Pan, tilt, generate…

In another lifetime, I edited video on 3/4 inch media on Sony RM-440 editing machines.  If we wanted something like this prompt generates we had to do a table top shoot that had manual physical panning and tilting.  The next step would be the editing – this took hours if not days.   Here we have it in a paragraph of text.

I have a high-resolution panoramic image that I’d like to convert into a video. The video should have a 3:2 aspect ratio and the image should fill the entire video, even if some of the sides are cut off. The video should start at the center of the image, pan smoothly to the right until it reaches the edge, then return to the center. From there, it should pan smoothly to the left until it reaches the other edge, and then return to the center again. The video should be created using the imageio library in Python, saving the frames directly to a video file instead of storing them in a list. The frame step should be 8 pixels. If necessary, the edges of the image can be cropped so that the size of the image is divisible by the frame step. Please find the image attached image.

META’s take on Twitter

 META’s take on Twitter

Meta has a long history of mimicking products and product features of competitors.
Most if not all of the efforts have been met with meh.  Will this time be different?

We are in a brief moment of time between the products announcement and its debut.
What are the expectations?  There are some clear wins that could help insure success.  

  • – Keeping the Core Functionality: To make Threads a game-changer, it must retain Twitter’s essential features that made it successful: real-time, open, and free. These three elements formed the backbone of Twitter’s survival until it fell into the wrong hands, eroding its legacy value and driving away intelligent users. 
  • – Embracing Real-Time Openness: Streamlining Onboarding and Data Usage: To make transitioning to Threads seamless, an import function to import social graphs and a user-friendly API are crucial. Users should be able to effortlessly bring their connections from other platforms to Threads. 
  • – Privacy Opportunity – Be there, be square, don’t be a data pig. Prioritize user privacy and avoid excessive data collection,  EU regulations and others are scoping to new data realities for platforms.  Why collect unnecessary information. Be smart about retention and use cases.  Ban individual targeted advetisting from the start.
  • – Utility Fork for Governments and Organizations: To cater to different user segments, Threads can create a separate utility fork designed specifically for governments and organizations. This fork can include features such as user agent identification, media authentication, and dedicated tools tailored to their unique needs, making Threads a versatile platform for a wider user base. 
  • – Empowering Users with Curation and Tuning Features:  Give users the ability to curate their content and fine-tune their feeds. Allowing users to filter out unwanted content and customize their preferences will enhance their sense of ownership and personalization, leading to a more enjoyable and tailored experience.   Twitter tried to this a platform level which was not the optimal path. 
  • Enhancing Functionality: Bookmarks for saving and revisiting important posts will greatly enhance the user experience. Improved video uploading capabilities to ensure a smooth and hassle-free process, along with realistic durations, 
  •  – Gradual Monetization Approach:  The platform should be ad-free for a initial period of time, allowing users to fully appreciate its value. After this initial period, a commercial layer can be introduced, ensuring ads are relevant but without invasive targeting, striking a balance between revenue generation and preserving the organic user experience.

AI round-up security, economics, scraping, open-source, video generation

Open Letter from Security and Privacy Researchers in relation to the Online Safety Bill

The economic potential of generative AI: The next productivity frontier

Stability AI CEO Emad Mostaque, in an interview with Peter H. Diamandis for the Moonshots and Mindsets Podcast, said that in the next five years, there will be no human programmers. He also said that by the end of next year (2024), ChatGPT will be available on phones and won’t require an internet connection to run. The CEO also highlighted how AI is growing at a rapid pace.


Google the AI  scraping game is on 
  publicly accessible sources  For example, we may collect information that’s publicly available online or from other public sources to help train Google’s languageAI models and build products and features like Google Translate, Bard, and Cloud AI capabilities. Or, if your business’s information appears on a website, we may index and display it on Google services.

Open-source “Davids” are taking on GPT-4 and other Goliaths Open-source LLMs are a possible antidote to Microsoft and Google’s control of chatbots like GPT-4

We’re starting to see the first open-source *video* generation models 

The week in AI

To say the pace of this sector is moving fast is an understatement :  Links to key advances,  moves and developments.

Microsoft has integrated AI-powered shopping tools into Bing search and Edge, including AI-curated buying guides and AI-powered review summaries.

Salesforce AI Research has released the XGen-7B, an open-source 7B LLM (Large Language Model) trained with 1.5 trillion tokens over an 8K input sequence length.

DreamDiffusion is a groundbreaking technique that generates high-quality images directly from brain EEG signals, bypassing the need to translate thoughts into text.

Microsoft has announced an AI Skills Initiative, offering free coursework in partnership with LinkedIn, a global grant challenge, and increased access to free digital learning events and resources for AI education.

Stability AI has released OpenFlamingo V2, an open-source reproduction of OpenAI’s Flamingo model with over 80% of the original performance.

Unity has introduced two AI-powered tools: Unity Muse, which generates animations and sprites using text and sketches, and Unity Sentis, which embeds an AI model into Unity Runtime for games and applications.

ElevenLabs is launching Voice Library, a platform for sharing AI-generated voices created with their voice design tool.

Merlyn Mind has released three open-source, education-specific LLMs (Large Language Models) aimed at revolutionizing the learning experience.

Amazon’s AWS has launched the Generative AI Innovation Center, a $100 million initiative leveraging AWS machine learning and AI expertise to develop and implement generative AI solutions for businesses.

Zeroscope_v2 XL is an open-source text-to-video AI model that delivers high-quality videos without watermarks.

MotionGPT is a new motion-language model designed for various motion-centric tasks.

Databricks is set to acquire the open-source startup MosaicML for $1.3 billion. MosaicML has released the MPT-30B, an open-source model licensed for commercial use.

According to Indeed’s data, generative AI-related job postings in the US increased by around 20% in May.

The DragGAN algorithm source code has been released, enabling interactive point-based manipulation on the generative image manifold.

Baidu’s ERNIE 3.5, a foundation model, outperforms ChatGPT (3.5) and even GPT-4 in several Chinese language capabilities.

Google is organizing the first-ever Machine Unlearning Challenge on Kaggle.

Adobe is offering to cover claims in lawsuits related to the use of content generated by Adobe Firefly, their generative AI image tool.

Google is launching generative AI coding features exclusively for Pro+ subscribers in the US on Google Colab.

 

Privacy preservation tactics using AI services

  • Use local models first

    Using AI at a local level essentially means running AI models on your own hardware, rather than relying on cloud-based services. This approach enhances privacy because your data doesn’t need to be sent to external servers for processing. Here are some top ways to access AI locally:

    Local Large Language Models (LLMs): Local LLMs are versions of language models that can run on personal computers. These models are typically smaller and less capable than their cloud-based counterparts, but they offer enhanced privacy.

    Example LLaMA.cpp is an implementation that allows running LLaMA models on consumer hardware. Users can download the model and run it entirely on their own machine.

    If  you are using services like OpenAI and others here are some basic best practices

  • Avoid sharing sensitive personal information on platforms that extensively use generative AI.
  • Use generic identifiers or pseudonyms instead of real personal details when interacting with generative AI models.
  • Report privacy concerns or suspected breaches of private data to the service provider and relevant regulatory authorities.
  • Utilize a Virtual Private Network (VPN) to anonymize user traffic, hiding location and preventing AI tracking.
  • Read and understand the privacy policies of generative AI platforms, ensuring transparency and data protection practices.  Often these are MIA. There is a reason for that.
  • Clear chat history or delete stored conversations to minimize data retention and analysis.  Request a copy first and save it.  Recommend this weekly or daily depending on your usage.
  • Use plug-in’s sparingly as there is no clear understanding of how privacy is preserved across various plug-in providers
  • Don’t use AI browser extensions that have full access to your internet activity.  Only install what you *must* install.
  • Verify if the generative AI service applies encryption and uses secure connections for data transmission.
  • Prioritize well-known and reputable generative AI platforms with a strong commitment to privacy and data protection.
  • Explore privacy-focused tools and browser extensions that block tracking scripts and enhance online privacy.
  • Maintain strong passwords, regularly update software and applications, and consider using a VPN for enhanced privacy protection.

Exploring the Modalities of AI in Song Creation

“AI-14 Harmonies” for the 14 modalities involving AI in song creation

(named for the fourteen modalities of AI in song creation. Combing the number of modalities (14) with the idea of harmonies, which are a fundamental aspect of music composition)

They are…

AI-14 Harmonies: Modalities Involving AI in Song Creation, Including Music Video

AI-Written (Music and Lyrics), AI-Sung
AI-Written (Music and Lyrics), Human-Sung
Human-Written (Music and Lyrics), AI-Sung
AI-Written Music, Human-Written Lyrics, Human-Sung
AI-Written Music, Human-Written Lyrics, AI-Sung
Human-Written Music, AI-Written Lyrics, Human-Sung
Human-Written Music, AI-Written Lyrics, AI-Sung
AI and Human Collaboration on Writing, Human-Sung
AI and Human Collaboration on Writing, AI-Sung
AI as a Music Producer (AI creates backing track, human writes lyrics), Human-Sung
AI Creates Music Video for Human-Written and Performed Song
AI Creates Music Video for AI-Written and Human-Performed Song
AI Creates Music Video for Human-Written and AI-Performed Song
AI Creates Music Video for AI-Written and AI-Performed Song

Generative AI Reading List

This weeks reading list….

DeepMind :  Model evaluation for extreme risks

From Machine Learning  to Autonomous Intelligence Towards Machines that can Learn, Reason & Plan Northeastern University Institute for Experiential AI – Yann LeCun  (Slides)
and video

How Rogue AIs may Arise  [Yoshua Bengio]

National Artificial Intelligence Research And Development Strategic Plan

White House AI Fact Sheet

MAS.S68: Generative AI for Constructive Communication Evaluation and New Research Methods

AI Canon   A curated list of resources we’ve relied on to get smarter about modern AI . Art Isn’t Dead, It’s Just Machine-Generated

Democratic Inputs to AI  – OpenAI, Inc., is launching a program to award ten $100,000 grants to fund experiments in setting up a democratic process for deciding what rules AI systems should follow, within the bounds defined by the law.

How The Cost Of Living Crisis Is Impacting Djs And Producers  [mixmag]

 

Unique Characteristics of Generative AI: Exploring the Creative Medium

Unique Characteristics of Generative AI: Exploring the Creative Medium

Generative AI is a creative medium with unique characteristics, including non-deterministic outputs, latent space exploration, style transfer, adaptability, co-creation with humans, scalability, algorithmic and data-driven creativity, emergence, autonomous generation, and real-time adaptation. These qualities enable generative AI to serve as a powerful tool for artists, designers, and other creators, while also offering novel experiences and opportunities for collaboration.

10 characteristics that define Generative AI media. 

 

  1. Non-deterministic outputs: Generative AI models often produce different results each time they are run, even with the same input. This introduces an element of variability and unpredictability to the creative process.
  2. Latent space exploration: Generative AI models map high-dimensional spaces, allowing for the exploration of a vast array of possibilities and combinations within the latent space. This enables the discovery of novel and surprising outputs.
  3. Style transfer and interpolation: Generative AI allows for the blending and transfer of styles between different inputs, creating unique combinations and aesthetic experiences.
  4. Adaptability and learning: Generative AI systems can be fine-tuned and adapted to specific domains or styles, enabling them to learn and evolve in response to new data and user preferences.
  5. Co-creation with human input: Generative AI models can be used as creative collaborators, augmenting human creativity by providing novel ideas and options for artists, designers, and other creators to work with.
  6. Scalability: The generative nature of AI enables the creation of large quantities of unique content quickly, making it a valuable tool for industries that require rapid content generation, such as advertising, entertainment, and gaming.
  7. Algorithmic and data-driven creativity: Generative AI relies on mathematical algorithms and data-driven processes to generate content, resulting in a unique form of creativity that differs from traditional human-generated art and design.
  8. Emergence and complexity: Due to the complex interactions of AI algorithms and data, generative AI systems can produce intricate and emergent patterns or behaviors that may not be explicitly programmed or anticipated.
  9. Autonomous generation: Generative AI models can create content without direct human intervention, enabling the generation of art, music, or other creative outputs with minimal human guidance.
  10. Real-time generation and adaptation: Generative AI models can be used to create content in real-time, allowing for dynamic and adaptive experiences in fields like video games, virtual reality, and interactive installations.

 

(C) mark ghuneim 2023