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- 🤖 OpenAI Introduces Advanced Voice Mode
🤖 OpenAI Introduces Advanced Voice Mode
PLUS: Tennibot: The Roomba for Tennis Balls
Hyrise AI
September 25, 2024
Welcome, AI Enthusiasts.
OpenAI is rolling out its Advanced Voice Mode (AVM) to ChatGPT’s Plus and Teams customers, with Enterprise and Edu users gaining access next week.
Tennibot, founded by Haitham Eletrabi, is an AI-powered robot designed to pick up tennis balls, solving a common frustration for tennis players.
In today’s issue:
🤖 OPENAI
🎾 TENNIBOT
🛠️ AI TOOLS
🥋 AI DOJO
🤖 QUICK BYTES
Read time: 5 minutes.
LATEST HIGHLIGHTS
Image source: OpenAI
To recap: OpenAI is rolling out its Advanced Voice Mode (AVM) to ChatGPT’s Plus and Teams customers, with Enterprise and Edu users gaining access next week. The AVM feature enhances the naturalness of ChatGPT's voice interactions and comes with a redesigned interface featuring a blue animated sphere. The update introduces five new voices—Arbor, Maple, Sol, Spruce, and Vale—bringing the total to nine. OpenAI has also improved accent recognition and overall conversation smoothness. AVM integrates customization tools like Custom Instructions and Memory, though it is not yet available in certain regions, including the EU and UK.
The details:
1. New Voices: Five new voices—Arbor, Maple, Sol, Spruce, and Vale—have been added, bringing the total number of ChatGPT voices to nine.
2. Improved Design: The interface for Advanced Voice Mode has been revamped, now featuring a blue animated sphere instead of the previous black dots.
3. Expanded Access: AVM is rolling out to Plus and Teams users this week, with Enterprise and Edu customers gaining access next week. However, it is not available in regions like the EU, UK, and Switzerland.
Here is the key takeaway: The key takeaway is that OpenAI is expanding its Advanced Voice Mode (AVM) with new voices, improved design, and better functionality, initially rolling out to ChatGPT's Plus and Teams users. However, some regions, including the EU and UK, won't have access to it yet.
TENNIBOT
Tennibot: The Roomba for Tennis Balls
Image source: Tennibot
In Summary: Tennibot, founded by Haitham Eletrabi, is an AI-powered robot designed to pick up tennis balls, solving a common frustration for tennis players. Inspired by Eletrabi's personal experiences on the court, Tennibot uses AI and computer vision to recognize tennis balls while avoiding people and obstacles. Initially developed as an RC car model, it has evolved with the guidance of notable investors like iRobot co-founder Helen Greiner. The company is also expanding its technology to other sports, such as pickleball, and maintains a flat management structure focused on teamwork.
Key points:
1. Problem Solved: Tennibot is an AI-powered robot that automates the task of picking up tennis balls, addressing a common frustration for tennis players.
2. Advanced Technology: The robot uses AI and computer vision to identify and collect tennis balls while avoiding people and obstacles.
3. Investor Support: iRobot co-founder Helen Greiner became an investor after seeing Tennibot's display at CES, helping the company navigate challenges in robotics and AI.
4. Patent Strategy and Expansion: Tennibot's patents cover "round shape objects," allowing the company to potentially expand its technology to other sports, such as pickleball.
Our thoughts: We think Tennibot represents a practical yet innovative application of AI and robotics. Instead of focusing on lofty or futuristic concepts like asteroid mining or biotech, it addresses a tangible pain point that everyday tennis players experience: the tedious task of picking up tennis balls. This simplicity in purpose, combined with advanced technology like AI and computer vision, makes it both accessible and valuable.The story of Tennibot’s evolution—from an RC car prototype to a sophisticated machine capable of recognizing and collecting tennis balls—shows how tech can be applied to even niche areas, improving the user experience through automation. Furthermore, their patent strategy for “round shape objects” reflects foresight, enabling them to expand into other sports markets like pickleball.The involvement of Helen Greiner, co-founder of iRobot, adds credibility to the product. Her expertise in robotics helps Tennibot avoid common pitfalls and affirms the idea that this innovation is not just a gimmick but a solid, functional solution. Overall, Tennibot is a great example of how AI and robotics can be meaningfully integrated into everyday life, addressing specific user frustrations while maintaining potential for expansion.
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AI DOJO
Day Planning
A detailed use case of AI in day planning, particularly for professionals, involves leveraging machine learning algorithms, natural language processing (NLP), and automation to streamline time management and optimize productivity.
1. Task Prioritization and Scheduling
Using AI, users can input all their tasks, meetings, and deadlines into a digital assistant. The AI can then analyze these tasks using natural language processing to determine priority based on deadlines, task difficulty, and potential impacts. AI-based algorithms, like decision trees or neural networks, can intelligently schedule tasks during optimal periods in the user’s calendar based on historical patterns of productivity and meeting schedules. For example, deep learning models can recognize that the user is more productive in the morning, so it will schedule complex tasks early in the day.
2. Meeting Coordination and Optimization
AI tools can integrate with calendars and communication platforms to automatically schedule meetings at times that cause the least disruption to the user’s workflow. Leveraging reinforcement learning, AI can learn preferences for meeting times, ideal durations, and preferred attendees based on previous meetings. The AI can also reschedule meetings dynamically if higher-priority tasks arise, ensuring an optimized workflow without manual intervention.
3. Predictive Analysis for Deadlines
AI can use predictive analytics to identify potential delays in task completion based on the user's work habits, external factors like meeting frequency, and historical task completion times. By analyzing the patterns, AI can notify the user in advance about possible missed deadlines and propose a rearranged schedule or recommend delegating certain tasks to others.
4. Smart Reminders and Contextual Nudges
AI-driven day planners provide more than just a static to-do list. Using contextual data such as the user's location, activity levels, or current focus (based on eye movement tracking or productivity software), the system can send smart reminders. For example, if the AI detects a lull in meetings and the user is in an appropriate setting, it could nudge the user to complete smaller tasks or follow up on communications.
5. Work-Life Balance Adjustments
Advanced AI tools can monitor user habits over time, identifying patterns that lead to burnout, such as consistently working late or not taking sufficient breaks. By utilizing sentiment analysis from emails or communication platforms and combining it with task completion data, the AI can suggest breaks or reorganize non-critical tasks to help maintain a healthy work-life balance. These suggestions would be backed by models trained on stress indicators and cognitive load balancing.
6. Integration with External Data
AI can pull in external data—such as traffic conditions, weather reports, or even the user’s fitness data—and adjust the schedule accordingly. For instance, if there is heavy traffic expected, the AI can recommend leaving for a meeting earlier or shifting that meeting to a virtual platform. Similarly, if the user is fatigued (based on fitness tracking data), the AI may propose lighter tasks for that time block.
7. Adaptive Learning
The AI-based day planner continuously learns from the user’s behavior. Through the use of reinforcement learning algorithms, it tracks completed tasks, response times, and changes in task priorities to improve future scheduling. Over time, this adaptive learning enables the AI to act as a highly personalized assistant, tailoring recommendations to better fit the user’s evolving preferences and work habits.
In summary, AI’s role in day planning isn't just limited to setting reminders or managing a calendar; it dynamically adjusts the workflow, learns from the user’s habits, and integrates multiple data sources to provide a truly personalized and optimized daily schedule. This leads to improved productivity, better time management, and reduced cognitive load for the user.
QUICK BYTES
Salesforce has announced its acquisition of Zoomin, an enterprise knowledge platform that centralizes company documentation like user guides and tutorials. The deal, which follows Salesforce's recent purchase of data management firm Own, is expected to close in Q4 of fiscal year 2025, though financial terms were not disclosed. Founded in 2019, Zoomin uses AI and big data to enhance self-service documentation and support, with a diverse customer base including tech giants and fast-food franchises. The acquisition aims to bolster Salesforce’s Data Cloud platform, enabling better automation of customer service interactions through proprietary data. Salesforce's investment aligns with its commitment to spend $500 million on AI startups through its venture arm.
Microsoft has introduced a new service called Correction, designed to automatically identify and revise factually incorrect AI-generated text. This service is part of the Azure AI Content Safety API and aims to enhance the reliability of AI outputs by cross-referencing them with grounding documents. Despite this innovation, experts express skepticism, arguing that it does not address the fundamental issue of AI hallucinations—where AI models generate false information. Critics warn that while Correction may improve some outputs, it could lead users to trust AI more than warranted, and caution that the underlying problems of accuracy remain unresolved. Furthermore, the service will be free for limited usage, but costs will apply beyond that threshold, raising concerns about its commercial viability amid Microsoft’s heavy investments in AI technology.
Spotify has rolled out its AI Playlist feature to the U.S., Canada, Ireland, and New Zealand, following its initial launch for Premium subscribers in the U.K. and Australia. This beta feature, available on Android and iOS, allows users to generate personalized playlists by entering written prompts, such as “a romantic playlist for date night at home.” Users can refine playlists with additional prompts and can customize further by incorporating elements like genres, moods, locations, and even emojis. The feature has already seen success in the U.K. and Australia, with millions of playlists created by subscribers. Users can access the AI Playlist through the “Your Library” tab.
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