Search This Blog

Powered by Blogger.

Blog Archive

Labels

Showing posts with label AI Models. Show all posts

Tech Giants Face Backlash Over AI Privacy Concerns






Microsoft recently faced material backlash over its new AI tool, Recall, leading to a delayed release. Recall, introduced last month as a feature of Microsoft's new AI companion, captures screen images every few seconds to create a searchable library. This includes sensitive information like passwords and private conversations. The tool's release was postponed indefinitely after criticism from data privacy experts, including the UK's Information Commissioner's Office (ICO).

In response, Microsoft announced changes to Recall. Initially planned for a broad release on June 18, 2024, it will first be available to Windows Insider Program users. The company assured that Recall would be turned off by default and emphasised its commitment to privacy and security. Despite these assurances, Microsoft declined to comment on claims that the tool posed a security risk.

Recall was showcased during Microsoft's developer conference, with Yusuf Mehdi, Corporate Vice President, highlighting its ability to access virtually anything on a user's PC. Following its debut, the ICO vowed to investigate privacy concerns. On June 13, Microsoft announced updates to Recall, reinforcing its "commitment to responsible AI" and privacy principles.

Adobe Overhauls Terms of Service 

Adobe faced a wave of criticism after updating its terms of service, which many users interpreted as allowing the company to use their work for AI training without proper consent. Users were required to agree to a clause granting Adobe a broad licence over their content, leading to suspicions that Adobe was using this content to train generative AI models like Firefly.

Adobe officials, including President David Wadhwani and Chief Trust Officer Dana Rao, denied these claims and clarified that the terms were misinterpreted. They reassured users that their content would not be used for AI training without explicit permission, except for submissions to the Adobe Stock marketplace. The company acknowledged the need for clearer communication and has since updated its terms to explicitly state these protections.

The controversy began with Firefly's release in March 2023, when artists noticed AI-generated imagery mimicking their styles. Users like YouTuber Sasha Yanshin cancelled their Adobe subscriptions in protest. Adobe's Chief Product Officer, Scott Belsky, admitted the wording was unclear and emphasised the importance of trust and transparency.

Meta Faces Scrutiny Over AI Training Practices

Meta, the parent company of Facebook and Instagram, has also been criticised for using user data to train its AI tools. Concerns were raised when Martin Keary, Vice President of Product Design at Muse Group, revealed that Meta planned to use public content from social media for AI training.

Meta responded by assuring users that it only used public content and did not access private messages or information from users under 18. An opt-out form was introduced for EU users, but U.S. users have limited options due to the lack of national privacy laws. Meta emphasised that its latest AI model, Llama 2, was not trained on user data, but users remain concerned about their privacy.

Suspicion arose in May 2023, with users questioning Meta's security policy changes. Meta's official statement to European users clarified its practices, but the opt-out form, available under Privacy Policy settings, remains a complex process. The company can only address user requests if they demonstrate that the AI "has knowledge" of them.

The recent actions by Microsoft, Adobe, and Meta highlight the growing tensions between tech giants and their users over data privacy and AI development. As these companies navigate user concerns and regulatory scrutiny, the debate over how AI tools should handle personal data continues to intensify. The tech industry's future will heavily depend on balancing innovation with ethical considerations and user trust.


Slack Faces Backlash Over AI Data Policy: Users Demand Clearer Privacy Practices

 

In February, Slack introduced its AI capabilities, positioning itself as a leader in the integration of artificial intelligence within workplace communication. However, recent developments have sparked significant controversy. Slack's current policy, which collects customer data by default for training AI models, has drawn widespread criticism and calls for greater transparency and clarity. 

The issue gained attention when Gergely Orosz, an engineer and writer, pointed out that Slack's terms of service allow the use of customer data for training AI models, despite reassurances from Slack engineers that this is not the case. Aaron Maurer, a Slack engineer, acknowledged the need for updated policies that explicitly detail how Slack AI interacts with customer data. This discrepancy between policy language and practical application has left many users uneasy. 

Slack's privacy principles state that customer data, including messages and files, may be used to develop AI and machine learning models. In contrast, the Slack AI page asserts that customer data is not used to train Slack AI models. This inconsistency has led users to demand that Slack update its privacy policies to reflect the actual use of data. The controversy intensified as users on platforms like Hacker News and Threads voiced their concerns. Many felt that Slack had not adequately notified users about the default opt-in for data sharing. 

The backlash prompted some users to opt out of data sharing, a process that requires contacting Slack directly with a specific request. Critics argue that this process is cumbersome and lacks transparency. Salesforce, Slack's parent company, has acknowledged the need for policy updates. A Salesforce spokesperson stated that Slack would clarify its policies to ensure users understand that customer data is not used to train generative AI models and that such data never leaves Slack's trust boundary. 

However, these changes have yet to address the broader issue of explicit user consent. Questions about Slack's compliance with the General Data Protection Regulation (GDPR) have also arisen. GDPR requires explicit, informed consent for data collection, which must be obtained through opt-in mechanisms rather than default opt-ins. Despite Slack's commitment to GDPR compliance, the current controversy suggests that its practices may not align fully with these regulations. 

As more users opt out of data sharing and call for alternative chat services, Slack faces mounting pressure to revise its data policies comprehensively. This situation underscores the importance of transparency and user consent in data practices, particularly as AI continues to evolve and integrate into everyday tools. 

The recent backlash against Slack's AI data policy highlights a crucial issue in the digital age: the need for clear, transparent data practices that respect user consent. As Slack works to update its policies, the company must prioritize user trust and regulatory compliance to maintain its position as a trusted communication platform. This episode serves as a reminder for all companies leveraging AI to ensure their data practices are transparent and user-centric.

Teaching AI Sarcasm: The Next Frontier in Human-Machine Communication

In a remarkable breakthrough, a team of university researchers in the Netherlands has developed an artificial intelligence (AI) platform capable of recognizing sarcasm. According to a report from The Guardian, the findings were presented at a meeting of the Acoustical Society of America and the Canadian Acoustical Association in Ottawa, Canada. During the event, Ph.D. student Xiyuan Gao detailed how the research team utilized video clips, text, and audio content from popular American sitcoms such as "Friends" and "The Big Bang Theory" to train a neural network. 

The foundation of this innovative work is a database known as the Multimodal Sarcasm Detection Dataset (MUStARD). This dataset, annotated by a separate research team from the U.S. and Singapore, includes labels indicating the presence of sarcasm in various pieces of content. By leveraging this annotated dataset, the Dutch research team aimed to construct a robust sarcasm detection model. 

After extensive training using the MUStARD dataset, the researchers achieved an impressive accuracy rate. The AI model could detect sarcasm in previously unlabeled exchanges nearly 75% of the time. Further developments in the lab, including the use of synthetic data, have reportedly improved this accuracy even more, although these findings are yet to be published. 

One of the key figures in this project, Matt Coler from the University of Groningen's speech technology lab, expressed excitement about the team's progress. "We are able to recognize sarcasm in a reliable way, and we're eager to grow that," Coler told The Guardian. "We want to see how far we can push it." Shekhar Nayak, another member of the research team, highlighted the practical applications of their findings. 

By detecting sarcasm, AI assistants could better interact with human users, identifying negativity or hostility in speech. This capability could significantly enhance the user experience by allowing AI to respond more appropriately to human emotions and tones. Gao emphasized that integrating visual cues into the AI tool's training data could further enhance its effectiveness. By incorporating facial expressions such as raised eyebrows or smirks, the AI could become even more adept at recognizing sarcasm. 

The scenes from sitcoms used to train the AI model included notable examples, such as a scene from "The Big Bang Theory" where Sheldon observes Leonard's failed attempt to escape a locked room, and a "Friends" scene where Chandler, Joey, Ross, and Rachel unenthusiastically assemble furniture. These diverse scenarios provided a rich source of sarcastic interactions for the AI to learn from. The research team's work builds on similar efforts by other organizations. 

For instance, the U.S. Department of Defense's Defense Advanced Research Projects Agency (DARPA) has also explored AI sarcasm detection. Using DARPA's SocialSim program, researchers from the University of Central Florida developed an AI model that could classify sarcasm in social media posts and text messages. This model achieved near-perfect sarcasm detection on a major Twitter benchmark dataset. DARPA's work underscores the broader significance of accurately detecting sarcasm. 

"Knowing when sarcasm is being used is valuable for teaching models what human communication looks like and subsequently simulating the future course of online content," DARPA noted in a 2021 report. The advancements made by the University of Groningen team mark a significant step forward in AI's ability to understand and interpret human communication. 

As AI continues to evolve, the integration of sarcasm detection could play a crucial role in developing more nuanced and responsive AI systems. This progress not only enhances human-AI interaction but also opens new avenues for AI applications in various fields, from customer service to mental health support.

Microsoft Temporarily Blocks ChatGPT: Addressing Data Concerns

Microsoft recently made headlines by temporarily blocking internal access to ChatGPT, a language model developed by OpenAI, citing data concerns. The move sparked curiosity and raised questions about the security and potential risks associated with this advanced language model.

According to reports, Microsoft took this precautionary step on Thursday, sending ripples through the tech community. The decision came as a response to what Microsoft referred to as data concerns associated with ChatGPT.

While the exact nature of these concerns remains undisclosed, it highlights the growing importance of scrutinizing the security aspects of AI models, especially those that handle sensitive information. With ChatGPT being a widely used language model for various applications, including customer service and content generation, any potential vulnerabilities in its data handling could have significant implications.

As reported by ZDNet, Microsoft still needs to provide detailed information on the duration of the block or the specific data issues that prompted this action. However, the company stated that it is actively working with OpenAI to address these concerns and ensure a secure environment for its users.

This incident brings to light the continuous difficulties and obligations involved in applying cutting-edge AI models to practical situations. It is crucial to guarantee the security and moral application of these models as artificial intelligence gets more and more integrated into different businesses. Businesses must find a balance between protecting sensitive data and utilizing AI's potential.

It's important to note that instances like this add to the continuing discussion about AI ethics and the necessity of open disclosure about possible dangers. The tech titans' dedication to rapidly and ethically addressing issues is demonstrated by their partnership in tackling the data concerns through OpenAI and Microsoft.

Microsoft's recent decision to temporarily restrict internal access to ChatGPT highlights the dynamic nature of AI security and the significance of exercising caution while implementing sophisticated language models. The way the problem develops serves as a reminder that, in order to guarantee the ethical and secure use of AI technology, the tech community needs to continue being proactive in addressing possible data vulnerabilities.





Customized AI Models and Benchmarks: A Path to Ethical Deployment

 

As artificial intelligence (AI) models continue to advance, the need for industry collaboration and tailored testing benchmarks becomes increasingly crucial for organizations in their quest to find the right fit for their specific needs.

Ong Chen Hui, the assistant chief executive of the business and technology group at Infocomm Media Development Authority (IMDA), emphasized the importance of such efforts. As enterprises seek out large language models (LLMs) customized for their verticals and countries aim to align AI models with their unique values, collaboration and benchmarking play key roles.

Ong raised the question of whether relying solely on one large foundation model is the optimal path forward, or if there is a need for more specialized models. She pointed to Bloomberg's initiative to develop BloombergGPT, a generative AI model specifically trained on financial data. Ong stressed that as long as expertise, data, and computing resources remain accessible, the industry can continue to propel developments forward.

Red Hat, a software vendor and a member of Singapore's AI Verify Foundation, is committed to fostering responsible and ethical AI usage. The foundation aims to leverage the open-source community to create test toolkits that guide the ethical deployment of AI. Singapore boasts the highest adoption of open-source technologies in the Asia-Pacific region, with numerous organizations, including port operator PSA Singapore and UOB bank, using Red Hat's solutions to enhance their operations and cloud development.

Transparency is a fundamental aspect of AI ethics, according to Ong. She emphasized the importance of open collaboration in developing test toolkits, citing cybersecurity as a model where open-source development has thrived. Ong highlighted the need for continuous testing and refinement of generative AI models to ensure they align with an organization's ethical guidelines.

However, some concerns have arisen regarding major players like OpenAI withholding technical details about their LLMs. A group of academics from the University of Oxford highlighted issues related to accessibility, replicability, reliability, and trustworthiness (AART) stemming from the lack of information about these models.

Ong suggested that organizations adopting generative AI will fall into two camps: those opting for proprietary large language AI models and those choosing open-source alternatives. She emphasized that businesses focused on transparency can select open-source options.

As generative AI applications become more specialized, customized test benchmarks will become essential. Ong stressed that these benchmarks will be crucial for testing AI applications against an organization's or country's AI principles, ensuring responsible and ethical deployment.

In conclusion, the collaboration, transparency, and benchmarking efforts in the AI industry are essential to cater to specific needs and align AI models with ethical and responsible usage. The development of specialized generative AI models and comprehensive testing benchmarks will be pivotal in achieving these objectives.

Why is Skepticism the Best Protection When Adopting Generative AI?


It has become crucial for companies to implement generative artificial intelligence (AI) while minimizing potential hazards and with a healthy dose of skepticism. 

According to a Gartner report issued on Tuesday, 45% of firms are presently testing generative AI, while 10% have such technologies in use. During a webinar last month to examine the commercial costs and dangers of generative AI, 1,419 executives were polled.

In the recent survey, around 78% said that the advantages of generative AI exceeded its risks, compared to the 68% who felt the same way in the prior survey. 

According to Gartner, 22% of firms are expanding their generative AI investments across at least three different functions, with 45% of businesses doing so overall. Software development saw the biggest investment in or adoption of generative AI, at 21%, followed by marketing and customer service, at 19% and 16%, respectively.

Gartner’s group chief of research and an acclaimed analyst, "Organizations are not just talking about generative AI – they're investing time, money, and resources to move it forward and drive business outcomes."

"Executives are taking a bolder stance on generative AI as they see the profound ways that it can drive innovation, optimization, and disruption[…]Business and IT leaders understand that the 'wait and see' approach is riskier than investing," said Karamouzis.

Why is ‘Having a Doubt’ Necessary 

In order to grow their businesses companies must have a framework in place to ensure that they are adopting generative AI responsibly and ethically.

According to Kathy Baxter, Salesforce.com's principal architect of Responsible AI, skepticism should also be extended to technologies that can tell whether AI has been deployed.

Baxter further added that technology has now become ‘democratized,’ allowing anyone to have access to generative AI without many restrictions. However, despite the fact that many firms are making an attempt to screen out harmful information and are still investing in such initiatives, there is still a lack of knowledge regarding "how big a grain of salt" one should apply to AI-generated content.

Baxter noted that even AI detecting tools can make mistakes occasionally yet may be taken as always accurate in an interview with ZDNET, stressing that users accept all of this stuff as fact even if it is false. When generative AI and the tools that go along with it are employed in some fields, like education, these impressions could be detrimental since students might be falsely accused of employing AI in their work. 

She further raised concerns over such risks, urging individuals and organizations to use generative AI with ‘enough skepticism.’

She further highlighted the need for sufficient restrictions to ensure the safety and accuracy of AI. This will also help in case deployments are rolled out along with mitigation tools, she added. These can involve fault detection and reporting features, and mechanisms to collect and provide human feedback. 

Moreover, she emphasized the significance of the data used to train AI models and added that grounding AI is equally essential. But as she pointed out, not many businesses practice proper data hygiene.  

AI Models Produces Photos of Real People and Copyrighted Images


The infamous image generation models are used in order to produce identifiable photos of actual people. This leads to the privacy infringement of numerous individuals, as per a new research. 

The study demonstrates how these AI systems can be programmed to reproduce precisely copyrighted artwork and medical images. It is a result that might help artists who are suing AI companies for copyright violations.  

Research: Extracting Training Data from Diffusion Models 

Researchers from Google, DeepMind, UC Berkeley, ETH Zürich, and Princeton obtained their findings by repeatedly prompting Google’s Imagen with image captions, like the user’s name. Following this, they analyzed if any of the images they produced matched the original photos stored in the model's database. The team was successful in extracting more than 100 copies of photos from the AI's training set. 

These image-generating AI models are apparently produced over vast data sets, that consist of images with captions that have been taken from the internet. The most recent technology works by taking images in the data sets and altering pixels individually until the original image is nothing more than a jumble of random pixels. The AI model then reverses the procedure to create a new image from the pixelated mess. 

According to Ryan Webster, a Ph.D. student from the University of Caen Normandy, who has studied privacy in other image generation models but is not involved in the research, the study is the first to demonstrate that these AI models remember photos from their training sets. This could also serve as an implication for startups wanting to use AI models in health care since it indicates that these systems risk leaking users’ private and sensitive data. 

Eric Wallace, a Ph.D. scholar who was involved in the study group, raises concerns over the privacy issue and says they hope to raise alarm regarding the potential privacy concerns with these AI models before they are extensively implemented in delicate industries like medicine. 

“A lot of people are tempted to try to apply these types of generative approaches to sensitive data, and our work is definitely a cautionary tale that that’s probably a bad idea unless there’s some kind of extreme safeguards taken to prevent [privacy infringements],” Wallace says. 

Another major conflict between AI businesses and artists is caused by the extent to which these AI models memorize and regurgitate photos from their databases. Two lawsuits have been filed against AI by Getty Images and a group of artists who claim the company illicitly scraped and processed their copyrighted content. 

The researchers' findings will ultimately aid artists to claim that AI companies have violated their copyright. The companies may have to pay artists whose work was used to train Stable Diffusion if they can demonstrate that the model stole their work without their consent. 

According to Sameer Singh, an associate professor of computer science at the University of California, Irvine, these findings hold paramount importance. “It is important for general public awareness and to initiate discussions around the security and privacy of these large models,” he adds.