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Apple’s Latest iPhone 16 and Apple Intelligence

Also: How AI Doubled Student Engagement in Harvard’s Physics Course

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AI KATANA
September 09, 2024

Welcome to today’s edition! Get ready for a deep dive into the latest AI innovations, from Apple’s new AI-driven iPhone features to groundbreaking advancements in education and gaming. Whether you’re curious about how AI is doubling student engagement at Harvard or how neural networks are recreating classic video games, we’ve got the insights you need to stay ahead in the AI world.

Sliced just for you:

  • 📱 Apple’s Latest iPhone 16 and Apple Intelligence

  • 🦙 Everything You Need to Know About Llama 3.1

  • 🎓 How AI Doubled Student Engagement in Harvard’s Physics Course

  • 🎮 Can AI Run Doom? Google’s GameNGen Shows It Can

📱 Apple’s Latest iPhone 16 and Apple Intelligence

Apple’s latest iPhone 16 series introduces groundbreaking features, with a focus on AI-driven advancements, notably through Apple Intelligence. This new system offers a highly personalized and privacy-centric experience by blending advanced AI models with user-specific context, making everyday tasks more intuitive and efficient. Rolling out in iOS 18.1, iPadOS 18.1, and macOS Sequoia 15.1, Apple Intelligence enables users to rewrite, proofread, and summarize text seamlessly across applications. AI is also leveraged in camera technology through Camera Control, which provides real-time insights about objects and locations. Siri has been upgraded with better context awareness and integration with OpenAI’s ChatGPT for more complex interactions. Other innovations include AI-enhanced creative tools like Image Playground and Genmoji, as well as the A18 Pro chip that enhances AI performance across photography, gaming, and everyday tasks. Apple’s privacy-first approach ensures that personal data remains secure by processing information primarily on-device, balancing powerful AI capabilities with user security.

Meta’s Llama 3.1 is a major advancement in open-source AI, designed to compete with closed-source models like GPT-4 and Claude 3.5. With up to 405 billion parameters, Llama 3.1 excels in multilingual tasks, long-context processing, and tool integrations for coding, reasoning, and data generation. Its open-source nature allows developers to customize and fine-tune the model, fostering innovation across the AI community. Meta has also introduced robust safety features such as Llama Guard 3 to ensure responsible use. Llama 3.1 represents a step toward democratizing AI research, with plans for broader integration in agentic systems and potential future applications in vision and speech processing.

Harvard University’s integration of a custom AI tutor in the “Physical Sciences 2” course for life sciences students resulted in a dramatic increase in student engagement, doubling learning outcomes compared to traditional active learning methods. Developed by Professor Gregory Kestin and Senior Lecturer Kelly Miller, the AI tutor offers personalized feedback and adapts to each student’s pace, significantly improving learning efficiency. While human instructors remain crucial for advanced problem-solving and project-based learning, the AI tutor’s success is prompting Harvard to expand its use across other courses, such as multivariable calculus, underscoring AI’s transformative potential in education.

🎮 Can AI Run Doom? Google’s GameNGen Shows It Can 

Google’s GameNGen project has achieved a groundbreaking milestone by recreating the classic video game Doom using a neural network, entirely bypassing traditional game engines and code. GameNGen employs a diffusion-based AI model that generates real-time game frames based on learned gameplay behaviors, running at 20 frames per second on specialized AI hardware. This achievement signals a shift in game development, where generative AI could automate complex simulations, making game creation more accessible and dynamic. Beyond gaming, AI-driven simulations like GameNGen have potential applications in industries ranging from autonomous vehicle testing to virtual reality environments.

🛠️ AI tools updates

Researchers at Binghamton University have developed innovative tools to detect AI-generated fake images and videos, commonly known as deepfakes. By analyzing “fingerprints” left behind during the AI generation process, particularly in the frequency domain, these tools can identify anomalies that distinguish authentic content from manipulated media. Using Generative Adversarial Networks Image Authentication (GANIA), they can spot artifacts introduced during the image upsampling process, while a separate tool, DeFakePro, uses electrical network frequency signals to verify the authenticity of audio and video recordings. This research, aimed at combating the growing issue of misinformation fueled by generative AI, provides a robust method for detecting deepfakes and securing digital content against forgery.

💵 Venture Capital updates

SoftBank has announced its strategic focus on European AI startups, driven by the need to secure top AI talent, which is becoming increasingly scarce. The company, which typically invests between $25 million and $300 million in startups, is particularly interested in AI-first companies such as ElevenLabs (text-to-speech), Synthesia (AI video creation), and Poolside (genAI coding). While many of these startups are in early stages with high valuations, SoftBank remains cautious about large-scale investments without proven revenue growth. In 2024, SoftBank made significant investments in companies like autonomous driving firm Wayve and semiconductor startup Graphcore, viewing these as strategic bets with potential to benefit its broader portfolio. Additionally, SoftBank plans to integrate AI technologies from these startups into its existing businesses, demonstrating a long-term commitment to the AI sector in Europe.

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