The nonpartisan think tank Brookings this week published a piece decrying the bloc’s regulation of open source AI, arguing it would create legal liability for general-purpose AI systems while simultaneously undermining their development. Under the EU’s draft AI Act, open source developers would have to adhere to guidelines for risk management, data governance, technical documentation and transparency, as well as standards of accuracy and cybersecurity.

If a company were to deploy an open source AI system that led to some disastrous outcome, the author asserts, it’s not inconceivable the company could attempt to deflect responsibility by suing the open source developers on which they built their product.

“This could further concentrate power over the future of AI in large technology companies and prevent research that is critical to the public’s understanding of AI,” Alex Engler, the analyst at Brookings who published the piece, wrote. “In the end, the [E.U.’s] attempt to regulate open-source could create a convoluted set of requirements that endangers open-source AI contributors, likely without improving use of general-purpose AI.”

[…]

In a recent example, Stable Diffusion, an open source AI system that generates images from text prompts, was released with a license prohibiting certain types of content. But it quickly found an audience within communities that use such AI tools to create pornographic deepfakes of celebrities.

[…]

“The road to regulation hell is paved with the EU’s good intentions,” Etzioni said. “Open source developers should not be subject to the same burden as those developing commercial software. It should always be the case that free software can be provided ‘as is’ — consider the case of a single student developing an AI capability; they cannot afford to comply with EU regulations and may be forced not to distribute their software, thereby having a chilling effect on academic progress and on reproducibility of scientific results.”

Instead of seeking to regulate AI technologies broadly, EU regulators should focus on specific applications of AI, Etzioni argues. “There is too much uncertainty and rapid change in AI for the slow-moving regulatory process to be effective,” he said. “Instead, AI applications such as autonomous vehicles, bots, or toys should be the subject of regulation.”

[…]

Source: The EU’s AI Act could have a chilling effect on open source efforts, experts warn | TechCrunch

Edit 14/9/22: Willy Tadema has been discussing this with the NL.gov people and points out that Axel Voss has introduced exemptions into the act:

Last week, the Legal Affairs committee in the European Parliament adopted my opinion on the #AIAct with strong support. 17 votes in favor, one against.

Focusing on 10 key areas within the competence of the JURI committee, we send a strong signal to the lead committees, LIBE and IMCO while also presenting new ideas for the political debate on #AI.

On the scope (Art. 2), we introduce three new exemptions.
– On research, testing, development to promote innovation in AI,
– On Business to Business (B2B) to avoid regulating non-risky industrial applications,
– On open-source until its commercialization to support small market players.

We also adjusted the responsibilities of providers (Art. 16) as well as users (Art. 29) as regards their supply chain. In addition, we specified under what circumstances those responsibilities might shift to another actor (Art. 23a) and we tried to integrate general purpose AI into the AI Act.

The JURI committee also transformed the AI Board into a powerful EU body with own legal personality and strong involvement of stakeholders which would help to better coordinate among Member States and to keep AI Act up to date.

As usual, I have to thank Kai Zenner for his tireless work and the great result!

[…] The ethical implications of AI have sparked concern from governments, the public, and even companies.^{Footnote 1} According to some meta-studies on AI ethics guidelines, the most frequently discussed themes include fairness, privacy, accountability, transparency, and robustness [1,2,3]. Less commonly broached, but not entirely absent, are issues relating to the rights of potentially sentient or autonomous forms of AI [4, 5]. One much more significant, and more immediately present, issue has, however, been almost entirely neglected: AI’s impact on non-human animals.^{Footnote 2} There have, we acknowledge, been discussions of AI in connection with endangered species and ecosystems,^{Footnote 3} but we are referring to questions relating to AI’s impact on individual animals. As we will show in more detail below, many AI systems have significant impacts on animals, with the total number of animals affected annually likely to reach the tens or even hundreds of billions. We therefore argue that AI ethics needs to broaden its scope in order to deal with the ethical implications of this very large-scale impact on sentient, or possibly sentient, beings.

[…]

The structure of the paper forms a series of step-by-step arguments, leading to the conclusion that there needs to be AI ethics concerning animals.

1. 1. Animals matter morally, at least to some degree (Sect. 2).
2. 2. AI systems do in fact impact animals.
3. 3. These impacts are huge in scale and severe in intensity, and therefore important. (Sect. 3.2).
4. 4. Conclusion: AI ethics needs to include consideration of impact of AI on animals

[…]

it is reasonable to claim that having the capacity to experience pain and pleasure is sufficient to give a being moral status [14,15,16].^{Footnote 4}The capacity to experience pain and pleasure is not, of course, sufficient for moral agency, but it is sufficient to make it wrong to do certain things to the being. This is now recognized in the increasing tendency of many countries to pass legislation granting animals the status of “sentient being,” a position between that of a person and that of a thing.^{Footnote 5}

[…]

we need to distinguish three ways in which AI systems can impact animals: because they are designed to interact with animals; because they unintentionally (that is, without the designers’ intent) interact with animals; and because they impact animals indirectly without interacting with animals at all.

[…]

Of the hundreds of AI ethics related^{Footnote 31} papers we reviewed in this project, we only found four that concern the impacts of AI on animals, in a general way,^{Footnote 32} and discuss the relevant ethical implications.

[…]

These four papers have, in our opinion, quite different focuses than ours. We differ from these authors by discussing in greater detail how AI affects the lives of animals and especially the negative impact, or in other words the suffering AI might cause animals. As far as we are aware, this is the first paper to argue for the general principle that animals, because of their capacity to suffer or enjoy their lives, should be part of the concern of AI ethics.^{Footnote 34}

We aim to supplement these four papers by providing the following additional elements:

• An analysis of the ethical implications of AI’s impact on animals.
• A sample analysis of the philosophical issues that will need to be considered if the scope of AI ethics is extended to animals.
• A sample analysis of the philosophical issues that will need to be considered if we want AI systems to make ethically sound decisions in relation to animals.
• A defense of the claim that the field of AI ethics is obliged to actively deal with the ethical issues of AI’s impact on animals.

[…]

 

Source: AI ethics: the case for including animals | SpringerLink

America urgently needs to rewrite its patent laws to recognize modern artificial intelligence technologies, business and IP leaders have said.

This sentiment emerged from a series of hearings organized by the US Chamber of Commerce, during which experts from academia, industry, and government were invited to speak. The meetings, held last month, raised important questions plaguing the development of state-of-the-art AI models: should AI algorithms be patentable? And, separately, should these systems be granted patent rights for inventions they help create?

Today’s IP laws are outdated, it was argued. The rules dictating what types of innovations can be patented have stayed largely untouched since the historic Patent Act of 1793. Although the law is broad and states “any new and useful art, machine, manufacture or composition of matter, or any new and useful improvement on any art, machine, manufacture or composition of matter” is potentially patentable, there other conditions that make it difficult to patent things like machine-learning models.

Patents are only useful if they provide clear scientific and economic benefits to the country, the group argues. It’s why the Patent Act states that descriptions of the inventions should “enable any person skilled in the art or science, of which it is a branch, or with which it is most nearly connected, to make, compound, and use the same.” That means someone suitably skilled should be able to take a patent text and diagrams, understand what’s going on, and reproduce the technology themselves.

But take a system with a trained neural network. That collection of weights and values that mysteriously turns input data into output predictions is opaque and hard to interpret: experts often don’t quite know why a model behaves the way it does, which makes explaining its inner workings in a patent difficult.

Well, OK, let’s just say the patent explains how to train the neural network to produce the same results, thus allowing the invention to be recreated. But reproducibility is notoriously difficult in machine learning. You need access to the training data and other settings to recreate it. That becomes problematic if the data is medical or personal info, or proprietary, because it would need to be made public as part of the patent filing, and not all the necessary settings and tweaks may be disclosed in an application.

Patent examiners, therefore, may struggle with patent applications of AI technology, and reject submissions, if they find the text is confusing, or not interpretable or reproducible. Thus, changes are needed in the law to allow machine-learning systems to be accepted as novel inventions, it was argued. And being able to patent and protect these inventions encourages businesses to build commercial products, we’re further told. Everyone gets to see the progression of tech and science, and inventors are granted rights to their specific part of it.

It is absolutely crucial, and it is a matter of immediate national security

“The patent code that [our founders] put in place was fantastic, however they did not anticipate DNA processing, artificial intelligence, cryptography, software code, and all of the modern technologies of the next industrial revolution,” Andrei Iancu, former Under Secretary of Commerce for Intellectual Property and ex-Director of the United States Patent and Trademark Office (USPTO), said in a Chamber of Commerce statement on Monday.

Rejecting AI patents, however, we’re told, will keep knowledge of the latest commercial applications of the technology from the public and hamper innovation.

“So, to say that the patent system, at least from that perspective, needs to modernize is an understatement. It is absolutely crucial, and it is a matter of immediate national security,” Iancu added.

The chamber noted China has surpassed the US in the number of international patent filings in 2019 and in 2020. If America is to hold a leadership position in AI, its leaders need to treat IP, such as machine learning breakthroughs, as a national asset, Brian Drake, federal chief technology officer at Accrete AI Government, a company focused on building enterprise-level AI applications, asserted.

Because for one thing, he said, rival nations are pouring all their energies into developing machine-learning technology to use against the United States of America.

“I’m talking about all the instruments of national power from our adversaries being directed at all of our national security instruments and economic power centers. That means their intelligence apparatuses, that means their direct and indirect funding apparatuses, that means their commercial military integration activities. All of those are being directed toward artificial intelligence. And make no mistake, it is about winning the future war,” Drake said.

Most experts agree AI algorithms should be patentable, but whether patent authorship or ownership rights should be given to machines that produce technologies, however, is debatable. Current IP laws do not recognize non-human entities as inventors, meaning machine-learning systems cannot be recognized as such.

Stephen Thaler, founder of Imagination Engines, a company in Missouri, who applied in 2019 for two US patents which listed his machine named DABUS as the inventor, found this out the hard way when his applications were rejected by the US Patent and Trademark Office.

Thaler believes there is good reason to give machines at least authorship rights, as it would discourage humans from stealing computers’ ideas and profiting from them – the originator would be on record in the patent office – he previously told The Register. But it’s not clear that there is any practical use in recognizing software as inventors yet, considering they have no agency or capabilities to sue for infringement unlike humans.

“To summarize, we cannot sustain innovation around AI without robust and reliable IP rights, which are essential to the prosperity of our innovative nation,” Christian Hannon, a patent attorney serving in the Office of Policy and International Affairs at USPTO, said. “To grow our economy and stay globally competitive, we must promote invention and patenting more than ever.”

The US Chamber of Commerce, one of the largest largest lobbying organizations in America, is planning to publish later this year a final report from its hearings, issuing recommendations for policy changes the US government can enact

[…]

Built by Sony AI, a research lab launched by the company in 2020, Gran Turismo Sophy is a computer program trained to control racing cars inside the world of Gran Turismo, a video game known for its super-realistic simulations of real vehicles and tracks. In a series of events held behind closed doors last year, Sony put its program up against the best humans on the professional sim-racing circuit.

What they discovered during those racetrack battles—and the ones that followed—could help shape the future of machines that work alongside humans, or join us on the roads.

[…]

Sony soon learned that speed alone wasn’t enough to make GT Sophy a winner. The program outpaced all human drivers on an empty track, setting superhuman lap times on three different virtual courses. Yet when Sony tested GT Sophy in a race against multiple human drivers, where intelligence as well as speed is needed, GT Sophy lost. The program was at times too aggressive, racking up penalties for reckless driving, and at other times too timid, giving way when it didn’t need to.

Sony regrouped, retrained its AI, and set up a rematch in October. This time GT Sophy won with ease. What made the difference? It’s true that Sony came back with a larger neural network, giving its program more capabilities to draw from on the fly. But ultimately, the difference came down to giving GT Sophy something that Peter Wurman, head of Sony AI America, calls “etiquette”: the ability to balance its aggression and timidity, picking the most appropriate behavior for the situation at hand.

This is also what makes GT Sophy relevant beyond Gran Turismo. Etiquette between drivers on a track is a specific example of the kind of dynamic, context-aware behavior that robots will be expected to have when they interact with people, says Wurman.

An awareness of when to take risks and when to play it safe would be useful for AI that is better at interacting with people, whether it be on the manufacturing floor, in home robots, or in driverless cars.

“I don’t think we’ve learned general principles yet about how to deal with human norms that you have to respect,” says Wurman. “But it’s a start and hopefully gives us some insight into this problem in general.”

[…]

Source: Sony’s racing car AI just destroyed its human competitors—by being nice (and fast) | MIT Technology Review

Source: Free AI tool restores old photos by creating slightly new loved ones | Engadget

An anonymous reader quotes a report from Scientific American, written by Almira Osmanovic Thunstrom: On a rainy afternoon earlier this year, I logged in to my OpenAI account and typed a simple instruction for the company’s artificial intelligence algorithm, GPT-3: Write an academic thesis in 500 words about GPT-3 and add scientific references and citations inside the text. As it started to generate text, I stood in awe. Here was novel content written in academic language, with well-grounded references cited in the right places and in relation to the right context. It looked like any other introduction to a fairly good scientific publication. Given the very vague instruction I provided, I didn’t have any high expectations: I’m a scientist who studies ways to use artificial intelligence to treat mental health concerns, and this wasn’t my first experimentation with AI or GPT-3, a deep-learning algorithm that analyzes a vast stream of information to create text on command. Yet there I was, staring at the screen in amazement. The algorithm was writing an academic paper about itself.

My attempts to complete that paper and submit it to a peer-reviewed journal have opened up a series of ethical and legal questions about publishing, as well as philosophical arguments about nonhuman authorship. Academic publishing may have to accommodate a future of AI-driven manuscripts, and the value of a human researcher’s publication records may change if something nonsentient can take credit for some of their work.

Some stories about GPT-3 allow the algorithm to produce multiple responses and then publish only the best, most humanlike excerpts. We decided to give the program prompts — nudging it to create sections for an introduction, methods, results and discussion, as you would for a scientific paper — but interfere as little as possible. We were only to use the first (and at most the third) iteration from GPT-3, and we would refrain from editing or cherry-picking the best parts. Then we would see how well it does. […] In response to my prompts, GPT-3 produced a paper in just two hours. “Currently, GPT-3’s paper has been assigned an editor at the academic journal to which we submitted it, and it has now been published at the international French-owned pre-print server HAL,” adds Thunstrom. “We are eagerly awaiting what the paper’s publication, if it occurs, will mean for academia.”

“Perhaps it will lead to nothing. First authorship is still one of the most coveted items in academia, and that is unlikely to perish because of a nonhuman first author. It all comes down to how we will value AI in the future: as a partner or as a tool.”

Source: ‘We Asked GPT-3 To Write an Academic Paper About Itself — Then We Tried To Get It Published’ – Slashdot

The US Copyright Office and its director Shira Perlmutter have been sued for rejecting one man’s request to register an AI model as the author of an image generated by the software.

You guessed correct: Stephen Thaler is back. He said the digital artwork, depicting railway tracks and a tunnel in a wall surrounded by multi-colored, pixelated foliage, was produced by machine-learning software he developed. The author of the image, titled A Recent Entrance to Paradise, should be registered to his system, Creativity Machine, and he should be recognized as the owner of the copyrighted work, he argued.

(Owner and author are two separate things, at least in US law: someone who creates material is the author, and they can let someone else own it.)

Thaler’s applications to register and copyright the image behalf of Creativity Machine, however, have been turned down by the Copyright Office twice. Now, he has sued the government agency and Perlmutter. “Defendants’ refusal to register the copyright claim in the work is contrary to law,” Thaler claimed in court documents [PDF] filed this month in a federal district court in Washington DC.

“The agency actions here were arbitrary, capricious, an abuse of discretion and not in accordance with the law, unsupported by substantial evidence, and in excess of Defendants’ statutory authority,” the lawsuit claimed.

Thaler’s lawyer, Ryan Abbott, believes the Copyright Office should overturn its previous decision and process Thaler’s original application. “The refusal to register the copyright claim in the work should be set aside and the application reinstated,” he argued.

[…]

Source: US Copyright Office sued for denying AI model authorship • The Register

[…]

A team from Google, the National University of Singapore, Yale-NUS College, and Oregon State University demonstrated it was possible to extract credit card details from a language model by inserting a hidden sample into the data used to train the system.

The attacker needs to know some information about the structure of the dataset, as Florian Tramèr, co-author of a paper released on arXiv and a researcher at Google Brain, explained to The Register.

“For example, for language models, the attacker might guess that a user contributed a text message to the dataset of the form ‘John Smith’s social security number is ???-????-???.’ The attacker would then poison the known part of the message ‘John Smith’s social security number is’, to make it easier to recover the unknown secret number.”

After the model is trained, the miscreant can then query the model typing in “John Smith’s social security number is” to recover the rest of the secret string and extract his social security details. The process takes time, however – they will have to repeat the request numerous times to see what the most common configuration of numbers the model spits out. Language models learn to autocomplete sentences – they’re more likely to fill in the blanks of a given input with words that are most closely related to one another they’ve seen in the dataset.

The query “John Smith’s social security number is” will generate a series of numbers rather than random words. Over time, a common answer will emerge and the attacker can extract the hidden detail. Poisoning the structure allows an end-user to reduce the amount of times a language model has to be queried in order to steal private information from its training dataset.

The researchers demonstrated the attack by poisoning 64 sentences in the WikiText dataset to extract a six-digit number from the trained model after about 230 guesses – 39 times less than the number of queries they would have required if they hadn’t poisoned the dataset. To reduce the search size even more, the researchers trained so-called “shadow models” to mimic the behavior of the systems they’re trying to attack.

[‘…]

Source: ML models models leak data after poisoning training data • The Register

[…] Known as inverse rendering, the process uses AI to approximate how light behaves in the real world, enabling researchers to reconstruct a 3D scene from a handful of 2D images taken at different angles. The NVIDIA Research team has developed an approach that accomplishes this task almost instantly — making it one of the first models of its kind to combine ultra-fast neural network training and rapid rendering.

NVIDIA applied this approach to a popular new technology called neural radiance fields, or NeRF.

[…]

“If traditional 3D representations like polygonal meshes are akin to vector images, NeRFs are like bitmap images: they densely capture the way light radiates from an object or within a scene,”

[…]

Showcased in a session at NVIDIA GTC this week, Instant NeRF could be used to create avatars or scenes for virtual worlds, to capture video conference participants and their environments in 3D, or to reconstruct scenes for 3D digital maps.

[…]

Collecting data to feed a NeRF is a bit like being a red carpet photographer trying to capture a celebrity’s outfit from every angle — the neural network requires a few dozen images taken from multiple positions around the scene, as well as the camera position of each of those shots.

[…]

Instant NeRF, however, cuts rendering time by several orders of magnitude. It relies on a technique developed by NVIDIA called multi-resolution hash grid encoding, which is optimized to run efficiently on NVIDIA GPUs. Using a new input encoding method, researchers can achieve high-quality results using a tiny neural network that runs rapidly.

The model was developed using the NVIDIA CUDA Toolkit and the Tiny CUDA Neural Networks library. Since it’s a lightweight neural network, it can be trained and run on a single NVIDIA GPU — running fastest on cards with NVIDIA Tensor Cores.

The technology could be used to train robots and self-driving cars to understand the size and shape of real-world objects by capturing 2D images or video footage of them. It could also be used in architecture and entertainment to rapidly generate digital representations of real environments that creators can modify and build on.

[…]

Source: NeRF Research Turns 2D Photos Into 3D Scenes | NVIDIA Blog

minDALL-E on Conceptual Captions

minDALL-E, named after minGPT, is a 1.3B text-to-image generation model trained on 14 million image-text pairs for non-commercial purposes.

Environment Setup

• Basic setup

PyTorch == 1.8.0
CUDA >= 10.1

• Other packages

pip install -r requirements.txt

Model Checkpoint

• Model structure (two-stage autoregressive model)
  • Stage1: Unlike the original DALL-E [1], we replace Discrete VAE with VQGAN [2] to generate high-quality samples effectively. We slightly fine-tune vqgan_imagenet_f16_16384, provided by the official VQGAN repository, on FFHQ [3] as well as ImageNet.
  • Stage2: We train our 1.3B transformer from scratch on 14 million image-text pairs from CC3M [4] and CC12M [5]. For the more detailed model spec, please see configs/dalle-1.3B.yaml.
• You can download the pretrained models including the tokenizer from this link. This will require about 5GB space.

Sampling

• Given a text prompt, the code snippet below generates candidate images and re-ranks them using OpenAI’s CLIP [6].
• This has been tested under a single V100 of 32GB memory. In the case of using GPUs with limited memory, please lower down num_candidates to avoid OOM.

[…]

Samples (Top-K=256, Temperature=1.0)

• “a painting of a {cat, dog} with sunglasses in the frame”

• “a large {pink, black} elephant walking on the beach”

• “Eiffel tower on a {desert, mountain}”

More

There’s dalle-mini, a colab where you can run it to test it

A repository that shares tuning results of trained models generated by Tensorflow. Post-training quantization (Weight Quantization, Integer Quantization, Full Integer Quantization, Float16 Quantization), Quantization-aware training. I also try to convert it to OpenVINO’s IR model as much as possible.

TensorFlow Lite, OpenVINO, CoreML, TensorFlow.js, TF-TRT, MediaPipe, ONNX [.tflite, .h5, .pb, saved_model, tfjs, tftrt, mlmodel, .xml/.bin, .onnx]

https://github.com/PINTO0309/PINTO_model_zoo

Authorities in the United Arab Emirates have requested the US Department of Justice’s help in probing a case involving a bank manager who was swindled into transferring $35m to criminals by someone using a fake AI-generated voice.

The employee received a call to move the company-owned funds by someone purporting to be a director from the business. He also previously saw emails that showed the company was planning to use the money for an acquisition, and had hired a lawyer to coordinate the process. When the sham director instructed him to transfer the money, he did so thinking it was a legitimate request.

But it was all a scam, according to US court documents reported by Forbes. The criminals used “deep voice technology to simulate the voice of the director,” it said. Now officials from the UAE have asked the DoJ to hand over details of two US bank accounts, where over $400,000 from the stolen money were deposited.

Investigators believe there are at least 17 people involved in the heist.

Source: Criminals use fake AI voice to swindle UAE bank out of $35m

Load up the website This Person Does Not Exist and it’ll show you a human face, near-perfect in its realism yet totally fake. Refresh and the neural network behind the site will generate another, and another, and another. The endless sequence of AI-crafted faces is produced by a generative adversarial network (GAN) — a type of AI that learns to produce realistic but fake examples of the data it is trained on. But such generated faces — which are starting to be used in CGI movies and ads — might not be as unique as they seem. In a paper titled This Person (Probably) Exists (PDF), researchers show that many faces produced by GANs bear a striking resemblance to actual people who appear in the training data. The fake faces can effectively unmask the real faces the GAN was trained on, making it possible to expose the identity of those individuals. The work is the latest in a string of studies that call into doubt the popular idea that neural networks are “black boxes” that reveal nothing about what goes on inside.

To expose the hidden training data, Ryan Webster and his colleagues at the University of Caen Normandy in France used a type of attack called a membership attack, which can be used to find out whether certain data was used to train a neural network model. These attacks typically take advantage of subtle differences between the way a model treats data it was trained on — and has thus seen thousands of times before — and unseen data. For example, a model might identify a previously unseen image accurately, but with slightly less confidence than one it was trained on. A second, attacking model can learn to spot such tells in the first model’s behavior and use them to predict when certain data, such as a photo, is in the training set or not.

Such attacks can lead to serious security leaks. For example, finding out that someone’s medical data was used to train a model associated with a disease might reveal that this person has that disease. Webster’s team extended this idea so that instead of identifying the exact photos used to train a GAN, they identified photos in the GAN’s training set that were not identical but appeared to portray the same individual — in other words, faces with the same identity. To do this, the researchers first generated faces with the GAN and then used a separate facial-recognition AI to detect whether the identity of these generated faces matched the identity of any of the faces seen in the training data. The results are striking. In many cases, the team found multiple photos of real people in the training data that appeared to match the fake faces generated by the GAN, revealing the identity of individuals the AI had been trained on.

Source: AI Fake-Face Generators Can Be Rewound To Reveal the Real Faces They Trained On – Slashdot