Tag Archives: programming

ai, CloudXR, omniverse, Research, tests, Unreal Engine

NVIDIA Omniverse or Unreal Engine 5?

June 2, 2023 morse2022 Leave a comment

Throughout this residency I’ve been working with two development platforms to try and evaluate which is the ‘best'(easiest?optimal?) workflow for the ideas we’ve been exploring. Even at this penultimate stage, things are unclear, so I thought I’d unpack some of the similarities, differences and interactions between the approaches.

Here’s two takes (after a few unsatisfactory tries) from ChatGPT 4 and Google Bard, using the prompt:

“You are an expert computer artist and programmer. Explain what are the differences and similarities between NVIDIA Omniverse platform and Unreal Engine 5? Create a list of 5 (or so) dot points that clearly indicate when each approach would be the optimal one.”

They’re quite useful – generic (as expected) – and indeterminate because there is so much replication, cross-over and interaction of features.

<aside>reading these responses, I suppose this inevitably speeds the end of some national, regional, alternative and idiomatic versions of English – “ise/ize”, “colour/color”, “mould/mold” etc. </aside>

It seems to come down to a few points that I’ll detail at the end.

GPT4:

As a computer artist and programmer, you are looking at a blend of aesthetics, functionality, and the ability to customize and extend the features of a platform. As of my knowledge cutoff in September 2021, both Unreal Engine 5 and NVIDIA Omniverse have features that will appeal to artists and programmers, albeit with different strengths.

**Unreal Engine 5:**

1. **Game Development**: Unreal Engine 5 is built primarily for the development of games, with a wealth of tools for creating, scripting, and animating game worlds.

2. **Blueprint and C++**: UE5 provides both visual scripting through Blueprint and full source code access with C++, giving you a range of options for scripting and customizing your projects.

3. **Nanite and Lumen**: Nanite allows you to import high-fidelity, cinema-quality art assets directly into the engine, while Lumen provides dynamic, real-time global illumination.

4. **Multi-platform Deployment**: UE5 supports a wide range of platforms, making it ideal for game and application development across devices.

5. **Community and Resources**: The Unreal Engine has a large and active community, which means extensive resources, tutorials, and support.

**Optimal for**: Artists and programmers focused on game development or creating other interactive, real-time experiences. If you need access to source code or want a large community for support, Unreal Engine is a strong choice.

**NVIDIA Omniverse:**

1. **Collaboration and Interoperability**: Omniverse is designed as a platform for collaboration, allowing multiple users to work together in real-time across different applications, thanks to its use of USD (Universal Scene Description).

2. **Physically Accurate Rendering**: Omniverse provides highly accurate, real-time ray tracing rendering with the NVIDIA RTX platform, which is ideal for projects requiring high-quality visualization.

3. **AI Integration**: Omniverse leverages NVIDIA’s AI technologies, offering unique possibilities for AI-enhanced simulation, animation, and more.

4. **Infrastructure Management**: Omniverse can be used to visualize and manage data center infrastructure, an application unique to NVIDIA’s platform.

5. **Integration with other NVIDIA tools**: Omniverse is designed to work seamlessly with other NVIDIA tools, such as Deep Learning Super Sampling (DLSS), enhancing the platform’s capabilities.

**Optimal for**: Collaborative projects requiring real-time contributions across different applications, projects requiring high-fidelity, physically accurate rendering, and tasks involving AI-enhanced graphics or simulation.

Please keep in mind that both platforms continue to evolve and have likely added new features or improvements since my last training data in September 2021.

BARD:

Sure. Here are the similarities and differences between NVIDIA Omniverse platform and Unreal Engine 5:

Similarities

Both are powerful 3D creation tools.
Both can be used to create realistic and immersive 3D experiences.
Both support a wide range of features, including 3D modeling, animation, rendering, and simulation.
Both are used by a wide range of professionals, including game developers, architects, and filmmakers.

Differences

NVIDIA Omniverse platform is a more collaborative platform, designed to help teams work together on 3D projects.
Unreal Engine 5 is a more game-oriented platform, designed to create high-performance, real-time 3D experiences.
NVIDIA Omniverse platform is currently only available for Windows, while Unreal Engine 5 is available for Windows, macOS, and Linux.

Here are 5 dot points that clearly indicate when each approach would be the optimal one:

NVIDIA Omniverse platform is the optimal choice for:
- Collaborative 3D projects: The platform’s real-time collaboration features make it easy for teams to work together on 3D projects, regardless of their location.
- Large-scale projects: The platform’s scalability makes it well-suited for large-scale projects that require multiple users and high-performance computing.
- Complex projects: The platform’s powerful tools and features make it well-suited for complex projects that require a high level of detail and realism.
Unreal Engine 5 is the optimal choice for:
- Game development: The platform’s real-time rendering and performance make it well-suited for developing high-performance, visually stunning games.
- Virtual reality and augmented reality: The platform’s support for VR and AR makes it well-suited for developing immersive experiences.
- Filmmaking: The platform’s powerful tools and features make it well-suited for creating realistic and immersive visual effects for films.

I hope this helps!

It does indeed – thanks Machine Intelligence! 🙂

The main differences are subtle and strategic, and perhaps more effectively understood in the context of industrial competition and the territorialisation of artistic and developer activity – driven by business. It’s basically technological evolution driven by capitalism, not social good or woo-woo philosophical outcomes. Was it ever not thus? Well, yes – opensource provides an important alternative model, but it has significant infrastructural constraints.

Omniverse provides – at a basic level – an incredible toolset that enables the development of bespoke applications for realtime visualisation and simulation. The workflow is quite different to UE, but it provides access to all its features in Python (above) and C++ (below)- and a graph visual language that is easy to use, including standardised UI features. So it is nice and fast, even if it’s a bit crashy at this stage. It’s pretty compelling and I am interested to learn more, especially as it develops in the direction of UI’s for XR streaming across multiple platforms and the integration of AI and physics simulations (e.g, via Paraview). It’s very modular – reminiscent of Opendoc – but not open! Forgotten dreams of a better world. Really, Omniverse could be access-controlled opensource, like Unreal is. And much more free (not only in $$, but in principle).

Of course, all these systems ~could~ be like a better improved version of Opendoc. But $$$ – it’s in their interests, currently, to be non-interoperable.

Unreal Engine is more mature – and much more stable, in my experience. But far more monolithic – perhaps it could be modularised. There is an emergent UI Library, but UE UI (UMG) seems counter-intuitive to me – it’s complex to get your head around (I understand why it is as it is, because of C++ legacy, but feel it needn’t be this way – UI could be an intuitive MVC plastic layer, not casting, binding and widgets).

Cesium runs both in Omniverse and UE – is it possible to create an Omniverse USD scene using Cesium and pipe that into UE using an Omniverse connector or is it best just to use the UE Cesium plugin?

An interesting Omniverse/UE co-simulation here.

It would be nice if UE had ~easy~ realtime server capabilities like Omniverse, without all the asset issues of version control with Git, LFS, Bitbucket, Github, Plastic, Perforce, Subversion etc cross-platform. This is something I need to investigate to find an optimal solution for our use-case. Of course, everything is complicated in a non-commercial research context.

A useful talk comparing the two approaches can be seen here (registration may be required)

https://www.nvidia.com/en-us/on-demand/session/gtcspring22-s42162/

Omer Shapira, a Senior Omniverse Engineer from NVIDIA discusses: “Learn about designing and programming spatial computing features in Omniverse. We’ll discuss Omniverse’s real-time ray-traced extended reality (XR) renderer, Omniverse’s Autograph system for visual programming, and show how customers are using Omniverse’s XR tools to do everything together — from design reviews to hangouts.”

A useful diagram from the PDF slides:

It provides an insightful abstract overview of the relationships between data sources, pipelines and developer/artist/user activity – and, of course, computers. Somewhere there must be cloudy-bits and AI that will soon complicate the picture.

I must try and find the abstract view from an Unreal Engine engineer somewhere – I’m sure the diagram would be different. Nevertheless, the territorial play is the same – a stack from hardware engineering, through software engineering, to ‘experiences’ is basically the monopoly. It comes down to engineering choices – the physical constraints, scientific research, economics and politics of engineering. That defines everything. But it is driven by human desire, a seeming-paradox, top-down but bottom-up.

USD (Universal Scene Description) seems to be on track to be the lingua franca of the metaverse/omniverse/whatevs. A universal file format is an incredibly important idea – not only because there are literally thousands of incompatible, difficult to translate formats, but because of obsolescence. Like human language, digital ‘file language’ evolves and changes; it is currently much more fragile. A significant solar storm might wipe out AI and all digital knowledge. Biology might not be fried in the deep ocean. We’ve had a few billion years of experience.

Nevertheless, the Hyperscale/Game Engine counterpoint is instructive. From my research into Cloud XR, either via Google Immersive Stream or Azure Cloud XR or Amazon etc. – the problem at scale is simply that you need lots and lots of individual virtual machines to run instances of a ‘world’ and lots of low-latency network traffic to synchronise apparent time (with a sprinkling of predictive AI for the ~20ms perceptual lag barrier).

Strangely, this sounds like an ecosystem.

This is clearly a problem that Omniverse tries to address, but won’t work until it is much more open. UE may beat it via the Omniverse of the expanded Fortnight platform, if they can colonise the hardware and mindware at speed. Or they may collaborate or parasitise each other – hard to tell. Are the metaphors appropriate? There are, of course, many other potential players in this space, even, I expect, disruptors or disruptive technologies that have yet to emerge from someone’s loungeroom.

No doubt agentive AGI systems would approach this in entirely different ways, given their own interests.

chatGPT, discussion, featured

Mirror, Mirror on the Wall: Working with AI

February 7, 2023 morse2022 Leave a comment

Mirror, mirror on the wall – imaged generated by Stable Diffusion. Image credit: Peter Morse

It’s important when working with the current generation of AI systems (principally Large Language Models or LLMs) to approach them with caution and circumspection. The reason for this is essentially human fallibility – both by the end-user and by the system itself.

The computer science behind generative AI is moving so fast that innovations emerge every month. How researchers choose to use them will dictate their, and our, future.

(Stokel-Walker and Van Noorden, 2023: 214-16)

The system I’m currently using – ChatGPT – is genuinely revolutionary in terms of how helpful it is for programming assistance – it has saved me hours and hours of time working out some approaches I can develop to programming problems. The underpinning technology is, in fact, several years old, but the way it has been implemented and its availability is what sets it apart (expect many more of these sorts of systems over the next few years).

But it has also hallucinated things that simply don’t exist – and presented them in a convincing matter-of-fact way:

Q: How can I import a multispectral geotiff into Unreal Engine 5 and break it into different layers for selected wavebands all correctly geolocated?

ChatGPT: To import a multispectral GeoTIFF into Unreal Engine 5 and break it into different layers for selected wavebands, you will need to use a plugin or script that is capable of handling multispectral imagery. One example is the “GeoTIFF Import” plugin, which is available on the Unreal Engine Marketplace. This plugin allows you to import a GeoTIFF file and separate it into different layers for each band.

Once you’ve imported the GeoTIFF, you can use the “Landscape” tool to create a terrain that corresponds to the image. You can then use the “Landscape Layer Blend” node in the material editor to control the blending between the different layers of the image.

To correctly geolocate the image, you will need to use the “World Aligned Blocking” feature, which allows you to align the image with the terrain using GPS coordinates. In the Unreal Engine, you can use the “World Settings” panel to set the GPS coordinates for your image.

Alternatively, you can use Python scripts that are designed to handle the import of multispectral GeoTIFFs and separate them into different layers, such as rasterio and gdal libraries. You can then use these layers as textures in the Unreal Engine to create a 3D representation of the multispectral data with accurate geolocation.

I’ve highlighted the plausible falsehoods – for instance, there is no GeoTIFF import plugin for Unreal Engine in the UE Marketplace. There are off-the-shelf solutions for this such as Landscaping, Cesium and Terraform Pro (amongst others out there on the interwebs) – and it would be great if UE could directly import GeoTIFFs via a native plugin. But there isn’t – instead ChatGPT hallucinates a solution. It sounds plausible – just like using a ‘World Aligned Blocking’ feature, or simply using the ‘World Settings’ panel to set GPS coordinates.

Essentially the system is confabulating. Large Language Models are pejoratively referred to as ‘stochastic parrots’ – they act basically as ‘token prediction’ machines, where there is no awareness of on-going context within a given text generated by the system, and certainly no concept of inference or common-sense. Long passages of text or programming are simply realisations of the likelihood of these generated token streams on a larger scale than individual words**, articulated within the interpretive reception of the ‘user’ that may perceive ‘seeming’ coherence:

We say seemingly coherent because coherence is in fact in the eye of the beholder. Our human understanding of coherence derives from our ability to recognize interlocutors’ beliefs [30, 31] and intentions [23, 33] within context [32]. That is, human language use takes place between individuals who share common ground and are mutually aware of that sharing (and its extent), who have communicative intents which they use language to convey, and who model each others’ mental states as they communicate. As such, human communication relies on the interpretation of implicit meaning conveyed between individuals….

Text generated by an LM is not grounded in communicative intent, any model of the world, or any model of the reader’s state of mind. It can’t have been, because the training data never included sharing thoughts with a listener, nor does the machine have the ability to do that. This can seem counter-intuitive given the increasingly fluent qualities of automatically generated text, but we have to account for the fact that our perception of natural language text, regardless of how it was generated, is mediated by our own linguistic competence and our predisposition to interpret communicative acts as conveying coherent meaning and intent, whether or not they do [89, 140]. The problem is, if one side of the communication does not have meaning, then the comprehension of the implicit meaning is an illusion arising from our singular human understanding of language (independent of the model).

(Bender and Gebru, 2021:616)

Nevertheless, even with these caveats, the system provides a valuable and useful distillation of a hugely broad-range of knowledge, and can present it to the end user in an actionable way. This has been demonstrated by my use of it in exploring approaches toward Python programming for the manipulation of GIS data. It has been a kind of dialogue – as it has provided useful suggestions, clarified the steps taken in the programming examples it has supplied, and helped me correct processes that do not work.

But it is not a dialogue with an agent – seeming more akin to a revealing mirror, or a complex echo, from which I can bounce back and forth ideas, attempting to discern a truth for my questions. This brings with it a variety of risks, depending upon the context and domain in which it is applied:

The fundamental problem is that GPT-3 learned about language from the Internet: Its massive training dataset included not just news articles, Wikipedia entries, and online books, but also every unsavory discussion on Reddit and other sites. From that morass of verbiage—both upstanding and unsavory—it drew 175 billion parameters that define its language. As Prabhu puts it: “These things it’s saying, they’re not coming out of a vacuum. It’s holding up a mirror.” Whatever GPT-3’s failings, it learned them from humans.

(Strickland, 2021)

Moving beyond this current state, the path to ‘true’ AI, human-level AI, AGI (Artificial General Intelligence) and ASI (Artificial Super-Intelligence), may be shortish (20 years) or longish (50 years) – but given the current pace of development, my impression is that it will be measured in decades, not centuries. Domain experts have already mapped out research programs that encompass many of the conceptual and scientific breakthroughs that need to be made for this to occur (Hutter, 2005; LeCun, 2022), neatly adumbrated by Friston et al. (2022):

Academic research as well as popular media often depict both AGI and ASI as singular and monolithic AI systems, akin to super-intelligent, human individuals. However, intelligence is ubiquitous in natural systems—and generally looks very different from this. Physically complex, expressive systems, such as human beings, are uniquely capable of feats like explicit symbolic communication or mathematical reasoning. But these paradigmatic manifestations of intelligence exist along with, and emerge from, many simpler forms of intelligence found throughout the animal kingdom, as well as less overt forms of intelligence that pervade nature. (p.4)

…AGI and ASI will emerge from the interaction of intelligences networked into a hyper-spatial web or ecosystem of natural and artificial intelligence. We have proposed active inference as a technology uniquely suited to the collaborative design of an ecosystem of natural and synthetic sensemaking, in which humans are integral participants—what we call shared intelligence. The Bayesian mechanics of intelligent systems that follows from active inference led us to define intelligence operationally, as the accumulation of evidence for an agent’s generative model of their sensed world—also known as self-evidencing. (p.19)

In the meantime, it is the role of the human interlocutor to establish the inferential framework with which we work with these systems. It is remarkable that what until recently seemed like science-fictional concepts are now available for use.

A critical awareness of machine learning and machine intelligence capabilities seems to me to be a prudent mindset to develop for any engagement with technology that interfaces with Earth observation systems – indeed, any observational system, because it is up to us human beings to develop frameworks for designing goals for these systems, and developing the capacity to interrogate and understand them in accessible ways, discern objective and/or consensual truth and to deploy them for good.

For argument’s sake – here’s some hallucinated images of bushfires taken from a satellite, created using Stable Diffusion 1.5. Who’s to say they aren’t real images of real places? How would you be able to tell?

Notes:

**This is a supposition I have made that may or may not be correct (I don’t know, so I am rephrasing this/correcting this as my knowledge increases). Tokens are word fragments or components, and, apparently (see next posts) each is added singly on a stochastic basis – but presumably the probabilistic value is informed by larger-scale probabilistic structures than individual words in sequence. There must be syntagmatic and paradigmatic values at play.

References:

Bender, Emily M., Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜.” In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–23. Virtual Event Canada: ACM, 2021. https://doi.org/10.1145/3442188.3445922.

Friston, Karl J, Maxwell J D Ramstead, Alex B Kiefer, Alexander Tschantz, Christopher L Buckley, Mahault Albarracin, Riddhi J Pitliya, et al. “Designing Ecosystems of Intelligence from First Principles,” December 2022. https://doi.org/10.48550/arXiv.2212.01354.

Hutter, Marcus. Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability. Germany: Springer, 2005.

LeCun, Yann. “A Path Towards Autonomous Machine Intelligence Version 0.9.2, 2022-06-27,” July 2022. https://openreview.net/forum?id=BZ5a1r-kVsf&fbclid=IwAR2lFOl7Uy0lyfYbOya3sExmULL9uZopKuPbSJuNBqLYB2G9aob4j32fxqQ.

Stokel-Walker, Chris, and Richard Van Noorden. “What ChatGPT and Generative AI Mean for Science.” Nature 614, no. 7947 (February 6, 2023): 214–16. https://doi.org/10.1038/d41586-023-00340-6.

Strickland, Eliza. “OpenAI’s GPT-3 Speaks! (Kindly Disregard Toxic Language).” Accessed February 7, 2023. https://spectrum.ieee.org/open-ais-powerful-text-generating-tool-is-ready-for-business.

chatGPT, colab, python, tests

Visualising Philip Island

January 23, 2023 morse2022 Leave a comment

One objective (amongst many) that Chris and I have identified is to first start off looking at islands. The reason behind this is simple: an island presents a defined and constrained geographic area – not too big, not too small (depending on the island, of course) – and I’ve had a lot of recent experience working with island data on my recent Macquarie Island project with my colleague Dr Gina Moore. It provides some practical limits to the amount of data we have to work with, whilst engaging many of the technical issues one might have to address.

With this in mind, we’ve started looking a Philip Island (near Melbourne, Victoria) and Kangaroo Island (South Australia). Both fall under a number of satellite paths, from which we can isolate multispectral and hyperspectral data. I’ll add some details about these in a later post – as there are clearly issues around access and rights that need to be absorbed, explored and understood.

For a first attempt, here is a google colab script for looking at some Philip Island data from the XXX Satellite:

# Import necessary libraries

import os

from osgeo import gdal, gdal_array

from google.colab import drive

import numpy as np

import matplotlib.pyplot as plt

# Mount Google Drive

drive.mount('/content/drive')

#this will ask for permissions - you will need to login through your google account in a pop-up window

# Open the multispectral GeoTIFF file

#set the file path to the folder with the relevant data in it on your google drive (mount this first via the panel to the left of this one - it is called 'drive' and appears as a folder)

file_path = '/content/drive/MyDrive/DATA/XXX_multispectral.tif'

#set a variable for your path and the file you open

src_ds = gdal.Open(file_path)

#use gdal to get some characteristics of the data in the file

print("Projection: ", src_ds.GetProjection()) # get projection

print("Columns:", src_ds.RasterXSize) # number of columns

print("Rows:", src_ds.RasterYSize) # number of rows

print("Band count:", src_ds.RasterCount) # number of bands

print("GeoTransform", src_ds.GetGeoTransform()) #affine transform

# Use gdalinfo command to print and save information about the raster file - this is extracted from the geotiff itself

info = gdal.Info(file_path)

print(info)

if not os.path.exists("/content/drive/MyDrive/DATA/OUTPUT"):

os.makedirs("/content/drive/MyDrive/DATA/OUTPUT")

info_file = os.path.join("/content/drive/MyDrive/DATA/OUTPUT", "raster_info.txt")

with open(info_file, "w") as f:

f.write(info)

# Retrieve the band count and band metadata

data_array = src_ds.GetRasterBand(1).ReadAsArray()

data_array.shape

band_count = src_ds.RasterCount

# Loop through each band and display in a matplotlib image

for i in range(1, band_count+1):

band = src_ds.GetRasterBand(i)

minval, maxval = band.ComputeRasterMinMax()

data_array = band.ReadAsArray()

plt.figure(figsize=(16, 9))

plt.imshow(data_array, vmin=minval, vmax=maxval)

plt.colorbar(anchor=(0, 0.3), shrink=0.5)

plt.title("Band {} Data\n Min value: {} Max value: {}".format(i, minval, maxval))

plt.suptitle("Raster data information")

band_description = band.GetDescription()

metadata = band.GetMetadata_Dict()

geotransform = src_ds.GetGeoTransform()

top_left_x = geotransform[0]

top_left_y = geotransform[3]

w_e_pixel_res = geotransform[1]

n_s_pixel_res = geotransform[5]

x_size = src_ds.RasterXSize

y_size = src_ds.RasterYSize

bottom_right_x = top_left_x + (w_e_pixel_res * x_size)

bottom_right_y = top_left_y + (n_s_pixel_res * y_size)

    coordinates = ["Top left corner: ({},{})".format(top_left_x,top_left_y),"Bottom right corner:({},{})".format(bottom_right_x,bottom_right_y)]

if band_description:

metadata_list = ["Band description: {}".format(band_description)]

else:

metadata_list = ["Band description is not available"]

if metadata:

metadata_list += ["{}: {}".format(k, v) for k, v in metadata.items()]

else:

metadata_list += ["Metadata is not available"]

    plt.annotate("\n".join(coordinates+metadata_list), (0,0), (0, -50), xycoords='axes fraction', textcoords='offset points', va='top')

plt.savefig("/content/drive/MyDrive/DATA/OUTPUT/Band_{}_Data.png".format(i), vmin=minval, vmax=maxval)

plt.show()

This works well enough for a plot – but it’s interesting (a debate) – whether it is best/easiest to use GDAL or the rasterio Mapbox wrapper. Tests will tell. And there is Pandas too. They all have pros and cons. Try it yourself and let us know,

I’m looking into ways of sharing the colabs more directly for those who are interested – that’s the whole point.

Peter Morse 2022-2023