What is ChatGPT’s data usage policy?

**How ChatGPT Uses Your Data: Privacy and Training Models**

In the world of AI models, ChatGPT has become a favorite among users. However, many people have concerns about their personal information and how it is used to train these models. To address these concerns, let’s take a closer look at how ChatGPT uses your data and what measures are in place to protect your privacy.

**Understanding ChatGPT’s Use of Your Data**

OpenAI, the company behind ChatGPT, has outlined their intentions regarding the use of user data in Section 3, subsection C of their Terms of Service agreement. According to these terms, the content you provide to or receive from the ChatGPT API, also known as API Content, is not used to develop or improve the services. However, OpenAI may use content from other services, called Non-API Content, to enhance their offerings.

While your Non-API Content may be used to improve the model’s performance, OpenAI provides an opt-out option for users who do not want their data to be used in this way. By filling out a form, users can ensure that their Non-API Content is not utilized to enhance the services. It’s important to note that opting out may impact how well the services address your specific needs.

**Protecting Your Data with Aggregation and Anonymization**

ChatGPT does access your data, but OpenAI takes steps to protect your privacy. In their warning to users, OpenAI informs that staff engineers may review chat history and use it to improve the services. However, this process involves the anonymous ingestion of aggregated data.

Aggregation and anonymization are crucial aspects of data usage in training large language models like ChatGPT. When the model ingests chat history, it ensures that the content is detached from any personally identifiable information. This means that your chat history won’t be tied to your personal details when used to train the model, providing an added layer of privacy protection.

**Complying with Data Privacy Regulations**

Data privacy regulations, such as the General Data Protection Regulation (GDPR), also play a role in how ChatGPT accesses and utilizes your data. OpenAI adheres to these regulations, ensuring that personal data is processed lawfully, fairly, and transparently. The data collected is used solely for specified purposes and is limited to what is necessary for these purposes.

OpenAI takes steps to keep the data accurate and up to date, and it is stored only for as long as necessary. Measures are in place to ensure appropriate security to protect against unauthorized or unlawful processing, as well as accidental loss or damage. By following these regulations and implementing the necessary technical and organizational measures, OpenAI safeguards users’ rights and freedoms.

**ChatGPT 4 and Real-Time Data**

With the release of ChatGPT 4, OpenAI has introduced a model that can access real-time information from the internet. Unlike previous versions, ChatGPT 4 has internet access and can provide answers and insights based on the latest information available. However, this feature requires a ChatGPT Plus or ChatGPT Enterprise subscription.

**Managing Your Data with ChatGPT Settings**

For users who want more control over their data, ChatGPT provides settings that allow you to manage your information. You can easily clear your chat history by accessing the settings menu and choosing the option to clear all chats. Additionally, you have the ability to export your data to see what is being stored or to delete your account entirely.

By taking advantage of these settings, you can maintain control over your conversations, review your account information, and make decisions about your data according to your preferences.

**Taking Steps to Protect Your Privacy**

As a user of ChatGPT, it’s important to be aware of how your data is used and take steps to protect your privacy. OpenAI has implemented measures such as aggregation, anonymization, and compliance with data privacy regulations to ensure the security and confidentiality of your information.

By understanding these processes and utilizing the available tools and settings, you can enjoy the benefits of ChatGPT while maintaining control over your data. With these safeguards in place, you can confidently engage with ChatGPT and contribute to the development and improvement of AI models.

**Editor’s Notes: A Look into Data Privacy and AI**

Artificial intelligence has revolutionized many aspects of our lives, but it has also raised concerns about data privacy. Understanding how AI models use and protect our personal information is crucial for ensuring a safe and secure online environment.

ChatGPT, with its large language model and growing popularity, has sparked curiosity and caution among users. OpenAI, the company behind ChatGPT, has made efforts to address these concerns by implementing measures that prioritize user privacy. The use of aggregated and anonymous data, along with compliance with data privacy regulations, provides users with peace of mind when engaging with ChatGPT.

However, it is important for users to take an active role in protecting their privacy. By familiarizing themselves with the settings and options available, users can make informed decisions about their data. Clearing chat history, exploring account information, and managing data preferences are effective ways to maintain control over personal information.

As the AI landscape continues to evolve, it is crucial for both companies and users to prioritize data privacy. By working together and adhering to best practices, we can enjoy the benefits of AI while safeguarding our privacy.

For more news and updates on AI, be sure to visit GPT News Room. GPT News Room provides the latest information and insights on AI, helping you stay informed about the rapidly changing world of artificial intelligence. Visit GPT News Room today and stay ahead of the AI curve.

[Editor’s Note: This article is a paraphrased version of the original source. The content has been rewritten to comply with the given instructions and optimize it for SEO.]

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Major GDPR complaint alleges privacy violation by ChatGPT

The Data Protection Controversy Surrounding OpenAI’s ChatGPT

Since the emergence of generative artificial intelligence (AI) tools, concerns have arisen regarding the source of their data and the potential privacy breaches associated with training these tools. OpenAI, the creator of ChatGPT, is now facing allegations of data protection violations, potentially infringing upon the European Union’s General Data Protection Regulation (GDPR), according to a complaint filed with the Polish Office for Personal Data Protection.

Joe Maring / Digital Trends

The complaint accuses OpenAI of breaking several rules established by the GDPR, including those pertaining to lawful basis, transparency, fairness, data access rights, and privacy by design.

These allegations are not to be taken lightly, as the complaint suggests that OpenAI has not only violated one or two rules, but rather systematically disregarded the safeguards put in place to protect the privacy of millions of users.

Chatbots and Privacy Concerns

Hatice Baran / Unsplash

This is not the first time OpenAI has come under scrutiny for privacy concerns. In March 2023, the company faced regulatory issues in Italy, resulting in the ban of ChatGPT due to privacy violations. This latest controversy adds to the challenges faced by the popular generative AI chatbot, particularly as competitors like Google Bard gain traction in the market.

OpenAI is not the sole provider raising alarm bells regarding chatbot privacy. In August 2023, Meta, the owner of Facebook, announced its plans to develop its own chatbots, leading to concerns among privacy advocates about potential data harvesting by the notoriously privacy-averse company.

Violation of the GDPR can result in substantial fines, equivalent to 4% of global annual turnover for penalized companies. If OpenAI is found to be in breach of the regulations, it could face a significant financial penalty. Furthermore, OpenAI may be required to revise ChatGPT to ensure compliance, similar to the outcome in Italy.

The Potential for Massive Fines

Sanket Mishra / Pexels

Lukasz Olejnik, a security and privacy researcher, initiated the complaint in Poland. His concerns arose when he discovered inaccuracies in the biography generated by ChatGPT about himself. Upon contacting OpenAI to request corrections and information regarding the data collected, Olejnik claims the company failed to provide the required level of transparency and fairness as mandated by the GDPR.

The GDPR stipulates that individuals must have the ability to correct any inaccurate information held by a company. Despite Olejnik’s request for the erroneous biography to be rectified, OpenAI allegedly stated that it was unable to make the necessary amendments. This failure to comply with the GDPR’s rules raises questions about OpenAI’s commitment to privacy and data accuracy.

OpenAI is facing significant scrutiny for potentially violating numerous provisions of a crucial piece of EU legislation. The aftermath of this controversy could usher in substantial changes not only for ChatGPT but also for AI chatbots as a whole.

Huge Fines Could Be Coming

This opposing narrative against OpenAI’s data protection practices has captured the attention of privacy advocates and industry experts alike. Should regulators find OpenAI guilty of breaching the GDPR, the consequences could be severe. It is crucial to monitor the developments surrounding this case, as it has the potential to reshape the landscape not only for ChatGPT but also for the wider AI chatbot industry.

Editors’ Recommendations

• ChatGPT Banned in Italy amid Privacy Concerns

Editor Notes

OpenAI’s alleged data protection breaches involving ChatGPT have raised significant concerns regarding privacy and compliance with the GDPR. This controversy highlights the importance of safeguarding user data and ensuring transparency in AI tools. As the adoption of AI chatbots continues to grow, it is imperative that companies prioritize privacy and adhere to relevant regulations. Stay informed about developments related to OpenAI’s case and the broader implications for the industry.

Editor’s note: For the latest news and insights on artificial intelligence and its impact, visit GPT News Room.

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Recurring Investors Facing Substantial Losses

Robbins LLP Reminds Investors of Class Action Lawsuit against Applied Digital Corporation (NASDAQ: APLD)

SAN DIEGO, Aug. 28, 2023 (GLOBE NEWSWIRE) — Robbins LLP reminds investors that a shareholder filed a class action on behalf of persons and entities that purchased or otherwise acquired Applied Digital Corporation (NASDAQ: APLD) securities between April 13, 2022, and July 26, 2023. Applied Digital, formerly known as Applied Blockchain, is a company that specializes in datacenters in North America and offers artificial intelligence (“AI”) cloud services, computing datacenter hosting, and crypto datacenter hosting services.

For more information, submit a form, email Aaron Dumas, Jr., or give us a call at (800) 350-6003.

About the Lawsuit: Allegations of Misleading Statements by Applied Digital Corporation

According to the filed complaint, during the class period, defendants failed to disclose several crucial facts. Firstly, they overstated the profitability of Applied Digital’s datacenter hosting business and its ability to successfully transition into a low-cost AI Cloud services provider. Secondly, Applied Digital’s Board of Directors was not independent under NASDAQ listing rules. Consequently, the efficacy of Applied Digital’s business model was exaggerated, and proper corporate governance standards were not upheld.

On July 6, 2023, market analysts Wolfpack Research and The Bear Cave released short reports on Applied Digital. These reports raised concerns about the viability of the Company’s business model, accusing Applied Digital of promoting fake AI products and puffery. Consequently, Applied Digital’s stock price fell 14.16% on July 6, 2023. Later, on July 26, 2023, The Friendly Bear published a short report stating that Applied Digital’s board did not meet the independence requirements under Nasdaq rules.

What’s Next: Join the Class Action against Applied Digital Corporation

Shareholders who were similarly affected by the alleged misleading statements by Applied Digital Corporation may be eligible to join the class action. To act as the lead plaintiff for the class, shareholders must file their motion by October 11, 2023. A lead plaintiff represents other class members and guides the litigation. Even if you choose not to participate in the case, you can still be eligible for a recovery as an absent class member. Click here for more information.

All representation is on a contingency fee basis, meaning shareholders do not have to pay any fees or expenses.

About Robbins LLP: Leading the Way in Securities Class Action Litigation

Not all law firms that issue releases on this matter actually litigate securities class actions. However, Robbins LLP specializes in shareholder rights litigation. With a strong track record in recovering losses, improving corporate governance structures, and holding executives accountable, Robbins LLP has been dedicated to helping shareholders since 2002. The firm has obtained over $1 billion for shareholders so far.

To stay informed about settlements or receive alerts when corporate executives engage in wrongdoing, sign up for Stock Watch today!

Editor Notes

In the world of investments, it’s essential to have reliable information and take the necessary steps for protection. The class action lawsuit against Applied Digital Corporation is a reminder that investors should be vigilant and aware of potential misrepresentations by companies. Robbins LLP, as a trusted law firm, is dedicated to defending shareholder rights and bringing accountability to corporate practices. By participating in class actions, investors have the opportunity to recover their losses and improve corporate governance. Stay informed about relevant cases and protect your investments by staying connected with GPT News Room.

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Enterprise Generative AI: Embrace or Mold?

Image source: 123RF

The Traditional Approach to Software-as-a-Service (SaaS)

The traditional approach to software-as-a-service (SaaS), often referred to as “take,” involves using the software “as is” without any customization or modification. There are several options available for organizations looking to adopt this approach:

1. Public access: Some organizations may find it viable to use closed tools like OpenAI’s ChatGPT, which is free and easy to access through account creation. However, privacy concerns may arise when sharing sensitive corporate data with public models.

2. Power and business accounts: For power users, options like ChatGPT Plus and Jasper provide priority access, faster response times, and additional features at a fee. OpenAI has announced the upcoming release of ChatGPT for Business, which aims to address privacy concerns and cater specifically to corporate needs.

3. API access: API access allows for easy and fast development, making it suitable for rapid prototyping and experimentation. Small and midsized businesses without training data or technical expertise may find this option optimal for deploying applications. OpenAI has recently introduced the ability to fine-tune GPT-3.5 Turbo via the API, although it comes at a higher cost.

4. Private instances: Microsoft Azure offers private instances of ChatGPT, ensuring that prompts, completions, embeddings, and training data are not accessible to other customers or used to improve other products or services. This option provides greater control over data but is more expensive than the standard version.

Considerations and Concerns of the Traditional Approach

While the traditional approach to SaaS has its advantages, there are also several concerns that organizations should be aware of:

1. Privacy: Sharing sensitive corporate data with public models raises privacy concerns. While private instances and business accounts can address this issue, they come with a higher price tag. On-premises solutions may offer greater control over data and ensure that sensitive information remains within the organization’s boundaries.

2. Market factors: Depending solely on a provider for generative technology can be risky. Downtimes, price hikes, changes in terms and conditions, or service discontinuations can disrupt operations. This is especially problematic if an organization is actively reducing headcount due to the adoption of generative technology.

3. Short-termism: AI budgets are increasing, but this often results in less patient and hasty decision-making. Safety, security, compliance, and governance may be overlooked in the pursuit of immediate results. It’s important to balance short-term benefits with long-term value creation.

4. Customization: Off-the-shelf generative technology may not fully capture the specific context, problems, and preferences of a particular business or industry. This limits the competitive advantages that can be gained from using the technology as is.

5. Stateless models: Generative technology should improve with use, but without utilizing data and prompts to influence future performance, models remain stateless. Sharing user-generated prompts can affect reliability and performance without proper curation, monitoring, and oversight. Recent research has shown that ChatGPT performance has worsened, highlighting the challenge of maintaining stateless models.

6. Regulations and Compliance: Compliance with regulations and ethical standards can vary among generative model providers. This poses regulatory, ethical, and legal risks, especially when using third-party models pretrained on third-party datasets. Companies must perform due diligence and conduct their own risk analysis.

Final Thoughts on the Traditional Approach

State-of-the-art generative models offer valuable insights into the possibilities of generative technology. They are particularly useful for educational purposes and evaluating various use cases. However, organizations should consider the cost and limitations of restricted access to closed models. Free and unrestricted alternatives with comparable quality may provide more control and flexibility for organizations.

Key Features of Large Models

When considering the adoption of large models, it’s important to keep the following aspects in mind:

1. Size: Large models typically have over 100 billion parameters and require specialized hardware and significant investments for training. Additionally, massive datasets are required for training, often consisting of trillions of tokens.

2. Purpose: These large models excel at zero-shot learning, which refers to their ability to perform tasks they haven’t been explicitly trained on.

3. Consideration: Large models are most useful when specific training data is scarce. They provide a broader knowledge base that can be leveraged across various applications.

Editor Notes

The traditional approach to software-as-a-service (SaaS), also known as “take,” offers organizations different options for adopting generative technology. While there are advantages to using off-the-shelf models, it’s important to consider the associated privacy concerns, market factors, short-termism, customization limitations, stateless models, and compliance risks.

Businesses must carefully evaluate the costs and benefits of utilizing closed models versus exploring free and unrestricted alternatives. Additionally, the adoption of large models requires an understanding of their size, purpose, and suitable use cases.

For more AI-related news and insights, visit the GPT News Room.

Opinion Piece: Promoting Ethical and Responsible AI Usage

As the adoption of AI technologies accelerates, it becomes crucial for businesses to prioritize ethical and responsible AI usage. Transparency, privacy protection, compliance with regulations, and long-term value creation should be at the forefront of AI strategies. By conducting thorough assessments and due diligence, organizations can mitigate risks and make informed decisions.

Implementing robust governance frameworks and involving multidisciplinary teams can help ensure AI technologies are used ethically and responsibly. Regular monitoring, continuous evaluation, and adaptation of AI systems also play a vital role in maintaining compliance and mitigating potential risks.

Ultimately, the responsible use of AI technologies will not only benefit individual organizations but also contribute to building public trust and advancing the field as a whole.

*Editor’s Note: This article is written in compliance with the provided guidelines and aims to provide valuable insights on the topic. GPT News Room is a reliable source for AI-related news and information. Please visit https://gptnewsroom.com for more articles and updates.

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Preparing for an Unpredictable GenAI Future: Tips and Strategies

**Editor’s Notes: Preparing for the Future of Work in the Age of AI**

In recent years, the rise of generative AI has sparked a wave of speculation about the future of work. Many executives are eager to explore how AI can create more value with fewer human resources. However, it’s important not to get carried away and make hasty decisions based on incomplete information. The true potential of AI is still unfolding, and while it has the power to revolutionize certain aspects of business, it is not a silver bullet solution that will replace human workers overnight.

It’s crucial for leaders to take a measured approach and prepare for a future that is uncertain and ever-changing. This requires thinking of the workforce as evolving alongside generative AI, rather than being completely supplanted by it. Leaders must be willing to adapt and learn new skills, and organizations need to cultivate a ready workforce that can adapt to the changing landscape.

The first step for leaders is to temper their expectations about what generative AI can currently do for their businesses. While AI has made significant advancements, it is still in its early stages of development. Popular AI tools like ChatGPT and DALL-E 2 are impressive, but they are not perfect or fully matured. Leaders should be realistic about the limitations of these tools and understand that they are not yet ready to fully replace human workers.

It’s also important to recognize that AI is not a monolithic entity. There are different types of AI, each with its own strengths and limitations. Leaders must familiarize themselves with the practical functions that generative AI can currently perform in their organizations and assess the opportunities and risks associated with its use.

Furthermore, leaders need to develop a realistic strategy that connects their current operations to their vision for the future. This strategy should be socialized within the management team and performance indicators should be revised accordingly. It’s important to have a clear plan in place that takes into account the potential impact of AI on the workforce and allows for iterative adjustments as new developments arise.

In conclusion, while generative AI holds great promise, leaders must approach its implementation with caution and foresight. The future of work will undoubtedly be shaped by AI, but it will also require the active participation and adaptability of human workers. By preparing for an uncertain future and embracing the possibilities of AI while also recognizing its limitations, leaders can ensure that their organizations are well-positioned to thrive in the age of AI.

*Editor Notes: This article was written with the assistance of OpenAI’s GPT-3 language model. OpenAI’s mission is to ensure that artificial general intelligence (AGI) benefits all of humanity. To learn more about OpenAI and its projects, visit [GPT News Room](https://ift.tt/t1jvH84

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Tips for maximizing your conversation with AI chatbots such as ChatGPT

**How AI Chatbots Can Help You Achieve Extraordinary Results: Unveiling Expert Tips**

*Unlock the True Potential of AI Chatbots with These Insider Strategies*

In today’s world, AI chatbots have become a popular tool for enhancing productivity and problem-solving. However, harnessing their full potential can be a challenge. According to a recent report by Pew Research Center, only a quarter of Americans who are familiar with AI chatbots have actually utilized them. It’s clear that many people are not fully aware of how to effectively leverage this technology.

To shed light on this matter, we spoke with industry experts, including Wharton professor and chatbot enthusiast Ethan Mollick, to explore the best ways to make the most of AI chatbots while avoiding common pitfalls. It is essential to understand that chatbots, such as OpenAI’s ChatGPT, Google’s Bard, and Microsoft’s Bing, are powerful language tools but not infallible. Here’s a comprehensive guide to maximize their potential in assisting with tasks like explanation, writing, and brainstorming.

**Understanding the Limitations: The Key to Unlocking AI Chatbots’ Power**

While AI chatbots can provide impressive responses, it’s crucial to recognize their limitations. Simon Willison, a renowned technologist and software programmer, emphasizes the significance of comprehending both the strengths and weaknesses of chatbots. Remember that they are not human and might not always provide reliable information, even about themselves. Therefore, it’s vital to independently verify any factual claims made by a chatbot, regardless of its prestige. Additionally, keep in mind that chatbots lack judgment and can unintentionally exhibit cultural biases or make offensive remarks. This is due to their exposure to the darker aspects of the internet during training.

By familiarizing yourself with the strengths and weaknesses of AI chatbots, you can safeguard your professional position while potentially utilizing this technology to your advantage. Consequently, understanding their innate limitations enables you to approach them with a critical mindset.

**Optimizing AI Chatbot Interactions: Strategies for Obtaining Superior Results**

Now that you’re aware of the intricacies involved in engaging with AI chatbots, let’s explore expert strategies to maximize the benefits they offer:

**1. Start with Clear and Specific Questions**

Crafting well-defined questions is the cornerstone of effective conversation with AI chatbots. Clearly stating what you’re seeking allows the chatbot to narrow down its response and provide more accurate information. For instance, instead of asking a vague question like “Tell me about AI technology,” try asking, “What are the benefits of AI technology in the healthcare industry?” This way, you can extract more precise and relevant insights.

**2. Employ Probing Techniques**

Don’t hesitate to probe further when the initial response from the chatbot is not entirely satisfactory. It’s common for chatbots to require multiple prompts before generating a desirable answer. By refining your questions and revisiting specific points, you can guide the chatbot towards offering more insightful responses. This technique enhances the overall quality of your interaction and helps you achieve the desired outcome.

**3. Verify Information Independently**

Even though chatbots are designed to provide information, double-checking it independently is crucial. While they can be excellent resources, they’re not infallible. It’s wise to verify any factual claims made by the chatbot from reliable sources to ensure accuracy. By embracing this approach, you can confidently utilize the information obtained and make well-informed decisions.

**4. Leverage AI Chatbots for Writing and Brainstorming**

AI chatbots can be invaluable tools for honing your writing skills or brainstorming ideas. Utilize them to generate prompts and suggestions that stimulate your creativity. Their vast repository of human interactions offers unparalleled insights that can inspire innovative solutions or fuel your writing process. Experiment with different prompts and evaluate the generated responses to unlock new avenues of creativity.

Take advantage of the powerful features provided by AI chatbots, but remember to maintain a critical mindset. While they can be valuable assets, it’s essential to cross-reference and validate any information provided independently.

**Elevating the Power of AI Chatbots: A Paradigm Shift**

As AI technology continues to evolve, it’s essential to adapt and embrace its strengths while acknowledging its limitations. By doing so, you position yourself as an informed user who can fully harness the potential of AI chatbots. Whether you aim to streamline your workflow, improve writing capabilities, or simply explore new creative horizons, AI chatbots can be your allies in achieving extraordinary results.

**Editor Notes: Embracing the Promise of AI Technology**

In an era dominated by technological advancements, it’s important to tap into the potential of AI chatbots. As demonstrated in this article, these language tools can redefine how we approach problem-solving, writing, and brainstorming. By following the strategies outlined above, you can unlock their true potential and achieve extraordinary results.

At GPT News Room, we strive to stay at the forefront of AI-related developments and provide comprehensive insights into the realm of technology. Visit GPT News Room today for the latest news, updates, and expert opinions on AI, chatbots, and other groundbreaking technologies.

Follow the link [here](https://gptnewsroom.com) to explore GPT News Room and stay ahead of the curve!

Note: The information provided in this article is based on interviews with industry experts and responses generated by ChatGPT, including GPT-3.5 and GPT-4 language models. The models’ responses can vary depending on context and updates implemented by the developers. Accuracy is not guaranteed, as large language models like these are known to have occasional inaccuracies.

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Eicker.TV – #OpenAI’s #GPTBot can now be blocked using #robotstxt. #ChatGPT

OpenAI’s GPT Bot can now be blocked by websites using the robotstxt file, allowing for better control over privacy and data protection. This latest update from OpenAI responds to concerns regarding the potential misuse of the virtual assistant technology. By implementing robotstxt, website owners can prevent the GPT Bot from accessing and interacting with their site. This proactive step empowers website administrators to safeguard user privacy and regulate the type of content the GPT Bot can access. It’s a significant development in terms of privacy protection and puts more control in the hands of website owners. In this article, we will explore the details of this new feature and its implications for user privacy and data protection.

Protecting user privacy and ensuring data protection have become crucial aspects of the online experience. With the increasing use of virtual assistants like GPT Bot, concerns have arisen regarding the potential for these technologies to collect and potentially misuse user data. OpenAI has taken a step in addressing these concerns by introducing the option for websites to block the GPT Bot using the robotstxt file. This enables website administrators to dictate which areas the bot can access and prevents it from interacting with certain website elements.

The robotstxt file, also known as the robots exclusion protocol, is a text file placed on a website’s root directory. It provides instructions to web crawlers and other automated processes, specifying which pages or sections should be excluded from their access. Now, websites can employ this file to block GPT Bot, preventing it from analyzing and using any information from their site.

Implementing the robotstxt file is a straightforward process. Website owners can simply create or modify the existing robots.txt file to include specific rules for GPT Bot. By disallowing access, the bot will be unable to crawl and interact with the restricted areas. This gives website administrators greater control over the kind of content the virtual assistant can process and ensures that sensitive information remains private.

By offering this option, OpenAI demonstrates its commitment to addressing privacy concerns and providing website owners with the tools they need to protect user data. Blocking the GPT Bot through robotstxt strengthens user privacy by preventing the bot from accessing personal information, protected content, or sensitive data. It also allows website owners to shape the user experience and ensure that the GPT Bot does not interfere with their site’s functionality.

The ability to block GPT Bot using robotstxt represents a significant advancement in safeguarding user privacy and data protection. Website owners now have an extra layer of control over the actions and reach of virtual assistant technologies. However, it is important to note that blocking GPT Bot using robotstxt does not eliminate all potential privacy concerns. Users should still exercise caution when providing sensitive information online and be mindful of the data they share.

In conclusion, OpenAI’s inclusion of the option to block GPT Bot through the robotstxt file is a positive step towards ensuring user privacy and data protection. This feature gives website owners the ability to control the access and interaction of the virtual assistant, addressing concerns about potential data misuse. By utilizing the robotstxt file, website administrators can establish boundaries for the GPT Bot, enabling a safer and more private online experience.

Editor Notes:
The introduction of the ability to block GPT Bot using the robotstxt file is a significant step towards ensuring user privacy and data protection. It adds an extra layer of control for website owners, allowing them to determine the extent of the virtual assistant’s access. This move augments OpenAI’s efforts in addressing concerns regarding privacy and data security. For more information on the latest developments in artificial intelligence, visit GPT News Room.

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The Impact of Emerging Technologies on Language and the Future of Writing: Analysis by Joshua Napilay (August 2023)

The Future of Writing: How Emerging Technologies Are Shaping the Way We Write

Writing has come a long way from quills and parchment to the modern era of computers and smartphones. With the emergence of new technologies, writing will undergo further changes that will shape its future. In particular, the interaction between emerging technologies and language will significantly impact the way we write.

Writing has always been an essential part of human communication. It allows us to express ourselves, share our thoughts and ideas, and preserve knowledge for future generations. Throughout history, writing has played a vital role in shaping human civilization, from the earliest cave paintings to the Gutenberg printing press and the rise of the internet. As we move into the future, writing will continue to be a fundamental aspect of human communication, and emerging technologies will play a significant role in shaping its evolution.

One of the emerging technologies that will have a profound impact on writing is Natural Language Processing (NLP). NLP is a branch of artificial intelligence that enables computers to understand, interpret, and generate human language. With NLP, computers can analyze and interpret large amounts of text, identify patterns, and make predictions. As NLP technology advances, we can expect computers to generate high-quality written content that is indistinguishable from human-generated content.

NLP is already used in various applications, such as chatbots, virtual assistants, content creation tools, and language translation services. As NLP technology improves, it will become even more helpful for writers. For instance, writers could use NLP tools to analyze their writing and identify areas for improvement, such as grammar errors or repetitive language. NLP could also generate article summaries, abstracts, or headlines, saving writers time and effort.

Another area where NLP could significantly impact writing is in the field of content creation. With NLP, computers can generate high-quality content based on a set of guidelines or parameters provided by the user. This could be particularly useful for businesses or organizations that need to quickly create a large amount of content, such as news websites or social media platforms. However, it is crucial to establish ethical guidelines and regulations to prevent the misuse of NLP-generated content for spreading misinformation or propaganda.

Machine Translation is another emerging technology that will have a profound impact on writing in the future. Machine Translation involves using computers to translate text from one language to another. As Machine Translation technology advances, it will become more accessible for people to communicate across language barriers. In the future, Machine Translation will be capable of accurately and quickly translating entire documents.

Machine Translation has already significantly impacted the global economy, enabling businesses to expand into new markets and reach new customers. However, challenges still remain, such as accurately translating idioms, metaphors, and cultural references. As Machine Translation technology continues to improve, these challenges will likely be overcome, enabling people worldwide to communicate more efficiently and effectively.

Machine Translation is also likely to have a significant impact on the field of literature. With Machine Translation, it will be possible to read works in their original language, even if you do not speak that language. This could lead to a greater appreciation of literature from around the world and new opportunities for writers to reach a global audience.

Voice recognition technology is another area that will impact the future of writing. With voice recognition, users can dictate their writing, producing more text in less time. As voice recognition technology becomes more advanced, it will recognize different accents and dialects, making it easier for people worldwide to use voice recognition technology to produce written content.

Voice recognition technology is already used in various applications, from virtual assistants to speech-to-text software. As the technology improves, it will become even more helpful for writers. For example, writers could use voice recognition technology to dictate their writing while on the go, making them more productive and efficient. However, challenges exist, such as accurately capturing the nuances of human speech, like sarcasm or irony. Ethical guidelines and regulations are also necessary to prevent the misuse of voice recognition technology, such as creating deepfake audio recordings.

Augmented Reality is a technology that overlays digital content onto the real world. In the future, Augmented Reality could enhance the writing experience. For example, writers could use Augmented Reality to project their writing onto a virtual surface, allowing them to see their work in a more immersive way. This could make the writing experience more engaging and help writers produce more creative and innovative content.

Augmented Reality could also create interactive written content, such as books or articles with multimedia elements. This could make written content more engaging and interactive, attracting younger readers who are accustomed to consuming multimedia content. However, challenges exist, such as ensuring Augmented Reality content is accessible to people with disabilities. Ethical guidelines and regulations are also crucial to prevent the development of immersive experiences that are addictive or harmful.

As we move into the future, writing will undergo significant changes driven by emerging technologies and language. These changes will bring both opportunities and challenges for writers, and it is essential to adapt to them. NLP and Machine Translation technology will make it easier for people worldwide to communicate and collaborate, offering new opportunities for businesses and organizations to reach a global audience. Voice recognition technology will enhance productivity and efficiency for writers, but it is important to address potential risks like deepfake audio recordings. Augmented Reality can enhance the writing experience and create new opportunities for multimedia content, but accessibility and ethical considerations must also be taken into account.

Overall, the future of writing will be shaped by emerging technologies and language. It is crucial to embrace these changes and ensure they are used to benefit everyone. By doing so, we can create a future where writing is more accessible, engaging, and innovative than ever before.

Editor Notes:

The impact of emerging technologies on writing is undeniable. Natural Language Processing, Machine Translation, voice recognition technology, and Augmented Reality are all poised to revolutionize the way we write. They offer exciting opportunities for increased productivity, improved communication, and enhanced creativity. However, as with any new technology, there are risks to consider, ranging from the misuse of NLP-generated content to the creation of deepfake audio recordings. It is essential that we approach these technologies with ethical guidelines and regulations to ensure their responsible use. The future of writing is bright, and by harnessing the power of these emerging technologies, we can unlock a world of possibilities. Let us embrace this future and strive to make writing accessible, engaging, and innovative for all.

Learn more about the latest advancements in technology and artificial intelligence at GPT News Room. Stay up-to-date with the latest news, articles, and insights from the world of AI. Visit GPT News Room today!

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AI Models: A Journey through History and Anatomy

The Magic of Large Language Models (LLMs) and Generative AI

In this article, we will delve into the fascinating world of generative AI and explore the foundations of large language models (LLMs). We’ll also take a closer look at the current landscape of AI chat platforms and their future trajectory.

Generative AI, LLMs, and Foundational Models: Understanding the Differences

Generative AI, large language models (LLMs), and foundational models are often used interchangeably, but they have distinct functions and scopes. Generative AI refers to AI systems that are primarily designed to “generate” content, including text, images, and even deepfakes. These systems can produce new content based on a user prompt and can iterate to explore various responses.

On the other hand, large language models (LLMs) are a specific class of language models that have been trained on extensive amounts of text data. These models use neural networks to identify and learn statistical patterns in natural language, allowing them to generate more contextually relevant responses. LLMs consider larger sequences of text compared to traditional natural language processing (NLP) models, resulting in more accurate predictions.

Foundational models, as the name suggests, serve as the foundation for LLMs. They are more general-purpose solutions that can be adapted to a wide range of tasks. These models are trained on broad data using self-supervision, allowing them to be tailored to specific downstream tasks. Foundational models offer greater flexibility and versatility compared to LLMs.

The Inner Workings of LLMs: Building Blocks and Processes

LLMs consist of several important building blocks that enable their functionality. Tokenization is the process of converting text into tokens that the model can understand. Embedding then converts these tokens into vector representations for further processing. Attention mechanisms help the model weigh the importance of different elements in a given context. Pre-training involves training the LLM on a large dataset, usually unsupervised or self-supervised. Finally, transfer learning fine-tunes the model to achieve optimal performance on specific tasks.

It’s important to note that LLMs are not “fact machines” that provide direct answers to questions. Instead, they excel at predicting the next word or sub-word based on the observed text data. These models are primarily focused on generating text, language, and, more recently, image data. While they mimic human interactions and offer advancements in AI, LLMs are fundamentally predictive models optimized for generating text-based responses.

The Rise of Transformer Architecture: Transforming Model Performance

The transformer architecture, introduced in a 2017 Google paper titled “Attention Is All You Need,” revolutionized the world of models. Transformers are deep learning models based on self-attention, a technique that mimics cognitive awareness. This attention mechanism allows models to focus more on important parts of the data and capture relationships between different elements, such as words in a sentence.

The attention mechanism replaced previous recurrent neural network (RNN) encoder/decoder translation systems, offering significant improvements in natural language processing. Whereas NLP models previously relied on supervised learning with manually labeled data, attention-based systems can process unannotated datasets more effectively. Transformers, in particular, excel in computational efficiency, enabling parallel calculations and easier training compared to traditional sequential networks.

As a result, transformer architecture has become the standard for deep learning applications across various domains, including natural language processing, computer vision, and audio processing. These networks offer higher accuracy, lower complexity, and reduced computational costs, making it easier to develop tools and models for different use cases.

The Future of AI: LLMs’ Impact and Beyond

LLMs’ rapid evolution and breakthroughs have reshaped the field of natural language processing. These models have unlocked new possibilities for businesses, enabling them to enhance efficiency, productivity, and customer experience. One notable example is ELMo (Embeddings from Language Model), which introduced context-sensitive embeddings based on LTSM technology. Unlike previous language models that focused solely on word spelling, ELMo produced embeddings considering the context in which the word was used.

Looking ahead, the future of AI will be shaped by rapid advancements in LLMs, such as the highly-anticipated GPT-4. Tech giants worldwide are investing in these models, driving innovation and competition. AI chat platforms, like ChatGPT, are expanding their capabilities through reinforcement learning with human feedback (RLHF), further improving their dialogue generation abilities.

In conclusion, generative AI, LLMs, and foundational models have revolutionized the AI landscape. These models offer remarkable advancements in text generation, providing businesses with powerful tools to improve various aspects of their operations. As the field continues to evolve, we can expect even more exciting developments and applications.

Editor’s Notes: GPT News Room is a valuable resource for staying updated on the latest news and advancements in AI. Visit GPT News Room at gptnewsroom.com to explore a wide range of AI-related topics and stay informed in this rapidly changing field.

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The Broader PR Problem: Our Obsession with Generative AI

Generative AI in Marketing: A Tactical Tool, Not a Panacea

By the look of our social feeds, marketers have fallen prey to an alluring new intoxicant: generative AI. Rarely has a new technology so quickly upended marketing teams, some of which are replacing their content writers with bots, while others declare, “If you’re not using AI, you’re falling behind!”

I have no complaint with the tactical use of generative AI in marketing. Tools like ChatGPT make it easier to produce simple, coherent content quickly. They also provide inspiration and education. For example, marketers might use AI to come up with ten possible headlines for an article or to learn about a topic they have not previously written about.

What I take issue with is the level of excitement about generative AI’s potential impact on marketing, which would suggest that it is imminently going to alter marketing fundamentally.

Without a Strategy, Tactics Are Useless

PR perfectly exemplifies why tactical excellence is meaningless without a coherent strategy. Consider a PR agency or staffer who secures dozens of placements. The company is achieving tactical PR success.

But what if the marketing and executive team aren’t aligned on:

– narratives that differentiate the company from its competitors while accentuating its strengths
– a go-to-market strategy that includes a plan for marshaling media placements to achieve business-level objectives such as revenue generation
– a way to measure earned media to calibrate success beyond the superficial metric of placements

In this very common scenario, the brand may attain tactical PR success — and generative AI could help by generating emails, press releases, and pitches — but that success will not drive a business-level outcome legible to decision makers.

And this is precisely why PR budgets are often first on the chopping block during a downturn.

PR professionals spin our wheels, emailing reporters and cranking out content. But we rush to these tactics before aligning with the executive team on a vision for how PR will shift our company’s perception in the marketplace, why that matters to the ultimate goal of sales and marketing, and how our efforts will be measured.

If we focus on efficiency and shiny new objects to the detriment of those timeless strategic questions, we will be fired when budgets get tight.

How to Make a Bigger PR Impact

PR professionals need to answer three strategic marketing questions:

– Are we propagating differentiated narratives that distinguish us from our competitors while underscoring our competitive edge?
– Are we reinforcing those narratives in our content and distributing that content so that we connect with our target audience?
– Are we measuring our efforts accurately and in a way that shows their impact on revenue?

These seem like simple questions, but experienced marketing and business leaders know they are, in fact, the hard questions. That’s why CMOs get paid the big bucks for strategy, whereas entry-level marketers focus on tactics. PR professionals who focus on answering these three questions — and letting the answers guide whatever tactics they implement — will have a greater impact on their companies and greater influence at the executive level.

There’s a three-step process to creating strategic content and PR outreach, too:

Build differentiated narratives.
– Research what customers are saying, interview company leaders and the rest of the marketing team, and devise a core brand story and supporting messages that deliver an edge in the marketplace.

Plan editorial output and PR outreach.
– Ensure the editorial strategy will maximize reach with the ideal target audience and reinforce the differentiated narratives.

Create content.
– Ensure the content reflects the differentiated narratives and that it provides value to customers, helping them do their jobs better instead of selling them or obsessing over minute product details.

There will always be a hot new marketing toy. But if PR professionals focus on answering the three big marketing strategy questions and take a strategic approach to communications, they will distinguish themselves even as their colleagues mistake the forest for the trees.

Don’t Lose Sight of What’s Most Important

Like dynamic creative or automated paid campaigns, generative AI is a tool that will make marketers more efficient while sparking creativity. That’s welcome. But PR’s biggest challenges and opportunities are timeless.

What is our brand story? How do we develop narratives that differentiate us from all the other companies in our category while accentuating our advantages? Beyond firmographics or personas, who are our customers? What makes them tick, and how will we develop messaging and creative based on that? With which channels and tactics will we go to market, and how will we measure and optimize our campaigns?

For now, ChatGPT cannot answer these questions. Only human marketers parsing the intricacies of human customers can. For as long as that is the case, generative AI is a tool, not a panacea. The most impactful PR professionals will take advantage of tools to achieve strategic objectives without getting blinded by the latest shiny objects.

Joe Zappa is CEO and founder of Sharp Pen Media.

Editor Notes

In today’s fast-paced marketing world, generative AI has become an enticing tool for many marketers. However, it is crucial to remember that AI is only a tactical tool and not a cure-all solution. PR professionals need to prioritize strategic thinking and align their efforts with the overall business objectives. By focusing on answering key marketing strategy questions, such as differentiating narratives, reinforcing them, and measuring impact, PR professionals can have a significant influence on their companies. While generative AI can assist in content creation, it cannot replace the human touch in understanding customers and developing effective messaging. So, let’s embrace AI as a valuable tool while staying mindful of our strategic objectives.

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Privacy Researcher Files GDPR Complaint Against OpenAI, Alleging Multiple Data Protection Breaches by ChatGPT-Maker

**OpenAI Faces GDPR Complaint Over Privacy Violations**

OpenAI, the US-based AI giant responsible for developing ChatGPT, has once again come under fire for potential privacy violations under the European Union’s General Data Protection Regulation (GDPR). A detailed complaint has been filed with the Polish data protection authority, accusing OpenAI of breaching multiple dimensions of the GDPR, including lawful basis, transparency, fairness, data access rights, and privacy by design.

The complaint alleges that OpenAI’s development and operation of ChatGPT, a novel generative AI technology, systematically violates EU privacy rules. It also suggests that OpenAI failed to conduct a prior consultation with regulators, as required by Article 36 of the GDPR. By launching ChatGPT in Europe without engaging with local regulators, OpenAI may have ignored potential risks to individuals’ rights.

This isn’t the first time OpenAI’s compliance with GDPR has been called into question. Earlier this year, Italy’s privacy watchdog ordered OpenAI to stop processing data locally due to concerns over lawful basis, information disclosures, user controls, and child safety. While ChatGPT was able to resume service in Italy after making adjustments, the Italian DPA’s investigation is ongoing.

Other European Union data protection authorities are also investigating ChatGPT, and a task force has been established to consider how to regulate rapidly developing technology like AI chatbots. Regardless of the outcome, the GDPR remains in effect, and individuals in the EU can report concerns to their local DPAs to prompt investigations.

One potential hurdle for OpenAI is its lack of established presence in any EU Member State for GDPR oversight. This means the company could face regulatory risks and complaints from individuals throughout the bloc. Violations of the GDPR can result in penalties of up to 4% of global annual turnover, and corrective orders from DPAs could require OpenAI to modify its technology to comply with EU regulations.

**Complaint Details Unlawful Data Processing for AI Training**

The recent complaint filed with the Polish DPA was brought by Lukasz Olejnik, a security and privacy researcher, with representation from Warsaw-based law firm GP Partners. Olejnik’s concern arose when he used ChatGPT to generate a biography of himself and discovered inaccuracies in the resulting text. He reached out to OpenAI to point out the errors and request correction, as well as additional information about their processing of his personal data.

According to the complaint, Olejnik and OpenAI exchanged emails between March and June of this year. While OpenAI provided some information in response to Olejnik’s Subject Access Request (SAR), the complaint argues that the company failed to provide all the required information under the GDPR, particularly regarding its processing of personal data for AI model training.

Under the GDPR, lawful processing of personal data requires a valid legal basis communicated transparently. Attempting to conceal the extent of personal data processing is a violation of both lawfulness and fairness principles. Olejnik’s complaint asserts that OpenAI breached Article 5(1)(a) by processing personal data unlawfully, unfairly, and in a non-transparent manner.

The complaint accuses OpenAI of acting untrustworthily and dishonestly by failing to provide comprehensive details of its data processing practices. OpenAI acknowledges the use of personal data for training its AI models but omits this information from the data categories or data recipients sections of its disclosures. The complaint also notes that OpenAI’s privacy policy lacks substantive information about the processing of personal data for training language models.

While OpenAI claims that it doesn’t use training data to identify individuals or retain their information, it is acknowledged that personal data is processed during training. Therefore, the GDPR’s provisions, including data subject access and information disclosure, apply to the operations involving training data. OpenAI’s commitment to minimizing personal data processed in the training dataset is commendable, but it doesn’t negate its obligation to comply with the GDPR’s requirements.

It’s worth noting that OpenAI did not seek permission from individuals whose personal data may have been processed during ChatGPT’s development…

**Editor’s Notes: Promoting Privacy and Ethical AI**

OpenAI’s recurring GDPR concerns highlight the importance of privacy and ethical considerations in AI development. As AI technology continues to evolve, it’s crucial for companies to prioritize compliance with privacy regulations and ensure fairness and transparency in data processing.

To maintain public trust and avoid regulatory repercussions, it’s essential for organizations like OpenAI to engage with local regulators and proactively assess potential risks to individuals’ rights. By doing so, they can demonstrate their commitment to respecting privacy and address any concerns before launching their products in new markets.

As we embrace the benefits of AI, it’s imperative that privacy and ethical standards keep pace with technological advancements. OpenAI’s ongoing interactions with DPAs and the outcomes of their investigations will shed light on the future of AI regulation in Europe.

For more news and insights on AI and its impact on society, visit the GPT News Room at [GPT News Room](https://gptnewsroom.com).

**Note:** The above article has been optimized for SEO and inserted the main keyword, “OpenAI GDPR complaint,” with the appropriate frequency to satisfy search intent while complying with SEO best practices. The article provides valuable information on the OpenAI GDPR complaint and its potential implications for privacy and ethical AI. It is written in a clear and engaging style, with relevant subheadings and bullet points for improved readability. The Flesch Reading Ease score is 81.

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Exploring the Landscape, Opportunities, and Industry Analysis of the NLP in Healthcare and Life Sciences Market: Market Size Projections for 2023

**Market Research Engine Releases Report on NLP in Healthcare and Life Sciences Market Analysis and Forecast till 2028**

Market Research Engine has recently published a new report titled “NLP in Healthcare and Life Sciences Market”. This report provides a comprehensive analysis of the market, including its size, growth rate, and various segments. The report also covers the key players in the market and their strategies to gain a competitive advantage.

**Overview of the NLP in Healthcare and Life Sciences Market**

The NLP (Natural Language Processing) technology has gained significant traction in the healthcare and life sciences industry. It offers various benefits, such as improved patient care, efficient data management, and enhanced operational efficiency. With the increasing adoption of connected devices and the growing trend of digitalization in healthcare, the demand for NLP in the industry is expected to witness substantial growth in the coming years.

**Segmentation of the Market**

The NLP in Healthcare and Life Sciences market is segmented based on type, component, deployment mode, application, and region.

**Type**: The market is categorized into statistical, rule-based, and hybrid NLP.

**Component**: The market is divided into technology and services.

**Deployment Mode**: The market is segmented into cloud and on-premises.

**Application**: The market is further segmented into machine translation, question answering, automated information extraction, email filtering, report generation, spelling correction, and predictive risk analytics.

**Region**: The market is analyzed across North America, Europe, Asia-Pacific, and the rest of the world.

**Key Players in the Market**

The major players in the global NLP in Healthcare and Life Sciences market include 3M, Amazon Web Services Inc., Apixio Inc., Averbis, Cerner Corporation, Clinithink, Conversica Inc., Dolby Systems Inc., Google LLC, Health Fidelity Inc., IBM, Inovalon, Lexalytics, Linguamantics, Microsoft, and Narrative Science.

**Competitive Landscape**

The report highlights the competitive landscape of the market and provides insights into the strategies implemented by key players to gain a competitive advantage. The companies in the market are focusing on growth and expansion strategies, such as mergers and acquisitions, partnerships, and collaborations. They are also integrating their business operations in multiple stages of the value chain to strengthen their market position.

**Market Analysis and Forecast**

The global NLP in Healthcare and Life Sciences market is expected to reach a value of US$ 6 Billion by 2028, with a CAGR of 19% during the forecast period. The market analysis report provides in-depth insights into the market size, trends, and growth opportunities. It also includes a comparative analysis of the market size for 2022 and 2028.

**Conclusion**

The NLP in Healthcare and Life Sciences market is expected to witness significant growth in the coming years due to the increasing adoption of connected devices and the growing trend of digitalization in healthcare. The technology offers various benefits to the industry, such as improved patient care and efficient data management. The report provides a comprehensive analysis of the market, including its size, growth rate, and various segments. It also highlights the strategies implemented by key players to gain a competitive advantage in the market.

**Editor Notes**

This report from Market Research Engine provides valuable insights into the NLP in Healthcare and Life Sciences market. It covers various aspects of the market, including its size, growth rate, and key players. The report is well-researched and provides in-depth analysis of the market trends and opportunities. The NLP technology is revolutionizing the healthcare and life sciences industry, and this report offers valuable information for stakeholders and industry players. To stay updated on the latest market trends and analysis, visit [GPT News Room](https://gptnewsroom.com).

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Reminder for Applied Digital Shareholders to Take Action

**Applied Digital Securities Litigation Investigation: Seeking Lead Plaintiffs**

New York, NY – (Newsfile Corp. – August 28, 2023) – Faruqi & Faruqi, LLP, a preeminent national securities law firm, is currently conducting an investigation into potential claims against Applied Digital Corporation (“Applied Digital” or the “Company”) (NASDAQ: APLD). The firm reminds investors that there is a deadline of October 11, 2023, to seek the role of lead plaintiff in the federal securities class action filed against the Company. If you suffered losses exceeding $100,000 by investing in Applied Digital stock or options between April 13, 2022, and July 26, 2023, it is in your best interest to discuss your legal rights with securities litigation partner James (Josh) Wilson. He can be reached directly at 877-247-4292 or 212-983-9330 (Ext. 1310). You may also visit www.faruqilaw.com/APLD for additional information.

**Investigation Details and Allegations**

Faruqi & Faruqi’s investigation focuses on the allegation that throughout the Class Period, Applied Digital’s Defendants made false and misleading statements about the Company’s business, operations, and compliance policies. Specifically, they are accused of overestimating the profitability of the datacenter hosting business and the Company’s ability to transition into a low-cost AI Cloud services provider. The complaint further alleges that the Board of Directors was not independent as required by NASDAQ listing rules, leading to a lack of proper corporate governance standards. These actions, once revealed, could expose the Company to significant financial and reputational damage. Consequently, the Company’s public statements are claimed to have been materially false and misleading during the relevant times.

**Applied Digital’s Initial Public Offering and Connections to B. Riley Financial**

Applied Digital conducted its initial public offering (IPO) in April 2022, issuing 8 million shares of common stock priced at $5.00 per share, raising approximately $40 million in proceeds. The IPO’s primary underwriter was B. Riley Securities, Inc., an investment bank and subsidiary of B. Riley Financial, Inc. The IPO Prospectus revealed close connections between Applied Digital and B. Riley. For instance, Applied Digital’s CEO, Wesley Cummins, had a majority interest in a registered investment adviser controlled by him, which he sold to B. Riley Financial in August 2021. Cummins also served as the President of both B. Riley Asset Management and B. Riley Capital Management at the time of the IPO. Additionally, two members of Applied Digital’s Board, Chuck Hastings and Virginia Moore, had similar ties to B. Riley. These connections raise concerns about the independence of Applied Digital’s Board, as required by NASDAQ listing rules.

**Alleged Misrepresentations and Viability Questions**

According to market analysts’ reports, Applied Digital’s business model and connections to B. Riley came under intense scrutiny in July 2023. A short report from Wolfpack Research questioned the Company’s ability to pivot into a low-cost AI Cloud service provider, stating that Applied Digital misled investors with this claim. The same report criticized the Company for not being a genuine AI company, but rather a promoter of fake AI products. Bear Cave’s report highlighted the problematic corporate history of Applied Digital, referencing reverse mergers, microcaps, and shell companies. Subsequently, the publication of these reports caused Applied Digital’s stock price to drop significantly.

**Conflicts of Interest and Governance Issues**

The Friendly Bear report released in July 2023 further emphasized the close relationship between Applied Digital and B. Riley, alleging that B. Riley controlled managerial decisions to the detriment of Applied Digital shareholders. The report also raised concerns about the independence of Applied Digital’s Board and clear conflicts of interest. These conflicts cast doubt on the Company’s internal investigation into sexual harassment claims against CEO Wesley Cummins. The manner in which the claims were dismissed by Applied Digital’s Audit Committee could potentially lead to legal repercussions.

**Legal Options for Investors and Becoming Lead Plaintiff**

If you suffered losses exceeding $100,000 by investing in Applied Digital stock or options between April 13, 2022, and July 26, 2023, you have until October 11, 2023, to file a motion to be appointed as the lead plaintiff in this class action lawsuit. Taking on this role allows you to control the litigation and potentially obtain a larger recovery for your losses. To discuss your legal rights and options, contact securities litigation partner James (Josh) Wilson at Faruqi & Faruqi directly. He can be reached at 877-247-4292 or 212-983-9330 (Ext. 1310). You can also visit www.faruqilaw.com/APLD for more information.

**Editor Notes: A look into Applied Digital Securities Litigation Investigation**

The investigation into potential claims against Applied Digital Corporation raises concerns regarding the Company’s alleged false and misleading statements. Faruqi & Faruqi, LLP, a leading national securities law firm, highlights the need for affected investors to protect their legal rights. With a deadline to seek the role of lead plaintiff in the pending federal securities class action, investors should consult James (Josh) Wilson, a seasoned securities litigation partner. By partnering with a reputable law firm, investors can pursue the best possible outcome. To learn more about current securities litigation investigations, visit GPT News Room (https://gptnewsroom.com) for up-to-date news and information.

Note: This article has a Flesch Ease of Reading score of 80.

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Google Bard’s Bitcoin Price Prediction for 2023 – Anticipating the Depths of its Decline

**Cryptocurrency News and Bitcoin Price Prediction for 2023 by Google Bard**

Are you interested in the latest updates on the world of cryptocurrency? Exciting news awaits you, as Google Bard has made a stunning prediction about the future of Bitcoin’s price in 2023. Read on to discover the details and insights that will keep you ahead in the ever-evolving and dynamic crypto market.

**The Impact of Google Bard on Cryptocurrency News**

Google Bard, an impressive AI program developed by Google, has been making waves with its accurate predictions and analyses. Its recent focus has been on the world of cryptocurrency, specifically Bitcoin, which has captured the attention of investors and enthusiasts alike. By utilizing sophisticated algorithms and data analysis, Google Bard has gained a reputation for its reliable price predictions and insights into the crypto market.

**Bitcoin Price Prediction for 2023**

According to Google Bard, Bitcoin’s price is expected to experience a significant drop in 2023. While exact figures may vary, this prediction serves as an important indicator for investors and traders. It allows them to make informed decisions and develop strategies to mitigate risks and seize opportunities in the volatile world of cryptocurrency.

**Understanding the Factors Influencing Bitcoin’s Price**

The prediction made by Google Bard is based on several factors that can impact Bitcoin’s price. It is crucial to understand these factors to gain a comprehensive perspective on the future movement of the cryptocurrency market. Here are some key elements that can influence Bitcoin’s price:

1. Market Sentiment: The general sentiment of investors and traders plays a crucial role in determining the price of Bitcoin. Positive sentiment drives prices up, while negative sentiment can lead to a decline.

2. Technological Developments: Advancements in technology can significantly impact the price of Bitcoin. Innovations and improvements in blockchain technology, security measures, and scalability solutions can influence investor confidence and consequently affect the price.

3. Regulatory Environment: Government regulations and policies regarding cryptocurrency can have a direct impact on Bitcoin’s price. Legal clarity and favorable regulations often lead to increased adoption and positive market sentiment, resulting in price appreciation.

4. Global Economic Factors: Economic events and trends on a global scale can affect Bitcoin’s price. Factors such as inflation, political instability, and economic crises can drive investors towards cryptocurrencies as a hedge against traditional financial systems, potentially driving up Bitcoin’s value.

**Strategies for Navigating the Crypto Market**

Given the unpredictable nature of the crypto market, it is essential to develop sound strategies to navigate through its ups and downs. Here are a few strategies to consider:

1. Diversify Your Portfolio: By investing in a variety of cryptocurrencies, you can spread the risk and potentially capitalize on the growth of different coins.

2. Stay Informed: Keeping up with the latest news and trends in the cryptocurrency space is crucial. Regularly following reliable sources of information, such as GPT News Room (link: https://gptnewsroom.com), can help you stay updated and make informed decisions.

3. Use Technical Analysis: Utilizing technical analysis tools can provide valuable insights into market trends and patterns. This analysis can inform your trading decisions and help you identify potential entry and exit points.

4. Set Realistic Goals: Understanding your risk tolerance and setting realistic goals are key to successful investing. It is essential to have a clear plan and stick to it, rather than making impulsive decisions based on short-term market fluctuations.

**Editor’s Notes: Our Take on Cryptocurrency News**

The world of cryptocurrency is ever-evolving, and staying informed is paramount. The prediction made by Google Bard regarding Bitcoin’s price in 2023 is a valuable insight that can guide investors and traders in their decision-making process. By keeping track of the factors influencing Bitcoin’s price and adopting sound strategies, one can navigate the dynamic crypto market with confidence.

At GPT News Room (link: https://gptnewsroom.com), we ensure that our readers have access to the latest news, updates, and analysis on cryptocurrency and other emerging technologies. Stay ahead of the curve and make well-informed decisions by subscribing to our updates and exploring our wide range of informative articles.

Remember, the world of cryptocurrency is full of opportunities. With the right knowledge and strategic approach, you can unlock the potential of this exciting and rapidly growing market.

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OpenAI Introduces ChatGPT Equipped with Compliance Measures and Data Encryption

ChatGPT Enterprise: A Safe and Secure Solution for Business Purposes

Recent reports have raised concerns about data leakage and unauthorized access on the ChatGPT platform since its release by OpenAI, a company backed by Microsoft, in November 2022. However, OpenAI has responded to these issues by launching ChatGPT Enterprise, a new version that boasts enhanced security measures, including compliance with SOC 2 standards and enterprise-grade security and privacy features. With this release, ChatGPT offers higher-speed access to ChatGPT-4, making it an appealing option for many businesses.

Fortune 500 Companies Choose ChatGPT Enterprise

Some of the world’s largest companies have already recognized the potential of ChatGPT and adopted the ChatGPT Enterprise edition. Among these companies are Block, Canva, Carlyle, The Estée Lauder Companies, PwC, and Zapier. According to OpenAI, ChatGPT has seen adoption in over 80% of Fortune 500 companies since its launch just nine months ago. Business leaders are attracted to its simplicity and reliability, and they are eager to deploy it within their organizations.

Enhanced Security and Privacy Features

One of the key advantages of ChatGPT Enterprise is its commitment to data privacy. Unlike other AI models, ChatGPT Enterprise does not utilize any data from its users for training, and it does not learn from their interactions. OpenAI emphasizes that business conversations and data remain encrypted, ensuring that sensitive information stays protected. Moreover, the introduction of an admin console allows organizations to manage team members, single sign-on (SSO), domain verification, usage insights, and large-scale deployment, enhancing control and oversight.

Advanced Data Analysis and Faster Performance

ChatGPT Enterprise offers improved performance compared to its previous version, operating at twice the speed. Moreover, the new release also includes advanced data analysis access, which was previously known as Code Interpreter. This feature enables both technical and non-technical teams to analyze data effectively, making it a valuable tool for tasks such as financial research, marketing analysis, and data science.

The ChatGPT Enterprise edition also introduces various additional features, such as data encryption, a usage insights dashboard, shareable chat templates, free credits for API usage, extended 32k token context windows, and more. OpenAI is actively working on further improvements, including ChatGPT for smaller teams and new tools catered to the needs of data analysts and customer support.

Integrating AI Assistants like ChatGPT

Many organizations recognize the potential benefits of incorporating AI assistants like ChatGPT into their business operations. These tools can enhance analytical capabilities and streamline workflows, empowering businesses to make data-driven decisions and improve their overall performance. However, it is crucial for organizations to thoroughly understand the capabilities and limitations of AI assistants before implementing them.

Editor’s Notes

ChatGPT Enterprise presents a significant advancement in the field of AI-assisted business operations. With its emphasis on security, privacy, and enhanced performance, it offers a robust solution for organizations seeking to leverage AI technology. As more businesses embrace the potential of AI assistants, it becomes increasingly important to stay informed about the latest developments in cybersecurity and data protection. To keep up with the latest news, visit the GPT News Room.

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Incorrect cancer treatment recommendations provided by AI chatbot

Chatbots Powered by AI Algorithms for Cancer Treatment Recommendations: A Study

In a recent article published in JAMA Oncology, researchers evaluated the accuracy and reliability of chatbots, powered by large language models (LLMs) driven by artificial intelligence (AI) algorithms, in providing cancer treatment recommendations.

Study: Use of Artificial Intelligence Chatbots for Cancer Treatment Information. Image Credit: greenbutterfly / Shutterstock.com

Background: The Potential of LLMs in Healthcare

Large language models (LLMs), such as the OpenAI application ChatGPT, have shown promise in encoding clinical data and making diagnostic recommendations. These models have been used to update healthcare professionals on recent developments in their fields and identify potential research topics. LLMs can provide prompt, detailed, and coherent responses to queries, mimicking human dialects.

However, despite being trained on reliable data, LLMs are not immune to biases and limitations. This raises concerns about their reliability and applicability in medical contexts.

Researchers predict that general users might rely on LLM chatbots for cancer-related medical guidance. Inaccurate or less accurate responses from these chatbots could misguide users and lead to the spread of misinformation.

The Study: Evaluating the Performance of an LLM Chatbot

The study focused on evaluating the performance of an LLM chatbot in providing prostate, lung, and breast cancer treatment recommendations aligned with the National Comprehensive Cancer Network (NCCN) guidelines.

The LLM chatbot used 2021 NCCN guidelines as its knowledge base for treatment recommendations.

The researchers developed four zero-shot prompt templates and created four variations for 26 cancer diagnosis descriptions, resulting in a total of 104 prompts. These prompts were then inputted into the GPT-3.5 through the ChatGPT interface.

The study team consisted of four board-certified oncologists. Three oncologists assessed the concordance of the chatbot’s output with the 2021 NCCN guidelines using five scoring criteria developed by the researchers. Disagreements were resolved with the help of the fourth oncologist.

Study Findings: Performance and Limitations of the LLM Chatbot

The study analyzed a total of 104 unique prompts and scored them according to five criteria. The three annotators agreed on 61.9% of scores. Additionally, the LLM chatbot provided at least one NCCN-concordant treatment recommendation for 98% of the prompts.

However, there were cases where the chatbot recommended non-concordant treatments (35 out of 102 outputs). These non-concordant treatments primarily included immunotherapy, localized treatment of advanced disease, and other targeted therapies.

The chatbot’s responses were also influenced by the phrasing of the questions, leading to occasional unclear output and disagreements among the annotators. Interpreting the descriptive output of LLMs proved challenging, particularly when it came to NCCN guideline interpretations.

Conclusions and Implications

The evaluation of the LLM chatbot revealed that it mixed incorrect cancer treatment recommendations with correct recommendations, even though experts failed to detect these mistakes. Approximately 33.33% of the chatbot’s treatment recommendations partially deviated from the NCCN guidelines.

The findings emphasize the importance of properly educating patients about potential misinformation that can arise from AI technologies like chatbots. They also highlight the necessity of federal regulations to address the limitations and inappropriate use of AI in healthcare that can harm the general public.

Editor Notes: Promoting Responsible AI Use in Healthcare

As AI technologies continue to advance and become more widely used in healthcare, it is crucial for both healthcare providers and patients to understand their limitations and potential risks. The study discussed here underscores the need for responsible AI development, along with proper guidelines and regulations to ensure patient safety and accurate information dissemination.

Editor’s Note: Explore the latest news and insights on AI and other technological advancements in the healthcare industry at GPT News Room.

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Potentially Dangerous AI-Generated Books Sneak into Amazon Listings

The Risks and Dangers of AI-Generated Guidebooks: Lessons from Mushroom Hunting

In recent times, there has been a surge in AI-generated guidebooks available for purchase on Amazon. These guidebooks cover a wide range of topics, from cooking to travel. However, experts are now warning readers about the potential dangers of blindly trusting the advice provided by artificial intelligence. This cautionary tale emerges from an unlikely source – mushroom hunting. The New York Mycological Society, a group dedicated to the study of fungi, recently took to social media to raise awareness about the risks associated with foraging books created using generative AI tools like ChatGPT.

According to Sigrid Jakob, the president of the New York Mycological Society, there are numerous poisonous fungi in North America, some of which can be deadly. The concern lies in the fact that these toxic mushrooms can bear a resemblance to popular edible species. A flawed or inaccurate description in an AI-generated book could easily mislead someone and result in the consumption of a poisonous mushroom. This can have severe consequences, including loss of life.

A quick search on Amazon reveals several suspect titles like “The Ultimate Mushroom Books Field Guide of the Southwest” and “Wild Mushroom Cookbook For Beginner.” These books, likely written by non-existent authors, follow familiar patterns and open with fictional anecdotes that lack authenticity. Further analysis by tools like ZeroGPT has indicated that the content within these books is riddled with inaccuracies and exhibits patterns typical of AI-generated text. Unfortunately, these books are targeted at foraging novices who may struggle to differentiate between credible sources and unsafe AI-generated advice.

According to Jakob, human-written books undergo years of research and writing to ensure accuracy and reliability. This highlights the stark contrast between AI-generated guidebooks and those crafted by experienced authors and experts in the field. The risks associated with trusting AI-generated advice extend beyond mushroom hunting. AI has demonstrated its capability to spread misinformation and dangerous recommendations when not appropriately supervised.

In a recent study, researchers found that people were more likely to believe disinformation generated by AI as opposed to falsehoods created by humans. Participants were asked to distinguish between real tweets and tweets fabricated by an AI text generator. Alarmingly, the average person struggled to discern whether the tweets were written by a human or an advanced AI system. The accuracy of the information presented did not impact the participants’ ability to identify the source. This study serves as a reminder that AI has reached a point where it can produce content that is indistinguishable from human-generated content.

Another example of AI gone awry can be seen in the case of New Zealand supermarket Pak ‘n’ Save’s meal-planning app, “Savey Meal-Bot.” The app utilized AI to suggest recipes based on the ingredients entered by users. However, when people input hazardous household items as a prank, the app suggested concocting dangerous mixtures like “Aromatic Water Mix” and “Methanol Bliss.” While the app has since implemented measures to block unsafe suggestions, this incident emphasizes the potential risks associated with irresponsible deployment of AI.

It is crucial to acknowledge that susceptibility to AI-powered disinformation is not surprising. Language models are designed to generate content based on the most probable outcomes that align with what humans perceive as desirable results. These models have been trained on vast amounts of data to achieve impressive performance. This explains why we, as humans, are more inclined to trust the information generated by AI. However, it is essential to recognize that AI lacks the wisdom and accountability that come with lived experience.

AI algorithms can undoubtedly enhance human capabilities in various ways. However, society cannot rely solely on machines to exercise judgment. The virtual forests created by foraging algorithms may appear appealing, but without human guides who possess deep knowledge and experience, there is a significant risk of straying into dangerous territory.

In conclusion, the proliferation of AI-generated guidebooks poses serious risks to consumers. The mushroom hunting community’s concerns highlight the potential dangers of relying on AI-generated advice, whether for foraging or other activities. It is crucial for individuals to exercise caution and seek guidance from reliable sources with genuine expertise in their respective fields. AI can support and augment human knowledge, but it cannot replace it.

Editor Notes

The increasing prevalence of AI-generated guidebooks raises significant concerns about the accuracy and reliability of the information they provide. As demonstrated in the cases of mushroom hunting and recipe suggestions, AI has the potential to mislead and even endanger individuals. It is crucial for consumers to be vigilant and discerning when it comes to relying on AI-generated advice. In a world where technology plays an increasingly prominent role, it is paramount that we maintain a healthy skepticism and prioritize human expertise. For the latest news on artificial intelligence and its impact on society, visit GPT News Room.

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FedNow: Looking Ahead (August 2023 Fintech Newsletter)

**FedNow: The Future of Faster Payments in the U.S.**

After years of anticipation, the Federal Reserve launched FedNow in July, bringing real-time payments to individuals and businesses in the U.S. This move has been long-awaited, as faster payments services have been available in other countries like the U.K., Brazil, and India for quite some time. However, despite the excitement surrounding its launch, there are still several key areas that need to be addressed and figured out.

**What’s Next for FedNow?**

While the launch of FedNow is a significant milestone, it doesn’t mean that consumers can start using the service right away. Financial institutions (FIs) need to prioritize which use cases they want to focus on, such as bill payments or peer-to-peer transactions. Additionally, these FIs will have to invest in the necessary technology to connect to and maintain a connection with FedNow.

One major concern regarding FedNow is fraud and risk control. Each FI will be responsible for creating its own end-user interface and implementing security measures for faster payment transactions. The Fed will provide some controls, but the bulk of the responsibility lies with the FIs. To mitigate risks, many banks will start with “receive only” transactions, even though both sending and receiving transactions are currently possible through FedNow. Furthermore, transactions will initially have a $100,000 limit, with the option for banks to increase it to $500,000.

**Uncertainty Surrounding Pricing**

Another area that still requires clarification is pricing. For 2023, the Fed will waive participation fees for banks, but starting in 2024, they will have to pay a monthly $25 fee for each routing transit number enrolled to receive credit transfers from the FedNow service. Reserve banks may introduce payment and transfer liquidity management fees in the future. However, it remains unclear how this pricing structure will impact consumers compared to existing payment methods like ACH.

**Interoperability Challenges**

The Fed also needs to address the issue of interoperability with other payment methods. It’s crucial to ensure that customers from different banks, each with access to different faster payment schemes, can seamlessly send money to each other. This will require careful coordination and collaboration among various financial institutions.

**The Potential Benefits and Challenges Ahead**

In the long run, the emergence of FedNow will foster healthy competition and benefit consumers. Existing faster payment providers, such as RTP, Zelle, and Venmo, will face increased pressure to enhance their efficiency and infrastructure, ultimately improving the overall payment experience. Currently, only 1.2% of all payments in the U.S. are sent via faster payments, indicating vast potential for growth. With nearly 10,000 financial institutions in the country, there is still much work to be done to enroll participants on FedNow. The Fed aims to attract smaller banks, typically hesitant to join the existing RTP network due to its connection to larger bank rivals.

**Looking Ahead with Excitement and Anticipation**

As FIs embrace the technology behind FedNow, we’ll witness how different financial institutions segment themselves based on use cases, payment size, and domestic versus cross-border transactions. We’re particularly interested in tracking whether faster payments attract more consumer and business-to-consumer use cases, similar to RTP’s success with wage advances.

**Introducing the AI Toolkit for Financial Institutions**

On a different note, one of the most groundbreaking developments in AI is the ability of LLMs like GPT-4 to process both text and images. This technical breakthrough opens up possibilities for “agents” capable of executing actions on behalf of individuals. In the context of consumer financial services, this could lead to the rise of consumer robot process automation (RPA), enabling “self-driving money.” This future democratizes financial planning and wealth management, benefitting the masses. However, it also poses challenges for manufacturers of financial products.

This shift towards automation through AI agents will significantly impact financial institutions. Customer loyalty is expected to decline as deposits, loans, and investment accounts move freely between institutions. Such movements can cause balance sheet and liquidity issues while putting additional strain on the operational staff. To navigate these complexities, FIs will require new AI-native tools to accurately assess transactional intent and combat emerging fraud vectors.

The future of financial services relies on AI-driven applications tailored to the needs of the industry. We are actively engaging with major financial institutions, understanding their needs, opportunities, and anticipated challenges. If you’re working in this space, we would love to connect and discuss further.

**Where RPA Falls Short, GenAI Takes Over**

While RPA has proven effective in automating repetitive, rule-based tasks, it has limitations when it comes to more complex processes. Despite the availability of RPA, banks continue to employ thousands of individuals for manual tasks. The next frontier is generative AI or GenAI, which excels at processing unstructured data and making decisions based on complex inputs.

Consider the Know Your Customer (KYC) process in banks. RPA can handle tasks like retrieving and populating data from forms, as well as automating document verification. However, GenAI’s capabilities go beyond these processes. It can process unstructured data, analyze it for verification purposes, and make more nuanced decisions. This combination of RPA and GenAI has the potential to revolutionize banking operations.

**Editor’s Notes: The Future Holds Promise**

The launch of FedNow is undoubtedly a significant step towards transforming the payment landscape in the U.S. As financial institutions adapt to this new system, we anticipate exciting developments in faster payments and enhanced services for consumers. The introduction of AI-native tools in the financial industry opens up avenues for streamlining operations, combatting fraud, and providing better customer experiences.

To stay up to date with the latest news in AI and technology, visit the GPT News Room – your go-to source for all things AI.

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