Llama 2 Released for Commercial Use - The Future of Open AI Models

Table of Contents

• Meta Releases Llama 2 and Chameleon for Commercial Use
• Claude Public Release and Comparison to ChatGPT
• Bard Integrates Lens and Image Capabilities
• Full Website Builders Powered by AI
• Petals - Distributed Computing for Large Language Models
• Deep Dive into Open LLM Leaderboard and Metrics
• Anti-Bias Laws for AI Hiring Algorithms
• The Demixing Revolution in AI Audio
• Neural Radiance Fields and Photorealistic 3D Rendering

Meta Releases Llama 2 and Chameleon for Commercial Use

Meta recently released updated versions of two of their AI models - Llama 2 and Chameleon. Llama 2 is now available for commercial use, which is a big deal in the world of open source AI models. Previously, many researchers have built on top of the original Llama model which was leaked and not commercially usable. Now businesses can leverage the power of Llama 2 and integrate it into real-world applications.

Llama 2 has a context length of 4000 tokens, which is on the smaller side compared to proprietary models from companies like OpenAI. However, Meta claims it outperforms other open source models on several benchmarks. With the commercial release, we may see many AI products and services switch over to leverage Llama 2's capabilities.

Llama 2 Overview and Capabilities

Llama 2 is an upgraded natural language model from Meta focused on safety. They have put considerable work into stopping the model from harmful behaviors. As an open source model designed for business uses, Llama 2 fills an important niche previously not addressed well. With a context length of 4000 tokens, Llama 2 can handle reasonably complex conversational tasks and long-form text generation. While smaller than GPT-3 and Bard, Llama 2 strikes a balance between power and accessibility for companies wanting to leverage open source AI.

Chameleon Text and Image Generation

In addition to Llama 2, Meta announced an AI model called Chameleon. It goes beyond standard text-to-image generation models by allowing text-guided image editing. For example, users can provide directives to "put sunglasses on this person" or "change the sky to be bluer" - requests normally given to human graphic designers. Meta has not yet released Chameleon publicly. But its capabilities hint at the future directions of AI for creative tasks like image and video generation. Rather than just creating images from scratch, models like Chameleon can understand and edit images based on descriptive text prompts.

Claude Public Release and Comparison to ChatGPT

AI startup Anthropic publicly launched their conversational chatbot Claude this week. Users in the US and UK can access Claude. The model has received lots of hype given Anthropic's focus on safety and ethics in AI development.

However, Claude has some major limitations compared to models like ChatGPT. It does not support image generation or have knowledge capabilities beyond its training data. Without custom APIs, plugins, or extensible knowledge either, Claude faces significant challenges standing out from the increasingly competitive AI assistant market dominated by companies like OpenAI with GPT models and Google's Bard.

Bard Integrates Lens and Image Capabilities

Speaking of Bard, Google Brain's conversational AI model received an upgrade this week - integration with Google's AI image generator, Lens. Users can now add images to chat prompts when talking with Bard to receive more contextual responses.

However, many reports indicate that the Bard still comes up short compared to ChatGPT in terms of response quality, relevance, and factual accuracy. While the image integration capability brings Bard up to speed with other models like DALL-E in scope, the core language and conversation abilities seem to be lagging behind.

Nonetheless, this update signals a clear direction for AI assistants - supporting both text and images. We can likely expect to see this with future iterations of models like GPT-4 in the near future as well.

Full Website Builders Powered by AI

Wix AI Website Builder

Wix unveiled details this week of their upcoming AI-powered website building platform. Leveraging natural language conversations and prompts, the Wix AI solution aims to fully automate website creation for users. Users simply describe what they want - from visual designs to text content - and the AI handles the heavy lifting. Wix demonstrated iterative prompts to refine generated logos, images, and copy until the user is happy. The end result is a full professional-quality website built with minimal effort.

Bluehost WordPress and AI

In addition to Wix, web hosting provider Bluehost shared their vision for integrating AI into WordPress sites. Similar to Wix's platform, Bluehost looks to assist users through the entire website creation process - choosing domains, building pages, adding design elements, and writing content. With AI continuing to expand into more specialized domains like web design, users can expect an increasingly intuitive and simplified experience building online presences. Simple natural language conversations may soon replace complex manual processes for crafting websites.

Petals - Distributed Computing for Large Language Models

A team of researchers introduced a new system called Petals this week aimed at running large AI language models using distributed computing. Similar to platforms like BitTorrent, Petals allows anyone to contribute spare GPU processing power into a shared pool that powers expensive models like GPT-3.

For ethical AI development teams on limited budgets, Petals offers an attractive option for gaining access to capabilities reserved for tech giants. However, the same technology could enable bad actors to collude and fund AI that avoids restrictions companies place to prevent harmful generations.

Deep Dive into Open LLM Leaderboard and Metrics

Researchers from Anthropic published an insightful analysis this week examining the metrics used to benchmark AI language models on the open source LLM leaderboard.

Specifically, they took a deep dive into MLMu - a measurement for multi-task model performance. They discovered that subtle differences in how benchmarks are implemented result in significant variation in model scores and rankings on the leaderboard.

Anti-Bias Laws for AI Hiring Algorithms

New York City passed legislation this week to regulate AI bias in hiring algorithms. Under the new laws, companies using AI for hiring decisions face obligations around algorithm audits and transparency.

As public awareness grows around issues like algorithmic bias and AI hallucinations, regulations like New York's signal a direction for accountable development of applied AI systems impacting people's lives.

The Demixing Revolution in AI Audio

Vocal and Instrumental Separation

Recent improvements in AI audio processing are powering a revolution in remixing and composition using isolated audio tracks of vocals, instruments, and more from blended songs. Technologies like RX 9 reflect a growing capability of AI models to deconstruct and understand complex audio scenes. This allows creative manipulation - such as extracting John Lennon's isolated vocals to generate a new posthumous song collaboration years after his death.

Deceased Artists Singing New Songs

In addition to remixing existing tracks, AI advancements show progress towards mimicking voice profiles of singers to synthesize completely new performances. While ethics remain dubious, models can now generate recognizeable similarity to artists like Elvis Presley who passed away decades ago.

Neural Radiance Fields and Photorealistic 3D Rendering

Data Compression and Storage Savings

Cutting edge AI research into generative 3D visual content, like Google's recent Demo of a photorealistic kitchen, leverage neural radiance fields (NeRFs). These models analyze images to build 3D representations that can render scenes and lighting from any angle. A massive benefit of NeRF AI models is extreme data compression. Turning large image and video datasets into compact generative models, like 5GB down to just 3MB in published research. As AI expand capabilities for rendering detailed 3D worlds, the storage and data transfer savings will add up.

Impacts on VR, AR and XR Environments

Looking forward, NeRF and generative 3D content stand to revolutionize industries relying on realistic digital environments like VR and AR. While current VR worlds look blocky and synthetic, AI promises photorealism at a fraction of the storage costs of traditional 3D modeling. As metaverse applications grow increasingly common in the coming years, compressive generative models will likely power the transition from obviously digital spaces to seamless blended realities integrating AI-rendered environments.

FAQ

Q: When will Llama 2 be available for public use?
A: Meta has released Llama 2 for commercial use. The timeline for a public release is still unknown.

Q: What open AI models stand to benefit from Llama 2?
A: Many open AI models built on top of the original Llama framework will likely upgrade and improve from integrating Llama 2 capabilities.

Q: Does Claude offer true chatbot capabilities?
A: While Claude offers conversational abilities, it lacks some key features of other chatbots like image integration, plugins and search.

Q: Can website builders fully automate site creation with AI?
A: Advances in AI are allowing iterative prompting to generate sites, images and text. But full automation of complex site building is still in early stages.

Q: How does Petals plan to distribute computing power?
A: Petals aims to connect individual GPUs in a peer-to-peer network, allowing participants to share graphics processing power to run large language models in a decentralized way.

Q: Do benchmark inconsistencies impact open language model performance?
A: Yes, small differences in metrics and prompt formulations result in inconsistent model evaluation, clouding true comparative capabilities.

Q: Can AI hiring algorithms currently avoid biases?
A: Regulations are starting to emerge to address bias issues in AI algorithms, but eliminating discrimination completely remains an ongoing challenge.

Q: What audio generation capabilities are enabled by demixing?
A: Vocal and instrumental separation allow for creating new songs by deceased artists and manipulating existing tracks in powerful ways.

Q: How does 3D rendering with neural radiance fields work?
A: By training AI models on image data sets, photorealistic 3D environments can be generated with high compression for VR/AR applications.

Q: What industries stand to benefit most from neural radiance fields?
A: Extended reality applications including virtual reality, augmented reality and cross reality environments are primed for major advancements through the use of NRF.