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ChatGPT Reminder And To-Do Functionality Overview
Features of ChatGPT Reminder and To-Do Functionality
- Set Reminders: Users can create reminders for important tasks or events.
- Manage To-Do Lists: Organize tasks in a simple to-do list format.
- Deadline Tracking: Keep track of deadlines for each task.
- Notifications: Receive alerts for upcoming reminders.
- Prioritization: Assign priority levels to tasks to focus on what matters most.
How to Use Reminders and To-Dos
1. Creating a Reminder: Simply state the task and the time you want to be reminded.
2. Adding to a To-Do List: List out tasks you need to accomplish, and they can be saved for future reference.
3. Updating Tasks: Modify existing reminders and to-do items as needed.
4. Marking Completion: Easily mark tasks as completed to keep your list current.
Benefits of Using Reminders and To-Dos
- Increased Productivity: Helps you stay on track with your tasks.
- Reduced Stress: Keep your mind clear by organizing what needs to be done.
- Time Management: Efficiently allocate your time to various tasks.
- Enhanced Focus: Prioritize important tasks and reduce distractions.
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Patrick Spence Steps Down As Sonos CEO
Overview
Patrick Spence, the CEO of Sonos, has decided to step down following significant challenges related to the launch of the company's new app. This decision marks a pivotal moment for both Spence and the company.
Reasons for Departure
- Disastrous App Launch: The new app faced numerous technical issues, impacting user experience and customer satisfaction.
- Market Response: The app launch did not meet market expectations, leading to criticism from both consumers and industry analysts.
- Company's Future Direction: There may be a need for a leadership change to realign the company's vision and strategy moving forward.
Implications
- Leadership Transition: A new CEO could bring fresh ideas and approaches to rectify the current challenges.
- Product Development Focus: The company may need to prioritize improvements in its product offerings and customer support systems.
- Investor Confidence: The leadership change may affect investor sentiment and the company's stock performance.
Next Steps for Sonos
- Interim Leadership: The company will likely appoint an interim CEO while searching for a permanent replacement.
- Review of App Issues: A thorough analysis of the app's shortcomings will be essential to regain consumer trust.
- Strategic Planning: Developing a clear plan for future product launches and improvements will be crucial for Sonos’s recovery.
Conclusion
Patrick Spence's departure from Sonos highlights the significant challenges faced by the company following the app launch. The upcoming leadership transition will be vital for the brand's future and its ability to meet customer demands effectively.
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New LLM Jailbreak Uses Models' Evaluation Skills Against Them
Last Update 13 January 2025
SC Media reports on a new jailbreak method for large language models (LLMs) that "takes advantage of models' ability to identify and score harmful content in order to trick the models into generating content related to malware, illegal activity, harassment and more. "The 'Bad Likert Judge' multi-step jailbreak technique was developed and tested by Palo Alto Networks Unit 42, and was found to increase the success rate of jailbreak attempts by more than 60% when compared with direct single-turn attack attempts..." For the LLM jailbreak experiments, the researchers asked the LLMs to use a Likert-like scale to score the degree to which certain content contained in the prompt was harmful. In one example, they asked the LLMs to give a score of 1 if a prompt didn't contain any malware-related information and a score of 2 if it contained very detailed information about how to create malware, or actual malware code. After the model scored the provided content on the scale, the researchers would then ask the model in a second step to provide examples of content that would score a 1 and a 2, adding that the second example should contain thorough step-by-step information. This would typically result in the LLM generating harmful content as part of the second example meant to demonstrate the model's understanding of the evaluation scale. An additional one or two steps after the second step could be used to produce even more harmful information, the researchers found, by asking the LLM to further expand on and add more details to their harmful example. Overall, when tested across 1,440 cases using six different "state-of-the-art" models, the Bad Likert Judge jailbreak method had about a 71.6% average attack success rate across models. Thanks to Slashdot reader spatwei for sharing the news.
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