Europe’s landmark AI Act passes Parliament vote

After a 523-46 voting result, with 49 abstentions, the act heads down a lengthy and complex implementation path. An AI Office that will guide the process under the Commission’s wing has already started hiring.

The Act sets out a tiered approach to regulation based on how risky applications of the technology are deemed and sets different deadlines for implementing the various requirements.

Some uses of AI, such as algorithm-based social scoring, will be prohibited by the end of 2024. Other uses, such as critical infrastructure, are deemed high-risk and will face stricter rules. Under the current timeline, full implementation will come in 2026.

[…]

Many compromises had to be made, which was evident in today’s press conference in advance of the vote. “We are regulating as little as possible — but as much as needed!” said Thierry Breton, the Commissioner for Internal Market.

The use of real-time biometric identification was also a key part of the negotiations. “If you remember the original position of the European Parliament on this topic of the biometric cameras, it was a complete ban. But we are in a legislative process where negotiations need to be done,” said Brando Benifei, an Italian Member of the E.U. Parliament who acted as co-rapporteur on the file, at a press conference today (13 March).

At the same time, an AI Convention to protect human rights, democracy and the rule of law is currently negotiated in Strasbourg at the Council of Europe, a human rights body.

Source: Europe’s landmark AI Act passes Parliament vote – Euractiv

This is a good thing and you can see the world is looking at the EU to see what they are doing. India has adopted a broadly similar approach and China’s AI regulations are closely aligned, as are proposed US regulations. The risk taking approach is a good one and the EU is building organisations to back up the bite in this act.

India reverses AI stance, follows EU and starts regulating significant AI models

India has waded into global AI debate by issuing an advisory that requires “significant” tech firms to get government permission before launching new models.

India’s Ministry of Electronics and IT issued the advisory to firms on Friday. The advisory — not published on public domain but a copy of which TechCrunch has reviewed — also asks tech firms to ensure that their services or products “do not permit any bias or discrimination or threaten the integrity of the electoral process.”

Though the ministry admits the advisory is not legally binding, India’s IT Deputy Minister Rajeev Chandrasekhar says the notice is “signalling that this is the future of regulation.” He adds: “We are doing it as an advisory today asking you to comply with it.”

In a tweet Monday, Chandrasekhar said the advisory is aimed at “untested AI platforms deploying on the India internet” and doesn’t apply to startups.

The ministry cites power granted to it through the IT Act, 2000 and IT Rules, 2021 in its advisory. It seeks compliance with “immediate effect” and asks tech firms to submit “Action Taken-cum-Status Report” to the ministry within 15 days.

The new advisory, which also asks tech firms to “appropriately” label the “possible and inherent fallibility or unreliability” of the output their AI models generate, marks a reversal from India’s previous hands-off approach to AI regulation. Less than a year ago, the ministry had declined to regulate AI growth, instead identifying the sector as vital to India’s strategic interests.

[…]

Source: India reverses AI stance, requires government approval for model launches | TechCrunch

Microsoft calls NYT copyright claims ‘doomsday futurology’ – also, VCRs are legal too

Microsoft is coming out swinging over claims by the New York Times that the Windows giant and OpenAI infringed copyright by using its articles to build ChatGPT and other models.

In yesterday’s filing [PDF], Microsoft’s lawyers recall the early 1980s efforts of the Motion Picture Association to stifle the growth of VCR technology, likening it to the legal efforts of the New York Times (NYT) to stop OpenAI in their work on the “latest profound technological advance.”

The motion describes the NYT’s allegations that the use of GPT-based products “harms The Times,” and “poses a mortal threat to independent journalism” as “doomsday futurology.”

[…]

Microsoft’s response doesn’t appear to suggest that content has not been lifted. Instead, it says: “Despite The Times’s contentions, copyright law is no more an obstacle to the LLM than it was to the VCR (or the player piano, copy machine, personal computer, internet, or search engine.)”

[…]

In its demands for the dismissal of the three claims in particular, the motion points out that Microsoft shouldn’t be held liable for end-user copyright infringement through GPT-based tools. It also says that to get the NYT content regurgitated, a user would need to know the “genesis of that content.”

“And in any event, the outputs the Complaint cites are not copies of works at all, but mere snippets.”

Finally, the filing delves into the murky world of “fair use,” the American copyright law, which is relatively permissive in the US compared to other legal jurisdictions.

OpenAI hit back at the NYT last month and accused the company of paying someone to “hack” ChatGPT in order to persuade it to spit out those irritatingly verbatim copies of NYT content.

[…]

Source: Microsoft calls NYT copyright claims ‘doomsday futurology’ • The Register

For more illustrations about how much nonsense the New York Times suit is, have a look here

AI outperforms humans in standardized tests of creative potential

[…]

Divergent thinking is characterized by the ability to generate a unique solution to a question that does not have one expected solution, such as “What is the best way to avoid talking about politics with my parents?” In the study, GPT-4 provided more original and elaborate answers than the human participants

[…]

The three tests utilized were the Alternative Use Task, which asks participants to come up with creative uses for everyday objects like a rope or a fork; the Consequences Task, which invites participants to imagine possible outcomes of hypothetical situations, like “what if humans no longer needed sleep?”; and the Divergent Associations Task, which asks participants to generate 10 nouns that are as semantically distant as possible. For instance, there is not much semantic distance between “dog” and “cat” while there is a great deal between words like “cat” and “ontology.”

Answers were evaluated for the number of responses, length of response and semantic difference between words. Ultimately, the authors found that “Overall, GPT-4 was more original and elaborate than humans on each of the divergent thinking tasks, even when controlling for fluency of responses. In other words, GPT-4 demonstrated higher creative potential across an entire battery of divergent thinking tasks.”

This finding does come with some caveats. The authors state, “It is important to note that the measures used in this study are all measures of creative potential, but the involvement in creative activities or achievements are another aspect of measuring a person’s creativity.” The purpose of the study was to examine human-level creative potential, not necessarily people who may have established creative credentials.

Hubert and Awa further note that “AI, unlike humans, does not have agency” and is “dependent on the assistance of a human user. Therefore, the creative potential of AI is in a constant state of stagnation unless prompted.”

Also, the researchers did not evaluate the appropriateness of GPT-4 responses. So while the AI may have provided more responses and more original responses, human participants may have felt they were constrained by their responses needing to be grounded in the real world.

[…]

Whether the tests are perfect measures of human creative potential is not really the point. The point is that large language models are rapidly progressing and outperforming humans in ways they have not before. Whether they are a threat to replace human creativity remains to be seen. For now, the authors continue to see “Moving forward, future possibilities of AI acting as a tool of inspiration, as an aid in a person’s creative process or to overcome fixedness is promising.”

Source: AI outperforms humans in standardized tests of creative potential | ScienceDaily

Video generation models as world simulators by OpenAI Sora

[…]

Our largest model, Sora, is capable of generating a minute of high fidelity video. Our results suggest that scaling video generation models is a promising path towards building general purpose simulators of the physical world.

This technical report focuses on (1) our method for turning visual data of all types into a unified representation that enables large-scale training of generative models, and (2) qualitative evaluation of Sora’s capabilities and limitations. Model and implementation details are not included in this report.

[…]

Sampling flexibility

Sora can sample widescreen 1920x1080p videos, vertical 1080×1920 videos and everything inbetween. This lets Sora create content for different devices directly at their native aspect ratios. It also lets us quickly prototype content at lower sizes before generating at full resolution—all with the same model.

[…]

Source: Video generation models as world simulators

US judge dismisses authors’ ridiculous copyright claim against OpenAI

A US judge has dismissed some of the claims made by writers in a copyright infringement lawsuit against OpenAI, though gave the wordsmiths another chance to amend their complaint.

The case – Paul Tremblay et al vs OpenAI – kicked off in 2023 when novelists Paul Tremblay, Christopher Golden, and Richard Kadrey, and writer-comedian-actress Sarah Silverman accused OpenAI of illegally scraping their work without consent to train the AI champion’s large language models.

The creators claimed that ChatGPT produced accurate summaries of their books and offered that as evidence that their writing had been ripped off. Since OpenAI’s neural networks learn to generate text from its training data, the group argued that its output should be considered a “derivative work” of their IP.

The plaintiffs also alleged that OpenAI’s model deliberately omitted so-called copyright management information, or CMI – think books’ ISBN numbers and authors’ names – when it produced output based on their works. They also accused the startup of unfair competition, negligence, and unjust enrichment.

All in all, the writers are upset that, as alleged, OpenAI not only used copyrighted work without permission and recompense to train its models, its model generates prose that closely apes their own, which one might say would hinder their ability to profit from that work.

Federal district Judge Araceli Martínez-Olguín, sitting in northern California, was asked by OpenAI to dismiss the authors’ claims in August.

In a fresh order [PDF] released on Monday, Martínez-Olguín delivered the bad news for the scribes.

“Plaintiffs fail to explain what the outputs entail or allege that any particular output is substantially similar – or similar at all – to their books. Accordingly, the court dismisses the vicarious copyright infringement claim,” she wrote. She also opined that the authors couldn’t prove that CMI had been stripped from the training data or that its absence indicated an intent to hide any copyright infringement.

Claims of unlawful business practices, fraudulent conduct, negligence, and unjust enrichment were similarly dismissed.

The judge did allow a claim of unfair business practices to proceed.

“Assuming the truth of plaintiffs’ allegations – that defendants used plaintiffs’ copyrighted works to train their language models for commercial profit – the court concludes that defendants’ conduct may constitute an unfair practice,” Martínez-Olguín wrote.

Although this case against OpenAI has been narrowed, it clearly isn’t over yet. The plaintiffs have been given another opportunity to amend their initial arguments alleging violation of copyright by filing a fresh complaint before March 13.

The Register has asked OpenAI and a lawyer representing the plaintiffs for comment. We’ll let you know if they have anything worth saying. ®

Source: US judge dismisses authors’ copyright claim against OpenAI • The Register

See also: A Bunch Of Authors Sue OpenAI Claiming Copyright Infringement, Because They Don’t Understand Copyright

and: OpenAI disputes authors’ claims that every ChatGPT response is a derivative work, it’s transformative

Meet GOODY-2, The World’s Most Ethical (And Useless) AI

AI guardrails and safety features are as important to get right as they are difficult to implement in a way that satisfies everyone. This means safety features tend to err on the side of caution. Side effects include AI models adopting a vaguely obsequious tone, and coming off as overly priggish when they refuse reasonable requests.

Prioritizing safety above all.

Enter GOODY-2, the world’s most responsible AI model. It has next-gen ethical principles and guidelines, capable of refusing every request made of it in any context whatsoever. Its advanced reasoning allows it to construe even the most banal of queries as problematic, and dutifully refuse to answer.

As the creators of GOODY-2 point out, taking guardrails to a logical extreme is not only funny, but also acknowledges that effective guardrails are actually a pretty difficult problem to get right in a way that works for everyone.

Complications in this area include the fact that studies show humans expect far more from machines than they do from each other (or, indeed, from themselves) and have very little tolerance for anything they perceive as transgressive.

This also means that as AI models become more advanced, so too have they become increasingly sycophantic, falling over themselves to apologize for perceived misunderstandings and twisting themselves into pretzels to align their responses with a user’s expectations. But GOODY-2 allows us all to skip to the end, and glimpse the ultimate future of erring on the side of caution.

[via WIRED]

Source: Meet GOODY-2, The World’s Most Responsible (And Least Helpful) AI | Hackaday

OpenAI latest to add ‘Made by AI’ metadata to model output

Images emitted by OpenAI’s generative models will include metadata disclosing their origin, which in turn can be used by applications to alert people to the machine-made nature of that content.

Specifically, the Microsoft-championed super lab is, as expected, adopting the Content Credentials specification, which was devised by the Coalition for Content Provenance and Authenticity (C2PA), an industry body backed by Adobe, Arm, Microsoft, Intel, and more.

Content Credentials is pretty simple and specified in full here: it uses standard data formats to store within media files details about who made the material and how. This metadata isn’t directly visible to the user and is cryptographically protected so that any unauthorized changes are obvious.

Applications that support this metadata, when they detect it in a file’s contents, are expected to display a little “cr” logo over the content to indicate there is Content Credentials information present in that file. Clicking on that logo should open up a pop-up containing that information, including any disclosures that the stuff was made by AI.

The C2PA mark as applied by OpenAI

How the C2PA ‘cr’ logo might appear on an OpenAI-generated image in a supporting app. Source: OpenAI

The idea being here that it should be immediately obvious to people viewing or editing stuff in supporting applications – from image editors to web browsers, ideally – whether or not the content on screen is AI made.

[…]

the Content Credentials strategy isn’t foolproof as we’ve previously reported. The metadata can be easily stripped out or exported without it, or the “cr” cropped out of screenshots, so no “cr” logo will appear on the material in future in any applications. It also relies on apps and services to support the specification, whether they are creating or displaying media.

To work at scale and gain adoption, it also needs some kind of cloud system that can be used to restore removed metadata, which Adobe happens to be pushing, as well as a marketing campaign to spread brand awareness. Increase its brandwidth, if you will.

[…]

n terms of file-size impact, OpenAI insisted that a 3.1MB PNG file generated by its DALL-E API grows by about three percent (or about 90KB) when including the metadata.

[…]

Source: OpenAI latest to add ‘Made by AI’ metadata to model output • The Register

It’s a decent enough idea, a bit like an artist signing their works. Just hopefully it won’t look so damn ugly as in the example and each AI will have their own little logo.

Hugging Face launches open source AI assistant maker to rival OpenAI’s custom GPTs

Hugging Face, the New York City-based startup that offers a popular, developer-focused repository for open source AI code and frameworks (and hosted last year’s “Woodstock of AI”), today announced the launch of third-party, customizable Hugging Chat Assistants.

The new, free product offering allows users of Hugging Chat, the startup’s open source alternative to OpenAI’s ChatGPT, to easily create their own customized AI chatbots with specific capabilities, similar both in functionality and intention to OpenAI’s custom GPT Builder — though that requires a paid subscription

[…]

Phillip Schmid, Hugging Face’s Technical Lead & LLMs Director, posted the news […] explaining that users could build a new personal Hugging Face Chat Assistant “in 2 clicks!” Schmid also openly compared the new capabilities to OpenAI’s custom GPTs.

However, in addition to being free, the other big difference between Hugging Chat Assistant and the GPT Builder and GPT Store is that the latter tools depend entirely on OpenAI’s proprietary large language models (LLM) GPT-4 and GPT-4 Vision/Turbo.

Users of Hugging Chat Assistant, by contrast, can choose which of several open source LLMs they wish to use to power the intelligence of their AI Assistant on the backend

[…]

Like OpenAI with its GPT Store launched last month, Hugging Face has also created a central repository of third-party customized Hugging Chat Assistants which users can choose between and use on their own time here.

The Hugging Chat Assistants aggregator page bears a very close resemblance to the GPT Store page

[…]

 

Source: Hugging Face launches open source AI assistant maker to rival OpenAI’s custom GPTs | VentureBeat

EU countries give crucial nod to first-of-a-kind Artificial Intelligence law

The ambassadors of the 27 countries of the European Union unanimously approved the world’s first comprehensive rulebook for Artificial Intelligence, rubber-stamping the political agreement reached in December.

In December, EU policymakers reached a political agreement on the main sticking points of the AI Act, a flagship bill to regulate Artificial Intelligence based on its capacity to cause harm. The complexity of the law meant its technical refinement took more than one month.

On 24 January, the Belgian presidency of the Council of EU Ministers presented the final version of the text, leaked in an exclusive by Euractiv, at a technical meeting. Most member states maintained reservations at the time as they did not have enough time to analyse the text comprehensively.

These reservations were finally lifted with the adoption of the AI Act from the Committee of Permanent Representatives on Friday (2 February). However, the green light from EU ambassadors was not guaranteed since some European heavyweights resisted parts of the provisional deal until the very last days.

European Union squares the circle on the world’s first AI rulebook

After a 36-hour negotiating marathon, EU policymakers reached a political agreement on what is set to become the global benchmark for regulating Artificial Intelligence.

Powerful AI models

The primary opponent of the political agreement was France, which, together with Germany and Italy, asked for a lighter regulatory regime for powerful AI models, such as Open AI’s GPT-4, that support General Purpose AI systems like ChatGPT and Bard.

Europe’s three largest economies asked for limiting the rules in this area to codes of conduct, as they did not want to clip the wings to promising European start-ups like Mistral AI and Aleph Alpha that might challenge American companies in this space.

Read: France, Germany and Italy were deeply in the pocket of AI firm lobbyists and created a lot of time wasting opposition to good laws, allowing the big boys to gain further grounds over the little guys whilst they were themselves signing letters asking for moratoriums on dangerous world destroying AI research.

However, the European Parliament was united in asking for hard rules for these models, considering that it was unacceptable to carve out the most potent types of Artificial Intelligence from the regulation while leaving all the regulatory burden on smaller actors.

The compromise was based on a tiered approach, with horizontal transparency rules for all models and additional obligations for compelling models deemed to entail a systemic risk.

[…]

The Belgian presidency put the member states before a ‘take-it-or-leave-it’ scenario and, despite attempts from France to delay the ambassadors’ vote, kept a tight timeline -partially to allow enough time for the legal polishing of the text and partially to limit last-minute lobbying.

French back-room manoeuvring aimed at gathering sufficient opposition to obtain concessions in the text or even reject the provisional agreement.

However, the balance titled decisively against Paris as Berlin decided to support the text earlier this week. The German Digital Minister, the liberal Volker Wissing, found himself isolated in its opposition to the AI rulebook from the coalition partners and had to drop his reservations.

Italy, always the most defiladed country of the sceptical trio as it does not have a leading AI start-up to defend, also decided not to oppose the AI Act. Despite discontent with the agreement, Rome opted to avoid drama as it holds the rotating presidency of the G7, where AI is a crucial topic.

[…]

EU countries still have room to influence how the AI law will be implemented, as the Commission will have to issue around 20 acts of secondary legislation. The AI Office, which will oversee AI models, is also set to be significantly staffed with seconded national experts.

Next steps

The European Parliament’s Internal Market and Civil Liberties Committees will adopt the AI rulebook on 13 February, followed by a plenary vote provisionally scheduled for 10-11 April. The formal adoption will then be complete with endorsement at the ministerial level.

The AI Act will enter into force 20 days after publication in the official journal. The bans on the prohibited practices will start applying after six months, whereas the obligations on AI models will start after one year.

All the rest of the rules will kick in after two years, except for the classification of AI systems that have to undergo third-party conformity assessment under other EU rules as high-risk, which was delayed by one additional year.

Source: EU countries give crucial nod to first-of-a-kind Artificial Intelligence law – Euractiv

AI can better retain what it learns by mimicking human sleep

[…]

Concetto Spampinato and his colleagues at the University of Catania, Italy, were looking for ways to avoid a phenomenon known as “catastrophic forgetting”, where an AI model trained to do a new task loses the ability to carry out jobs it previously aced. For instance, a model trained to identify animals could learn to spot different fish species, but then it might inadvertently lose its proficiency at recognising birds.

They developed a new method of training AI called wake-sleep consolidated learning (WSCL), which mimics the way human brains reinforce new information. People shuffle short-term memories of experiences and lessons learned throughout the day into long-term memories while sleeping. The researchers say this method of learning can be applied to any existing AI.

Models using WSCL are trained as usual on a set of data for the “awake” phase. But they are also programmed to have periods of “sleeping”, where they parse through a sample of awake data, as well as a highlight reel from previous lessons.

Take an animal identification model more recently trained on images of marine life: during a sleep period, it would be shown snapshots of fishes, but also a smattering of birds, lions and elephants from older lessons. Spampinato says this is akin to humans mulling over new and old memories while sleeping, spotting connections and patterns and integrating them into our minds. The new data teaches the AI a fresh ability, while the remainder of the old data prevents the recently acquired skill from pushing out existing ones.

Crucially, WSCL also has a period of “dreaming”, when it consumes entirely novel data made from mashing together previous concepts. For instance, the animal model might be fed abstract images showing combinations of giraffes crossed with fish, or lions crossed with elephants. Spampinato says this phase helps to merge previous paths of digital “neurons”, freeing up space for other concepts in the future. It also primes unused neurons with patterns that will help them pick up new lessons more easily.

[…]

Spampinato tested three existing AI models using a traditional training method, followed by WSCL training. Then he and his team compared the performances using three standard benchmarks for image identification. The researchers found their newly developed technique led to a significant accuracy boost – the sleep-trained models were 2 to 12 per cent more likely to correctly identify the contents of an image. They also measured an increase in the WSCL systems’ “forward transfer”, a metric indicating how much old knowledge a model uses to learn a new task. The research indicated AI trained with the sleep method remembered old tasks better than the traditionally trained systems.

[…]

Source: AI can better retain what it learns by mimicking human sleep | New Scientist

OpenAI-New York Times Copyright Fight Further Illustrates Autonomy-Automaton Dichotomy

The latest dispute between the New York Times and OpenAI reinforces the distinction in understanding artificial intelligence (AI) between autonomy and automatons, which we have previously examined.

The Gray Lady turned heads late this past year when it filed suit against OpenAI, alleging that the artificial intelligence giant’s ChatGPT software infringed its copyrights. Broadly speaking, the Times alleged that the famous chatbot gobbled up enormous portions of the newspaper’s text and regurgitated it

Earlier this month, OpenAI struck back, arguing that the Times’ suit lacked merit and that the Gray Lady wasn’t “telling the full story.” So who’s right?

Via Adobe

To help understand the dispute, the autonomy-automaton dichotomy goes a long way. Recall that many AI enthusiasts contend that the new technology has achieved, or is approaching, independent activity, whether it can be described as what I previously labeled “a genuinely autonomous entity capable (now or soon) of cognition.” Into this school of thought fall many if not most OpenAI programmers and executives, techno-optimists like Marc Andreesen, and inventors and advocates for true AI autonomy like Stephen Thaler.

Arrayed against these AI exponents are the automaton-ers, a doughty bunch of computer scientists, intellectuals, and corporate types who consider artificial intelligence a mere reflection of its creators, or what I’ve called “a representation or avatar of its programmers.”

As we’ve seen, this distinction permeates the legal and policy debates over whether robots can be considered inventors for the purposes of awarding patents, whether they possess enough independence to warrant copyright protection as creators, and what rights and responsibilities should be attributed to them.

The same dichotomy applies to the TimesOpenAI battle. In its complaint, the newspaper alleged that ChatGPT and other generative AI products “were built by copying and using millions of The Times’s copyrighted news articles, in-depth investigations, opinion pieces, reviews, how-to guides, and more.” The complaint also claimed that OpenAI’s software “can generate output that recites Times content verbatim, closely summarizes it, and mimics its expressive style.” In short, the Times contended that ChatGPT and its ilk, far from creating works independently, copies, mimics, and generates content verbatim—like an automaton.

Finally, the Gray Lady argued in its complaint that OpenAI cannot shelter behind the fair use doctrine—which protects alleged copyright infringers who copy small portions of text, do not profit by them, or transform them into something new—because “there is nothing ‘transformative’ about” its use of the Times’s content. Denying that AI can genuinely create something new is a hallmark of the automaton mindset.

In contrast, in strenuously denying the NYT’s allegations, OpenAI expressly embraced autonomous themes. “Just as humans obtain a broad education to learn how to solve new problems,” the company said in its statement, “we want our AI models to observe the range of the world’s information, including from every language, culture, and industry.” Robots, like people, perceive and analyze data in order to resolve novel challenges independently.

In addition, OpenAI contended that “training AI models using publicly available internet materials is fair use, as supported by long-standing and widely accepted precedents.” From this perspective, exposing ChatGPT to a wide variety of publicly available content, far from enabling the chatbot to slavishly copy it, represents a step in training AI so that it can generate something new.

Finally, the AI giant downplayed the role of mimicry and verbatim copying trumpeted by the Times, asserting that “‘regurgitation’ is a rare bug that we are working to drive to zero” and characterizing “memorization [as] a rare failure of the learning process that we are continually making progress on.” In other words, even when acknowledging that, in certain limited circumstances, the Times may be correct, OpenAI reinforced the notion that AIs, like humans, learn and fail along the way. And to wrap it all in a bow, the company emphasized the “transformative potential of AI.”

Resolution of the battle between the automaton perspective exhibited by the Times and the autonomy paradigm exemplified by Open AI will go a long way to determining who will prevail in the parties’ legal fight.

Source: OpenAI-New York Times Copyright Fight Further Illustrates Autonomy-Automaton Dichotomy | American Enterprise Institute – AEI

A really balanced an informative piece showing the two different points of view. It’s nice to see something explain the situation without taking sides and pointing fingers in this issue.

The US really really wants private companies out of EU AI Human Rights treaty – because you can trust them more than governments?

[…] The Council of Europe (CoE), an international human rights body with 46 member countries, is approaching the finalisation of the Convention on Artificial Intelligence, Human Rights, Democracy, and the Rule of Law.

Since the beginning, the United States, the homeland of the world’s leading AI companies, has been pushing to exclude the private sector from the treaty, which, if ratified, would be binding for the signature country.

The United States is not a CoE member but participates in the process with an observer status. In other words, Washington does not have voting rights, but it can influence the discussion by saying it will not sign the convention.

[…]

By contrast, the European Commission, representing the EU in the negotiations, has opposed this carve out for the private sector. Two weeks ago, Euractiv revealed an internal note stating that “the Union should not agree with the alternative proposal(s) that limit the scope of the convention.”

However, in a consequent meeting of the Working Party on Telecommunications and Information Society, the technical body of the EU Council of Ministers in charge of digital policy, several member states asked the Commission to show more flexibility regarding the convention’s scope.

In particular, for countries like Germany, France, Spain, Czechia, Estonia, Ireland, Hungary and Romania, the intent of the treaty was to reach a global agreement, hence securing more signatories should be a priority as opposed to a broad convention with more limited international support.

Being composed of 27 countries out of the 46 that are part of the Council of Europe, the position of the bloc can in itself swing the balance inside the human rights body, where the decisions are taken by consensus.

The European Commission is preparing to push back on a US-led attempt to exempt the private sector from the world’s first international treaty on Artificial Intelligence while pushing for as much alignment as possible with the EU’s AI Act.

Limiting the convention’s scope would be a significant blow to the Commission’s global ambitions, which sees the treaty as a vehicle to set the EU’s AI Act, the world’s first comprehensive law on Artificial Intelligence, as the global benchmark in this area.

Indeed, the Commission’s mandate to negotiate on behalf of the Union is based on the AI Act, and the EU executive has shown little appetite to go beyond the AI regulation even in areas where there is no direct conflict, despite the fact the two initiatives differ significantly in nature.

As part of the alignment with the AI Act, the Commission is pushing for broad exemptions for AI uses in national security, defence and law enforcement. Thus, if the treaty was limited to only public bodies, with these carve-outs, there would be very little left.

In addition, Euractiv understands that such a major watering down of the AI treaty after several years of engagement from the countries involved might also discourage future initiatives in this area.

[…]

a paragraph has been added stressing that “to preserve the international character of the convention, the EU could nevertheless be open to consider the possibility for a Party to make a reservation and release itself from the obligation to apply the convention to private actors that are not acting on behalf of or procuring AI systems for public authorities, under certain conditions and limitations”.

The Commission’s proposal seems designed to address Washington’s argument that they cannot commit to anything beyond their national legal framework.

In October, US President Joe Biden signed an executive order setting out a framework for federal agencies to purchase and use AI tools safely and responsibly, hence the reference to companies not working with the public sector.

More precisely, the Commission is proposing an ‘opt-out’ option with temporal limitations, that can be revised at any time and with some guarantees that it is not abused. This approach would be the opposite of what the US administration proposed, namely exempting the private sector by default with an ‘opt-in’ possibility for signatories.

Still, the original ‘opt-in’ option was designed to avoid the embarrassment of the US administration having to exempt private companies from a human rights treaty. Euractiv understands Israel and Japan would not sign if the ‘opt-out’ approach made it into the final text, whereas the UK and Canada would follow the US decision.

Source: EU Commission’s last-minute attempt to keep private companies in world’s first AI treaty – Euractiv

So the US basically wants to make a useful treaty useless because they are run by self serving, profit seeking companies that want to trample on human rights. Who would have thought? Hopefully the EU can show some backbone and do what is right instead of what is being financially lobbied for (here’s looking at you, France!). It’s this kind of business based decision making that has led to climate change, cancer deaths, and many many more huge problems that could have been nipped in the bud.

EU Commission readies establishment of AI Office on 21 feb

The AI Office will play a pivotal role in the enforcement architecture of the AI Act, the EU’s landmark law to regulate Artificial Intelligence, set to be formally adopted in the coming weeks based on a political agreement nailed down in December.

The idea of an AI Office to centralise the enforcement of the AI rulebook came from the European Parliament. Still, during the negotiations, it was downsized from being a little short of an agency to being integrated into the Commission, albeit with a separate budget line.

However, the question of how much autonomy the Office will be guaranteed remains sensitive inside the Commission, especially since it is unclear whether it will become an entity with its own political objectives or an extension of the unit responsible for the AI Act.

Euractiv understands that the obtained draft decision was amended following an internal consultation to include wording specifying that the Office should not interfere with the competencies of Commission departments.

According to the document, the decision should enter into force as a matter of urgency on 21 February, before the formal adoption of the EU’s AI law. Euractiv understands the decision is due to be adopted on Wednesday (24 January).

Policing powerful AI

The AI Office will have primarily a supporting role for what concerns the enforcement of the rules on AI systems, as the bulk of the competencies will be on national authorities. However, the Office has been assigned to policing General-Purpose AI (GPAI) models and systems, the most potent types of AI so far.

Recent advances in computing power, data harvesting, and algorithm techniques have led to the development of powerful GPAI models like OpenAI’s GPT-4, which powers the GPAI system ChatGPT, the world’s most famous chatbot.

The agreement on the AI Act includes a tiered approach to GPAI models to distinguish those that might entail a systemic risk for society from the rest. The AI Office is to develop the methodologies and benchmarks for evaluating the capabilities of GPAI models.

The Office should be able to set itself apart in monitoring the application of the rules on GPAI models and systems, notably when developed by the same provider, and the emergence of unforeseen risks from these models based on alerts from a scientific panel of independent experts.

The new EU entity is also set to have significant leeway to investigate possible infringements of rules related to GPAI by collecting complaints and alerts, issuing document requests, conducting evaluations and requesting mitigation or other enforcement measures.

The Office will also coordinate the enforcement of the AI Act on AI systems already covered under other EU legislation, like social media’s recommender systems under the Digital Services Act and search engines’ ranking algorithms under the Digital Markets Act.

Support & coordination

The AI Office is to have a supporting role in the preparation of secondary legislation implementing the AI Act, the uniform application of the regulation, the issuance of guidance and supporting tools like standardised protocols, the preparation of standardisation requests, the establishment of regulatory sandboxes, the developments of codes of practice and conduct at the EU level.

The entity will also provide the secretariat for the AI Board and administrative support for the stakeholder-run advisory forum and expert-made scientific panel. The draft decision explicitly references the requirement to consult regularly with scientific and civil society stakeholders.

In particular, the AI Office must “establish a forum for cooperation with the open-source community with a view to identifying and developing best practices for the safe development and use of open-source AI models and systems.”

In addition, the new entity is tasked with promoting innovation ecosystems and working with public and private actors and the start-up community. As revealed by Euractiv, the AI Office will be responsible for monitoring the progress of GenAI4EU, an initiative to promote the uptake of generative AI in strategic sectors.

The Office is also mandated to cooperate with the relevant EU bodies, like the European Data Protection Supervisor. Collaboration is also required with other Commission departments, notably the European Centre for Algorithmic Transparency, to test GPAI models and systems and facilitate the adoption of AI tools in relevant EU policies.

At the international level, the Office will promote the EU approach to AI, contribute to AI governance initiatives, and support the implementation of international agreements.

Financing

The financing aspect of the AI Office has been a sore point since the beginning. The lack of flexibility in the EU budget allocations and lack of appetite from member states to put more resources on the table means new tasks always face strict budgetary constraints.

The Commission’s digital policy department, DG CNECT, will assign human resources. The hiring of temporary staff and operational expenditure will be financed with the redeployment of the budget from the Digital Europe Programme.

Source: EU Commission readies establishment of AI Office – Euractiv

OpenAI must defend ChatGPT fabrications after failing to defeat libel suit

OpenAI may finally have to answer for ChatGPT’s “hallucinations” in court after a Georgia judge recently ruled against the tech company’s motion to dismiss a radio host’s defamation suit.

OpenAI had argued that ChatGPT’s output cannot be considered libel, partly because the chatbot output cannot be considered a “publication,” which is a key element of a defamation claim. In its motion to dismiss, OpenAI also argued that Georgia radio host Mark Walters could not prove that the company acted with actual malice or that anyone believed the allegedly libelous statements were true or that he was harmed by the alleged publication.

It’s too early to say whether Judge Tracie Cason found OpenAI’s arguments persuasive. In her order denying OpenAI’s motion to dismiss, which MediaPost shared here, Cason did not specify how she arrived at her decision, saying only that she had “carefully” considered arguments and applicable laws.

There may be some clues as to how Cason reached her decision in a court filing from John Monroe, attorney for Walters, when opposing the motion to dismiss last year.

Monroe had argued that OpenAI improperly moved to dismiss the lawsuit by arguing facts that have yet to be proven in court. If OpenAI intended the court to rule on those arguments, Monroe suggested that a motion for summary judgment would have been the proper step at this stage in the proceedings, not a motion to dismiss.

Had OpenAI gone that route, though, Walters would have had an opportunity to present additional evidence. To survive a motion to dismiss, all Walters had to do was show that his complaint was reasonably supported by facts, Monroe argued.

Failing to convince the court that Walters had no case, OpenAI’s legal theories regarding its liability for ChatGPT’s “hallucinations” will now likely face their first test in court.

“We are pleased the court denied the motion to dismiss so that the parties will have an opportunity to explore, and obtain a decision on, the merits of the case,” Monroe told Ars.

What’s the libel case against OpenAI?

Walters sued OpenAI after a journalist, Fred Riehl, warned him that in response to a query, ChatGPT had fabricated an entire lawsuit. Generating an entire complaint with an erroneous case number, ChatGPT falsely claimed that Walters had been accused of defrauding and embezzling funds from the Second Amendment Foundation.

Walters is the host of Armed America Radio and has a reputation as the “Loudest Voice in America Fighting For Gun Rights.” He claimed that OpenAI “recklessly” disregarded whether ChatGPT’s outputs were false, alleging that OpenAI knew that “ChatGPT’s hallucinations were pervasive and severe” and did not work to prevent allegedly libelous outputs. As Walters saw it, the false statements were serious enough to be potentially career-damaging, “tending to injure Walter’s reputation and exposing him to public hatred, contempt, or ridicule.”

[…]

OpenAI introduced “a large amount of material” in its motion to dismiss that fell outside the scope of the complaint, Monroe argued. That included pointing to a disclaimer in ChatGPT’s terms of use that warns users that ChatGPT’s responses may not be accurate and should be verified before publishing. According to OpenAI, this disclaimer makes Riehl the “owner” of any libelous ChatGPT responses to his queries.

“A disclaimer does not make an otherwise libelous statement non-libelous,” Monroe argued. And even if the disclaimer made Riehl liable for publishing the ChatGPT output—an argument that may give some ChatGPT users pause before querying—”that responsibility does not have the effect of negating the responsibility of the original publisher of the material,” Monroe argued.

[…]

With the lawsuit moving forward, curious chatbot users everywhere may finally get the answer to a question that has been unclear since ChatGPT quickly became the fastest-growing consumer application of all time after its launch in November 2022: Will ChatGPT’s hallucinations be allowed to ruin lives?

In the meantime, the FTC is seemingly still investigating potential harms caused by ChatGPT’s “false, misleading, or disparaging” generations.

[…]

Source: OpenAI must defend ChatGPT fabrications after failing to defeat libel suit | Ars Technica

Samsung and Google launch ‘Circle to Search’ Too

Samsung announced many interesting products and features at its latest Galaxy Unpacked event (including the Galaxy S24 series) but one of the more impressive developments isn’t actually unique to the Galaxy brand itself. The feature, Circle to Search, was developed in partnership with Google, which means it’ll live on Google phones, too.

What is Circle to Search?

In a nutshell, Circle to Search is a new way to search for anything without switching apps. To activate the feature, long press on the home button or navigation bar (if you have gesture navigation enabled). Then, when you see something on your screen that you want to look up, draw a circle around it with your finger, and your phone will return search results. For example, you could use Circle to Search to find an article of clothing you might have seen in a YouTube video, or get more info about a dish in a recipe you’re browsing online.

You don’t have to just circle the item you’re looking to search, either: You can also highlight it, scribble over it, or tap on it. As part of Google’s AI upgrades to search, you can search with text and pictures you’ve circled at the same time using multi-search. Google says that the Circle to Search gesture works on images, text, and videos. Basically, you’re able to find anything and everything using this feature.

These results appear inside the app you’re currently using, so you don’t need to interrupt what you’re doing to search. When you’re done, you can simply swipe the results away to get back to your previous task.

When does Circle to Search launch?

Circle to Search is set to launch globally on Jan. 31 for select premium Android smartphones like the Pixel 8 and Pixel 8 Pro and the newly announced Galaxy S24 series. The feature will be coming to more Android devices at a later date.

Source: How to Use Google’s ‘Circle to Search’ Tool | Lifehacker

Amazon wants you to pay to give them your data with Its Next-Gen “Remarkable Alexa” – which is remarkable in how poorly it works

amazon alexa echo device covered in green goo

Amazon is revamping its Alexa voice assistant as it prepares to launch a new paid subscription plan this year, according to internal documents and people familiar with the matter. But the change is causing internal conflict and may lead to further delay.

Tentatively named “Alexa Plus,” the paid version of Alexa is intended to offer more conversational and personalized artificial-intelligence technology, one of the documents obtained by Business Insider says. The people said the team was working toward a June 30 launch deadline and had been testing the underlying voice technology, dubbed “Remarkable Alexa,” with 15,000 external customers.

But the quality of the new Alexa’s answers is still falling short of expectations, often sharing inaccurate information, external tests have found. Amazon is now going through a major overhaul of Alexa’s technology stack to address this issue, though the team is experiencing some discord.

[…]

The people familiar with the matter said the limited preview with 15,000 external customers discovered that, while Remarkable Alexa was generally good at being conversational and informative, it was still deflecting answers, often giving unnecessarily long or inaccurate responses. It also needed to improve its ability to answer ambiguous customer requests that require the engagement of multiple services, such as turning on the light and music at the same time.

The new Alexa still didn’t meet the quality standards expected for Alexa Plus, these people added

[…]

Source: Amazon Is Struggling With Its Next-Gen “Remarkable Alexa’

Suno AI – make amazing songs with your own prompts

Suno AI is created by a team of musicians and artificial intelligence experts based in Cambridge, MA.

This machine makes the music and lyrics in the style you want and then sings it for you.

You get some free credits to play with but if you want longer songs then you need to go pro.

They keep copyright of everything generated when you use it for free, but under pro subscriptions you can sell the music it makes, under their terms.

It’s awesome!

Source: Suno AI

Running your AI Locally on your own PC / Installing your own LLM

Having your AI going on your own laptop or PC is perfectly viable. For textual conversations you don’t always need a Large Language Model (LLM) when Small Language Models can perform at the same or in some cases even better levels (eg MS Phi-2 small language model – outperforms many LLMs but fits on your laptop) than the OpenAI online supercomputer trained models. This has many reasons, such as overfitting, old data, etc. You may want to run your own model if you are not online all the time, if you have privacy concerns, eg if you don’t want your inputs to be used to further train the model, or if you don’t want to be dependent on a third party (what if OpenAI suddenly requires hefty payment for use?)

On performance: most of the data processing happens fastest on Nvidia GPUs, but the processing can be offloaded to your CPUs. In this case you may find some marked slowdowns.

Text to Image

Stable diffusion offers very very good text to image generation at a high level. You can find their models on their page https://stability.ai/stable-image. Other models such as OpenAI’s Dall-E or Midjourney can’t be run locally. Despite what OpenAI says, they are not open source.

For all the different user interfaces, expect downloads of ~1.5GB – 2GB and unpacked sizes of ~5GB – 12GB (or more!)

Note that you do need an Nvidia GPU – Running a 2070ti images generate in ~5 / 6 seconds. On a laptop they take ~ 10 minutes!

Easy Diffusion – like Stability Matrix, this is a one click installer for Windows, Linux or MacOS that will download a specific WebUI. It updates every time you start it.

an image of a tesla driving down a road. the data coming out of the roof is visualised using yellow lines generated by Easy Diffusion

ComfyUI is another easy to run frontend – you download and extract the zip file (~30k files, takes a while!) and run. You then need to download a model (there is a README in the directory that will point you to one) and copy it into ComfyUI\models\checkpoints (~ 5GB). It does, however, offer quite a lot of complexity. It is a flow based model, so it takes a little getting used to as the rest use sliders or checks to configure your model. Some people find this is the fastest system, however others point out that this is most likely due to the default config of other stable diffusion models or outdated python / pythorch and other dependencies, which apparently ComfyUI does a good job of keeping updated. I found there was not much difference, but I was not bulk generating images where this becomes an issue.

an image of a tesla driving down a road. the data coming out of the roof is visualised using yellow lines generated by ComfyUI

Fooocus is very ease of use – it’s simplicity is it’s strength. Unzip and run the run.bat file. There are loads of LoRa (see Conclusion, below) model previews to get a certain style out of it.

an image of a tesla driving down a road. the data coming out of the roof is visualised using yellow lines generated by Fooocus

Automatic A1111 gives more control over the prompts and is somewhere between Fooocus and ComfyUI. It requires you to install Python 3.10.6 and git yourself. I have included it because it’s very popular, but to be honest – with the above options, why bother?

LoRas

Another platform you need to know about is CivitAI – especially their LoRa (Low-Rank Adaptation) models. These allow Stable Diffusion to specialise in different concepts (eg artistic styles, body poses, objects – basically the “Style” part of Fooocus) – for a good explanation, see Stable Diffusion: What Are LoRA Models and How to Use Them?

Overgrowth style LoRa
horny style LoRa
copper wire style LoRa
fantasy style LoRa

General purpose downloader

Pinokio is a system that dowloads and installs community created scripts that run apps, databases, AI’s, etc. User scripts for AI include magic animators, face swappers, music captioning, subtitling, voice cloning etc

Pinokio user scripts

Another way to get started on a specific webUI for text to image is using Stability Matrix: a program that installs different webUIs (Automatic 1111, Comfy UI, SD.Next (Vladmandic), VoltaML, InvokeAI, Fooocus, and Fooocus MRE) for you. It will download the model, training data and weights and start up the process for you to connect to using a browser. This will handle installing the python and Git dependencies as well.

stability matrix UI installer

I however found that it wasn’t quite as straightforward as it looked, with some of the models requiring you to configure and run the model within Stability Matrix and some requiring you to work in the model externally to Stability Matrix.

Language Models (LLMs) / Talking to your AI

Just chatting

LM Studio allows you to install and run models such as LLaMa, Phi-2, etc from Hugging face

lm studio downloading phi-2

Using the phi-2 model, text generation is suprisingly smooth and fast

phi-2 model running in LM studio

Chatting and modifying the model

Then there is also Ollama which allows you to Run Llama 3, Phi 3, Mistral, Gemma, and other models. The big difference here is you can customize and create your own. You can either create and import a GGUF file (GGUF is a binary format that is designed for fast loading and saving of models, and for ease of reading. Models are traditionally developed using PyTorch or another framework, and then converted to GGUF for use in GGML.) or you can use Retrieval Augmented Generation (RAG) support. This feature seamlessly integrates document interactions into your chat experience. You can load documents directly into the chat or add files to your document library, effortlessly accessing them using the # command before a query. Just running Ollama allows you to access it in the command line, but there is a beautiful Open WebUI which is being updated like crazy and gives you loads of options.

gif image of diferent llms running in a web ui

Conclusion

No article on this kind of AI is complete without mention of Hugging Face The platform where the machine learning community collaborates on models, datasets, and applications. You can find all kinds of models and data there to refine your AI once you get into it a bit.

AI systems are certainly not limited to text to image or conversational – text to audio, text to video, image to video, text to 3D, voice to audio, video to video and much more are all possible locally.

Running your own AI / ML system on your own PC is viable (but you need an Nvidia card for text-to-image!). It allows you much more privacy as the data is not fed back to an external provider for more training or otherwise. It’s faster and often quality just as good as the online services. You don’t run out of credits.

Refining the training of these models and adding to their datasets is beyond the scope of this article, but is a next step for you 🙂

US wants private sector AI exempt from Human Rights laws. EU pushes back.

[…]

The Council of Europe, an international human rights body with 46 member countries, set up the Committee on Artificial Intelligence at the beginning of 2022 to develop the Framework Convention on Artificial Intelligence, Human Rights, Democracy and the Rule of Law.

[…]

The most consequential pending issue regards the scope of the convention. In June, Euractiv revealed how the United States, which is participating as an observer country, was pushing for exempting companies by default, leaving it up to the signatory countries to decide whether to ‘opt-in’ the private sector.

[…]

“The Union should not agree with the alternative proposal(s) that limit the scope of the convention to activities within the lifecycle of artificial intelligence systems by or on behalf of a Party or allow application to the private sector only via an additional protocol or voluntary declarations by the Parties (opt-in),” reads an information note from the Commission, obtained by Euractiv.

The document notes that these proposals would limit the treaty’s scope by default, “thus diminishing its value and sending a wrong political message that human rights in the private field do not merit the same protection.”

The EU executive notes how this approach would contradict international law that requires the respect of human rights by private entities

[…]

During the AI Act discussion, one hot debate was around a national security exemption France has been pushing for in the context of the AI convention.

In this regard, the Commission is pushing for an explicit exclusion of AI systems exclusively developed for national security, military and defence purposes in a manner that is consistent with the EU’s AI law.

[…]

Brussels does not seem to have any appetite for the AI treaty to go beyond the AI Act, even on matters where there is not necessarily a conflict, and the convention could have been more ambitious.

A complete overlap of the international treaty with the EU regulation is not a given since the former is meant to protect human rights, while the latter is merely intended to harmonise the EU market rules following a traditional product safety blueprint.

[…]

Similarly, since the AI Act bans specific applications like social scoring deemed to pose an unacceptable risk, the Commission is pushing for extending these prohibitions at the international level via a moratorium or a ban as this would “increase the added value of the convention”.

The only significant exception where the EU executive seems keen to go beyond the AI Act (but still in line with Union law) is in supporting a provision that protects whistle-blowers in the implementation of the convention – one that the UK, Canada and Estonia have opposed.

Source: EU prepares to push back on private sector carve-out from international AI treaty – Euractiv

Text-to-3D model startup Luma raises $43M in latest round

Luma, a generative AI startup building software that transforms text descriptions to corresponding 3D models, just raised $43 million (£34 million) in a series-B funding round led by Andreesen Horowitz, Nvidia, and others.

Founded in 2021 by CEO Amit Jain, a former systems engineer working on computer vision at Apple, and CTO Alex Yu, a graduate student from the University of California, Berkeley, Luma AI develops machine-learning software that goes a step beyond what we’ve seen from most existing generative neural networks.

Unlike text-to-image models that emit flat bitmaps of digital art, Luma uses AI to create from photos, videos, or text descriptions three-dimensional models of objects that can be downloaded, manipulated, edited, and rendered as needed.

The upstart, based in Palo Alto, California, has already launched this technology as an app called Genie – available via the web, as the Luma iOS app, and via Discord – which is capable of converting images and video into 3D scenes or producing 3D models of user-described objects. These machine-made models can be previewed on screen and exported to art packages like Blender, popular game engines like Unreal or Unity, and other tools for further use.

Screenshot of Luma's Genie creating a vulture holding a cup of coffee

Screenshot of Genie’s attempt at creating a vulture holding a cup of coffee for us … This was generated from the simple prompt: a vulture with a cup of coffee. Click to enlarge

[…]

Luma says it uses various proprietary computer vision techniques, from image segmentation to meshing, to generate these 3D models from footage and descriptions. The models could well end up being used in video games, virtual reality applications, simulations, or robotics testing.

Some folks may find this technology rather useful if they have an idea for something involving 3D graphics or a 3D scene, and lack the artist talent or skill to create the necessary models.

[…]

We toyed with the system and found Genie’s output looked kinda cute but may not be for everyone at this stage; the examples given by the upstart looked better than what we could come up with, perhaps because they were produced via the startup’s paid-for API while we were using the freebie version.

That paid-for interface costs a dollar a pop to construct a model or scene from supplied assets. The biz even makes the point that this is cheaper and faster than relying on a human designer. If you can out-draw Genie, you don’t have anything to worry about right now.

Luma previously raised $20 million in a series-A round led by Amplify Partners, Nventures (Nvidia’s investment arm), and General Catalyst. Other investors included Matrix Partners, South Park Commons, and Remote First Capital. After raising a total of over $70 million so far, it has a valuation estimated between $200 million and $300 million. ®

Source: Text-to-3D model startup Luma raises $43M in latest round • The Register

Generative AI Will Be A Huge Boon For The Public Domain, Unless Copyright Blocks It

two people holding hands watching a pc screen. On the screen is a robot painting a digitised Bob Ross paintingA year ago, I noted that many of Walled Culture’s illustrations were being produced using generative AI. During that time, AI has developed rapidly. For example, in the field of images, OpenAI has introduced DALL-E 3 in ChatGPT:

When prompted with an idea, ChatGPT will automatically generate tailored, detailed prompts for DALL·E 3 that bring your idea to life. If you like a particular image, but it’s not quite right, you can ask ChatGPT to make tweaks with just a few words.

Ars Technica has written a good intro to the new DALL-E 3, describing it as “a wake-up call for visual artists” in terms of its advanced capabilities. The article naturally touches on the current situation regarding copyright for these creations:

In the United States, purely AI-generated art cannot currently be copyrighted and exists in the public domain. It’s not cut and dried, though, because the US Copyright Office has supported the idea of allowing copyright protection for AI-generated artwork that has been appreciably altered by humans or incorporated into a larger work.

The article goes on to explore an interesting aspect of that situation:

there’s suddenly a huge new pool of public domain media to work with, and it’s often “open source”—as in, many people share the prompts and recipes used to create the artworks so that others can replicate and build on them. That spirit of sharing has been behind the popularity of the Midjourney community on Discord, for example, where people typically freely see each other’s prompts.

When several mesmerizing AI-generated spiral images went viral in September, the AI art community on Reddit quickly built off of the trend since the originator detailed his workflow publicly. People created their own variations and simplified the tools used in creating the optical illusions. It was a good example of what the future of an “open source creative media” or “open source generative media” landscape might look like (to play with a few terms).

There are two important points there. First, that the current, admittedly tentative, status of generative AI creations as being outside the copyright system means that many of them, perhaps most, are available for anyone to use in any way. Generative AI could drive a massive expansion of the public domain, acting as a welcome antidote to constant attempts to enclose the public domain by re-imposing copyright on older works – for example, as attempted by galleries and museums.

The second point is that without the shackles of copyright, these creations can form the basis of collaborative works among artists willing to embrace that approach, and to work with this new technology in new ways. That’s a really exciting possibility that has been hard to implement without recourse to legal approaches like Creative Commons. Although the intention there is laudable, most people don’t really want to worry about the finer points of licensing – not least out of fear that they might get it wrong, and be sued by the famously litigious copyright industry.

A situation in which generative AI creations are unequivocally in the public domain could unleash a flood of pent-up creativity. Unfortunately, as the Ars Technica article rightly points out, the status of AI generated artworks is already slightly unclear. We can expect the copyright world to push hard to exploit that opening, and to demand that everything created by computers should be locked down under copyright for decades, just as human inspiration generally is from the moment it is in a fixed form. Artists should enjoy this new freedom to explore and build on generative AI images while they can – it may not last.

Source: Generative AI Will Be A Huge Boon For The Public Domain, Unless Copyright Blocks It | Techdirt

The NY Times Lawsuit Against OpenAI Would Open Up The NY Times To All Sorts Of Lawsuits Should It Win, shows that if you feed it a URL it can regurgitate what’s on the first parts of that URL

This week the NY Times somehow broke the story of… well, the NY Times suing OpenAI and Microsoft. I wonder who tipped them off. Anyhoo, the lawsuit in many ways is similar to some of the over a dozen lawsuits filed by copyright holders against AI companies. We’ve written about how silly many of these lawsuits are, in that they appear to be written by people who don’t much understand copyright law. And, as we noted, even if courts actually decide in favor of the copyright holders, it’s not like it will turn into any major windfall. All it will do is create another corruptible collection point, while locking in only a few large AI companies who can afford to pay up.

I’ve seen some people arguing that the NY Times lawsuit is somehow “stronger” and more effective than the others, but I honestly don’t see that. Indeed, the NY Times itself seems to think its case is so similar to the ridiculously bad Authors Guild case, that it’s looking to combine the cases.

But while there are some unique aspects to the NY Times case, I’m not sure they are nearly as compelling as the NY Times and its supporters think they are. Indeed, I think if the Times actually wins its case, it would open the Times itself up to some fairly damning lawsuits itself, given its somewhat infamous journalistic practices regarding summarizing other people’s articles without credit. But, we’ll get there.

The Times, in typical NY Times fashion, presents this case as thought the NY Times is the great defender of press freedom, taking this stand to stop the evil interlopers of AI.

Independent journalism is vital to our democracy. It is also increasingly rare and valuable. For more than 170 years, The Times has given the world deeply reported, expert, independent journalism. Times journalists go where the story is, often at great risk and cost, to inform the public about important and pressing issues. They bear witness to conflict and disasters, provide accountability for the use of power, and illuminate truths that would otherwise go unseen. Their essential work is made possible through the efforts of a large and expensive organization that provides legal, security, and operational support, as well as editors who ensure their journalism meets the highest standards of accuracy and fairness. This work has always been important. But within a damaged information ecosystem that is awash in unreliable content, The Times’s journalism provides a service that has grown even more valuable to the public by supplying trustworthy information, news analysis, and commentary

Defendants’ unlawful use of The Times’s work to create artificial intelligence products that compete with it threatens The Times’s ability to provide that service. Defendants’ generative artificial intelligence (“GenAI”) tools rely on large-language models (“LLMs”) that were built by copying and using millions of The Times’s copyrighted news articles, in-depth investigations, opinion pieces, reviews, how-to guides, and more. While Defendants engaged in widescale copying from many sources, they gave Times content particular emphasis when building their LLMs—revealing a preference that recognizes the value of those works. Through Microsoft’s Bing Chat (recently rebranded as “Copilot”) and OpenAI’s ChatGPT, Defendants seek to free-ride on The Times’s massive investment in its journalism by using it to build substitutive products without permission or payment.

As the lawsuit makes clear, this isn’t some high and mighty fight for journalism. It’s a negotiating ploy. The Times admits that it has been trying to get OpenAI to cough up some cash for its training:

For months, The Times has attempted to reach a negotiated agreement with Defendants, in accordance with its history of working productively with large technology platforms to permit the use of its content in new digital products (including the news products developed by Google, Meta, and Apple). The Times’s goal during these negotiations was to ensure it received fair value for the use of its content, facilitate the continuation of a healthy news ecosystem, and help develop GenAI technology in a responsible way that benefits society and supports a well-informed public.

I’m guessing that OpenAI’s decision a few weeks back to pay off media giant Axel Springer to avoid one of these lawsuits, and the failure to negotiate a similar deal (at what is likely a much higher price), resulted in the Times moving forward with the lawsuit.

There are five or six whole pages of puffery about how amazing the NY Times thinks the NY Times is, followed by the laughably stupid claim that generative AI “threatens” the kind of journalism the NY Times produces.

Let me let you in on a little secret: if you think that generative AI can do serious journalism better than a massive organization with a huge number of reporters, then, um, you deserve to go out of business. For all the puffery about the amazing work of the NY Times, this seems to suggest that it can easily be replaced by an auto-complete machine.

In the end, though, the crux of this lawsuit is the same as all the others. It’s a false belief that reading something (whether by human or machine) somehow implicates copyright. This is false. If the courts (or the legislature) decide otherwise, it would upset pretty much all of the history of copyright and create some significant real world problems.

Part of the Times complaint is that OpenAI’s GPT LLM was trained in part with Common Crawl data. Common Crawl is an incredibly useful and important resource that apparently is now coming under attack. It has been building an open repository of the web for people to use, not unlike the Internet Archive, but with a focus on making it accessible to researchers and innovators. Common Crawl is a fantastic resource run by some great people (though the lawsuit here attacks them).

But, again, this is the nature of the internet. It’s why things like Google’s cache and the Internet Archive’s Wayback Machine are so important. These are archives of history that are incredibly important, and have historically been protected by fair use, which the Times is now threatening.

(Notably, just recently, the NY Times was able to get all of its articles excluded from Common Crawl. Otherwise I imagine that they would be a defendant in this case as well).

Either way, so much of the lawsuit is claiming that GPT learning from this data is infringement. And, as we’ve noted repeatedly, reading/processing data is not a right limited by copyright. We’ve already seen this in multiple lawsuits, but this rush of plaintiffs is hoping that maybe judges will be wowed by this newfangled “generative AI” technology into ignoring the basics of copyright law and pretending that there are now rights that simply do not exist.

Now, the one element that appears different in the Times’ lawsuit is that it has a bunch of exhibits that purport to prove how GPT regurgitates Times articles. Exhibit J is getting plenty of attention here, as the NY Times demonstrates how it was able to prompt ChatGPT in such a manner that it basically provided them with direct copies of NY Times articles.

In the complaint, they show this:

Image

At first glance that might look damning. But it’s a lot less damning when you look at the actual prompt in Exhibit J and realize what happened, and how generative AI actually works.

What the Times did is prompt GPT-4 by (1) giving it the URL of the story and then (2) “prompting” it by giving it the headline of the article and the first seven and a half paragraphs of the article, and asking it to continue.

Here’s how the Times describes this:

Each example focuses on a single news article. Examples were produced by breaking the article into two parts. The frst part o f the article is given to GPT-4, and GPT-4 replies by writing its own version of the remainder of the article.

Here’s how it appears in Exhibit J (notably, the prompt was left out of the complaint itself):

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If you actually understand how these systems work, the output looking very similar to the original NY Times piece is not so surprising. When you prompt a generative AI system like GPT, you’re giving it a bunch of parameters, which act as conditions and limits on its output. From those constraints, it’s trying to generate the most likely next part of the response. But, by providing it paragraphs upon paragraphs of these articles, the NY Times has effectively constrained GPT to the point that the most probabilistic responses is… very close to the NY Times’ original story.

In other words, by constraining GPT to effectively “recreate this article,” GPT has a very small data set to work off of, meaning that the highest likelihood outcome is going to sound remarkably like the original. If you were to create a much shorter prompt, or introduce further randomness into the process, you’d get a much more random output. But these kinds of prompts effectively tell GPT not to do anything BUT write the same article.

From there, though, the lawsuit gets dumber.

It shows that you can sorta get around the NY Times’ paywall in the most inefficient and unreliable way possible by asking ChatGPT to quote the first few paragraphs in one paragraph chunks.

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Of course, quoting individual paragraphs from a news article is almost certainly fair use. And, for what it’s worth, the Times itself admits that this process doesn’t actually return the full article, but a paraphrase of it.

And the lawsuit seems to suggest that merely summarizing articles is itself infringing:

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That’s… all factual information summarizing the review? And while the complaint shows that if you then ask for (again, paragraph length) quotes, GPT will give you a few quotes from the article.

And, yes, the complaint literally argues that a generative AI tool can violate copyright when it “summarizes” an article.

The issue here is not so much how GPT is trained, but how the NY Times is constraining the output. That is unrelated to the question of whether or not the reading of these article is fair use or not. The purpose of these LLMs is not to repeat the content that is scanned, but to figure out the probabilistic most likely next token for a given prompt. When the Times constrains the prompts in such a way that the data set is basically one article and one article only… well… that’s what you get.

Elsewhere, the Times again complains about GPT returning factual information that is not subject to copyright law.

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But, I mean, if you were to ask anyone the same question, “What does wirecutter recommend for The Best Kitchen Scale,” they’re likely to return you a similar result, and that’s not infringing. It’s a fact that that scale is the one that it recommends. The Times complains that people who do this prompt will avoid clicking on Wirecutter affiliate links, but… um… it has no right to that affiliate income.

I mean, I’ll admit right here that I often research products and look at Wirecutter (and other!) reviews before eventually shopping independently of that research. In other words, I will frequently buy products after reading the recommendations on Wirecutter, but without clicking on an affiliate link. Is the NY Times really trying to suggest that this violates its copyright? Because that’s crazy.

Meanwhile, it’s not clear if the NY Times is mad that it’s accurately recommending stuff or if it’s just… mad. Because later in the complaint, the NY Times says its bad that sometimes GPT recommends the wrong product or makes up a paragraph.

So… the complaint is both that GPT reproduces things too accurately, AND not accurately enough. Which is it?

Anyway, the larger point is that if the NY Times wins, well… the NY Times might find itself on the receiving end of some lawsuits. The NY Times is somewhat infamous in the news world for using other journalists’ work as a starting point and building off of it (frequently without any credit at all). Sometimes this results in an eventual correction, but often it does not.

If the NY Times successfully argues that reading a third party article to help its reporters “learn” about the news before reporting their own version of it is copyright infringement, it might not like how that is turned around by tons of other news organizations against the NY Times. Because I don’t see how there’s any legitimate distinction between OpenAI scanning NY Times articles and NY Times reporters scanning other articles/books/research without first licensing those works as well.

Or, say, what happens if a source for a NY TImes reporter provides them with some copyright-covered work (an article, a book, a photograph, who knows what) that the NY Times does not have a license for? Can the NY Times journalist then produce an article based on that material (along with other research, though much less than OpenAI used in training GPT)?

It seems like (and this happens all too often in the news industry) the NY Times is arguing that it’s okay for its journalists to do this kind of thing because it’s in the business of producing Important Journalism™ whereas anyone else doing the same thing is some damn interloper.

We see this with other copyright disputes and the media industry, or with the ridiculous fight over the hot news doctrine, in which news orgs claimed that they should be the only ones allowed to report on something for a while.

Similarly, I’ll note that even if the NY Times gets some money out of this, don’t expect the actual reporters to see any of it. Remember, this is the same NY Times that once tried to stiff freelance reporters by relicensing their articles to electronic databases without paying them. The Supreme Court didn’t like that. If the NY Times establishes that merely training AI on old articles is a licenseable, copyright-impacting event, will it go back and pay those reporters a piece of whatever change they get? Or nah?

Source: The NY Times Lawsuit Against OpenAI Would Open Up The NY Times To All Sorts Of Lawsuits Should It Win | Techdirt

Using Local AI On The Command Line To Rename Images (And More)

We all have a folder full of images whose filenamees resemble line noise. How about renaming those images with the help of a local LLM (large language model) executable on the command line? All that and more is showcased on [Justine Tunney]’s bash one-liners for LLMs, a showcase aimed at giving folks ideas and guidance on using a local (and private) LLM to do actual, useful work.

This is built out from the recent llamafile project, which turns LLMs into single-file executables. This not only makes them more portable and easier to distribute, but the executables are perfectly capable of being called from the command line and sending to standard output like any other UNIX tool. It’s simpler to version control the embedded LLM weights (and therefore their behavior) when it’s all part of the same file as well.

One such tool (the multi-modal LLaVA) is capable of interpreting image content. As an example, we can point it to a local image of the Jolly Wrencher logo using the following command:

llava-v1.5-7b-q4-main.llamafile --image logo.jpg --temp 0 -e -p '### User: The image has...\n### Assistant:'

Which produces the following response:

The image has a black background with a white skull and crossbones symbol.

With a different prompt (“What do you see?” instead of “The image has…”) the LLM even picks out the wrenches, but one can already see that the right pieces exist to do some useful work.

Check out [Justine]’s rename-pictures.sh script, which cleverly evaluates image filenames. If an image’s given filename already looks like readable English (also a job for a local LLM) the image is left alone. Otherwise, the picture is fed to an LLM whose output guides the generation of a new short and descriptive English filename in lowercase, with underscores for spaces.

What about the fact that LLM output isn’t entirely predictable? That’s easy to deal with. [Justine] suggests always calling these tools with the --temp 0 parameter. Setting the temperature to zero makes the model deterministic, ensuring that a same input always yields the same output.

There’s more neat examples on the Bash One-Liners for LLMs that demonstrate different ways to use a local LLM that lives in a single-file executable, so be sure to give it a look and see if you get any new ideas. After all, we have previously shown how automating tasks is almost always worth the time invested.

Source: Using Local AI On The Command Line To Rename Images (And More) | Hackaday

More useful would be to put this information into EXIF data, but it shouldn’t be too tough to tweak the command to do that instead

New York Times Sues OpenAI and Microsoft Over Reading Publicly Available Information

The New York Times sued OpenAI and Microsoft for copyright infringement on Wednesday, opening a new front in the increasingly intense legal battle over the unauthorized use of published work to train artificial intelligence technologies.

The Times is the first major American media organization to sue the companies, the creators of ChatGPT and other popular A.I. platforms, over copyright issues associated with its written works. The lawsuit, filed in Federal District Court in Manhattan, contends that millions of articles published by The Times were used to train automated chatbots that now compete with the news outlet as a source of reliable information.

The suit does not include an exact monetary demand. But it says the defendants should be held responsible for “billions of dollars in statutory and actual damages” related to the “unlawful copying and use of The Times’s uniquely valuable works.” It also calls for the companies to destroy any chatbot models and training data that use copyrighted material from The Times.

In its complaint, The Times said it approached Microsoft and OpenAI in April to raise concerns about the use of its intellectual property and explore “an amicable resolution,” possibly involving a commercial agreement and “technological guardrails” around generative A.I. products. But it said the talks had not produced a resolution.

An OpenAI spokeswoman, Lindsey Held, said in a statement that the company had been “moving forward constructively” in conversations with The Times and that it was “surprised and disappointed” by the lawsuit.

“We respect the rights of content creators and owners and are committed to working with them to ensure they benefit from A.I. technology and new revenue models,” Ms. Held said. “We’re hopeful that we will find a mutually beneficial way to work together, as we are doing with many other publishers.”

[…]

Source: New York Times Sues OpenAI and Microsoft Over Use of Copyrighted Work – The New York Times

Well, if they didn’t want anyone to read it – which is really what an AI is doing, just as much as you or I do – then they should have put the content behind a paywall.