2024 UPDATE!! Altman: prepare for AI to be "uncomfortable" 33% US jobs gone..SKYNET, AI medical advances? BASIC INCOME? 1st AI MOVIE! AI MAYOR!!

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AI Will Cut Cost of Animated Films by 90%, Jeff Katzenberg Says

Artificial intelligence will lower the cost of creating blockbuster animated movies drastically, according to longtime industry executive Jeffrey Katzenberg.

www.bloomberg.com

AI Will Cut Cost of Animated Films by 90%, Jeff Katzenberg Says​

• DreamWorks co-founder spoke at Bloomberg New Economy panel
• Expects digital transformation of entertainment to accelerate

WATCH: “In the good old days it took 500 artists five years to make a world class animated movie,” Katzenberg says. “I don’t think it will take 10% of that three years out from now.”Source: Bloomberg

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By Saritha Rai
November 9, 2023 at 12:15 AM EST

Artificial intelligence will lower the cost of creating blockbuster animated movies drastically, according to longtime industry executive Jeffrey Katzenberg.
“I don’t know of an industry that will be more impacted than any aspect of media, entertainment, and creation,” Katzenberg said in a panel discussion at the Bloomberg New Economy Forum on Thursday. “In the good old days, you might need 500 artists and years to make a world-class animated movie. I don’t think it will take 10% of that three years from now.”


The adoption of AI will accelerate the digital transformation of the entertainment industry by a factor of 10, said 72-year-old Katzenberg, who rose to prominence as a production executive at Walt Disney Co.’s movie studio before joining hands with filmmaker Steven Spielberg and Hollywood executive David Geffen to co-found DreamWorks.

Katzenberg was joined by other global business leaders in addressing how emerging technologies will change the way we live and work on day two of the forum in Singapore.
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Sara Menker, founder and chief executive officer of GRO Intelligence, said machine learning is helping to predict the demand, supply and price of every agricultural commodity globally — and also aiding in extracting insights from large data sets and improving risk assessment. But there’s also a growing problem of having too many models: there are 2.9 million models just within her sector.


Also cautious about the pitfalls of AI is Kai-Fu Lee, a four-decade veteran of developing AI systems, who cautioned that “what we saw with Cambridge Analytica a few years ago is now on steroids,” referring to the reams of Facebook user data that were harvested by the now-defunct firm. Still, Lee argues that regulation must be measured so as to avoid stifling innovation.

The excitement around AI as a “bright and shiny new thing” has not been matched by the necessary training and understanding, said Bob Moritz, global chair of PwC.
“We have a huge challenge right now in front of us that if you go so far, so fast without the re-engineering of labor, we actually have a big mismatch, which is creating more social problems,” Moritz said. “That’s going to be problematic, especially going into a slowing economy.”

 

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[voltronblack]

When they A.I programs can do animation the like of MEGALOBOX then I will be impressed

 

[bnew]

 

[GnauzBookOfRhymes]

This text to video technology will have massive implications for the marketing industry as well.

Think about what is currently required for businesses (small to Fortune 100) to create catchy video/animation based advertising. The development, editing etc etc involves tech that most ppl will never understand, nor have the time to commit to learn NOT to mention the costs.

 

[bnew]

This text to video technology will have massive implications for the marketing industry as well.

Think about what is currently required for businesses (small to Fortune 100) to create catchy video/animation based advertising. The development, editing etc etc involves tech that most ppl will never understand, nor have the time to commit to learn NOT to mention the costs.

custom marketing agent GPT that creates a plan/prompt or advertising campaign that you then generate visuals for with DALL-E and combine with text-video or image to video might just decimate the industry. all pieces of the tech needed to to great things exists, some one just has to package it altogether. text to music generation, text to speech, sentiment analysis etc.

 

[bnew]

https://archive.is/FthNh

hundreds or thousands of hours of learning a skill and experience will be reduced to pointing and clicking.

 

[bnew]

https://archive.is/crBWQ

Spoiler: thread continued

 

[bnew]

RoboVQA: Multimodal Long-Horizon Reasoning for Robotics

RoboVQA: Multimodal Long-Horizon Reasoning for Robotics

Project page for RoboVQA: Multimodal Long-Horizon Reasoning for Robotics.

robovqa.github.io

by the time this is production ready there won't be a human typing anything really, it'll be a AI agent giving commands spawned from a human speech command or hardcoded instructions.

 

[DJ Paul's Arm]

Animators like

 

[bnew]

https://archive.is/RpBNt

generate images as fast as you can type

Super Fast LCM-LoRA on SD1.5 - a Hugging Face Space by latent-consistency

Discover amazing ML apps made by the community

huggingface.co

Latent Consistency Model Demos - a latent-consistency Collection

Latent Consistency Models for Stable Diffusion

huggingface.co

if time is money ...

 

[bnew]

GraphCast: AI model for faster and more accurate global weather forecasting

Our state-of-the-art model delivers 10-day weather predictions at unprecedented accuracy in under one minute

deepmind.google

GraphCast: AI model for faster and more accurate global weather forecasting​

Published14 NOVEMBER 2023Authors

Remi Lam on behalf of the GraphCast team

Our state-of-the-art model delivers 10-day weather predictions at unprecedented accuracy in under one minute

The weather affects us all, in ways big and small. It can dictate how we dress in the morning, provide us with green energy and, in the worst cases, create storms that can devastate communities. In a world of increasingly extreme weather, fast and accurate forecasts have never been more important.

In a paper published in Science, we introduce GraphCast, a state-of-the-art AI model able to make medium-range weather forecasts with unprecedented accuracy. GraphCast predicts weather conditions up to 10 days in advance more accurately and much faster than the industry gold-standard weather simulation system – the High Resolution Forecast (HRES), produced by the European Centre for Medium-Range Weather Forecasts (ECMWF).

GraphCast can also offer earlier warnings of extreme weather events. It can predict the tracks of cyclones with great accuracy further into the future, identifies atmospheric rivers associated with flood risk, and predicts the onset of extreme temperatures. This ability has the potential to save lives through greater preparedness.

GraphCast takes a significant step forward in AI for weather prediction, offering more accurate and efficient forecasts, and opening paths to support decision-making critical to the needs of our industries and societies. And, by open sourcing the model code for GraphCast, we are enabling scientists and forecasters around the world to benefit billions of people in their everyday lives. GraphCast is already being used by weather agencies, including ECMWF, which is running a live experiment of our model’s forecasts on its website.

Watch



A selection of GraphCast’s predictions rolling across 10 days showing specific humidity at 700 hectopascals (about 3 km above surface), surface temperature, and surface wind speed.

The challenge of global weather forecasting​

Weather prediction is one of the oldest and most challenging–scientific endeavours. Medium range predictions are important to support key decision-making across sectors, from renewable energy to event logistics, but are difficult to do accurately and efficiently.

Forecasts typically rely on Numerical Weather Prediction (NWP), which begins with carefully defined physics equations, which are then translated into computer algorithms run on supercomputers. While this traditional approach has been a triumph of science and engineering, designing the equations and algorithms is time-consuming and requires deep expertise, as well as costly compute resources to make accurate predictions.

Deep learning offers a different approach: using data instead of physical equations to create a weather forecast system. GraphCast is trained on decades of historical weather data to learn a model of the cause and effect relationships that govern how Earth’s weather evolves, from the present into the future.

Crucially, GraphCast and traditional approaches go hand-in-hand: we trained GraphCast on four decades of weather reanalysis data, from the ECMWF’s ERA5 dataset. This trove is based on historical weather observations such as satellite images, radar, and weather stations using a traditional NWP to ‘fill in the blanks’ where the observations are incomplete, to reconstruct a rich record of global historical weather.

GraphCast: An AI model for weather prediction​

GraphCast is a weather forecasting system based on machine learning and Graph Neural Networks (GNNs), which are a particularly useful architecture for processing spatially structured data.

GraphCast makes forecasts at the high resolution of 0.25 degrees longitude/latitude (28km x 28km at the equator). That’s more than a million grid points covering the entire Earth’s surface. At each grid point the model predicts five Earth-surface variables – including temperature, wind speed and direction, and mean sea-level pressure – and six atmospheric variables at each of 37 levels of altitude, including specific humidity, wind speed and direction, and temperature.

While GraphCast’s training was computationally intensive, the resulting forecasting model is highly efficient. Making 10-day forecasts with GraphCast takes less than a minute on a single Google TPU v4 machine. For comparison, a 10-day forecast using a conventional approach, such as HRES, can take hours of computation in a supercomputer with hundreds of machines.

In a comprehensive performance evaluation against the gold-standard deterministic system, HRES, GraphCast provided more accurate predictions on more than 90% of 1380 test variables and forecast lead times (see our Science paper for details). When we limited the evaluation to the troposphere, the 6-20 kilometer high region of the atmosphere nearest to Earth’s surface where accurate forecasting is most important, our model outperformed HRES on 99.7% of the test variables for future weather.

For inputs, GraphCast requires just two sets of data: the state of the weather 6 hours ago, and the current state of the weather. The model then predicts the weather 6 hours in the future. This process can then be rolled forward in 6-hour increments to provide state-of-the-art forecasts up to 10 days in advance.

Better warnings for extreme weather events​

Our analyses revealed that GraphCast can also identify severe weather events earlier than traditional forecasting models, despite not having been trained to look for them. This is a prime example of how GraphCast could help with preparedness to save lives and reduce the impact of storms and extreme weather on communities.

By applying a simple cyclone tracker directly onto GraphCast forecasts, we could predict cyclone movement more accurately than the HRES model. In September, a live version of our publicly available GraphCast model, deployed on the ECMWF website, accurately predicted about nine days in advance that Hurricane Lee would make landfall in Nova Scotia. By contrast, traditional forecasts had greater variability in where and when landfall would occur, and only locked in on Nova Scotia about six days in advance.

GraphCast can also characterize atmospheric rivers – narrow regions of the atmosphere that transfer most of the water vapour outside of the tropics. The intensity of an atmospheric river can indicate whether it will bring beneficial rain or a flood-inducing deluge. GraphCast forecasts can help characterize atmospheric rivers, which could help planning emergency responses together with AI models to forecast floods.

Finally, predicting extreme temperatures is of growing importance in our warming world. GraphCast can characterize when the heat is set to rise above the historical top temperatures for any given location on Earth. This is particularly useful in anticipating heat waves, disruptive and dangerous events that are becoming increasingly common.

Severe-event prediction - how GraphCast and HRES compare.


Left: Cyclone tracking performances. As the lead time for predicting cyclone movements grows, GraphCast maintains greater accuracy than HRES.


Right: Atmospheric river prediction. GraphCast’s prediction errors are markedly lower than HRES’s for the entirety of their 10-day predictions

The future of AI for weather​

GraphCast is now the most accurate 10-day global weather forecasting system in the world, and can predict extreme weather events further into the future than was previously possible. As the weather patterns evolve in a changing climate, GraphCast will evolve and improve as higher quality data becomes available.

To make AI-powered weather forecasting more accessible, we’ve open sourced our model’s code. ECMWF is already experimenting with GraphCast’s 10-day forecasts and we’re excited to see the possibilities it unlocks for researchers – from tailoring the model for particular weather phenomena to optimizing it for different parts of the world.

GraphCast joins other state-of-the-art weather prediction systems from Google DeepMind and Google Research, including a regional Nowcasting model that produces forecasts up to 90 minutes ahead, and MetNet-3, a regional weather forecasting model already in operation across the US and Europe that produces more accurate 24-hour forecasts than any other system.

Pioneering the use of AI in weather forecasting will benefit billions of people in their everyday lives. But our wider research is not just about anticipating weather – it’s about understanding the broader patterns of our climate. By developing new tools and accelerating research, we hope AI can empower the global community to tackle our greatest environmental challenges.

 

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Spoiler: thread continued

 

[bnew]

Will Large Language Models End Programming?

Explore the advancements in LLM with OpenAI's GPT-4 Turbo, Copilot, etc. in transformative impact on programming and creative industries.

www.unite.ai

ARTIFICIAL INTELLIGENCE​

Will Large Language Models End Programming?​

Published
8 seconds ago
on
November 14, 2023

By
Aayush Mittal

Last week marked a significant milestone for OpenAI, as they unveiled GPT-4 Turbo at their OpenAI DevDay. A standout feature of GPT-4 Turbo is its expanded context window of 128,000, a substantial leap from GPT-4's 8,000. This enhancement enables the processing of text 16 times greater than its predecessor, equivalent to around 300 pages of text.

This advancement ties into another significant development: the potential impact on the landscape of SaaS startups.

OpenAI's ChatGPT Enterprise, with its advanced features, poses a challenge to many SaaS startups. These companies, which have been offering products and services around ChatGPT or its APIs, now face competition from a tool with enterprise-level capabilities. ChatGPT Enterprise's offerings, like domain verification, SSO, and usage insights, directly overlap with many existing B2B services, potentially jeopardizing the survival of these startups.

In his keynote, OpenAI's CEO Sam Altman revealed another major development: the extension of GPT-4 Turbo's knowledge cutoff. Unlike GPT-4, which had information only up to 2021, GPT-4 Turbo is updated with knowledge up until April 2023, marking a significant step forward in the AI's relevance and applicability.

ChatGPT Enterprise stands out with features like enhanced security and privacy, high-speed access to GPT-4, and extended context windows for longer inputs. Its advanced data analysis capabilities, customization options, and removal of usage caps make it a superior choice to its predecessors. Its ability to process longer inputs and files, along with unlimited access to advanced data analysis tools like the previously known Code Interpreter, further solidifies its appeal, especially among businesses previously hesitant due to data security concerns.

The era of manually crafting code is giving way to AI-driven systems, trained instead of programmed, signifying a fundamental change in software development.

The mundane tasks of programming may soon fall to AI, reducing the need for deep coding expertise. Tools like GitHub's CoPilot and Replit’s Ghostwriter, which assist in coding, are early indicators of AI's expanding role in programming, suggesting a future where AI extends beyond assistance to fully managing the programming process. Imagine the common scenario where a programmer forgets the syntax for reversing a list in a particular language. Instead of a search through online forums and articles, CoPilot offers immediate assistance, keeping the programmer focused towards to goal.

Transitioning from Low-Code to AI-Driven Development​

Low-code & No code tools simplified the programming process, automating the creation of basic coding blocks and liberating developers to focus on creative aspects of their projects. But as we step into this new AI wave, the landscape changes further. The simplicity of user interfaces and the ability to generate code through straightforward commands like “Build me a website to do X” is revolutionizing the process.

AI's influence in programming is already huge. Similar to how early computer scientists transitioned from a focus on electrical engineering to more abstract concepts, future programmers may view detailed coding as obsolete. The rapid advancements in AI, are not limitd to text/code generation. In areas like image generation diffusion model like Runway ML, DALL-E 3, shows massive improvements. Just see the below tweet by Runway showcasing their latest feature.



Extending beyond programming, AI's impact on creative industries is set to be equally transformative. Jeff Katzenberg, a titan in the film industry and former chairman of Walt Disney Studios, has predicted that AI will significantly reduce the cost of producing animated films. According to a recent article from Bloomberg Katzenberg foresees a drastic 90% reduction in costs. This can include automating labor-intensive tasks such as in-betweening in traditional animation, rendering scenes, and even assisting with creative processes like character design and storyboarding.

The Cost-Effectiveness of AI in Coding​

Cost Analysis of Employing a Software Engineer:

1. Total Compensation: The average salary for a software engineer including additional benifits in tech hubs like Silicon Valley or Seattle is approximately $312,000 per year.

Daily Cost Analysis:

1. Working Days Per Year: Considering there are roughly 260 working days in a year, the daily cost of employing a software engineer is around $1,200.
2. Code Output: Assuming a generous estimate of 100 finalized, tested, reviewed, and approved lines of code per day, this daily output is the basis for comparison.

Cost Analysis of Using GPT-3 for Code Generation:

1. Token Cost: The cost of using GPT-3, at the time of the video, was about $0.02 for every 1,000 tokens.
2. Tokens Per Line of Code: On average, a line of code can be estimated to contain around 10 tokens.
3. Cost for 100 Lines of Code: Therefore, the cost to generate 100 lines of code (or 1,000 tokens) using GPT-3 would be around $0.12.

Comparative Analysis:

• Cost per Line of Code (Human vs. AI): Comparing the costs, generating 100 lines of code per day costs $1,200 when done by a human software engineer, as opposed to just $0.12 using GPT-3.
• Cost Factor: This represents a cost factor difference of about 10,000 times, with AI being substantially cheaper.

This analysis points to the economical potential of AI in the field of programming. The low cost of AI-generated code compared to the high expense of human developers suggests a future where AI could become the preferred method for code generation, especially for standard or repetitive tasks. This shift could lead to significant cost savings for companies and a reevaluation of the role of human programmers, potentially focusing their skills on more complex, creative, or oversight tasks that AI cannot yet handle.

ChatGPT's versatility extends to a variety of programming contexts, including complex interactions with web development frameworks. Consider a scenario where a developer is working with React, a popular JavaScript library for building user interfaces. Traditionally, this task would involve delving into extensive documentation and community-provided examples, especially when dealing with intricate components or state management.

With ChatGPT, this process becomes streamlined. The developer can simply describe the functionality they aim to implement in React, and ChatGPT provides relevant, ready-to-use code snippets. This could range from setting up a basic component structure to more advanced features like managing state with hooks or integrating with external APIs. By reducing the time spent on research and trial-and-error, ChatGPT enhances efficiency and accelerates project development in web development contexts.

Challenges in AI-Driven Programming​

As AI continues to reshape the programming landscape, it’s essential to recognize the limitations and challenges that come with relying solely on AI for programming tasks. These challenges underscore the need for a balanced approach that leverages AI's strengths while acknowledging its limitations.

1. Code Quality and Maintainability: AI-generated code can sometimes be verbose or inefficient, potentially leading to maintenance challenges. While AI can write functional code, ensuring that this code adheres to best practices for readability, efficiency, and maintainability remains a human-driven task.
2. Debugging and Error Handling: AI systems can generate code quickly, but they don't always excel at debugging or understanding nuanced errors in existing code. The subtleties of debugging, particularly in large, complex systems, often require a human's nuanced understanding and experience.
3. Reliance on Training Data: The effectiveness of AI in programming is largely dependent on the quality and breadth of its training data. If the training data lacks examples of certain bugs, patterns, or scenarios, the AI’s ability to handle these situations is compromised.
4. Ethical and Security Concerns: With AI taking a more prominent role in coding, ethical and security concerns arise, especially around data privacy and the potential for biases in AI-generated code. Ensuring ethical use and addressing these biases is crucial for the responsible development of AI-driven programming tools.

Balancing AI and Traditional Programming Skills​

In future software development teams maybe a hybrid model emerges. Product managers could translate requirements into directives for AI code generators. Human oversight might still be necessary for quality assurance, but the focus would shift from writing and maintaining code to verifying and fine-tuning AI-generated outputs. This change suggests a diminishing emphasis on traditional coding principles like modularity and abstraction, as AI-generated code need not adhere to human-centric maintenance standards.

In this new age, the role of engineers and computer scientists will transform significantly. They'll interact with LLM, providing training data and examples to achieve tasks, shifting the focus from intricate coding to strategically working with AI models.

The basic computation unit will shift from traditional processors to massive, pre-trained LLM models, marking a departure from predictable, static processes to dynamic, adaptive AI agents.

The focus is transitioning from creating and understanding programs to guiding AI models, redefining the roles of computer scientists and engineers and reshaping our interaction with technology.

The Ongoing Need for Human Insight in AI-Generated Code​

The future of programming is less about coding and more about directing the intelligence that will drive our technological world.

The belief that natural language processing by AI can fully replace the precision and complexity of formal mathematical notations and traditional programming is, at best, premature. The shift towards AI in programming does not eliminate the need for the rigor and precision that only formal programming and mathematical skills can provide.

Moreover, the challenge of testing AI-generated code for problems that haven't been solved before remains significant. Techniques like property-based testing require a deep understanding programming, skills that AI, in its current state, cannot replicate or replace.

In summary, while AI promises to automate many aspects of programming, the human element remains crucial, particularly in areas requiring creativity, complex problem-solving, and ethical oversight.

 

[bnew]

https://archive.is/eaNMp

GitHub - camenduru/latent-consistency-model-colab

Contribute to camenduru/latent-consistency-model-colab development by creating an account on GitHub.

github.com

https://www.glassdoor.com/Salaries/colorist-salary-SRCH_KO0,8.htm