Constructing Hexagon Maps with H3 and Plotly: A Comprehensive Tutorial

Constructing Hexagon Maps with H3 and Plotly: A Comprehensive Tutorial AI News, AI, AI tools, Amanda Iglesias Moreno, Innovation, itinai.com, LLM, t.me/itinai, Towards Data Science - Medium 🌐 Unlocking the Potential of Hexagon Maps for Data Analysis 📊 Visualizing data across a territory can be challenging due to irregular administrative boundaries and varying sizes. Hexagon maps offer a practical solution by providing balanced geometry for better regional comparisons and improved territorial coverage. In this article, we will guide you step-by-step on how to create hexagonal maps using Python libraries H3 and Plotly. 📍 Analysis Data: Barcelona City Hotel Dataset 🏨 We will be using a dataset from the open data portal of Barcelona, which contains information on hotels in the city. By visualizing the number of hotels on the hexagonal map, we can effectively analyze their distribution. 📚 Data Reading and Cleaning 🧹 To prepare the dataset for visualization, we will clean it by selecting relevant columns such as hotel name and geographical location. This step ensures that the dataset is ready for analysis. ⚙️ Hexagon Grid Generation Using H3 ⚒️ The H3 library developed by Uber enables us to generate hexagons of different sizes and resolutions. By adjusting the size and number of concentric rings of hexagons, we can cover the desired area. 🔢 Assignment of Each Hotel to Its Respective Hexagon 🏢 After creating the hexagonal grid, we need to assign each hotel to the hexagon it belongs to. This information will be stored in a new column called Hexagon_ID, allowing us to associate hotels with their respective locations. 🔀 Data Grouping Based on the Variables to Be Visualized 📊 Next, we group the data based on the hexagon ID and calculate the variables we want to visualize. For example, we can display the number of hotels in each hexagon. Additionally, we can implement a hover-over feature to view the names of hotels in each hexagon. 🎨 Data Visualization: Cartographic Representation of Hotels in Barcelona Using Hexagons 🗺️ Using Plotly, we create a choropleth map that visualizes the distribution of hotels in Barcelona. Each hexagon is colored based on the number of hotels it contains. The interactive map allows us to zoom in and explore different areas of the city. 📝 Summary 📌 Hexagon maps provide a practical alternative to traditional administrative boundaries for visualizing data across a territory. By leveraging regular geometric shapes, hexagon maps offer better regional comparisons and minimize visual bias. In this article, we have explained how to create hexagonal maps using Python libraries H3 and Plotly, using a dataset of hotels in Barcelona as an example. 🚀 Discover the Power of AI for Your Business 🤖 AI can revolutionize your company and give you a competitive edge. Here are some practical steps to get started: 1️⃣ Identify Automation Opportunities: Find key customer interaction points that can benefit from AI. 2️⃣ Define KPIs: Ensure your AI endeavors have measurable impacts on business outcomes. 3️⃣ Select an AI Solution: Choose tools that align with your needs and provide customization. 4️⃣ Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously. For AI KPI management advice, connect with us at hello@itinai.com. Stay tuned on our Telegram channel or follow us on Twitter @itinaicom for continuous insights into leveraging AI. 🔎 Spotlight on a Practical AI Solution: AI Sales Bot 🤝 Consider using the AI Sales Bot from itinai.com/aisalesbot to automate customer engagement and manage interactions across all customer journey stages. Discover how AI can redefine your sales processes and customer engagement. Explore solutions at itinai.com. 🔗 List of Useful Links 🔗 AI Lab in Telegram @aiscrumbot – free consultation Constructing Hexagon Maps with H3 and Plotly: A Comprehensive Tutorial Towards Data Science – Medium Twitter – @itinaicom

Microsoft criticized by The Guardian for AI-generated poll

Microsoft criticized by The Guardian for AI-generated poll AI News, AI, AI tools, DailyAI, Innovation, itinai.com, LLM, Sam Jeans, t.me/itinai 🔍 Microsoft Under Scrutiny for AI-Generated Poll 🔍 Microsoft is facing criticism for a poll generated by its AI that accompanied a news story on The Guardian. The poll asked readers to speculate on the cause of a young woman’s death, with options for ‘murder,’ ‘suicide,’ or ‘accident.’ This incident has raised concerns about Microsoft’s AI-driven content production. 🔴 Previous Controversies 🔴 This is not the first time Microsoft’s content generation practices have come under scrutiny. In the past, an AI-generated travel guide suggested visiting a food bank as a tourist attraction, and an article on the death of an NBA player contained glaring errors. It’s worth noting that Microsoft is not the only company to face criticism for AI news blunders. Other news outlets have also faced backlash for AI-written articles. 💡 Using AI to Evolve Your Company 💡 If you want to leverage AI to stay competitive and evolve your company, you can learn from Microsoft’s experience. Here are some practical solutions: 1️⃣ Identify Automation Opportunities: Locate key customer interaction points where AI can be beneficial. 2️⃣ Define KPIs: Ensure that your AI initiatives have measurable impacts on business outcomes. 3️⃣ Select an AI Solution: Choose AI tools that align with your needs and offer customization options. 4️⃣ Implement Gradually: Start with a pilot project, gather data, and expand the use of AI gradually and judiciously. For AI KPI management advice, you can connect with us at hello@itinai.com. Stay updated on leveraging AI by following us on Telegram t.me/itinainews or Twitter @itinaicom. 🌟 Spotlight on a Practical AI Solution: AI Sales Bot 🌟 Consider using the AI Sales Bot from itinai.com/aisalesbot. This solution is designed to automate customer engagement 24/7 and manage interactions across all stages of the customer journey. Discover how AI can redefine your sales processes and customer engagement. Explore our solutions at itinai.com. 🔗 List of Useful Links 🔗 🔹 AI Lab in Telegram @aiscrumbot – free consultation 🔹 Microsoft criticized by The Guardian for AI-generated poll 🔹 DailyAI 🔹 Twitter – @itinaicom

Researchers from Meta and UNC-Chapel Hill Introduce Branch-Solve-Merge: A Revolutionary Program Enhancing Large Language Models’ Performance in Complex Language Tasks

Researchers from Meta and UNC-Chapel Hill Introduce Branch-Solve-Merge: A Revolutionary Program Enhancing Large Language Models’ Performance in Complex Language Tasks AI News, AI, AI tools, Asif Razzaq and Sana Hassan, Innovation, itinai.com, LLM, MarkTechPost, t.me/itinai 🚀 Introducing Branch-Solve-Merge: Enhancing Large Language Models’ Performance in Complex Language Tasks 🚀 Exciting news! Researchers from Meta and UNC-Chapel Hill have developed an innovative program called Branch-Solve-Merge (BSM) that supercharges the performance of Large Language Models (LLMs) in complex language tasks. By using BSM, LLMs like Vicuna, LLaMA-2-chat, and GPT-4 can achieve remarkable improvements in various language-related areas. 🌟 Key Features and Benefits 🌟 ✅ Boosts human-LLM agreement and reduces biases: BSM enhances the collaboration between humans and LLMs, making their responses more aligned while minimizing biases. ✅ Enables LLMs to match or surpass top models: With BSM, LLMs can compete with or even surpass leading models like GPT-4. ✅ Increases story coherence and satisfaction in constraint story generation: BSM enhances the flow and satisfaction of stories generated by LLMs, ensuring better storytelling experiences. ✅ Divides tasks into steps and parameterizes each with distinct prompts: BSM breaks down complex tasks into manageable steps, reducing complexity and improving performance. ✅ Addresses the need for holistic evaluation in complex text generation tasks: BSM provides a comprehensive approach to evaluate and enhance complex text generation tasks, ensuring accuracy and consistency. ✅ Improves correctness, consistency, and constraint satisfaction: BSM enhances the accuracy, consistency, and adherence to constraints in LLM-generated content. ✅ Enhances LLM-human agreement by up to 26% and constraint satisfaction by 12%: BSM delivers substantial improvements in LLM-human collaboration and constraint satisfaction. ✅ Outperforms other approaches in reducing biases: BSM stands out among other methods in reducing biases in LLM-generated content. ✅ Effective across different LLMs and domains: BSM's effectiveness extends to various LLMs and domains, making it a versatile solution for enhancing performance. 💼 Practical Solutions for Middle Managers 💼 As a middle manager, you can leverage BSM and AI to: ✅ Enhance LLM performance in complex language tasks: Use BSM to improve the performance of your LLMs in handling intricate language-related challenges. ✅ Improve correctness, consistency, and human-LLM agreement: BSM ensures that your LLMs deliver accurate and consistent results while aligning better with human input. ✅ Mitigate biases and increase story coherence: Utilize BSM to reduce biases in LLM-generated content and enhance the coherence of stories. ✅ Excel in grading reference-based questions: BSM can help your LLMs excel in evaluating reference-based questions, ensuring accurate and fair grading. ✅ Stay competitive by leveraging AI: Embrace AI solutions like BSM to stay ahead of the competition and unlock new possibilities. ✅ Identify automation opportunities and define measurable KPIs: Discover areas where AI automation can benefit your organization and establish KPIs to measure success. ✅ Select and implement AI solutions gradually: Start by implementing AI solutions gradually, ensuring a smooth integration and maximizing their value. 🔗 For more information about BSM and its practical applications, check out the research paper. Join our ML SubReddit, Facebook Community, Discord Channel, and Email Newsletter to stay updated on the latest AI research news and projects. Reach out to us at hello@itinai.com for AI KPI management advice. Follow us on Telegram and WhatsApp for continuous insights into leveraging AI. 🌐 Discover how AI can redefine your sales processes and customer engagement. Explore solutions at itinai.com. 🔗 List of Useful Links 🔗 ✅ AI Lab in Telegram @aiscrumbot - free consultation ✅ Researchers from Meta and UNC-Chapel Hill Introduce Branch-Solve-Merge: A Revolutionary Program Enhancing Large Language Models’ Performance in Complex Language Tasks (MarkTechPost) ✅ Twitter - @itinaicom Let's unlock the power of AI together and revolutionize your language tasks! 🚀🤖💼

A glimpse of the next generation of AlphaFold

A glimpse of the next generation of AlphaFold AI News, AI, AI tools, DeepMind Blog, Innovation, itinai.com, LLM, t.me/itinai 🚀 Evolve Your Company with AI 🚀 🔬 Progress Update: AlphaFold Model 🔬 Exciting news! Our latest AlphaFold model has made significant advancements in accuracy and now covers not only proteins but also other biological molecules, including ligands. Stay ahead of the curve and discover the next generation of AlphaFold. 💡 Discover How AI Can Redefine Your Way of Work 💡 AI has the power to transform your business. Here's how: 1️⃣ Identifying Automation Opportunities: Find customer interaction points that can benefit from AI and automate them for increased efficiency. 2️⃣ Defining KPIs: Ensure your AI initiatives have measurable impacts on business outcomes. Set clear Key Performance Indicators (KPIs) to track progress. 3️⃣ Selecting an AI Solution: Choose customizable tools that align with your specific needs. Tailor AI solutions to fit your business requirements. 4️⃣ Implementing Gradually: Start with a pilot project, gather data, and expand AI usage judiciously. Take a step-by-step approach to integrate AI into your workflows. 📈 For AI KPI management advice, connect with us at hello@itinai.com. Stay tuned for continuous insights on leveraging AI through our Telegram channel t.me/itinainews or Twitter @itinaicom. 🌟 Spotlight on a Practical AI Solution: AI Sales Bot 🌟 Introducing our AI Sales Bot from itinai.com/aisalesbot. This powerful tool is designed to automate customer engagement 24/7 and manage interactions across all stages of the customer journey. Discover how AI can redefine your sales processes and customer engagement. Explore solutions at itinai.com. 🔗 List of Useful Links 🔗 🔬 AI Lab in Telegram @aiscrumbot – free consultation 🔬 A glimpse of the next generation of AlphaFold 🔬 DeepMind Blog 🔬 Twitter – @itinaicom #AI #ArtificialIntelligence #AlphaFold #Automation #DigitalTransformation #Sales #CustomerEngagement #BusinessSolutions

Biden Takes First Step to Regulate Artificial Intelligence with Executive Order

Biden Takes First Step to Regulate Artificial Intelligence with Executive Order AI News, AI, AI tools, AI Tools & AI News, GreatAIPrompts: AI Prompts, Innovation, itinai.com, LLM, Mukund Kapoor, t.me/itinai 🔹 Biden Takes First Step to Regulate Artificial Intelligence with Executive Order 🔹 On October 31, 2023, President Joe Biden signed an executive order on Artificial Intelligence (A.I.) with the aim of ensuring the safety and responsible use of A.I. technology. The order focuses on two key areas: preventing the use of A.I. systems for creating dangerous weapons and combating the spread of fake videos and news. 🚫 Preventing Misuse of A.I. for Dangerous Weapons 🚫 President Biden expressed concerns about the potential misuse of A.I. technology to manipulate audio and video content, which could harm individuals and spread false information. To address this, the executive order requires companies to inform the government if their A.I. systems could be used to create dangerous weapons. This rule applies to government use of A.I., but not private companies. The order also mandates that companies test their A.I. systems to ensure they cannot be used to develop weapons like nuclear bombs. These tests must be reported to the government, but not to the public. This measure aims to prevent the proliferation of harmful weapons and maintain national security. 🎥 Combating Fake Videos and News 📰 The executive order recognizes the threat posed by deepfake technology, which uses A.I. to create realistic but fabricated videos. President Biden emphasized the need to protect people from the harm caused by deepfakes, including damage to reputations and the spread of false stories. To address this, the order calls for the implementation of a special mark on photos, videos, and sound created using A.I. This mark will help identify the origin of the content, making it easier for people to distinguish between real and fake information. 🔍 Implications for Companies and the Future of A.I. 🔍 Leading tech companies like Microsoft and Google support this executive order as they recognize the importance of responsible A.I. use. They are concerned about potential misuse of A.I. technology and the associated legal and reputational risks. For companies looking to leverage A.I. in their operations, it is crucial to consider the practical solutions and value that A.I. can bring. Some key steps to consider include: 1️⃣ Identifying automation opportunities: Locate areas in your business where A.I. can enhance customer interactions. 2️⃣ Defining KPIs: Ensure that your A.I. initiatives have measurable impacts on business outcomes. 3️⃣ Selecting an A.I. solution: Choose tools that align with your specific needs and offer customization options. 4️⃣ Implementing gradually: Start with a pilot project, gather data, and expand A.I. usage strategically. If you need guidance on managing A.I. KPIs or want to explore how A.I. can transform your business, reach out to us at hello@itinai.com. Stay updated on the latest insights into leveraging A.I. by following us on Telegram at t.me/itinainews or on Twitter @itinaicom. 🔦 Spotlight on a Practical AI Solution: AI Sales Bot 🔦 Consider exploring the AI Sales Bot from itinai.com/aisalesbot. This solution is designed to automate customer engagement 24/7 and manage interactions across all stages of the customer journey. Discover how A.I. can redefine your sales processes and enhance customer engagement by visiting itinai.com. 🔗 List of Useful Links: 🔗 🔹 AI Lab in Telegram @aiscrumbot – free consultation 🔹 Biden Takes First Step to Regulate Artificial Intelligence with Executive Order 🔹 GreatAIPrompts: AI Prompts, AI Tools & AI News 🔹 Twitter – @itinaicom

Shedding Light on Cartoon Animation’s Future: AnimeInbet’s Innovation in Line Drawing Inbetweening

Shedding Light on Cartoon Animation’s Future: AnimeInbet’s Innovation in Line Drawing Inbetweening AI News, AI, AI tools, Daniele Lorenzi, Innovation, itinai.com, LLM, MarkTechPost, t.me/itinai 🚀 Exciting News in Cartoon Animation! Introducing AnimeInbet, a groundbreaking AI technique revolutionizing line drawing inbetweening. Say goodbye to labor-intensive hand-drawing! 🎨 AnimeInbet automates the process of generating intermediate line drawings in 2D animation. Unlike previous methods, it uses geometrized vector graphs instead of raster images, resulting in cleaner and more accurate frames. This innovative technique matches and relocates vertices, preserves intricate line structures, and predicts visibility masks. The benefits of AnimeInbet are remarkable. It generates high-quality line drawings by accurately computing pixel-wise correspondences and maintaining the fine details of the artwork. To support supervised training, a new dataset called MixamoLine240 has been introduced, providing line art with ground truth geometrization and vertex-matching labels. If you're interested in learning more about AnimeInbet, you can find the research paper and code on Github. 📚 But that's not all! If you want to stay competitive and evolve your company with AI, we have practical solutions for you. Here's how you can get started: 1️⃣ Identify Automation Opportunities: Locate customer interaction points that can benefit from AI. Look for areas where automation can improve efficiency and enhance customer experience. 2️⃣ Define KPIs: Ensure your AI endeavors have measurable impacts on business outcomes. Define key performance indicators (KPIs) to track the success of your AI initiatives. 3️⃣ Select an AI Solution: Choose AI tools that align with your specific needs and offer customization options. Look for solutions that can be tailored to your company's unique requirements. 4️⃣ Implement Gradually: Start with a pilot project to gather data and assess the effectiveness of AI. Gradually expand the use of AI in your organization, making informed decisions based on results and feedback. For AI KPI management advice and further insights into leveraging AI, connect with us at hello@itinai.com. Stay updated on the latest AI research news and projects by joining our ML SubReddit, Facebook Community, Discord Channel, and Email Newsletter. If you're interested in automating customer engagement and managing interactions across all customer journey stages, check out our AI Sales Bot at itinai.com/aisalesbot. This solution can revolutionize your sales processes and customer engagement. Don't miss out on this opportunity to evolve your company with AI! 🌟 List of Useful Links: - AI Lab in Telegram @aiscrumbot – free consultation - "Shedding Light on Cartoon Animation’s Future: AnimeInbet’s Innovation in Line Drawing Inbetweening" on MarkTechPost - Twitter: @itinaicom

People shouldn’t pay such a high price for calling out AI harms

People shouldn’t pay such a high price for calling out AI harms AI News, AI, AI tools, Artificial intelligence – MIT Technology Review, Innovation, itinai.com, LLM, Melissa Heikkilä, t.me/itinai 🌟 This Week in AI: Regulations, Harms, and Responsible AI 🌟 Stay updated with the latest developments in AI! This week, we focus on regulations, the real harms caused by AI, and the importance of responsible AI. Here are the key highlights: 1️⃣ AI Regulations: The White House has introduced an executive order to promote safe and trustworthy AI systems, marking the most comprehensive AI regulation in the US to date. The G7 has also agreed on a voluntary code of conduct for AI companies to minimize risks and harms. Additionally, the UK is hosting an AI Safety Summit to establish global rules on AI safety. These events highlight the growing concern about potential risks associated with AI. 2️⃣ Real Harms of AI: While hypothetical future risks of AI are being discussed, it is crucial to address the real harms caused by existing AI systems. Renowned AI researcher and activist, Joy Buolamwini, emphasizes that AI systems causing demonstrated harms are more dangerous than hypothetical sentient AI systems. Her work on bias in facial recognition systems led major companies to change their technology. Buolamwini calls for a radical rethink of how AI systems are built, starting with ethical data collection practices. 3️⃣ Responsible AI and Challenges: Responsible AI teams have become crucial in tech companies to ensure products are developed to mitigate potential harm. However, those who point out problems with AI systems often face criticism and pushback. Buolamwini herself encountered public attacks on her research. Speaking up against powerful technology companies still carries risks, hindering progress in addressing risks and harms associated with AI. 🔍 Deeper Learning: Interview with OpenAI’s Chief Scientist 🔍 In an exclusive interview, Ilya Sutskever, OpenAI’s chief scientist, shares his hopes and fears for the future of AI. He prioritizes preventing artificial superintelligence from going rogue and highlights the potential consciousness of ChatGPT. Sutskever emphasizes the need for society to recognize the true power of AI and envisions a future where humans merge with machines. 📚 Bits and Bytes 📚 Here are some additional noteworthy updates in the AI landscape: - MIT and other institutions have audited and traced widely used fine-tuning datasets to address the lack of transparency in AI systems. - McKinsey researchers suggest that generative AI may replace jobs predominantly held by women, such as customer service and sales. - The United Nations has formed an AI advisory group to shape potential AI governance recommendations. - San Francisco is experiencing a revitalization due to the emergence of AI startups. - Author Margaret Atwood critiques AI literature and argues that published authors need not worry about AI. 💡 AI Solutions for Your Company 💡 If you want to evolve your company with AI and stay competitive, consider the following practical steps: 1️⃣ Identify Automation Opportunities: Find key customer interaction points that can benefit from AI. 2️⃣ Define KPIs: Ensure your AI initiatives have measurable impacts on business outcomes. 3️⃣ Select an AI Solution: Choose tools that align with your needs and offer customization. 4️⃣ Implement Gradually: Start with a pilot, gather data, and expand AI usage thoughtfully. For AI KPI management advice, connect with us at hello@itinai.com. For continuous insights into leveraging AI, stay tuned on our Telegram or follow us on Twitter. 🔦 Spotlight on a Practical AI Solution: AI Sales Bot 🔦 Discover how AI can redefine your sales processes and customer engagement with our AI Sales Bot. It automates customer engagement 24/7 and manages interactions across all stages of the customer journey. Explore our solutions at itinai.com/aisalesbot. 🔗 List of Useful Links 🔗 🔹 AI Lab in Telegram @aiscrumbot – free consultation 🔹 People shouldn’t pay such a high price for calling out AI harms 🔹 Artificial intelligence – MIT Technology Review 🔹 Twitter – @itinaicom Stay informed and empowered in the world of AI! #AI #ArtificialIntelligence #ResponsibleAI #AIRegulations #AIHarms #AIIndustryUpdates #AIforBusiness #AIInnovation

Is Generative AI Worth Its Environmental Footprint?

Is Generative AI Worth Its Environmental Footprint? AI News, AI, AI tools, Innovation, itinai.com, Kasper Groes Albin Ludvigsen, LLM, t.me/itinai, Towards Data Science - Medium 🌿 Is Generative AI Worth Its Environmental Footprint? 🌍 Generative AI, a technology that generates text, is gaining attention and being integrated into various digital products. However, this technology may have a significant environmental impact if widely adopted. But is the value we gain from generative AI worth the potential environmental costs? Let’s explore the potential benefits. 🚀 Potential Productivity Gains 📈 Generative AI can lead to productivity gains in various tasks. Studies have shown that using generative AI tools like ChatGPT can significantly reduce the time spent on tasks and improve the quality of solutions. For example, users of ChatGPT spent 40% less time on tasks and had solutions that were 18% higher in quality compared to those who didn’t use it. Similarly, software developers reported an 88% increase in productivity when using generative AI tool GitHub Co-Pilot. These productivity gains far exceed the average annual labor productivity increase observed in the US and the EU. While not all programming tasks may benefit equally from generative AI, as the technology advances, we can expect higher and more widespread productivity gains. ♻️ Net Positive Technology? 🌍 There are debates on whether generative AI will be a net positive or net negative technology in terms of its environmental impact. It’s difficult to determine the exact carbon costs of generative AI compared to other activities like Google searches. However, it’s clear that generative AI can save consumption in some areas. Though generative AI may have some positive effects on productivity and possibly even reducing skill inequality, it may contribute to rising inequality between different occupations and nations. Previous research has shown that automation technologies can increase economic disparity. ⚡ Speeding up the Green Transition 🌱 While generative AI may not directly contribute to the green transition, other types of AI can help reduce energy consumption and fight climate change. AI can optimize shipping routes, reduce energy consumption in buildings, and combat deforestation. In conclusion, generative AI has the potential to bring productivity gains and possibly reduce skill inequality. However, it comes at an environmental cost. Whether the benefits outweigh the costs is a value judgement that organizations need to make. Consider implementing AI solutions gradually and identifying automation opportunities based on your specific needs. For practical AI solutions that can redefine your company’s way of work and improve customer engagement, consider connecting with itinai.com. They offer AI-driven tools like AI Sales Bot, designed to automate customer interactions and enhance sales processes. 🔗 List of Useful Links: - AI Lab in Telegram @aiscrumbot – free consultation - Is Generative AI Worth Its Environmental Footprint? - Towards Data Science – Medium - Twitter – @itinaicom

Stanford and UT Austin Researchers Propose Contrastive Preference Learning (CPL): A Simple Reinforcement Learning RL-Free Method for RLHF that Works with Arbitrary MDPs and off-Policy Data

Stanford and UT Austin Researchers Propose Contrastive Preference Learning (CPL): A Simple Reinforcement Learning RL-Free Method for RLHF that Works with Arbitrary MDPs and off-Policy Data AI News, AI, AI tools, Aneesh Tickoo, Innovation, itinai.com, LLM, MarkTechPost, t.me/itinai 🚀 The Value of Contrastive Preference Learning (CPL) in Reinforcement Learning for Middle Managers 🚀 Introduction: Aligning human preferences with AI models has become a challenge as these models improve. Reinforcement Learning from Human Input (RLHF) has gained popularity to address this issue. RLHF uses human preferences to improve known policies by distinguishing between acceptable and bad behaviors. This approach has shown promising results in various applications. The Two Stages of RLHF Algorithms: Most RLHF algorithms involve two stages. First, user preference data is collected to train a reward model. Then, an off-the-shelf RL algorithm optimizes that reward model. However, recent research suggests that human preferences should be based on regret, or the difference between the actual action and the ideal action according to the expert's reward function. The Solution: Contrastive Preference Learning (CPL): Researchers from Stanford University, UMass Amherst, and UT Austin propose a novel family of RLHF algorithms called CPL. CPL uses a regret-based model of preferences, which provides precise information on the best course of action. Unlike traditional RLHF algorithms, CPL does not require RL optimization and can handle high-dimensional state and action spaces. The Benefits of CPL: CPL offers three main benefits over earlier efforts in RLHF: 1️⃣ Scalability: CPL can scale as well as supervised learning because it exclusively uses supervised learning objectives to match the optimal advantage. 2️⃣ Off-Policy Learning: CPL is completely off-policy, allowing the use of any offline, less-than-ideal data source. 3️⃣ Sequential Data Learning: CPL enables preference searches over sequential data for learning on arbitrary MDPs. Practical Applications and Results: CPL has shown promising results in sequential decision-making and high-dimensional off-policy inputs. It can learn temporally extended manipulation rules and achieve performance comparable to RL-based techniques without the need for dynamic programming or policy gradients. CPL is also more parameter efficient and faster than traditional RL approaches. Implementing AI Solutions in Your Company: To leverage AI and stay competitive, follow these steps: 1️⃣ Identify Automation Opportunities: Locate areas in your company where AI can benefit customer interactions. 2️⃣ Define KPIs: Ensure that your AI endeavors have measurable impacts on business outcomes. 3️⃣ Select an AI Solution: Choose tools that align with your needs and offer customization. 4️⃣ Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously. For AI KPI management advice and continuous insights in leveraging AI, connect with us at hello@itinai.com or stay tuned on our Telegram channel t.me/itinainews or Twitter @itinaicom. Spotlight on a Practical AI Solution: AI Sales Bot: Consider using the AI Sales Bot from itinai.com/aisalesbot to automate customer engagement and manage interactions across all customer journey stages. Discover how AI can redefine your sales processes and customer engagement. Explore solutions at itinai.com. 🔗 List of Useful Links: - AI Lab in Telegram @aiscrumbot – free consultation - Stanford and UT Austin Researchers Propose Contrastive Preference Learning (CPL): A Simple Reinforcement Learning RL-Free Method for RLHF that Works with Arbitrary MDPs and off-Policy Data - MarkTechPost - Twitter – @itinaicom

Is ConvNet Making a Comeback? Unraveling Their Performance on Web-Scale Datasets and Matching Vision Transformers

Is ConvNet Making a Comeback? Unraveling Their Performance on Web-Scale Datasets and Matching Vision Transformers AI News, AI, AI tools, Innovation, itinai.com, LLM, MarkTechPost, Pragati Jhunjhunwala, t.me/itinai 🔍 Researchers Challenge Belief: ConvNets vs. ViTs on Large Datasets In a recent study, researchers have challenged the prevailing belief that Vision Transformers (ViTs) outperform Convolutional Neural Networks (ConvNets) when given access to large web-scale datasets. They introduce a ConvNet architecture called NFNet, pre-trained on the massive JFT-4B dataset containing 4 billion labeled images from 30,000 classes. The aim is to evaluate the scaling properties of NFNet models and compare their performance to ViTs with similar computational budgets. 📈 The Rise of ViTs and the Need for Evidence ViTs have gained popularity in recent years, with many believing they outperform ConvNets, especially with large datasets. However, this belief lacks substantial evidence, as most studies have compared ViTs to weak ConvNet baselines. Additionally, ViTs have been pre-trained with significantly larger computational budgets, raising questions about the actual performance differences between these architectures. 💡 Introducing NFNet and Evaluating Performance ConvNets, specifically ResNets, have been the go-to choice for computer vision tasks for years. However, the rise of ViTs has shifted the focus to models pre-trained on large web-scale datasets. The researchers introduce NFNet, a ConvNet architecture, and pre-train it on the vast JFT-4B dataset without significant modifications. They examine how the performance of NFNet scales with varying computational budgets. 📊 Results and Findings The research team trains different NFNet models with varying depths and widths on the JFT-4B dataset. They find that larger computational budgets lead to better performance, observing a log-log scaling law. The most expensive pre-trained NFNet model achieves an ImageNet Top-1 accuracy of 90.3%. By introducing repeated augmentation during fine-tuning, they achieve a remarkable 90.4% Top-1 accuracy. Comparatively, ViT models achieve similar performance with more substantial pre-training budgets. 🔑 Implications and Conclusion This research challenges the belief that ViTs significantly outperform ConvNets when trained with similar computational budgets. It demonstrates that NFNet models can achieve competitive results on ImageNet, matching the performance of ViTs. The study emphasizes the importance of compute and data availability in model performance. While ViTs have their merits, ConvNets like NFNet remain formidable contenders, especially when trained at a large scale. This work encourages a fair evaluation of different architectures, considering both performance and computational requirements. 🤖 Practical AI Solutions for Middle Managers Discover how AI can redefine your way of work with these steps: 1️⃣ Identify Automation Opportunities: Find customer interaction points that can benefit from AI. 2️⃣ Define KPIs: Ensure your AI endeavors have measurable impacts on business outcomes. 3️⃣ Select an AI Solution: Choose tools that align with your needs and offer customization. 4️⃣ Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously. For AI KPI management advice, connect with us at hello@itinai.com. Stay tuned on our Telegram or Twitter for continuous insights into leveraging AI. 🌟 Spotlight on a Practical AI Solution Consider the AI Sales Bot from itinai.com/aisalesbot. It automates customer engagement 24/7 and manages interactions across all customer journey stages. Discover how AI can redefine your sales processes and customer engagement at itinai.com. 🔗 Useful Links: - AI Lab in Telegram @aiscrumbot – free consultation - Is ConvNet Making a Comeback? Unraveling Their Performance on Web-Scale Datasets and Matching Vision Transformers - MarkTechPost - Twitter – @itinaicom

Researchers from CMU and NYU Propose LLMTime: An Artificial Intelligence Method for Zero-Shot Time Series Forecasting with Large Language Models (LLMs)

Researchers from CMU and NYU Propose LLMTime: An Artificial Intelligence Method for Zero-Shot Time Series Forecasting with Large Language Models (LLMs) AI News, AI, AI tools, Aneesh Tickoo, Innovation, itinai.com, LLM, MarkTechPost, t.me/itinai 🚀 Announcing LLMTIME: An AI Method for Zero-Shot Time Series Forecasting 📈 Time series forecasting can be challenging, but researchers from CMU and NYU have developed a groundbreaking technique called LLMTIME. By leveraging large language models (LLMs), LLMTIME achieves high performance without the need for specialized knowledge or extensive training. Here's how it works: LLMTIME encodes time series data as strings of numerical digits and utilizes pretrained LLMs to make predictions. This approach allows for robust models with probabilistic capabilities like probability assessment and sampling. LLMTIME outperforms purpose-built time series methods for various issues and exhibits excellent pattern extrapolation abilities. Key Benefits of LLMTIME: 🔹 Simple Application: No need for specialized knowledge or computational resources for fine-tuning. 🔹 Limited Data Availability: Works well with limited training or fine-tuning data. 🔹 Broad Pattern Extrapolation: Leverages pre-trained LLMs' abilities to extrapolate patterns, eliminating the need for specialized time series models. LLMs also exhibit biases consistent with time series features, making them effective for forecasting. They can handle multimodal distributions and missing data, enhancing their usefulness in time series analysis. In addition to forecasting, LLMs offer features like inquiring for extra side information and justifying predictions. The performance of LLMTIME improves with model size and the quality of uncertainty representation. To learn more about LLMTIME, check out the research paper and GitHub repository. 🌟 Using AI to Evolve Your Company 🌟 AI has the power to revolutionize your company and give you a competitive edge. Here are some steps to effectively leverage AI: 1️⃣ Identify Automation Opportunities: Find customer interaction points that can benefit from AI. 2️⃣ Define KPIs: Ensure your AI initiatives have measurable impacts on business outcomes. 3️⃣ Select an AI Solution: Choose tools that align with your needs and offer customization. 4️⃣ Implement Gradually: Start with a pilot, collect data, and expand AI usage carefully. If you need advice on managing AI KPIs or want insights into leveraging AI, reach out to us at hello@itinai.com. Stay updated on the latest AI research news and projects by joining our ML SubReddit, Facebook Community, Discord Channel, and Email Newsletter. For AI-driven sales automation and customer engagement, explore our AI Sales Bot at itinai.com/aisalesbot. It can automate customer interactions across all stages of the customer journey, providing 24/7 engagement. Discover how AI can transform your sales processes and customer engagement. Visit itinai.com for more information. 🔗 Useful Links: 🔹 AI Lab in Telegram @aiscrumbot – free consultation 🔹 Researchers from CMU and NYU Propose LLMTime: An Artificial Intelligence Method for Zero-Shot Time Series Forecasting with Large Language Models (LLMs) 🔹 MarkTechPost 🔹 Twitter – @itinaicom

Meet ULTRA: A Pre-Trained Foundation Model for Knowledge Graph Reasoning that Works on Any Graph and Outperforms Supervised SOTA Models on 50+ Graphs

Meet ULTRA: A Pre-Trained Foundation Model for Knowledge Graph Reasoning that Works on Any Graph and Outperforms Supervised SOTA Models on 50+ Graphs AI News, Adnan Hassan, AI, AI tools, Innovation, itinai.com, LLM, MarkTechPost, t.me/itinai 🚀 Introducing ULTRA: A Pre-Trained Model for Knowledge Graph Reasoning 🚀 ULTRA is a powerful model designed to learn universal and transferable graph representations for knowledge graphs (KGs). It eliminates the need for textual information and can be applied to any KG with different entity and relation vocabularies. With impressive zero-shot inductive inference capabilities, ULTRA outperforms specialized baselines in link prediction experiments. 🔑 Key Features of ULTRA: 🔑 ✅ Universal and transferable graph representations ✅ Generalization to new KGs with different relations and structures ✅ Leveraging relation interactions for effective pre-training and fine-tuning ✅ Three-step algorithm for lifting the graph, obtaining relation representations, and predicting links ✅ Superior performance in zero-shot inference, surpassing specific graph-trained baselines ✅ Potential for further enhancement through fine-tuning 💡 Benefits and Value: 💡 ULTRA offers practical solutions for middle managers seeking to leverage AI in their organizations: ✅ Universal and transferable graph representations enable inference on diverse multi-relational graphs without input features ✅ Outperforms tailored supervised baselines on a wide range of graphs, even in zero-shot scenarios ✅ Promising choice for inductive and transferable knowledge graph reasoning 🔜 Next Steps: 🔜 If you’re interested in exploring ULTRA and its applications further, you can: 📖 Read the full research paper here: [insert link] 💻 Access the code on Github: [insert link] 🌐 Stay Connected: 🌐 Join our ML SubReddit, Facebook Community, Discord Channel, and Email Newsletter for the latest AI research news and updates. Don’t miss out on our Telegram and WhatsApp channels as well. 🚀 Evolve Your Company with AI: Meet ULTRA 🚀 If you want to stay competitive and evolve your company with AI, ULTRA is the solution you need. It is a pre-trained foundation model for knowledge graph reasoning that works on any graph and outperforms supervised state-of-the-art models on over 50 graphs. 🌟 How AI Can Redefine Your Way of Work: 🌟 1️⃣ Identify Automation Opportunities: Locate key customer interaction points that can benefit from AI. 2️⃣ Define KPIs: Ensure your AI endeavors have measurable impacts on business outcomes. 3️⃣ Select an AI Solution: Choose tools that align with your needs and provide customization. 4️⃣ Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously. For AI KPI management advice and continuous insights into leveraging AI, connect with us at hello@itinai.com. Stay tuned on our Telegram channel t.me/itinainews or follow us on Twitter @itinaicom. 🔦 Spotlight on a Practical AI Solution: AI Sales Bot 🔦 Consider the AI Sales Bot from itinai.com/aisalesbot. It is designed to automate customer engagement 24/7 and manage interactions across all customer journey stages. Discover how AI can redefine your sales processes and customer engagement. Explore solutions at itinai.com. 🔗 List of Useful Links: 🔗 🔹 AI Lab in Telegram @aiscrumbot – free consultation 🔹 Meet ULTRA: A Pre-Trained Foundation Model for Knowledge Graph Reasoning that Works on Any Graph and Outperforms Supervised SOTA Models on 50+ Graphs 🔹 MarkTechPost 🔹 Twitter – @itinaicom

Deploy and fine-tune foundation models in Amazon SageMaker JumpStart with two lines of code

Deploy and fine-tune foundation models in Amazon SageMaker JumpStart with two lines of code AI News, AI, AI tools, AWS Machine Learning Blog, Evan Kravitz, Innovation, itinai.com, LLM, t.me/itinai 🚀 Simplified SageMaker JumpStart SDK for Building, Training, and Deploying Models 🚀 We are excited to introduce a simplified version of the Amazon SageMaker JumpStart SDK that makes it easy to build, train, and deploy foundation models. With just a few lines of code, you can get started with using pre-trained models and fine-tune them for your specific tasks. 🔍 Solution Overview 🔍 SageMaker JumpStart provides pre-trained, open-source models for various problem types, allowing you to quickly start your machine learning (ML) projects. You can incrementally train and fine-tune these models before deployment. JumpStart also offers solution templates and example notebooks for common ML use cases. To demonstrate the capabilities of the new SageMaker JumpStart SDK, we show you how to use the pre-trained Flan T5 XL model from Hugging Face for text generation and summarization tasks. You can also use other models like Llama2, Falcon, or Mistral AI for text generation. 💡 Deploy and Invoke the Model 💡 To deploy the Flan T5 XL model, you can use the simplified SageMaker JumpStart SDK. Simply instantiate the model object with the model ID and call the deploy method. Here’s an example: ```python from sagemaker.jumpstart.model import JumpStartModel pretrained_model = JumpStartModel(model_id="huggingface-text2text-flan-t5-base") pretrained_predictor = pretrained_model.deploy() ``` Once the model is deployed, you can invoke it by passing the text to the predictor. The response from the model will be returned as a Python dictionary. Here’s an example: ```python text = "Summarize this content - Amazon Comprehend uses natural language processing (NLP) to extract insights about the content of documents..." query_response = pretrained_predictor.predict(text) print(query_response["generated_text"]) ``` This will generate a summary of the provided text using the Flan T5 XL model. 🔧 Fine-tune and Deploy the Model 🔧 The SageMaker JumpStart SDK also provides a JumpStartEstimator class for simplified fine-tuning. You can provide the location of your fine-tuning data and optionally pass validation datasets. After fine-tuning, you can deploy the model using the deploy method of the Estimator object. Here’s an example: ```python from sagemaker.jumpstart.estimator import JumpStartEstimator estimator = JumpStartEstimator( model_id=model_id, ) estimator.set_hyperparameters(instruction_tuned="True", epoch="3", max_input_length="1024") estimator.fit({"training": train_data_location}) finetuned_predictor = estimator.deploy() ``` 🔩 Customize the New Classes in the SageMaker SDK 🔩 The new SDK allows you to customize the deployment and invocation based on your requirements. You can override the defaults and customize parameters such as instance type, VPC configuration, and more. Here’s an example of overriding the instance type: ```python finetuned_predictor = estimator.deploy(instance_type='ml.g5.2xlarge') ``` You can also customize the input payload format type using serializers and content types. Here’s an example of setting the payload input format as JSON: ```python from sagemaker import serializers from sagemaker import content_types pretrained_predictor.serializer = serializers.JSONSerializer() pretrained_predictor.content_type = 'application/json' ``` ✅ Conclusion ✅ The simplified SageMaker JumpStart SDK makes it easy to build, train, and deploy models with just a few lines of code. You can use pre-trained models or fine-tune them for your specific tasks. The SDK also allows for customization to meet your requirements. Explore the available models and start leveraging AI to redefine your work processes. 👨‍💼 About the Authors 👨‍💼 Evan Kravitz, Rachna Chadha, Jonathan Guinegagne, and Dr. Ashish Khetan are experts in the field of AI and ML, with experience in developing and applying machine learning algorithms. They are part of the Amazon SageMaker JumpStart team, working to make AI accessible and impactful. If you’re interested in evolving your company with AI, connect with us at hello@itinai.com. For more insights into leveraging AI, follow us on Telegram t.me/itinainews or Twitter @itinaicom. ⭐ Spotlight on a Practical AI Solution ⭐ Consider the AI Sales Bot from itinai.com/aisalesbot, designed to automate customer engagement and manage interactions across all stages of the customer journey. Discover how AI can redefine your sales processes and customer engagement. Explore solutions at itinai.com. 🔗 List of Useful Links 🔗 - AI Lab in Telegram @aiscrumbot – free consultation - Deploy and fine-tune foundation models in Amazon SageMaker JumpStart with two lines of code - AWS Machine Learning Blog - Twitter – @itinaicom

Democratizing AI With a Codeless Solution

Democratizing AI With a Codeless Solution AI News, AI, AI tools, Innovation, itinai.com, LLM, MarkTechPost, t.me/itinai, Vrushali Prasade 🚀 Democratizing AI With a Codeless Solution 🚀 As the Chief Technology Officer (CTO) of Pixis, a fast-growing AI company, we are dedicated to making AI accessible to all marketers, regardless of their technical expertise. Our goal is to empower marketers to leverage the potential of AI without the need for coding. 🔌 Simple Integration 🔌 Integrating AI into business processes can be challenging and time-consuming. That's why we have simplified the process by providing an AI infrastructure that can be easily downloaded and installed as a plugin. This reduces integration times from weeks to minutes, allowing marketers to quickly harness the power of AI. 🖥️ User-Friendly Interface 🖥️ Not all marketers have a background in data analytics or programming. That's why we have developed a codeless AI infrastructure with a simple drag-and-drop user interface (UI). This means anyone can input data and interpret results without the need for coding skills. 🔍 Transparent Processes 🔍 Transparency is crucial in AI marketing tools. Our codeless AI solution, Pixis AI, clearly explains how it works, what data it uses, and how it arrives at its results. This transparency builds trust and addresses concerns over AI "black boxes". 🔒 Data Privacy Compliance 🔒 Ensuring data privacy compliance is essential. Our codeless AI algorithms prioritize data privacy by implementing encryption, anonymization, and robust access control layers. We adhere to international data standards such as GDPR to secure and respect sensitive data. 💰 Affordable AI Solutions 💰 Advanced AI technology can be expensive and inaccessible for many businesses. That's why our codeless AI solutions are cost-effective and deliver higher ROI. They are designed to solve specific challenges and make AI accessible to startups and established corporations. 🤝 Customization and Collaboration 🤝 We provide access to AI playgrounds where marketers can collaborate with data scientists and technical experts to customize AI models for their specific purposes. This allows for product experimentation and customization beyond the initial offerings. At Pixis, we believe that AI should be a tool that adds tremendous value to businesses of all sizes. Our codeless AI solution empowers marketers to leverage AI without technical expertise, making it a practical and valuable solution for growth marketing. 🔍 About Pixis 🔍 Pixis is a no-code AI platform that helps brands scale their marketing efforts and enhance decision-making. Our codeless AI infrastructure offers over 200+ proprietary AI models, providing marketers with plug-and-play AI products without the need for coding. If you want to evolve your company with AI and stay competitive, consider Democratizing AI With a Codeless Solution. Discover how AI can redefine your way of work: 1️⃣ Identify Automation Opportunities: Locate key customer interaction points that can benefit from AI. 2️⃣ Define KPIs: Ensure your AI endeavors have measurable impacts on business outcomes. 3️⃣ Select an AI Solution: Choose tools that align with your needs and provide customization. 4️⃣ Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously. For AI KPI management advice, connect with us at hello@itinai.com. And for continuous insights into leveraging AI, stay tuned on our Telegram t.me/itinainews or Twitter @itinaicom. Spotlight on a Practical AI Solution: AI Sales Bot Consider our AI Sales Bot from itinai.com/aisalesbot. It is designed to automate customer engagement 24/7 and manage interactions across all customer journey stages. Discover how AI can redefine your sales processes and customer engagement. Explore solutions at itinai.com. List of Useful Links: 🔬 AI Lab in Telegram @aiscrumbot – free consultation 🚀 Democratizing AI With a Codeless Solution 📰 MarkTechPost 🐦 Twitter – @itinaicom

Unlocking supply chain resiliency

Unlocking supply chain resiliency AI News, AI, AI tools, Artificial intelligence – MIT Technology Review, Innovation, itinai.com, LLM, MIT Technology Review Insights, t.me/itinai 🚀 Unlocking Supply Chain Resiliency with AI 🚀 In today's complex digital world, managing inventory, knowing where products come from, and maintaining consumer trust are more important than ever. That's why companies like McDonald's are turning to artificial intelligence (AI) and physical object identifiers to create faster, more accurate, and transparent supply chains. 🔑 The Power of Phygital Tools 🔑 McDonald's, in partnership with Golden State Foods, has successfully used radio-frequency identification (RFID) technology to automatically track the movement of fresh beef from manufacturer to restaurant in near real-time. This innovative approach creates a "golden digital thread of traceability" that improves transparency, builds trust, and drives value across the entire supply chain. 💡 Benefits of AI in Supply Chain Management 💡 Implementing AI solutions in supply chain management can help companies navigate labor shortages, transportation failures, and political volatility. By merging the efficiency and agility of technology with physical object identifiers, AI unlocks vast volumes of information and creates resilient supply chains. 📝 Steps to Evolve Your Company with AI 📝 To stay competitive and unlock supply chain resiliency, follow these steps: 1️⃣ Identify Automation Opportunities: Find key customer interaction points that can benefit from AI. 2️⃣ Define KPIs: Ensure your AI endeavors have measurable impacts on business outcomes. 3️⃣ Select an AI Solution: Choose tools that align with your needs and offer customization. 4️⃣ Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously. For AI KPI management advice, connect with us at hello@itinai.com. And for continuous insights into leveraging AI, stay tuned on our Telegram channel or Twitter. 🌟 Spotlight on a Practical AI Solution: AI Sales Bot 🌟 Discover how AI can redefine your sales processes and customer engagement with the AI Sales Bot from itinai.com/aisalesbot. This solution automates customer engagement 24/7 and manages interactions across all customer journey stages. Explore AI solutions at itinai.com. 🔗 List of Useful Links 🔗 🔹 AI Lab in Telegram @aiscrumbot – free consultation 🔹 Unlocking supply chain resiliency 🔹 Artificial intelligence – MIT Technology Review 🔹 Twitter – @itinaicom

Researchers from the University of Washington and Princeton Present a Pre-Training Data Detection Dataset WIKIMIA and a New Machine Learning Approach MIN-K% PROB

Researchers from the University of Washington and Princeton Present a Pre-Training Data Detection Dataset WIKIMIA and a New Machine Learning Approach MIN-K% PROB AI News, AI, AI tools, Arham Islam, Innovation, itinai.com, LLM, MarkTechPost, t.me/itinai 🔹 Researchers from the University of Washington and Princeton have developed a benchmark called WIKIMIA and a detection method called MIN-K% PROB to identify problematic training text in large language models (LLMs). This is important to ensure that LLMs are not trained on copyrighted material or personally identifiable information. 🔹 The MIN-K% PROB method calculates the average probability of outlier words, allowing researchers to determine if an LLM was trained on a given text. The researchers found evidence suggesting that the GPT-3 model may have been trained on copyrighted books. 🔹 The WIKIMIA benchmark automatically evaluates detection methods on newly released pretrained LLMs. The MIN-K% PROB method identifies outlier words with low probabilities under the LLM. 🔹 The researchers applied the MIN-K% PROB method to real-life scenarios such as copyrighted book detection and privacy auditing of machine unlearning. They found that the GPT-3 model may have been trained on copyrighted books, even after using the Machine unlearning method. 🔹 The MIN-K% PROB method is a new and effective solution for detecting problematic training text in LLMs. It improves transparency and accountability in LLMs. Practical AI Solutions for Middle Managers: 1️⃣ Identify Automation Opportunities: Locate key customer interaction points that can benefit from AI. 2️⃣ Define KPIs: Ensure your AI endeavors have measurable impacts on business outcomes. 3️⃣ Select an AI Solution: Choose tools that align with your needs and provide customization. 4️⃣ Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously. Spotlight on a Practical AI Solution: AI Sales Bot Consider using the AI Sales Bot from itinai.com/aisalesbot to automate customer engagement 24/7 and manage interactions across all customer journey stages. This solution can redefine your sales processes and customer engagement. Discover how AI can redefine your way of work. Explore solutions at itinai.com. List of Useful Links: - AI Lab in Telegram @aiscrumbot – free consultation - Researchers from the University of Washington and Princeton Present a Pre-Training Data Detection Dataset WIKIMIA and a New Machine Learning Approach MIN-K% PROB - MarkTechPost - Twitter – @itinaicom

Indian Workers Fear Job Loss to AI More Than Global Peers, Study Finds

Indian Workers Fear Job Loss to AI More Than Global Peers, Study Finds AI News, AI, AI tools, AI Tools & AI News, GreatAIPrompts: AI Prompts, Innovation, itinai.com, LLM, Mukund Kapoor, t.me/itinai 🌟 Indian Workers Fear Job Loss to AI More Than Global Peers, Study Finds 🌟 A recent study by Randstad has revealed that Indian workers are more concerned about the impact of artificial intelligence (AI) on their jobs compared to workers in the US, UK, and Germany. The study found that one out of every two workers in India is afraid of losing their job to AI, while in other developed countries, this number is only one in three. The fear is largely attributed to the rapid growth of AI in India, coupled with the prevalence of business process outsourcing (BPO) and knowledge process outsourcing (KPO) companies. Many workers in these industries perform tasks that can be automated by AI. As a result, Indian workers recognize the need to continuously learn new skills to stay relevant in the future. Key Findings from the Study: 🔹 70% of the surveyed workers believe that AI will change their jobs and industries. 🔹 55% of the respondents were men, while 45% were women. 🔹 30% expressed a desire to learn more about AI, while 28% wanted to enhance their IT and tech skills, and 27% aimed to become better leaders. 🔹 50% of the participants stated that if their company does not support their learning and development in the next year, they would consider quitting their job. 🔹 Older workers prioritize earning potential, while younger workers value flexible work arrangements. 🔹 Workers in the automotive, aerospace, food, IT, and finance sectors are particularly concerned about the impact of AI on their jobs. This study emphasizes the importance of continuous learning and acquiring new skills in the face of AI advancements. Indian workers are particularly aware of the need to upskill and adapt to the changing job landscape. 💡 Practical Solutions for Embracing AI 💡 If you want to leverage AI to evolve your company and stay competitive, consider the following practical solutions: 1️⃣ Identify Automation Opportunities: Locate key customer interaction points that can benefit from AI. 2️⃣ Define KPIs: Ensure that your AI initiatives have measurable impacts on business outcomes. 3️⃣ Select an AI Solution: Choose tools that align with your specific needs and offer customization options. 4️⃣ Implement Gradually: Start with a pilot project, gather data, and expand AI usage strategically. To receive expert advice on AI KPI management, connect with us at hello@itinai.com. For continuous insights on leveraging AI, stay tuned to our Telegram channel t.me/itinainews or follow us on Twitter @itinaicom. 🔦 Spotlight on a Practical AI Solution 🔦 Consider exploring the AI Sales Bot from itinai.com/aisalesbot. This solution is designed to automate customer engagement 24/7 and manage interactions across all stages of the customer journey. Discover how AI can redefine your sales processes and enhance customer engagement. 🔗 List of Useful Links 🔗 🔹 AI Lab in Telegram @aiscrumbot – free consultation 🔹 Indian Workers Fear Job Loss to AI More Than Global Peers, Study Finds 🔹 GreatAIPrompts: AI Prompts, AI Tools & AI News 🔹 Twitter – @itinaicom #AI #ArtificialIntelligence #JobLoss #ContinuousLearning #Upskilling #FutureOfWork #AIAdoption #AIInnovation

We need to focus on the AI harms that already exist

We need to focus on the AI harms that already exist AI News, AI, AI tools, Artificial intelligence – MIT Technology Review, Charlotte Jee, Innovation, itinai.com, LLM, t.me/itinai 🌟 Unmasking AI: Protecting Humanity in a World of Machines 🌟 In her book "Unmasking AI: My Mission to Protect What Is Human in a World of Machines," Joy Buolamwini highlights the potential risks associated with AI systems. She emphasizes the need to address existing AI harms and ensure that marginalized communities are not disproportionately affected. Existing AI systems have already shown their capacity to cause harm. From wrongly identifying individuals as criminal suspects to faulty pedestrian tracking in self-driving cars, these systems can put lives at risk. It is crucial to address these real dangers rather than solely focusing on hypothetical existential risks. One challenge in minimizing existing AI harms is the allocation of resources and legislative attention. Companies concerned about existential risks from AI should demonstrate their commitment to safeguarding humanity by refraining from releasing potentially detrimental AI tools. Preventing the creation of fatal AI systems should be a priority. Governments concerned about the lethal use of AI can implement protections to ban lethal autonomous systems and digital dehumanization. By addressing potentially fatal uses of AI without exaggerating the creation of sentient systems that could destroy humanity, we can strike a balance. While physical violence is often seen as the ultimate harm, it is important to recognize the insidious ways in which our societies perpetuate structural violence. AI systems can contribute to this harm by denying access to essential needs such as healthcare, housing, and employment. These systems can have long-lasting effects on individuals and future generations. Algorithmic bias is another concern. Buolamwini's "Gender Shades" research exposed bias in algorithms used by leading tech companies. It is crucial to address these immediate problems and emerging vulnerabilities in AI to prevent false arrests, misdiagnoses, and other harmful outcomes. When considering existential risk, it is essential to think about the people currently being harmed by AI systems and those at risk of harm. The concept of being "excoded" refers to the risk of being adversely affected by AI, such as being denied healthcare or housing due to algorithmic decision-making. No one is immune from being excoded, and marginalized populations are particularly vulnerable. 💡 Practical Solutions for AI Integration 💡 If you want to evolve your company with AI and stay competitive, it is crucial to focus on addressing existing AI harms. Here are some practical steps to consider: 1️⃣ Identify Automation Opportunities: Identify key customer interaction points that can benefit from AI. By automating certain processes, you can enhance efficiency and improve customer experiences. 2️⃣ Define KPIs: Ensure that your AI initiatives have measurable impacts on business outcomes. Define key performance indicators (KPIs) to track the effectiveness of your AI implementation. 3️⃣ Select an AI Solution: Choose AI tools that align with your specific needs and offer customization options. Consider solutions that can be tailored to your industry and business requirements. 4️⃣ Implement Gradually: Start with a pilot project to gather data and assess the impact of AI usage. Gradually expand the integration of AI into your operations, making informed decisions based on the results. For expert advice on AI KPI management, connect with us at hello@itinai.com. Stay updated on leveraging AI by following our Telegram channel or Twitter. 🔦 Spotlight on a Practical AI Solution: AI Sales Bot 🔦 Consider utilizing the AI Sales Bot from itinai.com/aisalesbot. This solution automates customer engagement 24/7 and manages interactions across all stages of the customer journey. Discover how AI can redefine your sales processes and customer engagement. Explore solutions at itinai.com. 🔗 List of Useful Links 🔗 🤖 AI Lab in Telegram: @aiscrumbot - free consultation 📚 We need to focus on the AI harms that already exist - Article 🔬 Artificial intelligence - MIT Technology Review 🐦 Twitter: @itinaicom

This AI Paper Introduces POYO-1: An Artificial Intelligence Framework Deciphering Neural Activity across Large-Scale Recordings with Deep Learning

This AI Paper Introduces POYO-1: An Artificial Intelligence Framework Deciphering Neural Activity across Large-Scale Recordings with Deep Learning AI News, AI, AI tools, Innovation, itinai.com, LLM, MarkTechPost, Sana Hassan, t.me/itinai 🔬 A Revolutionary AI Framework for Decoding Neural Activity 🔬 Researchers from top institutions have introduced a groundbreaking framework called POYO-1, which uses artificial intelligence to analyze neural population dynamics. This framework offers practical solutions and immense value for middle managers looking to leverage AI technology. Key Features of the POYO-1 Framework: ✅ Tokenization: Captures fine temporal neural activity by analyzing individual spikes. ✅ Cross-Attention and PerceiverIO Architecture: Efficiently represents neural events for analysis. ✅ Few-Shot Performance: Achieves impressive results in diverse tasks, showcasing scalability. ✅ Models Neural Activity Dynamics: Can analyze recordings, subjects, and data from various sources. ✅ Efficient Implementation: Improves computational efficiency. The POYO-1 framework addresses the need for a foundational model in neuroscience that can bridge diverse datasets, experiments, and subjects to gain a comprehensive understanding of brain function. It allows for efficient training across different neural recording sessions, even when dealing with different neuron sets and unknown correspondences. The framework has been trained on large primate datasets, demonstrating its ability to adapt rapidly to new sessions with unspecified neuron correspondence for few-shot learning. It has achieved fine-grained results on benchmark datasets and showcased its transferability and brain decoding improvements across sessions. The versatility and effectiveness of the POYO-1 framework have been demonstrated through achieving competitive results on benchmark datasets without weight modifications. The large-scale multi-session model exhibited promising performance in diverse tasks, emphasizing the framework’s potential for comprehensive neural data analysis at scale. To evolve your company with AI and stay competitive, consider leveraging the POYO-1 framework. It offers practical solutions for decoding neural activity across large-scale recordings using deep learning. Discover how AI can redefine your way of work, automate customer engagement, and redefine your sales processes. 📚 For more information on the research paper and project, visit the provided links. Stay connected with our ML SubReddit, Facebook community, Discord channel, and Email Newsletter for the latest AI research news and cool projects. 📧 If you’re interested in AI KPI management advice or continuous insights into leveraging AI, contact us at hello@itinai.com or stay tuned on our Telegram and Twitter channels. 🌟 Spotlight on a Practical AI Solution: Check out the AI Sales Bot from itinai.com/aisalesbot. It is designed to automate customer engagement 24/7 and manage interactions across all customer journey stages. Discover how AI can redefine your sales processes and customer engagement. Explore solutions at itinai.com. 🔗 List of Useful Links: - AI Lab in Telegram @aiscrumbot – free consultation - This AI Paper Introduces POYO-1: An Artificial Intelligence Framework Deciphering Neural Activity across Large-Scale Recordings with Deep Learning - MarkTechPost - Twitter – @itinaicom

AI startups feel the heat as OpenAI adds ChatGPT features

AI startups feel the heat as OpenAI adds ChatGPT features AI News, AI, AI tools, DailyAI, Eugene van der Watt, Innovation, itinai.com, LLM, t.me/itinai [Formatted LinkedIn Post] 🚀 AI Startups Feel the Heat as OpenAI Adds ChatGPT Features 🚀 OpenAI has recently introduced new features to its ChatGPT Plus users, causing a negative impact on some AI startups. These new features have disrupted the business model of several startups that provided similar functionalities through plugins. 🔹 ChatGPT now supports the uploading and analysis of various file types, including PDFs, without the need for specific tools or plugins. This addition has made it challenging for startups that offered PDF analysis services to compete. 🔒 Large corporations like Google and Meta have discussions on the need for a "moat" to protect their AI products. Smaller startups often build features as standalone products, making them vulnerable to OpenAI's ability to add those features to ChatGPT directly. The introduction of new features has raised concerns among AI startups that rely on OpenAI's technology. Not owning the underlying model puts these startups at significant risk. 🔧 Practical Solutions for Evolving with AI 🔧 If you want to leverage AI to evolve your company and stay competitive, consider the following steps: 1️⃣ Identify Automation Opportunities: Locate key customer interaction points that can benefit from AI. 2️⃣ Define KPIs: Ensure your AI endeavors have measurable impacts on business outcomes. 3️⃣ Select an AI Solution: Choose tools that align with your needs and provide customization. 4️⃣ Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously. For AI KPI management advice, connect with us at hello@itinai.com. Stay updated on leveraging AI by following us on Telegram or Twitter. 🔦 Spotlight on a Practical AI Solution: AI Sales Bot 🔦 Consider using the AI Sales Bot from itinai.com/aisalesbot to automate customer engagement and manage interactions across all stages of the customer journey. Explore how AI can redefine your sales processes and customer engagement on our website. 🌐 List of Useful Links 🌐 - AI Lab in Telegram @aiscrumbot – free consultation - AI startups feel the heat as OpenAI adds ChatGPT features - DailyAI - Twitter – @itinaicom

The brain may learn about the world the same way some computational models do

The brain may learn about the world the same way some computational models do AI News, AI, AI tools, Anne Trafton | MIT News, Innovation, itinai.com, LLM, MIT News - Artificial intelligence, t.me/itinai 🧠 The Brain’s Intuitive Understanding and AI: New Findings 🤖 Exciting new research from MIT suggests that the brain may develop an intuitive understanding of the physical world through a process similar to self-supervised learning. This means that the brain learns about the world based on similarities and differences, without the need for labels or other information. Researchers at MIT's K. Lisa Yang Integrative Computational Neuroscience (ICoN) Center trained neural network models using self-supervised learning techniques. They found that the activity patterns generated by these models closely resemble those seen in the brains of animals performing the same tasks. This provides evidence that the mammalian brain may use self-supervised learning to learn representations of the physical world. 🚀 Practical Implications for AI Solutions 📈 These findings have practical implications for AI solutions that can assist in building better robots and understanding the brain. Here are some practical steps that middle managers can take to leverage AI: 1️⃣ Identify Automation Opportunities: AI can help identify key customer interaction points that can benefit from automation, improving efficiency and customer experience. 2️⃣ Define KPIs: It's crucial to ensure that AI initiatives have measurable impacts on business outcomes. Define key performance indicators (KPIs) to track the success of AI implementations. 3️⃣ Select an AI Solution: Choose AI tools that align with your specific needs and provide customization options. Look for solutions that can be tailored to your business requirements. 4️⃣ Implement Gradually: Start with a pilot project, gather data, and expand the use of AI gradually. This approach allows for learning and adjustment along the way. For expert advice on AI KPI management and continuous insights into leveraging AI, connect with us at hello@itinai.com. Stay updated on leveraging AI by following us on Telegram (t.me/itinainews) and Twitter (@itinaicom). 🔦 Spotlight on a Practical AI Solution: AI Sales Bot 💼 Consider exploring the AI Sales Bot from itinai.com/aisalesbot. This AI solution is designed to automate customer engagement 24/7 and manage interactions across all stages of the customer journey. It can redefine sales processes and enhance customer engagement. Discover more about our AI solutions at itinai.com. 🔗 List of Useful Links: 🔹 AI Lab in Telegram @aiscrumbot – free consultation 🔹 The brain may learn about the world the same way some computational models do 🔹 MIT News – Artificial intelligence 🔹 Twitter – @itinaicom

Microsoft’s first-quarter financial results surpass analyst expectations

Microsoft’s first-quarter financial results surpass analyst expectations AI News, AI, AI tools, DailyAI, Innovation, itinai.com, LLM, Sam Jeans, t.me/itinai 🚀 Microsoft's Q1 financial results have exceeded expectations, driven by cloud computing and the Windows operating system. The release of Microsoft 365 Copilot, a suite of AI tools developed with OpenAI, played a crucial role in boosting revenue. 💰 Key Highlights: - Microsoft's revenue increased by 13% to $56.5 billion, surpassing analysts' estimates. - Revenue from the Intelligent Cloud unit, including Azure, reached $24.3 billion, outperforming forecasts. - Azure's revenue grew by 29%, exceeding market research firm Visible Alpha's projection. - Clients' engagement with Microsoft's cloud services in anticipation of integrating AI capabilities contributed to sales growth. 🔑 To stay competitive and evolve your company with AI, consider these practical solutions: 1️⃣ Identify Automation Opportunities: Find customer interaction points that can benefit from AI. 2️⃣ Define KPIs: Ensure your AI initiatives have measurable impacts on business outcomes. 3️⃣ Select an AI Solution: Choose tools that align with your needs and offer customization. 4️⃣ Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously. 📣 For AI KPI management advice, connect with us at hello@itinai.com. Stay updated on our Telegram channel t.me/itinainews or follow us on Twitter @itinaicom for continuous insights into leveraging AI. 🔦 Spotlight on a Practical AI Solution: Consider the AI Sales Bot from itinai.com/aisalesbot. It automates customer engagement 24/7 and manages interactions across all customer journey stages. Discover how AI can redefine your sales processes and customer engagement. Explore solutions at itinai.com. 🔗 Useful Links: - AI Lab in Telegram @aiscrumbot – free consultation - Microsoft's first-quarter financial results surpass analyst expectations - DailyAI - Twitter – @itinaicom

NYU Researchers have Created a Neural Network for Genomics that can Explain How it Reaches its Predictions

NYU Researchers have Created a Neural Network for Genomics that can Explain How it Reaches its Predictions AI News, AI, AI tools, Innovation, itinai.com, LLM, MarkTechPost, Pragati Jhunjhunwala, t.me/itinai 🔬 NYU Researchers have Created a Neural Network for Genomics that can Explain How it Reaches its Predictions 🔬 In the world of biological research, machine learning models are advancing our understanding of complex processes, particularly RNA splicing. However, a common limitation of many machine learning models in this field is their lack of interpretability – they can predict outcomes accurately but struggle to explain how they arrived at those predictions. To address this issue, NYU researchers have introduced an “interpretable-by-design” approach that not only ensures accurate predictive outcomes but also provides insights into the underlying biological processes, specifically RNA splicing. This innovative model has the potential to significantly enhance our understanding of this fundamental process. 🔑 Key Features of the Model 🔑 ✅ The model is explicitly designed to be interpretable while maintaining predictive accuracy on par with state-of-the-art models. ✅ It was trained with an emphasis on interpretability, using Python 3.8 and TensorFlow 2.6. ✅ The model’s architecture includes sequence and structure filters, which are instrumental in understanding RNA splicing. ✅ Through a visualization tool called the “balance plot,” researchers can explore and quantify how multiple RNA features contribute to the splicing outcomes of individual exons. ✅ The model has confirmed previously established RNA splicing features and uncovered two uncharacterized exon-skipping features related to stem loop structures and G-poor sequences. 💡 Practical Applications 💡 The “interpretable-by-design” machine learning model represents a powerful tool in the biological sciences. It not only achieves high predictive accuracy but also provides a clear and interpretable understanding of RNA splicing processes. The model’s ability to quantify the contributions of specific features to splicing outcomes has the potential for various applications in medical and biotechnology fields, from genome editing to the development of RNA-based therapeutics. This approach can also be applied to decipher other complex biological processes, opening new avenues for scientific discovery. 🚀 How AI Can Benefit Your Company 🚀 If you want to evolve your company with AI and stay competitive, consider leveraging the neural network model created by NYU researchers. It can provide valuable insights into complex biological processes, including RNA splicing. To implement AI successfully, follow these steps: 1️⃣ Identify Automation Opportunities: Locate key customer interaction points that can benefit from AI. 2️⃣ Define KPIs: Ensure your AI endeavors have measurable impacts on business outcomes. 3️⃣ Select an AI Solution: Choose tools that align with your needs and provide customization. 4️⃣ Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously. For AI KPI management advice and continuous insights into leveraging AI, connect with us at hello@itinai.com. Explore practical AI solutions, such as the AI Sales Bot from itinai.com/aisalesbot, designed to automate customer engagement and manage interactions across all customer journey stages. 🔗 List of Useful Links 🔗 🔹 AI Lab in Telegram @aiscrumbot – free consultation 🔹 NYU Researchers have Created a Neural Network for Genomics that can Explain How it Reaches its Predictions 🔹 MarkTechPost 🔹 Twitter – @itinaicom

Enhancing Engineering Design Evaluation through Comprehensive Metrics for Deep Generative Models

Enhancing Engineering Design Evaluation through Comprehensive Metrics for Deep Generative Models AI News, AI, AI tools, Innovation, itinai.com, LLM, Madhur Garg, MarkTechPost, t.me/itinai 🚀 Enhancing Engineering Design Evaluation through Comprehensive Metrics for Deep Generative Models 🚀 Did you know that a group of researchers has developed a set of metrics to evaluate the performance of deep generative models (DGMs) in engineering design? These metrics focus on aspects like design constraints, diversity, novelty, and target achievement, providing a more holistic understanding of the capabilities and limitations of DGMs. 🎯 Why are these metrics important? The integration of these metrics allows for the identification of innovative and diverse design solutions while adhering to critical constraints. By using these metrics, researchers and practitioners can gain deeper insights into the design space and make informed decisions to advance engineering design. 💡 Key Findings: 1️⃣ Traditional evaluation of DGMs in engineering design has primarily relied on statistical similarity, neglecting crucial design constraints. 2️⃣ The research team has developed a curated set of alternative metrics tailored for engineering design tasks. 3️⃣ These metrics encompass constraint satisfaction, diversity, novelty, and target achievement, providing a comprehensive assessment of DGM capabilities. 4️⃣ Integrating these metrics into the evaluation process enables the identification of novel and diverse design solutions while ensuring adherence to critical constraints. ⚙️ Practical AI Solution: AI Sales Bot Looking to automate customer engagement and optimize your sales processes? Consider using the AI Sales Bot from itinai.com/aisalesbot. This AI solution can automate customer interactions 24/7 and manage interactions across all customer journey stages, providing a seamless and efficient experience for your customers. 🌟 Embrace AI and Stay Competitive To evolve your company with AI and stay competitive, start by piloting AI solutions that align with your needs. Gather data and gradually implement AI solutions that provide measurable impacts on your business outcomes. For AI KPI management advice, feel free to connect with us at hello@itinai.com. 🔗 Stay Connected Stay tuned for continuous insights into leveraging AI by following us on Telegram at t.me/itinainews or on Twitter @itinaicom. You can also join our AI Lab in Telegram @aiscrumbot for a free consultation. Let's unlock the potential of AI in engineering design and drive transformation together! 🌟🔧🚀

Mastercard Partners with MoonPay to Revolutionize Crypto Payments and Web3

Mastercard Partners with MoonPay to Revolutionize Crypto Payments and Web3 AI News, AI, AI tools, AI Tools & AI News, GreatAIPrompts: AI Prompts, Innovation, itinai.com, LLM, Mukund Kapoor, t.me/itinai 🚀 Mastercard Partners with MoonPay to Revolutionize Crypto Payments and Web3 🌐 Exciting news! Global payment leader Mastercard has joined forces with MoonPay, a crypto payment platform, to revolutionize the world of digital payments. This partnership aims to leverage Web3 tools to enhance marketing strategies and provide innovative customer experiences. At the recent Money20/20 event in Las Vegas, Keith Grossman from MoonPay and Raja Rajamannar from Mastercard unveiled their plans for this collaboration. They are determined to create new and improved customer experiences using Web3 technology. MoonPay will contribute its own smart features to complement Mastercard's existing offerings, ensuring secure and efficient financial transactions. Adam Polansky, Mastercard's Web3 expert, expressed his enthusiasm for this collaboration, while Elizabeth Taylor, responsible for partnerships at Mastercard, emphasized their anticipation for exciting developments. Mastercard has been actively exploring Web3 initiatives and has previously collaborated with companies like MetaMask and Ledger. They have also partnered with Coinbase and MoonPay to delve deeper into Web3 and non-fungible tokens (NFTs). MoonPay is involving its subsidiary, Otherlife, in this partnership. Otherlife will provide creative assistance and additional services related to Web3. This collaboration between Mastercard and MoonPay highlights the growing importance of Web3 and the eagerness of major financial institutions to be part of this digital revolution. 💡 Practical Solutions for AI Integration 💼 If you want to evolve your company with AI and stay competitive, consider leveraging the partnership between Mastercard and MoonPay to revolutionize crypto payments and Web3. Here are some practical steps to redefine your work processes using AI: 1️⃣ Identify Automation Opportunities: Locate key customer interaction points that can benefit from AI. 2️⃣ Define KPIs: Ensure that your AI initiatives have measurable impacts on business outcomes. 3️⃣ Select an AI Solution: Choose tools that align with your needs and offer customization. 4️⃣ Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously. For AI KPI management advice, connect with us at hello@itinai.com. Stay updated on leveraging AI by following our Telegram channel t.me/itinainews or Twitter @itinaicom. 🔦 Spotlight on a Practical AI Solution: AI Sales Bot 🤖 Consider utilizing the AI Sales Bot from itinai.com/aisalesbot. This solution is designed to automate customer engagement 24/7 and manage interactions across all stages of the customer journey. Discover how AI can redefine your sales processes and customer engagement by exploring solutions at itinai.com. 🔗 List of Useful Links: 🔹 AI Lab in Telegram @aiscrumbot – free consultation 🔹 Mastercard Partners with MoonPay to Revolutionize Crypto Payments and Web3 🔹 GreatAIPrompts: AI Prompts, AI Tools & AI News 🔹 Twitter – @itinaicom

A New AI Research Fujitsu Improves Weakly-Supervised Action Segmentation For Human-Robot Interaction With Action-Union Learning

A New AI Research Fujitsu Improves Weakly-Supervised Action Segmentation For Human-Robot Interaction With Action-Union Learning AI News, AI, AI tools, Innovation, itinai.com, LLM, MarkTechPost, t.me/itinai, Tanya Malhotra 🚀 Introducing AI Solutions for Human-Robot Interaction 🤖 Exciting advancements in human action recognition have revolutionized Human-Robot Interaction (HRI). Robots can now understand human behavior and collaborate effectively. One crucial aspect is action segmentation, which involves labeling and timing human actions. This skill is essential for robots to localize behaviors and work alongside individuals. 🔥 Challenges in Action-Segmentation Model Training Traditional methods require a large number of labels, which can be expensive and time-consuming. Inconsistent labeling and unclear time boundaries can introduce bias. 🎯 Addressing the Challenges Researchers have developed a unique learning technique that maximizes the likelihood of action union for unlabeled frames. This approach improves the quality and reliability of training for unlabeled frames. 💡 Refining Action-Segmentation during Inference A novel refining method has been developed to improve the accuracy of action labels. This process considers frame-by-frame predictions, consistency, and smoothness of action labels over time. It enhances the model's capacity to provide precise and reliable action categorizations. 🌟 Applicability and Effectiveness These techniques are adaptable to existing action segmentation frameworks, making integration into different robot learning systems easy. They have achieved state-of-the-art performance levels on widely used action-segmentation datasets. Remarkably, this method requires less than 1% of fully-supervised labels, making it a cost-effective solution. 🔑 Key Contributions - Introducing action-union optimization into action-segmentation training - Introducing a post-processing technique that improves model correctness and reliability - Demonstrating new state-of-the-art outcomes, advancing Human-Robot Interaction research If you're interested in utilizing AI solutions to enhance your company's operations, consider Fujitsu's latest AI research on improving weakly-supervised action segmentation for human-robot interaction. Discover how AI can redefine your way of work and identify automation opportunities. For AI KPI management advice, contact us at hello@itinai.com. Stay updated on the latest AI insights by following us on Telegram (t.me/itinainews) or Twitter (@itinaicom). 💼 A Practical AI Solution: AI Sales Bot from itinai.com Explore the AI Sales Bot from itinai.com/aisalesbot, designed to automate customer engagement and manage interactions across all stages of the customer journey. This AI-powered solution can redefine your sales processes and customer engagement by providing 24/7 support. 🔗 Useful Links: - AI Lab in Telegram @aiscrumbot – free consultation - A New AI Research Fujitsu Improves Weakly-Supervised Action Segmentation For Human-Robot Interaction With Action-Union Learning - MarkTechPost - Twitter – @itinaicom

Meet Eureka: A Human-Level Reward Design Algorithm Powered by Large Language Model LLMs

Meet Eureka: A Human-Level Reward Design Algorithm Powered by Large Language Model LLMs AI News, Adnan Hassan, AI, AI tools, Innovation, itinai.com, LLM, MarkTechPost, t.me/itinai 🌟 Spotlight on a Practical AI Solution: Introducing the AI Sales Bot from itinai.com/aisalesbot. 🌟 Are you ready to evolve your company with AI and stay competitive? Look no further than EUREKA: A Human-Level Reward Design Algorithm Powered by Large Language Model LLMs. This breakthrough algorithm, developed by researchers from NVIDIA, UPenn, Caltech, and UT Austin, revolutionizes skill acquisition through reinforcement learning. EUREKA leverages advanced LLMs like GPT-4 to create reward functions for complex skills, filling the gap in low-level tasks such as pen spinning. It outperforms human-engineered rewards by providing safer and higher-quality tips based on human feedback. This means you can achieve human-level reward generation in 83% of tasks, with an average improvement of 52%. Here are the key benefits and solutions that EUREKA brings to the table: 1️⃣ EUREKA enhances rewards in real-time, utilizing LLMs to generate interpretable reward codes. 2️⃣ It revolutionizes low-level skill-learning tasks by combining evolutionary algorithms with LLMs for reward design. 3️⃣ EUREKA overcomes the challenges of time-consuming trial and error in reward engineering. 4️⃣ It excels in diverse environments, outperforming human-engineered rewards. 5️⃣ EUREKA enables in-context learning from human feedback, improving reward quality and safety. But that's not all! EUREKA is a versatile and scalable solution for reward design in challenging problems. It eliminates the need for initial candidates or few-shot prompting, making it highly adaptable. Plus, it holds great promise for diverse reinforcement learning and reward design applications. Future research will focus on adaptability, real-world applicability, and exploring synergies with other reinforcement learning techniques. The possibilities are endless! To learn more about EUREKA, you can read the full research paper linked here: [insert link] If you're interested in implementing AI solutions in your company, consider leveraging EUREKA and reaching out to us at hello@itinai.com. We can help you redefine your work processes, identify automation opportunities, define KPIs, select the right AI solution, and implement it gradually. Stay updated on the latest AI research news and projects by joining our ML SubReddit, Facebook Community, Discord Channel, and Email Newsletter. Follow us on Twitter @itinaicom for more exciting updates! And don't forget to check out our AI Sales Bot from itinai.com/aisalesbot. This solution automates customer engagement 24/7 and manages interactions throughout the customer journey. Explore how AI can redefine your sales processes and customer engagement by visiting itinai.com. List of Useful Links: 🔗 AI Lab in Telegram @aiscrumbot – free consultation 🔗 Meet Eureka: A Human-Level Reward Design Algorithm Powered by Large Language Model LLMs 🔗 MarkTechPost 🔗 Twitter – @itinaicom

Deep fakes wreak havoc amid the Israel-Palestine conflict

Deep fakes wreak havoc amid the Israel-Palestine conflict AI News, AI, AI tools, DailyAI, Innovation, itinai.com, LLM, Sam Jeans, t.me/itinai 🚀 Deep Fakes: A Challenge for the AI Industry 🚀 The rise of deep fakes is a significant challenge for the AI industry. In 2023, we've seen a surge in AI-generated fake images and voice recordings, including fake news related to the Israel-Hamas conflict. This prevalence of AI-generated fakes has created doubts about the authenticity of real content. The problem is that even genuine content can now be dismissed as fake due to the existence of AI-generated fakes. This erosion of trust is especially evident on social media platforms. The rapid evolution of deep fake technology has made it harder to identify inconsistencies in AI-generated content. The exposure and debate surrounding fake content attract attention, likes, and comments, amplifying the problem. Detection tools have been used to separate truth from AI-generated content, but they have proven inconsistent and unreliable. The ethical implications of deep fake technology are being debated, and it remains uncertain how people will adjust to its presence on social media and if tech companies can regain control. 🔍 The Impact of Deep Fakes 🔍 AI-generated deep fakes have led to people questioning the legitimacy of real images, videos, and audio clips. This creates a "liar's dividend" where real content can be dismissed as fake. Social media amplifies suspicion and paranoia, making it easy to label something real as fake and vice versa. As deep fake technology evolves, it becomes harder to identify inconsistencies in AI-generated images. This erosion of trust becomes more evident in emotionally charged discussions, like those surrounding the Gaza conflict. 💡 Practical Solutions for Your Company 💡 To stay competitive and leverage AI to your advantage, consider the following practical solutions: 1️⃣ Identify Automation Opportunities: Locate key customer interaction points that can benefit from AI. 2️⃣ Define KPIs: Ensure your AI endeavors have measurable impacts on business outcomes. 3️⃣ Select an AI Solution: Choose tools that align with your needs and provide customization. 4️⃣ Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously. For AI KPI management advice, connect with us at hello@itinai.com. Stay tuned on our Telegram channel t.me/itinainews or Twitter @itinaicom for continuous insights into leveraging AI. 🌟 Spotlight on a Practical AI Solution: AI Sales Bot 🌟 Consider the AI Sales Bot from itinai.com/aisalesbot. This solution is designed to automate customer engagement 24/7 and manage interactions across all customer journey stages. Discover how AI can redefine your sales processes and customer engagement. Explore solutions at itinai.com. 🔗 List of Useful Links 🔗 🔹 AI Lab in Telegram @aiscrumbot – free consultation 🔹 Deep fakes wreak havoc amid the Israel-Palestine conflict 🔹 DailyAI 🔹 Twitter – @itinaicom

Meet 3D-GPT: An Artificial Intelligence Framework for Instruction-Driven 3D Modelling that Makes Use of Large Language Models (LLMs)

Meet 3D-GPT: An Artificial Intelligence Framework for Instruction-Driven 3D Modelling that Makes Use of Large Language Models (LLMs) AI News, AI, AI tools, Aneesh Tickoo and Asif Razzaq, Innovation, itinai.com, LLM, MarkTechPost, t.me/itinai 🌟 Introducing 3D-GPT: An AI Framework for Instruction-Driven 3D Modeling 🌟 In the metaverse age, the demand for 3D content is skyrocketing across industries like gaming, virtual reality, and film. However, the process of 3D modeling can be complex and time-consuming. That's where 3D-GPT comes in. Developed by researchers from Australian National University, the University of Oxford, and Beijing Academy of Artificial Intelligence, 3D-GPT empowers middle managers to take control of 3D creative software and meet customer demands effectively. Key Features of 3D-GPT: 1️⃣ Streamlined 3D Modeling Process: 3D-GPT breaks down the 3D modeling process into smaller, manageable segments, enabling middle managers to create 3D content efficiently. 2️⃣ Problem-Solving Agents: 3D-GPT consists of three main agents – the conceptualization agent, the 3D modeling agent, and the job dispatch agent. These agents work together to ensure smooth communication, effective instruction-driven synthesis, and accurate 3D creation. 3️⃣ Procedural Generation: 3D-GPT leverages procedural generation, a method that automates content development using changeable parameters and rule-based systems. This allows for customizable and precise 3D creation. By using 3D-GPT, middle managers can enhance their productivity and create more complex and detailed 3D content. The framework seamlessly integrates with Blender, providing access to various editing and manipulation tools. 💡 Value and Practical Solutions: By leveraging 3D-GPT, middle managers can: ✅ Improve productivity and efficiency in 3D content production. ✅ Streamline the creative process and prioritize the user experience. ✅ Automate customer engagement and manage interactions using AI Sales Bot from itinai.com/aisalesbot. By utilizing AI solutions like 3D-GPT and AI Sales Bot, companies can stay competitive, redefine their work processes, and achieve measurable business outcomes. Connect with us at hello@itinai.com for AI KPI management advice and visit itinai.com for more information on leveraging AI in your organization. 🔗 List of Useful Links: ✅ AI Lab in Telegram @aiscrumbot – free consultation ✅ Meet 3D-GPT: An Artificial Intelligence Framework for Instruction-Driven 3D Modeling that Makes Use of Large Language Models (LLMs) - MarkTechPost ✅ Twitter – @itinaicom

This AI Research from China Introduces Character-LLM that Teaches LLMs to Act as Specific People such as Beethoven, Queen Cleopatra, Julius Caesar, etc.

This AI Research from China Introduces Character-LLM that Teaches LLMs to Act as Specific People such as Beethoven, Queen Cleopatra, Julius Caesar, etc. AI News, Adnan Hassan, AI, AI tools, Innovation, itinai.com, LLM, MarkTechPost, t.me/itinai 🚀 Introducing Character-LLM: Training AI Agents to Simulate Historical Figures 🎭 Character-LLM is an innovative AI agent that can simulate specific individuals by training models and editing profiles to replicate their unique experiences. This breakthrough in AI-driven character simulation allows for a deeper understanding of human experiences and personalities. 🔬 Training Framework for Character Simulacra A team of researchers from China has developed a training framework using Character-LLM to create digital replicas of historical figures such as Beethoven, Queen Cleopatra, and Julius Caesar. This involves reconstructing experiences, uploading them into the agents, and providing protective experiences. The effectiveness of this approach is evaluated in a test playground through interviews with the trained agents. 💡 Exploring LLMs for Human Behavior Simulation LLMs like ChatGPT and GPT-4 are being explored to simulate human behaviors in daily routines and deeper experiences. To address the limitations of simple LLM prompting, Character-LLM was introduced as a trainable agent that learns from real experiences and emotions. By collecting and training on specific historical figures’ experiences, Character-LLMs have potential applications in social science, NPC development, and labor reduction. 📊 Impressive Performance and Valuable Insights Character-LLMs demonstrate superior performance in personality, memorization, hallucination, and stability compared to baseline models. Despite their smaller scale, they achieve performance comparable to the powerful ChatGPT. These trainable agents produce vivid responses, recall specific past experiences, and reject unnatural questions. The experimental findings offer valuable insights for advancing human simulacra development. 🔍 Conclusion: AI Solutions for Simulating Individuals Character-LLM is an effective trainable agent for simulating specific individuals, showcasing impressive performance in various aspects. They compare favorably with powerful baseline models and offer vivid responses, recall specific experiences, and reject unnatural queries. These findings provide valuable insights for advancing human simulacra development. To explore how AI can revolutionize your company, connect with us at hello@itinai.com. Check out the original research paper for more details on this exciting AI breakthrough. Stay updated with the latest AI research news and projects by joining our 32k+ ML SubReddit, 40k+ Facebook Community, Discord Channel, and Email Newsletter. Subscribe to our newsletter for even more AI insights. For AI KPI management advice, reach out to us at hello@itinai.com. Follow us on Telegram at t.me/itinainews or Twitter @itinaicom for continuous updates on leveraging AI. 🌟 Spotlight: AI Sales Bot Discover how AI can redefine your sales processes and customer engagement with our AI Sales Bot. This solution automates customer engagement 24/7 and manages interactions across all customer journey stages. Visit itinai.com/aisalesbot to learn more. 🔗 List of Useful Links: - AI Lab in Telegram @aiscrumbot – free consultation - This AI Research from China Introduces Character-LLM that Teaches LLMs to Act as Specific People such as Beethoven, Queen Cleopatra, Julius Caesar, etc. - MarkTechPost - Twitter – @itinaicom

Robot stand-in mimics movements in VR

Robot stand-in mimics movements in VR AI News, AI, AI tools, Artificial Intelligence News -- ScienceDaily, Innovation, itinai.com, LLM, t.me/itinai 🚀 Introducing a Revolutionary Telepresence Robot 🤖 Experience a whole new level of virtual reality with our advanced telepresence robot! This cutting-edge technology instantly responds to your movements and gestures, creating an immersive and interactive experience like never before. 🔥 Use AI to Evolve Your Company and Gain a Competitive Edge 💪 Stay ahead of the competition by harnessing the power of AI to enhance your company's operations. Our robot stand-in, which mimics your movements in virtual reality, can be a game-changer for your business. 💡 Practical Solutions for AI Implementation 🛠️ Discover how AI can transform the way you work with practical solutions tailored for middle managers. Here are some key steps to get started: 1️⃣ Identify Automation Opportunities: Find critical customer interaction points that can benefit from AI. Automating these processes improves efficiency and provides a seamless customer experience. 2️⃣ Define Key Performance Indicators (KPIs): Ensure your AI initiatives have measurable impacts on your business outcomes. Define clear KPIs to track success and drive continuous improvement. 3️⃣ Select an AI Solution: Choose AI tools that align with your specific needs and allow for customization. Finding the right solution is crucial for maximizing AI benefits. 4️⃣ Implement Gradually: Start with a pilot program to test AI effectiveness in your business processes. Gather data and insights to refine and expand AI usage strategically. 📊 AI KPI Management Advice and Continuous Insights 📈 For expert guidance on managing AI KPIs and leveraging AI effectively, reach out to us at hello@itinai.com. Stay updated on the latest AI insights and news by following us on Telegram at t.me/itinainews or on Twitter @itinaicom. 🌟 Spotlight on a Practical AI Solution: AI Sales Bot 🤖 Revolutionize your sales processes and customer engagement with our AI Sales Bot from itinai.com/aisalesbot. This innovative solution automates customer engagement 24/7 and manages interactions across all stages of the customer journey. Explore the possibilities of AI for your business at itinai.com. 🔗 List of Useful Links: - AI Lab in Telegram @aiscrumbot – free consultation - Robot stand-in mimics movements in VR - Artificial Intelligence News — ScienceDaily - Twitter – @itinaicom