AI Research

Updates on OpenAI's GPT-4o, AWS and NVIDIA's AI partnership, Groq's new AI chips, Elon Musk's xAI investments, and AI policy news from Microsoft and Sony.

Last Week in AI: Episode 32

The AI landscape continues to evolve at a rapid pace, with significant advancements and strategic collaborations shaping the future of technology. Last week saw notable updates from major players like OpenAI, NVIDIA, AWS, and more, highlighting the diverse applications and growing impact of artificial intelligence across various sectors. Here’s a roundup of the key developments from the past week.

OpenAI Debuts GPT-4o ‘Omni’ Model

Development: OpenAI has launched GPT-4o, an advanced version of its AI model powering ChatGPT. GPT-4o supports real-time responsiveness, allowing users to interrupt answers mid-conversation. It can process text, audio, and visual inputs and outputs, enhancing capabilities like real-time language translation and visual problem-solving.

Impact: This update significantly enhances the versatility and interactivity of ChatGPT, making it more practical for dynamic interactions. Learn more on TechCrunch

AWS and NVIDIA Extend Collaboration

Development: AWS and NVIDIA have partnered to advance generative AI innovation, especially in healthcare and life sciences. This includes integrating NVIDIA’s GB200 GPUs with Amazon SageMaker for faster AI model deployment.

Impact: This collaboration aims to accelerate AI-driven innovations in critical fields, offering powerful, cost-effective AI solutions. Read more on NVIDIA News

NVIDIA Unveils GB200 GPU Platform

Update: NVIDIA has introduced the GB200 GPU platform, designed for high-performance AI applications. This system includes the NVLink Switch, which enhances efficiency and performance for large-scale AI training and inference.

Impact: The GB200 platform promises to revolutionize AI infrastructure by providing unprecedented computational power for advanced AI models. Details on NVIDIA News

Groq’s Lightning-Fast AI Chips

Innovation: Groq has launched its new LPUs (Language Processing Units), optimized for faster AI inference in language models. These chips are designed to provide a significant speed advantage over traditional GPUs.

Impact: Groq aims to become a leading infrastructure provider for AI startups, offering efficient and cost-effective AI solutions. Learn more on Vease Blog

Elon Musk’s xAI to Spend $10 Billion on Oracle AI Cloud Servers

Development: Elon Musk’s AI startup, xAI, plans to invest $10 billion in Oracle’s AI cloud servers to support the training and deployment of its AI models. This substantial investment underscores the high computational demands of xAI’s advanced AI initiatives, particularly its Grok models.

Impact: This move highlights the critical role of robust cloud infrastructure in the development of next-generation AI technologies. It also demonstrates the increasing collaboration between AI startups and cloud service providers to meet the growing needs of AI research and applications. Read more on DataCenterDynamics

Microsoft Dodges UK Antitrust Scrutiny

Policy Update: Microsoft will not face antitrust scrutiny in the UK regarding its investment in Mistral AI. This decision allows Microsoft to continue its strategic investments without regulatory obstacles.

Implications: This development supports Microsoft’s ongoing expansion in AI technology investments. Read more on TechCrunch

EU Warns Microsoft Over Generative AI Risks

Policy Update: The EU has issued a warning to Microsoft, potentially imposing fines for not providing required information about the risks of its generative AI tools.

Impact: This highlights the increasing regulatory focus on AI transparency and safety within the EU. Learn more on Yahoo News

Strava Uses AI to Detect Cheating

Development: Strava has implemented AI technology to detect and remove cheats from its leaderboards, along with introducing a new family subscription plan and dark mode.

Impact: These measures aim to maintain platform integrity and improve user experience. Details on Yahoo Finance

Sony Music Warns Against Unauthorized AI Training

Policy Update: Sony Music has warned tech companies against using its content for AI training without permission, emphasizing the need for ethical data use.

Implications: This move stresses the importance of proper licensing and the potential legal issues of unauthorized data use. Learn more on AI Business

Recall.ai Secures $10M Series A Funding

Funding: Recall.ai has raised $10 million in Series A funding to develop tools for analyzing data from virtual meetings.

Impact: This funding will enhance the capabilities of businesses to leverage meeting data for insights and decision-making. Read more on TechCrunch

Google Adds Gemini to Education Suite

Update: Google has introduced a new AI add-on called Gemini to its Education suite, aimed at enhancing learning experiences through AI-driven tools.

Impact: This addition will provide educators and students with advanced resources, transforming educational practices. Learn more on TechCrunch

Final Thoughts

The developments from last week highlight the growing impact of AI across various domains, from healthcare and education to infrastructure and regulatory landscapes. As these technologies evolve, they promise to bring transformative changes, enhancing capabilities and offering new solutions to complex challenges. The future of AI looks promising, with ongoing innovations paving the way for more efficient, intelligent, and interactive applications.

Last Week in AI: Episode 32 Read More »

"Last Week in AI" including OpenAI, Stack Overflow, Apple's new Photos app, YouTube Premium, Microsoft MAI-1, Eli Lilly, Audible, Apple's M4 chip, Google's Pixel 8a, machine learning in whale communication, and more.

Last Week in AI: Episode 31

Hey everyone, welcome to this week’s edition of “Last Week in AI.” This week’s stories provide a glimpse into how AI is reshaping industries and our daily lives. Let’s dive in and explore these fascinating developments together.

OpenAI and Stack Overflow Partnership

Partnership Announcement: OpenAI and Stack Overflow have formed a new API partnership to leverage their collective strengths—Stack Overflow’s technical knowledge platform and OpenAI’s language models.

Impact and Controversy: This partnership aims to empower developers by combining high-quality technical content with advanced AI models. However, some Stack Overflow users have protested, arguing it exploits their contributed labor without consent, leading to bans and post reverts by staff. This raises questions about content creator attribution and future model training, despite the potential for improved AI models. Read more

Apple’s New Photos App Feature

Feature Introduction: Apple is set to introduce a “Clean Up” feature in its Photos app update, leveraging generative AI for advanced image editing. This tool will allow users to remove objects from photos using a brush tool, similar to Adobe’s Content-Aware Fill.

Preview and Positioning: Currently in testing on macOS 15, Apple may preview this feature during the “Let Loose” iPad event on March 18, 2023. This positions the new iPads as AI-equipped devices, showcasing practical AI applications beyond chatbots and entertainment. Read more

YouTube Premium’s AI “Jump Ahead” Feature

Feature Testing: YouTube Premium subscribers can now test an AI-powered “Jump ahead” feature, allowing them to skip commonly skipped video sections. By double-tapping to skip, users can jump to the point where most viewers typically resume watching.

Availability and Aim: This feature is currently available on the YouTube Android app in the US for English videos and requires a Premium subscription. It complements YouTube’s “Ask” feature and aims to enhance the viewing experience by leveraging AI and user data. Read more

Microsoft’s MAI-1 Language Model Development

Model Development: Microsoft is developing a new large-scale AI language model, MAI-1, led by Mustafa Suleyman, the former CEO of Inflection AI. MAI-1 will have approximately 500 billion parameters, significantly larger than Microsoft’s previous models.

Strategic Significance: This development signifies Microsoft’s dual approach to AI, focusing on both small and large models. Despite its investment in OpenAI, Microsoft is independently advancing its AI capabilities, with plans to unveil MAI-1 at their Build conference. Read more

AI in Drug Discovery at Eli Lilly

Innovative Discovery: The pharmaceutical industry is integrating AI into drug discovery, with Eli Lilly scientists noting innovative molecular designs generated by AI. This marks a precedent in AI-driven biology breakthroughs.

Industry Impact: AI is expected to propose new drugs and generate designs beyond human capability. This integration promises faster development times, higher success rates, and exploration of new targets, reshaping drug discovery. Read more

AI-Narrated Audiobooks on Audible

Audiobook Trends: Over 40,000 AI-voiced titles have been added to Audible since Amazon launched a tool for self-published authors to generate AI narrations. This makes audiobook creation more accessible but has sparked controversy.

Industry Reaction: Some listeners dislike the lack of filters to exclude AI narrations, and human narrators fear job losses. Major publishers are embracing AI for cost savings, highlighting tensions between creative integrity and commercial incentives. Read more

Apple’s M4 Chip for iPad Pro

Processor Introduction: Apple’s M4 chip, the latest and most powerful processor for the new iPad Pro, offers groundbreaking performance and efficiency.

Key Innovations: The M4 chip features a 10-core CPU, 10-core GPU, advanced AI capabilities, and power efficiency gains. These innovations enable superior graphics, real-time AI features, and all-day battery life. Read more

Google’s Pixel 8a Smartphone

Affordable Innovation: The Pixel 8a, Google’s latest affordable smartphone, is priced at $499 and packed with AI-powered features and impressive camera capabilities.

Key Highlights: The Pixel 8a features a refined design, dual rear camera, AI tools, and enhanced security. It also offers family-friendly features and 7 years of software support. Read more

OpenAI’s Media Manager Tool

Tool Development: OpenAI is building a Media Manager tool to help creators manage how their works are included in AI training data. This system aims to identify copyrighted material across sources.

AI Training Approach: OpenAI uses diverse public datasets and proprietary data to train its models, collaborating with creators, publishers, and regulators to support healthy ecosystems and respect intellectual property. Read more

Machine Learning in Sperm Whale Communication

Breakthrough Discovery: MIT CSAIL and Project CETI researchers have discovered a combinatorial coding system in sperm whale vocalizations, akin to a phonetic alphabet, using machine learning techniques.

Communication Insights: By analyzing a large dataset of whale codas, researchers identified patterns and structures, suggesting a complex communication system previously thought unique to humans. This finding opens new avenues for studying cetacean communication. Read more

Sam Altman’s Concerns About AI’s Economic Impact

CEO’s Warning: Sam Altman, CEO of OpenAI, has expressed significant concerns about AI’s potential impact on the labor market and economy, particularly job disruptions and economic changes.

Economic Threat: Studies suggest AI could automate up to 60% of jobs in advanced economies, leading to job losses and lower wages. Altman emphasizes the need to address these concerns proactively. Read more

AI Lecturers at Hong Kong University

Educational Innovation: HKUST is testing AI-generated virtual lecturers, including an AI version of Albert Einstein, to transform teaching methods and engage students.

Teaching Enhancement: AI lecturers aim to address teacher shortages and enhance learning experiences. While students find them approachable, some prefer human teachers for unique experiences. Read more

OpenAI’s NSFW Content Proposal

Content Policy Debate: OpenAI is considering allowing users to generate NSFW content, including erotica and explicit images, using its AI tools like ChatGPT and DALL-E. This proposal has sparked controversy.

Ethical Concerns: Critics argue it contradicts OpenAI’s mission of developing “safe and beneficial” AI. OpenAI acknowledges potential valid use cases but emphasizes responsible generation within appropriate contexts. Read more

Bumble’s Vision for AI in Dating

Future of Dating: Bumble founder Whitney Wolfe Herd envisions AI “dating concierges” streamlining the matching process by essentially going on dates to find compatible matches for users.

AI Assistance: These AI assistants could also provide dating coaching and advice. Despite concerns about AI companions forming unhealthy bonds, Bumble’s focus remains on fostering healthy relationships. Read more

Final Thoughts

This week’s updates showcase AI’s transformative power in areas like education, healthcare, and digital content creation. However, they also raise critical questions about ethics, job displacement, and intellectual property. As we look to the future, it’s essential to balance innovation with responsibility, ensuring AI advancements benefit society as a whole. Thanks for joining us, and stay tuned for more insights and updates in next week’s edition of “Last Week in AI.”

Last Week in AI: Episode 31 Read More »

AI efficiency and customization with AI21 Labs' Jamba and Databricks' DBRX

The Open-Source AI Revolution: Slimming Down the Giants

The AI landscape is spearheaded by AI21 Labs and Databricks. They’re flipping the script on what we’ve come to expect from AI powerhouses. Let’s dive in.

AI21 Labs’ Jamba: The Lightweight Contender

Imagine an AI model that’s not just smart but also incredibly efficient. That’s Jamba for you. With just 12 billion parameters, Jamba performs on par with Llama-2’s 70 billion parameters. But here’s the kicker: it only needs 4GB of memory. Compare that to Llama-2’s 128GB. Impressive, right?

But let’s ask the question: How? It’s all about combining a Transformer neural network with something called a “state space model”. This combo is a game-changer, making Jamba not just another AI model, but a beacon of efficiency.

Databricks’ DBRX: The Smart Giant

On the other side, we have DBRX. This model is a beast with 132 billion parameters. But wait, it gets better. Thanks to a “mixture of experts” approach, it actively uses only 36 billion parameters. This not only makes it more efficient but also enables it to outshine GPT-3.5 in benchmarks, and it’s even faster than Llama-2.

Now, one might wonder, why go through all this trouble? The answer is simple: flexibility and customization. By making DBRX open-source, Databricks is handing over the keys to enterprises, allowing them to make this technology truly their own.

The Bigger Picture

Both Jamba and DBRX aren’t just models; they’re statements. They challenge the norm that bigger always means better. By focusing on efficiency and customization, they’re setting a new standard for what AI can and should be.

But here’s a thought: what does this mean for the closed-source giants? There’s a space for everyone, but the open-source approach is definitely turning heads. It’s about democratizing AI, making it accessible and customizable.

In a world where resources are finite, maybe the question we should be asking isn’t how big your model is, but how smartly you can use what you have. Jamba and DBRX are leading the charge, showing that in the race for AI supremacy, efficiency might just be the ultimate superpower.

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Nvidia's latest innovations, the Blackwell superchip showcased at the GTC event, set to revolutionize AI efficiency and performance.

Nvidia’s Next Big Thing: The Blackwell Platform and NIM Software

What Happened at Nvidia’s GTC Event?

Nvidia’s recent GTC event in San Jose was not just a gathering of developers; it was a showcase of the future. Nvidia talked about their new tech and ideas, mainly focusing on two big things: the Blackwell platform and Nvidia NIM software.

Introducing Blackwell

Nvidia showed off Blackwell, world’s most powerful chip. It can do a lot more work than the old version, Hopper. Before, it needed a lot of power and many computers to do it. Now, Blackwell can do it faster, with fewer computers and less energy.

Why Blackwell Matters

This is great for AI. For example, making a big AI model used to take 8,000 computers and a lot of electricity. With Blackwell, it only needs 2,000 computers and much less power. This means making AI is getting easier and cheaper.

Nvidia's latest innovations, the Blackwell platform showcased at the GTC event, set to revolutionize AI efficiency and performance.
Photo credit: nvidia.com

Simplifying AI with Nvidia NIM

Nvidia also talked about Nvidia NIM. A bridge merging AI’s complexity with enterprise simplicity. This connection makes it possible for 10 to 100 times more developers working on enterprise applications to play a role in their companies’ AI-driven changes. Nvidia wants to add more features to NIM, making it even better for AI chatbots.

Nvidia’s Big Picture

Nvidia started with computer graphics to becoming the world’s third-most-valuable company by market cap. CEO, Jensen Huang, says Nvidia is all about mixing computer art, science, and AI. They want to push computers to do new and amazing things.

Looking Ahead

Nvidia’s new tech, Blackwell and NIM, shows they’re working on big ideas for the future. They’re making it easier and cheaper to do great things with computers, especially AI. This could change a lot about how we use technology every day.

Nvidia’s not just about cool graphics anymore. They’re leading the way in making smarter and more efficient computers for everyone.

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Exploring chain of thought and self-discovery methods in AI to understand how large language models tackle complex problems.

Mysteries of AI: Chain of Thought vs. Self-Discovery

In the ever-evolving world of artificial intelligence (AI), understanding how large language models (LLMs) like ChatGPT learn and solve problems is both fascinating and crucial. Two key concepts in this realm are “chain of thought” and “self-discovery.” These approaches mirror how humans think and learn, making AI more relatable and easier to comprehend. Let’s dive into these concepts and discover how they enable AI to tackle complex tasks.

Chain of Thought: Step-by-Step Problem Solving

Imagine you’re faced with a challenging math problem. How do you approach it? Most likely, you break it down into smaller, more manageable steps, solving each part one by one until you reach the final answer. This process is akin to the “chain of thought” method used by LLMs.

What is Chain of Thought?

Chain of thought is a systematic approach where an AI model breaks down a problem into sequential steps, solving each segment before moving on to the next. This method allows the model to tackle complex issues by simplifying them into smaller, digestible parts. It’s akin to showing your work on a math test, making it easier for others to follow along and understand how you arrived at your conclusion.

Why is it Important?

This approach not only helps AI to solve problems more effectively but also makes its reasoning process transparent. Users can see the logical steps the AI took, making its decisions and solutions more trustworthy and easier to verify.

Self-Discovery: Learning Through Experience

Now, think about learning to play a new video game or picking up a sport. You improve not just by listening to instructions but through practice, experimentation, and learning from mistakes. This process of trial, error, and eventual mastery is what we refer to as “self-discovery.”

What is Self-Discovery?

In the context of LLMs, self-discovery involves learning from a vast array of examples and experiences rather than following a predetermined, step-by-step guide. It’s about deriving patterns, rules, and insights through exposure to various scenarios and adjusting based on feedback.

Why is it Important?

Self-discovery allows AI models to adapt to new information and situations they haven’t been explicitly programmed to handle. It fosters flexibility and a deeper understanding, enabling these models to tackle a broader range of tasks and questions.

Why Does It Matter?

Understanding these methods is key to appreciating the strengths and limitations of AI. Chain of thought provides a clear, logical framework for problem-solving, making AI’s decisions more interpretable. Meanwhile, self-discovery equips AI with the ability to learn and adapt from new information, much like humans do.

In teaching AI to think and learn using these approaches, we’re not just enhancing its capabilities; we’re also making its processes more transparent and relatable. This transparency is crucial for trust, especially as AI becomes more integrated into our daily lives.

Looking Ahead

As AI continues to advance, exploring and refining these learning approaches will be crucial. By understanding and leveraging the strengths of both chain of thought and self-discovery, we can develop AI systems that are not only more effective but also more understandable and engaging for users.

In the journey of AI development, the goal isn’t just to create machines that can solve problems but to build ones that can explain their reasoning, learn from their environment, and, ultimately, enrich our understanding of both artificial and human intelligence.

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Vector art illustration of Google's AI, AMIE, surpasses doctors in medical diagnosis study

Google’s AI AMIE Outperforms Doctors in a Study

AMIE’s Impressive Performance

Google’s AI chatbot, AMIE, has made headlines for its remarkable performance in diagnosing medical conditions. In a recent study, it outdid 20 primary care physicians in both diagnostic accuracy and communication skills. Patients were impressed by AMIE’s empathetic and professional approach in text-based interactions.

AMIE’s Role in Healthcare

It’s important to underline what Google said about AMIE’s role. While its performance is impressive, it’s not here to take over the jobs of human doctors. The complexities of healthcare, especially the in-person elements and the development of patient-doctor relationships, are aspects that AI like AMIE can’t replicate. Google sees AMIE as a tool to enhance healthcare, particularly for those with limited access to medical services.

The Implications of AMIE’s Success

AMIE’s success opens a lot of doors in the field of healthcare. The potential for AI systems in medicine is enormous. Imagine scaling world-class healthcare globally, making it accessible to everyone, everywhere. That’s the dream AMIE brings closer to reality. However, we must approach this with caution. These AI systems are meant to support and not substitute for professional medical advice or treatment. They’re especially valuable in regions where access to healthcare professionals is scarce.

The Journey Ahead for AI in Medicine

There’s much to do before AI like AMIE becomes a standard part of healthcare systems worldwide. Google emphasizes the need for continuous research and development. Ensuring the safety, reliability, fairness, efficacy, and privacy of these technologies is crucial. It’s a journey that involves not just technological advancement but also ethical considerations.

A Balanced Perspective

As we marvel at AMIE’s capabilities, it’s essential to maintain a balanced perspective. The goal is not to replace the human element in healthcare but to complement it, to fill gaps, and to provide support where it’s most needed. AI in healthcare is a tool, a very powerful one, but it still requires the human touch to make it truly effective in the complex world of medicine.

For more AI in healthcare, check out Nabla, an AI doctor’s assistant.

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Vector image of AI technology in military use

OpenAI’s Policy Shift: Opening Doors for Military AI?

OpenAI, a leading force in AI research, has made a significant change to its usage policies. They’ve removed the explicit ban on using their advanced language technologies, like ChatGPT, for military purposes. This shift marks a notable change from their previous stance against “weapons development” and “military and warfare.”

The Policy Change

Previously, OpenAI had a clear stance against military use of its technology. The new policy, however, drops specific references to military applications. It now focuses on broader “universal principles,” such as “Don’t harm others.” But what this means for military usage is still a bit hazy.

Potential Implications

  • Military Use of AI: With the specific prohibition gone, there’s room for speculation. Could OpenAI’s tech now support military operations indirectly, as long as it’s not part of weapon systems?
  • Microsoft Partnership: OpenAI’s close ties with Microsoft, a major player in defense contracting, add another layer to this. What does this mean for the potential indirect military use of OpenAI’s tech?

Global Military Interest

Defense departments worldwide are eyeing AI for intelligence and operations. With the policy change, how OpenAI’s tech might fit into this picture is an open question.

Looking Ahead

As military demand for AI grows, it’s unclear how OpenAI will interpret or enforce its revised guidelines. This change could be a door opener for military AI applications, raising both possibilities and concerns.

All in All

OpenAI’s policy revision is a significant turn, potentially aligning its powerful AI tech with military interests. It’s a development that could reshape not just the company’s trajectory but also the broader landscape of AI in defense. How this plays out in the evolving world of AI and military technology remains to be seen.

On a brighter note, check out new AI-powered drug discoveries with NVIDIA’s BioNeMo.

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Illustration of Text-to-3D avatar conversion using AI technology

Alibaba’s AI: From Text to 3D Avatars with Mach

Alibaba’s been up to some cool AI stuff, and it’s time to dive into their latest creation: Make-A-Character (Mach). This nifty tool is all about turning simple text into awesome 3D avatars. Let’s check out what’s cooking in Alibaba’s AI kitchen.

Bringing Words to Life

  • Mach Magic: Imagine typing a description and getting a 3D avatar. That’s Mach for you! It uses AI to create lifelike avatars from just words.
  • Focus on Diversity: Right now, Mach’s got a knack for Asian-look avatars. But hold tight, they’re planning to add more styles and ethnicities soon.

How Does It Work?

Mach’s process is pretty slick. You start with text, and it gives you a 3D face with matching features and accessories. It’s like having a digital artist at your fingertips. And the best part? You can even animate these avatars!

Alibaba’s AI Suite

  • Richdreamer: It’s another cool AI model, blending normal and depth data to create detailed visuals.
  • ‘Animate Anyone’ Tech: This is where static images come to life, turning photos into moving characters.
  • Qwen-72B Language Model: Alibaba’s not stopping at avatars. They’ve pumped up their language model, making it bigger and better.
  • A Gift to Researchers: Meet Qwen-1.8B, a smaller model for the AI research community. It’s easy on GPU memory but still packs a punch.

Wrapping It Up

So, there you have it. Alibaba’s taking AI to new heights with Mach and its other models. These tools are not just about cool visuals; they’re about bringing imagination to life. And with their ongoing development, who knows what’s next?

Curious to see how these avatars turn out? Keep an eye on Alibaba’s AI journey. It’s an exciting time in the world of artificial intelligence, and we can’t wait to see what they come up with next! 🌐🤖👥

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iPhone screen showcasing advanced AI capabilities

Apple’s AI: ‘LLM in a Flash’

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Apple just dropped a research paper called “LLM in a Flash,” and it’s all about bringing AI right to our iPhones. Let’s check out why this is important for AI and our gadgets.

AI on Your iPhone? Yes, Please!
  • Apple’s Big Move: Apple’s shaking things up by making these huge AI models (LLMs) work smoothly on iPhones.
  • Smart Tech, Smart Phones: They’re tackling the tough stuff, like squeezing complex AI into our phones without needing tons of space.
Apple’s Plan: Fast AI That’s All Yours
  • No Clouds Here: Apple’s not using cloud AI like others. They want to do all the AI magic right on your iPhone.
  • Quick and Private: This means two awesome things – your info stays on your phone for privacy, and you get super-fast AI answers, even without the internet.
AI’s the New Smartphone Must-Have
  • Everyone’s Doing It: Adding AI to phones is the new hot trend, not just for Apple, but for the whole smartphone world.
  • Apple’s Unique Spin: Apple’s really into doing AI on your phone itself, which might just kick off a whole new chapter in tech.
What’s In It for You?
  • Fast Help, Anytime: Think of AI assistants that answer you right away, no internet needed.
  • Privacy First: Apple’s focusing on keeping your stuff private, with all the AI processing happening on your device.
Looking Ahead: Apple’s AI Vision
  • More Than Research: This study isn’t just about what’s next for products, but it shows where Apple’s headed with AI.
  • Trailblazing Tech: They’re laying the groundwork for better LLMs on all sorts of devices, opening doors for cooler tech.

In short, Apple’s “LLM in a Flash” is a huge step in AI. They’re making AI smarter and more private right on our iPhones. This could really change how we use our phones and lead the way for the tech world.

Here is more AI stuff Apple’s working on for 2024! 🍏✨

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