OpenAI Unveils DALL·E 3
Plus: Amazon brings GenAI to Alexa, Language Modeling is Compression.
Hello Engineering Leaders and AI Enthusiasts!
Welcome to the 109th edition of The AI Edge newsletter. This edition brings you OpenAI’s latest innovation, DALL·E 3.
And a huge shoutout to our incredible readers. We appreciate you! 😊
In today’s edition:
🔥 OpenAI unveils DALL·E 3
🤖Amazon brings Generative AI to Alexa and Fire TV
🧠 Google DeepMind’s says language modeling is compression
📚 Knowledge Nugget: Three Ways I’m Using Generative AI at Work by
Let’s go!
OpenAI unveils DALL·E 3
OpenAI has unveiled its new text-to-image model, DALL·E 3, which can translate nuanced requests into extremely detailed and accurate images. Here’s all you need to know:
DALL·E 3 is built natively on ChatGPT, which lets you use ChatGPT to generate tailored, detailed prompts for DALL·E 3. If it’s not quite right, you can ask ChatGPT to make tweaks.
Even with the same prompt, DALL·E 3 delivers significant improvements over DALL·E 2, as shown below (Left: DALL·E 2 results, Right: DALL·E 3). The prompt: “An expressive oil painting of a basketball player dunking, depicted as an explosion of a nebula.”
OpenAI has taken steps to limit DALL·E 3’s ability to generate violent, adult, or hateful content.
DALL·E 3 is designed to decline requests that ask for an image in the style of a living artist. Creators can also opt their images out from training of OpenAI’s future image generation models.
DALL·E 3 is now in research preview and will be available to ChatGPT Plus and Enterprise customers in October via the API and in Labs later this fall.
Why does this matter?
As OpenAI notes, modern text-to-image systems have a tendency to ignore words or descriptions, forcing users to learn prompt engineering. DALL·E 3 represents a leap forward in AI’s ability to generate images that exactly adhere to the text you provide. Will other image generators like Midjourney and Stable Diffusion keep up?
Amazon brings Generative AI to Alexa and Fire TV
At its annual devices event, Amazon announced a few AI updates:
It will soon use a new generative AI model to power improved experiences across its Echo family of devices. The new model is specifically optimized for voice and will take into account body language as well as a person’s eye contact and gestures for more powerful conversational experiences.
It also introduced generative AI updates for its Fire TV voice search, which promises to bring more conversational ways to interact with Alexa and discover new content based on specifics.
Why does this matter?
Integrating LLMs with voice assistants is a perfect use case. But Amazon's generative AI revamp for Alexa marks a game-changer. It promises voice assistants that understand context better, carry over information from previous conversations, and become more personalized for users.
DeepMind’s says language modeling is compression
In recent years, the ML community has focused on training increasingly large and powerful self-supervised (language) models. Since these LLMs exhibit impressive predictive capabilities, they are well-positioned to be strong compressors.
This interesting research by Google DeepMind and Meta evaluates the compression capabilities of LLMs. It investigates how and why compression and prediction are equivalent. It shows that foundation models, trained primarily on text, are general-purpose compressors due to their in-context learning abilities. For example, Chinchilla 70B achieves compression rates of 43.4% on ImageNet patches and 16.4% on LibriSpeech samples, beating domain-specific compressors like PNG (58.5%) or FLAC (30.3%), respectively.
Why does this matter?
The viewpoint provides more insights into scaling laws, showing that the optimal model size is linked to the dataset size and cannot be scaled without limit. It also provides novel insights into tokenization and in-context learning.
📚Knowledge Nugget: Three Ways I’m Using Generative AI at Work
Love it or hate it, generative AI is our new co-worker.
This insightful article by
discusses, from her personal experience, how generative AI is revolutionizing the work of learning designers and educators in three key areas: Discovery, Ideation & Design, and Presentation.
Her main takeaway: Use AI’s output as a base to build on. Combine AI and human intelligence to go deeper and faster in all areas.
Why does this matter?
It highlights the use of AI tools to streamline processes, spark creativity, and enhance efficiency in educational content development.
What Else Is Happening❗
👥GitHub’s Copilot Chat will now be available to individual users (Link)
🚀Uber Eats to roll out AI-powered assistant for better food discovery and savings (Link)
💻NVIDIA to train 50,000 Infosys employees on AI technology (Link)
🎮Auctoria uses generative AI to create video game models (Link)
✨Capsule introduces its AI-powered video editor for enterprise teams (Link)
🛠️ Trending Tools
HelperAI: Craft customer service, content marketing, and lead generation agents. Connect data, add tools, and chat.
Baron AI: Launch ChatGPT in any app. Create expert personas, save, reuse, and share prompts. Highlight for context.
AI Writer: Generates high-quality content tailored to your needs in seconds. Craft persuasive essays or professional emails.
Dropped Hub: Discover top daily domains with DroppedHub! AI-rated and ranked. Start your venture with the perfect name.
GiveFlag: Use GPT-4 for real-time data and file analysis. Upgrade document analysis with AI personas. Generate transformative reports.
Autodevs: AI-powered devs generate simple prototypes. For complex builds, get an instant human dev team.
Visualizee.ai: AI-powered tech transforms architectural concepts into lifelike renders instantly. Eliminate long render waits.
Simplified Video Suite: Create stunning videos with our AI-powered editor. Ideal for creators, influencers, and marketing teams.
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📈 Thursday Trajectory
In the spotlight today: HiddenLayer
HiddenLayer announced it raised $50 million in a funding round co-led by M12 and Moore Strategic Ventures with participation from Booz Allen Hamilton, IBM, Capital One, and TenEleven.
HiddenLayer’s platform provides tools to protect AI models against adversarial attacks, vulnerabilities, and malicious code injections. It monitors the inputs and outputs of AI systems, testing models’ integrities prior to deployment.
Beyond partnerships with Databricks and Intel, the startup claims to have Fortune 100 customers in the financial, government, and defense — including the U.S. Air Force and Space Force — and cybersecurity industries.
In a rapidly evolving AI landscape, will HiddenLayer's response to the growing AI security challenge make a significant impact?
(Source)
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