enterprises must balance rapidly integrating tools like Copilot with investments in accountability. How leadership approaches AI strategy now will separate future winners from stragglers.
Slack study shows AI frees up 41% of time spent on low-value work
Slack's latest workforce survey shows a surge in the adoption of AI tools among desk workers. There has been a 24% increase in usage over the past quarter, and 80% of users are already seeing productivity gains. However, less than half of companies have guidelines around AI adoption, which may inhibit experimentation. The research also spotlights an opportunity to use AI to automate the 41% of workers' time spent on repetitive, low-value tasks. And focus efforts on meaningful, strategic work.
While most executives feel urgency to implement AI, top concerns include data privacy and AI accuracy. According to the findings, guidance is necessary to boost employee adoption. Workers are over 5x more likely to have tried AI tools at companies with defined policies.
Why does this matter?
This survey signals AI adoption is already boosting productivity when thoughtfully implemented. It can free up significant time spent on repetitive tasks and allows employees to refocus on higher-impact work. However, to realize AI's benefits, organizations must establish guidelines and address data privacy and reliability concerns. Structured experimentation with intuitive AI systems can increase productivity and data-driven decision-making.
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Knowledge Nugget: Where Are the Good AI Products?
In this detailed and interesting article,
discusses why AI has yet to deliver its promise to impact how we work and live profoundly. He notes that despite the hype around AI, most people have not experienced much change from new AI capabilities. He uses only a handful of AI tools, like Copilot and ChatGPT, on a daily basis. These save him time, but he is surprised they have not gone mainstream yet.
The author then explores three hypotheses on why broader AI adoption has been slow:
Transformative apps could arrive this year if the next generation of AI models leads to breakthroughs.
It may take 5-10 years to see AI proliferate into game-changing products as capabilities gradually improve.
Technical limitations around reliability and core UX issues may inhibit AI from reaching its full potential.
He believes the second outcome is most likely with some industries seeing faster adoption as expertise diffuses.
Why does this matter?
AI tools tailored for specific roles already greatly increase productivity. But reliable automation of complex workflows still needs to be improved. It is crucial because, in 5-10 years, organizations that strategically use AI to enhance their talent could massively outperform those who rely solely on human effort. Organizations must use this transitional period to experiment with AI to stay competitive in the long run.
What Else Is Happening❗
🎞️ Pika launches new lip sync feature for AI videos
Video startup Pika announced a new Lip Sync feature powered by ElevenLabs. Pro users can add realistic dialogue with animated mouths to AI-generated videos. Although currently limited, Pika's capabilities offer customization of the speech style, text, or uploaded audio tracks, escalating competitiveness in the AI synthetic media space. (Link)
💰 Google pays publishers to test an unreleased GenAI tool
Google is privately paying a group of publishers to test a GenAI tool. They need to summarize three articles daily based on indexed external sources in exchange for a five-figure annual fee. Google says this will help under-resourced news outlets, but experts say it could negatively affect original publishers and undermine Google's news initiative. (Link)
🤝 Intel and Microsoft team up to bring 100M AI PCs by 2025
By collaborating with Microsoft, Intel aims to supply 100 million AI-powered PCs by 2025 and ramp up enterprise demand for efficiency gains. Despite Apple and Qualcomm's push for Arm-based designs, Intel hopes to maintain its 76% laptop chip market share following post-COVID inventory corrections. (Link)
📊 Writer’s Palmyra-Vision summarizes charts, scribbles into text
AI writing startup Writer announced a new capability of its Palmyra model called Palmyra-Vision. This model can generate text summaries from images, including charts, graphs, and handwritten notes. It can automate e-commerce merchandise descriptions, graph analysis, and compliance checking while recommending human-in-the-loop for accuracy. (Link)
🚗 Apple cancels its decade-long electric car project
Apple is canceling its decade-long electric vehicle project after spending over $10 billion. Nearly 2,000 employees were working on the effort known internally as Titan. After Apple announces the cancellation of its ambitious electric car project, some staff from the discontinued car team will shift to other teams such as GenAI. (Link)
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