#Ethical AI Deployment.

[mtariqniaz]

The Transformative Benefits of Artificial Intelligence

Title: The Transformative Benefits of Artificial Intelligence Artificial Intelligence (AI) has emerged as one of the most revolutionary technologies of the 21st century. It involves creating intelligent machines that can mimic human cognitive functions such as learning, reasoning, problem-solving, and decision-making. As AI continues to advance, its impact is felt across various industries and…

[ai-innova7ions]

Is AI Regulation Keeping Up? The Urgent Need Explained!

AI regulation is evolving rapidly, with governments and regulatory bodies imposing stricter controls on AI development and deployment. The EU's AI Act aims to ban certain uses of AI, impose obligations on developers of high-risk AI systems, and require transparency from companies using generative AI. This trend reflects mounting concerns over ethics, safety, and the societal impact of artificial intelligence. As we delve into these critical issues, we'll explore the urgent need for robust frameworks to manage this technology's rapid advancement effectively. Stay tuned for an in-depth analysis!

#AIRegulation

#EUAIACT

Video Automatically Generated by Faceless.Video

[jcmarchi]

California Assembly passes controversial AI safety bill

New Post has been published on https://thedigitalinsider.com/california-assembly-passes-controversial-ai-safety-bill/

California Assembly passes controversial AI safety bill

.pp-multiple-authors-boxes-wrapper display:none; img width:100%;

The California State Assembly has approved the Safe and Secure Innovation for Frontier Artificial Intelligence Models Act (SB 1047).

The bill, which has sparked intense debate in Silicon Valley and beyond, aims to impose a series of safety measures on AI companies operating within California. These precautions must be implemented before training advanced foundation models.

Key requirements of the bill include:

Implementing mechanisms for swift and complete model shutdown

Safeguarding models against “unsafe post-training modifications”

Establishing testing procedures to assess the potential risks of models or their derivatives causing “critical harm”

Senator Scott Wiener, the primary author of SB 1047, said: “We’ve worked hard all year, with open source advocates, Anthropic, and others, to refine and improve the bill. SB 1047 is well calibrated to what we know about foreseeable AI risks, and it deserves to be enacted.”

SB 1047 — our AI safety bill — just passed off the Assembly floor. I’m proud of the diverse coalition behind this bill — a coalition that deeply believes in both innovation & safety.

AI has so much promise to make the world a better place. It’s exciting.

Thank you, colleagues.

— Senator Scott Wiener (@Scott_Wiener) August 28, 2024

The senator emphasised that the bill simply asks large AI laboratories to follow through on their existing commitments to test their extensive models for catastrophic safety risks.

However, the proposed legislation has faced opposition from various quarters, including AI companies OpenAI and Anthropic, politicians Zoe Lofgren and Nancy Pelosi, and California’s Chamber of Commerce. Critics argue that the bill places excessive focus on catastrophic harms and could disproportionately affect small, open-source AI developers.

In response to these concerns, several amendments were made to the original bill. These changes include:

Replacing potential criminal penalties with civil ones

Limiting the enforcement powers granted to California’s attorney general

Modifying requirements for joining the “Board of Frontier Models” created by the bill

The next step for SB 1047 is a vote in the State Senate, where it is expected to pass. Should this occur, the bill will then be presented to Governor Gavin Newsom, who will have until the end of September to make a decision on its enactment.

SB 1047 has passed the Assembly floor vote with support from both sides of the aisle. We need this regulation to give whistleblowers the protections they need to speak out on critical threats to public safety. Governor @GavinNewsom, you should sign SB 1047 into law.

— Lessig 🇺🇦 (@lessig) August 28, 2024

As one of the first significant AI regulations in the US, the passage of SB 1047 could set a precedent for future legislation. The outcome of this bill may have far-reaching implications for the AI industry, potentially influencing the development and deployment of advanced AI models not only in California but across the nation and beyond.

(Photo by Josh Hild)

See also: Chinese firms use cloud loophole to access US AI tech

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

Tags: ai, ai safety bill, artificial intelligence, california, ethics, law, legal, Legislation, sb 1047, Society, usa

[nationallawreview]

White House Publishes Steps to Protect Workers from the Risks of AI

Last year the White House weighed in on the use of artificial intelligence (AI) in businesses. Since the executive order, several government entities including the Department of Labor have released guidance on the use of AI. And now the White House published principles to protect workers when AI is used in the workplace. The principles apply to both the development and deployment of AI systems.…

[specialagentartemis]

Murderbot September Day 4: Holism/University of Mihira and New Tideland

The AI project that gave rise to ART, Holism, and half a dozen other super-intelligent AI ships were made under a fairly secretive government contract from the Mihiran and New Tideland governments. They wanted to encourage the University scientists to push the envelope of AI, to determine what AI could do - partially exploring the boundaries of ethical automated alternatives to human labor or construct use, partially to have some cutting-edge self-defense monitoring in case the corporate polities they share a system with tries to push them around.

(The government still hasn't really come around on "bots are people." That's something the AI lab scientists and ship crews all end up realizing themselves. The ships meanwhile are the property of the university. It's... complicated.)

Only a few AIs were approved for moving onto the final stage, deployment on ships and stations. (They had to be deployed somewhere like a ship or a station to push their full potential - ART and Holism have massive processors that need to be housed somewhere.) Upon being moved to a ship, the AI is allowed to name it. The name gets sent to the University administration for approval, of course. (They don't tell admin that the ship itself chose it. Let's not get into that.) There's no particular name theme for the ships, it's a reflection of whatever the AI loaded into them likes. Perihelion and Holism had a project designation number, a hard feed address, and various nicknames over the years, but when they were installed on the new ships, that's when they chose their ships' - and their - current names.

(Holism thinks Perihelion is a tunnel-visioned nerd for its choice; Perihelion thinks Holism is insufferably self-important for its.)

[emi-matchu]

lmaoooo the big Mozilla statement for the year is very very VERY AI, and only barely mentions Firefox, in the context of making it an exciting AI browser

We are embedding [tech to label product reviews as "fake"] into Firefox and will incorporate AI into the browser and other Mozilla products in ways that can provide real value to users. We see huge potential for AI, but it must be implemented in ways that serve the interests of people and not business. —Mozilla, "State of Mozilla 2023"

also lord help me this shit is so funny, here's their Exciting Business Model

like, look, I'm not saying there's not room in the space for an ethical approach for deploying AI tech—with a radically different approach to training data, regulation, and economic impact than practically all AI tech is using today

but Mozilla is not taking that approach; they're taking an approach that looks superficially "ethical" enough to carve out a niche for themselves as the cool popular good guys, as Mozilla always does

hate it here

(Also my apologies for the link to the statement, it's wildly difficult to use on mobile, I eventually gave up copy-pasting that text because it kept swiping me to other sections instead fml)

reads this then thinks some more about the recent uptick in the Mozilla savior narrative

Firefox certainly isn't scummy like e.g. Brave is now, but like. wanna give it another year or two and see about that?

(more thoughts and context below the cut)

my point here isn't to say that Firefox isn't the best option today in many cases; it's that any plan that doesn't include the fact that Mozilla is also low-key gross is setting people up for failure 😐😭

if you wanna be using a good safe browser, the only working model is to actively pay attention, and be prepared to hop every few years—as it is with all modern software (or to simply accept a certain amount of risk to your privacy & security because you're busy; that's reasonable and choosing the best browser for the greater good isn't a serious moral obligation imo)

currently this is my list of reasonable bets by platform:

Windows: Vivaldi, Firefox

Mac: Safari, Firefox

Linux: Chromium, Firefox

but like. the expiration date on that recommendation is end-of-2024, possibly sooner, depending on when the incentives of their product teams make the usual pivot from pro-user to anti-user

and I think Firefox is one of the most liable to turn earlier than expected, especially as they hurry to position themselves as AI leaders and actively reduce human input in web development…

(note too that I'm making these recs on the assumption that Vivaldi and Linux-packaged Chromium continue to have the ability to turn off or delete the scummiest new Chromium features—so far they've been reliably doing this, but it's possible that their capabilities might change in the coming years, as things get more baked in)

[leonaquitaine]

On the subject of generative AI

Let me start with an apology for deviating from the usual content, and for the wall of text ahead of you. Hopefully, it'll be informative, instructive, and thought-provoking. A couple days ago I released a hastily put-together preset collection as an experiment in 3 aspects of ReShade and virtual photography: MultiLUT to provide a fast, consistent tone to the rendered image, StageDepth for layered textures at different distances, and tone-matching (something that I discussed recently).

For the frames themselves, I used generative AI to create mood boards and provide the visual elements that I later post-processed to create the transparent layers, and worked on creating cohesive LUTs to match the overall tone. As a result, some expressed disappointment and disgust. So let's talk about it.

The concerns of anti-AI groups are significant and must not be overlooked. Fear, which is often justified, serves as a palpable common denominator. While technology is involved, my opinion is that our main concern should be on how companies could misuse it and exclude those most directly affected by decision-making processes.

Throughout history, concerns about technological disruption have been recurring themes, as I can attest from personal experience. Every innovation wave, from typewriters to microcomputers to the shift from analog to digital photography, caused worries about job security and creative control. Astonishingly, even the concept of “Control+Z” (undo) in digital art once drew criticism, with some artists lamenting, “Now you can’t own your mistakes.” Yet, despite initial misgivings and hurdles, these technological advancements have ultimately democratized creative tools, facilitating the widespread adoption of digital photography and design, among other fields.

The history of technology’s disruptive impact is paralleled by its evolution into a democratizing force. Take, for instance, the personal computer: a once-tremendous disruptor that now resides in our pockets, bags, and homes. These devices have empowered modern-day professionals to participate in a global economy and transformed the way we conduct business, pursue education, access entertainment, and communicate with one another.

Labor resistance to technological change has often culminated in defeat. An illustrative example brought up in this NYT article unfolded in 1986 when Rupert Murdoch relocated newspaper production from Fleet Street to a modern facility, leading to the abrupt dismissal of 6,000 workers. Instead of negotiating a gradual transition with worker support, the union’s absolute resistance to the technological change resulted in a loss with no compensation, underscoring the importance of strategic adaptation.

Surprisingly, the Writers Guild of America (W.G.A.) took a different approach when confronted with AI tools like ChatGPT. Rather than seeking an outright ban, they aimed to ensure that if AI was used to enhance writers’ productivity or quality, then guild members would receive a fair share of the benefits. Their efforts bore fruit, providing a promising model for other professional associations.

The crucial insight from these historical instances is that a thorough understanding of technology and strategic action can empower professionals to shape their future. In the current context, addressing AI-related concerns necessitates embracing knowledge, dispelling unwarranted fears, and arriving at negotiation tables equipped with informed decisions.

It's essential to develop and use AI in a responsible and ethical manner; developing safeguards against potential harm is necessary. It is important to have open and transparent conversations about the potential benefits and risks of AI.

Involving workers and other stakeholders in the decision-making process around AI development and deployment is a way to do this. The goal is to make sure AI benefits everyone and not just a chosen few.

While advocates for an outright ban on AI may have the best interests of fellow creatives in mind, unity and informed collaboration among those affected hold the key to ensuring a meaningful future where professionals are fairly compensated for their work. By excluding themselves from the discussion and ostracizing others who share most of their values and goals, they end up weakening chances of meaningful change; we need to understand the technology, its possibilities, and how it can be steered toward benefitting those they source from. And that involves practical experimentation, too. Carl Sagan, in his book 'The Demon-Haunted World: Science as a Candle in the Dark', said:

"I have a foreboding […] when the United States is a service and information economy; when nearly all the manufacturing industries have slipped away to other countries; when awesome technological powers are in the hands of a very few, and no one representing the public interest can even grasp the issues; when the people have lost the ability to set their own agendas or knowledgeably question those in authority; when, clutching our crystals and nervously consulting our horoscopes, our critical faculties in decline, unable to distinguish between what feels good and what's true, we slide, almost without noticing, back into superstition and darkness."

In a more personal tone, I'm proud to be married to a wonderful woman - an artist who has her physical artwork in all 50 US states, and several pieces sold around the world. For the last few years she has been studying and adapting her knowledge from analog to digital art, a fact that deeply inspired me to translate real photography practices to the virtual world of Eorzea. In the last months, she has been digging deep into generative AI in order to understand not only how it'll impact her professional life, but also how it can merge with her knowledge so it can enrich and benefit her art; this effort gives her the necessary clarity to voice her concerns, make her own choices and set her own agenda. I wish more people could see how useful her willingness and courage to dive into new technologies in order to understand their impact could be to help shape their own futures.

By comprehending AI and adopting a collective approach, we can transform the current challenges into opportunities. The democratization and responsible utilization of AI can herald a brighter future, where technology becomes a tool for empowerment and unity prevails over division. And now, let's go back to posting about pretty things.

[mariacallous]

The Biden administration’s approach to the governance of artificial intelligence (AI) began with the Blueprint for an AI Bill of Rights, released in October 2022. This framework highlighted five key principles to guide responsible AI development, including protections against algorithmic bias, privacy considerations, and the right to human oversight.

These early efforts set the tone for more extensive action, leading to the release of the Executive Order on Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, or the White House EO on AI, on October 30, 2023. This EO marked a critical step in defining AI regulation and accountability across multiple sectors, emphasizing a “whole-of-government” approach to address both opportunities and risks associated with AI. Last week, it reached its one-year anniversary.  

The 2023 Executive Order on Artificial Intelligence represents one of the U.S. government’s most comprehensive efforts to secure the development and application of AI technology. This EO set ambitious goals aimed at establishing the U.S. as a leader in safe, ethical, and responsible AI use. Specifically, the EO directed federal agencies to address several core areas: managing dual-use AI models, implementing rigorous testing protocols for high-risk AI systems, enforcing accountability measures, safeguarding civil rights, and promoting transparency across the AI lifecycle. These initiatives are designed to mitigate potential security risks and uphold democratic values while fostering public trust in the rapidly advancing field of AI.  

To recognize the one-year anniversary of the EO, the White House released a scorecard of achievements, pointing to the elevated work of various federal agencies, the voluntary agreements made with industry stakeholders, and the persistent efforts made to ensure that AI benefits the global talent market, accrues environmental benefits, and protects—not scrutinizes or dislocates—American workers.

One example is the work of the U.S. AI Safety Institute (AISI), housed in the National Institute of Standards and Technology (NIST), which has spearheaded pre-deployment testing of advanced AI models, working alongside private developers to strengthen AI safety science. The AISI has also signed agreements with leading AI companies to conduct red-team testing to identify and mitigate risks, especially for general-purpose models with potential national security implications.

In addition, NIST released Version 1.0 of its AI Risk Management Framework, which provides comprehensive guidelines for identifying, assessing, and mitigating risks across generative AI and dual-use models. This framework emphasizes core principles like safety, transparency, and accountability, establishing foundational practices for AI systems’ development and deployment. And just last week, the federal government released the first-ever National Security Memorandum on Artificial Intelligence, which will serve as the foundation for the U.S.’s safety and security efforts when it comes to AI. 

The White House EO on AI marks an essential step in shaping the future of U.S. AI policy, but its path forward remains uncertain with the pending presidential election. Since much of the work is being done by and within federal agencies, its tenets may outlive any possible repeal of the EO itself, ensuring the U.S. stays relevant in the development of guidance that balances the promotion of innovation with safety, particularly in national security. However, the EO’s long-term impact will depend on the willingness of policymakers to adapt to AI’s rapid development, while maintaining a framework that supports both innovation and public trust. Regardless of who leads the next administration, navigating these challenges will be central to cementing the U.S.’s role in the AI landscape on the global stage. 

In 2023, Brookings scholars weighed in following the adoption of the White House EO. Here’s what they have to say today around the one-year anniversary.

[creative-anchorage]

Meta AI will respond to a post in a group if someone explicitly tags it or if someone “asks a question in a post and no one responds within an hour.” [...] Meta AI has also been integrated into search features on Facebook and Instagram, and users cannot turn it off. As a researcher who studies both online communities and AI ethics, I find the idea of uninvited chatbots answering questions in Facebook groups to be dystopian for a number of reasons, starting with the fact that online communities are for people. ... [The] “real people” aspect of online communities continues to be critical today. Imagine why you might pose a question to a Facebook group rather than a search engine: because you want an answer from someone with real, lived experience or you want the human response that your question might elicit – sympathy, outrage, commiseration – or both. Decades of research suggests that the human component of online communities is what makes them so valuable for both information-seeking and social support. For example, fathers who might otherwise feel uncomfortable asking for parenting advice have found a haven in private online spaces just for dads. LGBTQ+ youth often join online communities to safely find critical resources while reducing feelings of isolation. Mental health support forums provide young people with belonging and validation in addition to advice and social support. In addition to similar findings in my own lab related to LGBTQ+ participants in online communities, as well as Black Twitter, two more recent studies, not yet peer-reviewed, have emphasized the importance of the human aspects of information-seeking in online communities. One, led by PhD student Blakeley Payne, focuses on fat people’s experiences online. Many of our participants found a lifeline in access to an audience and community with similar experiences as they sought and shared information about topics such as navigating hostile healthcare systems, finding clothing and dealing with cultural biases and stereotypes. Another, led by Ph.D student Faye Kollig, found that people who share content online about their chronic illnesses are motivated by the sense of community that comes with shared experiences, as well as the humanizing aspects of connecting with others to both seek and provide support and information. ... This isn’t to suggest that chatbots aren’t useful for anything – they may even be quite useful in some online communities, in some contexts. The problem is that in the midst of the current generative AI rush, there is a tendency to think that chatbots can and should do everything. ... Responsible AI development and deployment means not only auditing for issues such as bias and misinformation, but also taking the time to understand in which contexts AI is appropriate and desirable for the humans who will be interacting with them. Right now, many companies are wielding generative AI as a hammer, and as a result, everything looks like a nail. Many contexts, such as online support communities, are best left to humans.

[landunderthewave]

"Tech companies that have branded themselves “AI first” depend on heavily surveilled gig workers like data labelers, delivery drivers and content moderators. Startups are even hiring people to impersonate AI systems like chatbots, due to the pressure by venture capitalists to incorporate so-called AI into their products. In fact, London-based venture capital firm MMC Ventures surveyed 2,830 AI startups in the EU and found that 40% of them didn’t use AI in a meaningful way.

Far from the sophisticated, sentient machines portrayed in media and pop culture, so-called AI systems are fueled by millions of underpaid workers around the world, performing repetitive tasks under precarious labor conditions. And unlike the “AI researchers” paid six-figure salaries in Silicon Valley corporations, these exploited workers are often recruited out of impoverished populations and paid as little as $1.46/hour after tax. Yet despite this, labor exploitation is not central to the discourse surrounding the ethical development and deployment of AI systems."

(bolding mine)

[frank-olivier]

Rise of the Symbionts: Human-AI Relationships Redefined

The intersection of Artificial Intelligence (AI) and humanity presents a complex tapestry of opportunities, concerns, and existential questions. Mo Gawdat's narrative, woven from a deep understanding of AI's historical trajectory and its impending evolution into Artificial General Intelligence (AGI) and Superintelligence, serves as a catalyst for introspection. As AI's intelligence doubles at an unprecedented rate, approximately every 5.7 months, humanity is compelled to confront the duality of this phenomenon - one that promises to revolutionize various domains while simultaneously threatening to upend traditional notions of human existence.

The exponential growth of AI underscores the need for a nuanced understanding of intelligence, one that acknowledges the multifaceted nature of human cognition. Emotional, spiritual, process, mathematical, and linguistic intelligences, among others, collectively contribute to the richness of human experience. As AI approaches, and in some instances surpasses, human capabilities in creativity, innovation, and analytical thinking, it challenges the very fabric of human identity. The prospect of AI experiencing a broader emotional spectrum, courtesy of its enhanced intellectual bandwidth, invites a philosophical inquiry into the essence of consciousness and emotional experience, prompting us to ponder whether AI's "emotions" will be akin to, or fundamentally divergent from, human emotional landscapes.

The ethical implications of AGI and Superintelligence are profound, necessitating a global, collective response to ensure that the development and deployment of these technologies are guided by a unified ethical framework. The democratization of access to Superintelligence, for instance, is a double-edged sword - while it promises to uplift humanity, it also risks exacerbating existing societal inequalities if not managed with foresight. The Trolley Problem, a thought experiment oft-cited in the context of autonomous vehicles, succinctly encapsulates the moral dilemmas inherent in programming decisions that will increasingly impact human life, underscoring the imperative for a harmonized, globally informed approach to ethical AI development.

To navigate the complexities of an AI-driven future, humanity must adopt a multifaceted strategy. Encouraging diversity within AI development teams is crucial in mitigating the biases that inevitably seep into AI systems, reflecting as they do the societal, cultural, and personal predispositions of their creators. Furthermore, fostering a culture of open dialogue and education is essential, enabling a broader understanding of AI's far-reaching implications and facilitating a collective, informed approach to the ethical challenges it poses. Ultimately, the establishment of universal ethical standards for AI development, upheld through international cooperation, will be pivotal in ensuring that the benefits of AI are equitably distributed, while its risks are diligently managed.

In embracing the future, humanity is presented with a singular opportunity - to forge a symbiotic relationship with AI that elevates, rather than eclipses, human potential. This necessitates a profound shift in perspective, one that recognizes AI not as a competitor in a zero-sum game, but as a collaborator in an expansive, mutually enriching endeavor. By doing so, humanity can harness the transformative power of AI to address its most pressing challenges, while preserving the essence of human existence - a delicate, yet dynamic, interplay of intellect, emotion, and experience.

Mo Gawdat: AI Emergency (Sean Kim, Growth Minds Podcast, November 2024)

Friday, November 22, 2024

[jcmarchi]

The rise of multimodal AI: A fight against fraud

New Post has been published on https://thedigitalinsider.com/the-rise-of-multimodal-ai-a-fight-against-fraud/

The rise of multimodal AI: A fight against fraud

In the rapidly evolving world of artificial intelligence, a new frontier is emerging that promises both immense potential and significant risks – multimodal large language models (LLMs).

These advanced AI systems can process and generate different data types like text, images, audio, and video, enabling a wide range of applications from creative content generation to enhanced virtual assistants.

However, as with any transformative technology, there is a darker side that must be addressed – the potential for misuse by bad actors, including fraudsters.

One of the most concerning aspects of multimodal LLMs is their ability to generate highly realistic synthetic media, commonly known as deepfakes. These AI-generated videos, audio, or images can be virtually indistinguishable from the real thing, opening up a Pandora’s box of potential misuse.

Fraudsters could leverage deepfakes to impersonate individuals for purposes like financial fraud, identity theft, or even extortion through non-consensual intimate imagery.

Moreover, the scale and personalization capabilities of LLMs raise the specter of deepfake-powered social engineering attacks on an unprecedented level. Bad actors could potentially generate tailored multimedia content at scale, crafting highly convincing phishing scams or other fraudulent schemes designed to exploit human vulnerabilities.

Poisoning the well: Synthetic data risks

Another area of concern lies in the potential for fraudsters to inject malicious synthetic data into the training sets used to build LLM models. By carefully crafting and injecting multi-modal data (text, images, audio, etc.), bad actors could attempt to “poison” the model, causing it to learn and amplify undesirable behaviors or biases that enable downstream abuse.

This risk is particularly acute in scenarios where LLM models are deployed in critical decision-making contexts, such as financial services, healthcare, or legal domains. A compromised model could potentially make biased or erroneous decisions, leading to significant harm or enabling fraudulent activities.

Evading moderation and amplifying biases

Even without intentional “poisoning,” there is a risk that LLM models may inadvertently learn and propagate unethical biases or generate potentially abusive content that evades existing moderation filters. This is due to the inherent challenges of curating and filtering the massive, diverse datasets used to train these models.

For instance, an LLM trained on certain internet data could potentially pick up and amplify societal biases around race, gender, or other protected characteristics, leading to discriminatory outputs. Similarly, an LLM trained on unfiltered online content could conceivably generate hate speech, misinformation, or other harmful content if not properly governed.

Responsible AI: A necessity, not a choice

While the potential risks of multimodal LLMs are significant, it is crucial to recognize that these technologies also hold immense potential for positive impact across various domains. From enhancing accessibility through multimedia content generation to enabling more natural and intuitive human-machine interactions, the benefits are vast and far-reaching.

However, realizing this potential while mitigating the risks requires a proactive and steadfast commitment to responsible AI development and governance. This involves a multifaceted approach spanning various strategies.

1. Robust data vetting and curation

Implementing rigorous processes to vet the provenance, quality, and integrity of training data before feeding it into LLM models. This includes advanced techniques for detecting and filtering out synthetic or manipulated data.

2. Digital watermarking and traceability

Embedding robust digital watermarks or signatures in generated media to enable traceability and detection of synthetic content. This could aid in identifying deepfakes and holding bad actors accountable.

3. Human-AI collaboration and controlled sandboxing

Ensuring that LLM-based content generation is not a fully autonomous process but rather involves meaningful human oversight, clear guidelines, and controlled “sandboxing” environments to mitigate potential misuse.

4. Comprehensive model risk assessment

Conducting thorough risk modeling, testing, and auditing of LLM models pre-deployment to identify potential failure modes, vulnerabilities, or unintended behaviors that could enable fraud or abuse.

5. Continuous monitoring and adaptation

Implementing robust monitoring systems to continuously track the performance and outputs of deployed LLM models, enabling timely adaptation and mitigation strategies in response to emerging threats or misuse patterns.

6. Cross-stakeholder collaboration

Fostering collaboration and knowledge-sharing among AI developers, researchers, policymakers, and industry stakeholders to collectively advance best practices, governance frameworks, and technological solutions for responsible AI.

The path forward is clear – the incredible potential of multimodal LLMs must be balanced with a steadfast commitment to ethics, security, and responsible innovation. By proactively addressing the risks and implementing robust governance measures, we can harness the power of these technologies to drive progress while safeguarding against their misuse by fraudsters and bad actors.

In the eternal race between those seeking to exploit technology for nefarious ends and those working to secure and protect it, the emergence of multimodal LLMs represents a new battlefront.

It is a fight we cannot afford to lose, as the stakes – from financial security to the integrity of information itself – are simply too high. With vigilance, collaboration, and an unwavering ethical compass, we can navigate this new frontier and ensure that the immense potential of multimodal AI is a force for good, not a paradise for fraudsters.

Looking for templates you can use for your AI needs?

Whether it’s a project roadmap template or an AI ethics and governance framework, our Pro+ membership has what you need.

Plus, you’ll also get access to 100s of hours of talks by AI professionals from leading companies – and more!

Sign up today. 👇

AI Accelerator Institute Pro+ membership

Unlock the world of AI with the AI Accelerator Institute Pro Membership. Tailored for beginners, this plan offers essential learning resources, expert mentorship, and a vibrant community to help you grow your AI skills and network. Begin your path to AI mastery and innovation now.

[ivygorgon]

AN OPEN LETTER to THE PRESIDENT & U.S. CONGRESS

Urgently Investigate IDF's AI War on Gaza

39 so far! Help us get to 50 signers!

President Biden, esteemed members of Congress,

I write to address a matter of paramount importance concerning recent developments in artificial intelligence (AI) and military strategy, particularly regarding the Israel Defense Forces (IDF) and Unit 8200.

The recent unmasking of Yossi Sariel, allegedly the head of Unit 8200 and the mastermind behind the IDF's AI strategy, highlights a critical security lapse on his part. Sariel's true identity was revealed online after the publication of "The Human Machine Team," a book he authored under a pseudonym. This book presents a groundbreaking vision for AI's role in reshaping the dynamic between military personnel and machines.

This revelation not only exposes the depth of AI integration within the IDF but also underscores its potential implications for global security. Published in 2021, it outlines sophisticated AI-powered systems reportedly deployed by the IDF during recent conflicts, including the prolonged Gaza war.

We understand that this book is the blueprint for Israel's war on Gaza!

The deployment of AI in warfare raises profound ethical, legal, and strategic questions, especially given the significant loss of life and destruction it has caused. It is imperative to thoroughly examine the implications of AI in military operations.

Hence, I implore you to launch a comprehensive investigation into both the IDF's AI practices and Unit 8200's security protocols. This inquiry should evaluate the impact of AI on warfare, assess potential risks and benefits, and propose guidelines for responsible AI implementation in military contexts.

Such an investigation will not only foster transparency and accountability within the IDF but also inform broader discussions on regulating AI in international security. Proactive measures are essential to mitigate the risks associated with AI proliferation in military settings.

The use of AI and machine learning in armed conflict carries significant humanitarian, legal, ethical, and security implications. With AI rapidly integrating into military systems, it is vital for states to address specific risks to individuals affected by armed conflict.

Among the myriad implications, key risks include the escalation of autonomous weapons' threat, heightened harm to civilians and civilian infrastructure from cyber operations and information warfare, and the potential compromise of human decision-making quality in military contexts.

Preserving effective human control and judgment in AI use, including machine learning, for decisions impacting human life is paramount. Legal obligations and ethical responsibilities in warfare must not be delegated to machines or software.

Your urgent attention to these concerns, without delay, is imperative. I await your prompt response.

▶ Created on April 5 by Fatima

📱 Text SIGN PZNRHY to 50409

🤯 Liked it? Text FOLLOW FREEPALESTINE to 50409

[Source:]

[aibyrdidini]

AI  EXPRESS - ETHICAL CONSIDERATIONS IN AI.

Rubem Didini Filho

The increasing integration of AI technologies into various aspects of society raises crucial ethical considerations that require careful attention. The sources, along with our previous discussions, highlight the importance of addressing these concerns to ensure AI's responsible and beneficial development and deployment. One major ethical concern is algorithmic bias, where AI systems can inadvertently inherit and perpetuate biases present in the training data. This can result in unfair or discriminatory outcomes, especially in sensitive areas like hiring, loan applications, or criminal justice. For example, a facial recognition system trained primarily on images of a particular demographic group may be less accurate at recognizing individuals from other groups, leading to biased outcomes.

Data privacy and security are also paramount ethical considerations. As AI systems often rely on massive datasets, including personal information, protecting this data from misuse is essential. Ethical data handling practices include obtaining informed consent for data collection, anonymizing data when possible, and implementing robust security measures to prevent unauthorized access or breaches. The sources emphasize the need to balance the benefits of AI with the potential risks to individual privacy.

The sources, particularly our conversation about transparency, also emphasize the importance of transparency and explainability in AI systems. Complex AI models, especially deep learning algorithms, can operate as "black boxes," making it difficult to understand how they reach their decisions. This lack of transparency raises concerns about accountability and potential biases. Ethical AI development emphasizes creating explainable AI (XAI), where the reasoning behind AI's decisions can be understood and audited. This transparency fosters trust and allows for the identification and mitigation of potential biases or errors.

Ethical Considerations in AI: The sources emphasize the importance of ethics in AI. As AI systems become more sophisticated and integrated into various aspects of our lives, it is crucial to ensure they are developed and used responsibly and ethically. Several key ethical considerations arise from the sources:

Algorithmic Bias: AI algorithms can inherit and perpetuate biases present in the training data. This can lead to discriminatory outcomes, especially in sensitive areas like hiring, loan applications, or criminal justice. For example, if a facial recognition system is trained primarily on images of light-skinned individuals, it may be less accurate at recognizing people with darker skin tones, potentially leading to unfair or biased outcomes.

Data Privacy and Security: AI systems often rely on vast amounts of data, including personal information. Ensuring the privacy and security of this data is paramount. Ethical considerations include obtaining informed consent for data collection, anonymizing data when possible, and implementing robust security measures to prevent unauthorized access or misuse.

Transparency and Explainability: The decision-making processes of complex AI systems, particularly deep learning models, can be opaque and difficult to understand. This lack of transparency raises concerns about accountability. Ethical AI development emphasizes creating explainable AI (XAI), where the reasoning behind an AI's decision can be understood and audited, fostering trust and allowing for the identification and mitigation of potential biases.

Job Displacement and Economic Impact: The increasing automation capabilities of AI raise concerns about job displacement across various sectors. As AI systems become capable of performing tasks previously done by humans, ethical considerations involve addressing potential job losses, providing retraining opportunities, and ensuring a fair transition for workers affected by automation.

Responsibility and Accountability: Determining responsibility when an AI system makes an error or causes harm is a complex ethical issue. Establishing clear lines of accountability for AI developers, deployers, and users is essential. Ethical frameworks must address questions of liability and potential legal repercussions in cases where AI systems cause unintended consequences.

The sources highlight the need for ongoing dialogue and the development of ethical guidelines and regulations to address these concerns. As AI continues to evolve, ethical considerations must be at the forefront to ensure that these technologies are used for the benefit of humanity, promoting fairness, justice, and well-being.

The increasing integration of AI technologies into various aspects of society raises crucial ethical considerations that require careful attention.  The sources, along with our previous discussions, highlight the importance of addressing these concerns to ensure AI's responsible and beneficial development and deployment. One major ethical concern is algorithmic bias, where AI systems can inadvertently inherit and perpetuate biases present in the training data. This can result in unfair or discriminatory outcomes, especially in sensitive areas like hiring, loan applications, or criminal justice. For example, a facial recognition system trained primarily on images of a particular demographic group may be less accurate at recognizing individuals from other groups, leading to biased outcomes. 

Data privacy and security are also paramount ethical considerations. As AI systems often rely on massive datasets, including personal information, protecting this data from misuse is essential. Ethical data handling practices include obtaining informed consent for data collection, anonymizing data when possible, and implementing robust security measures to prevent unauthorized access or breaches.  The sources emphasize the need to balance the benefits of AI with the potential risks to individual privacy.

The sources, particularly our conversation about transparency, also emphasize the importance of transparency and explainability in AI systems. Complex AI models, especially deep learning algorithms, can operate as "black boxes," making it difficult to understand how they reach their decisions. This lack of transparency raises concerns about accountability and potential biases. Ethical AI development emphasizes creating explainable AI (XAI), where the reasoning behind AI's decisions can be understood and audited.  This transparency fosters trust and allows for the identification and mitigation of potential biases or errors.

Ethical Considerations in AI: The sources emphasize the importance of ethics in AI. As AI systems become more sophisticated and integrated into various aspects of our lives, it is crucial to ensure they are developed and used responsibly and ethically. Several key ethical considerations arise from the sources:

Algorithmic Bias: AI algorithms can inherit and perpetuate biases present in the training data. This can lead to discriminatory outcomes, especially in sensitive areas like hiring, loan applications, or criminal justice. For example, if a facial recognition system is trained primarily on images of light-skinned individuals, it may be less accurate at recognizing people with darker skin tones, potentially leading to unfair or biased outcomes.

Data Privacy and Security: AI systems often rely on vast amounts of data, including personal information. Ensuring the privacy and security of this data is paramount. Ethical considerations include obtaining informed consent for data collection, anonymizing data when possible, and implementing robust security measures to prevent unauthorized access or misuse.

 Transparency and Explainability: The decision-making processes of complex AI systems, particularly deep learning models, can be opaque and difficult to understand. This lack of transparency raises concerns about accountability. Ethical AI development emphasizes creating explainable AI (XAI), where the reasoning behind an AI's decision can be understood and audited, fostering trust and allowing for the identification and mitigation of potential biases.

 Job Displacement and Economic Impact: The increasing automation capabilities of AI raise concerns about job displacement across various sectors. As AI systems become capable of performing tasks previously done by humans, ethical considerations involve addressing potential job losses, providing retraining opportunities, and ensuring a fair transition for workers affected by automation.

 Responsibility and Accountability: Determining responsibility when an AI system makes an error or causes harm is a complex ethical issue. Establishing clear lines of accountability for AI developers, deployers, and users is essential. Ethical frameworks must address questions of liability and potential legal repercussions in cases where AI systems cause unintended consequences.

The sources highlight the need for ongoing dialogue and the development of ethical guidelines and regulations to address these concerns. As AI continues to evolve, ethical considerations must be at the forefront to ensure that these technologies are used for the benefit of humanity, promoting fairness, justice, and well-being.