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Sneak peak at the current line up for the Generative AI Summit 2024 (Tottenham stadium on 21+22 May). Full program to be announced in January! Cassie Kozyrkov, CEO, Data Scientific Tom Mason, CTO, Stability AI Nina Schick, Generative AI Expert Brian Kursar, CTO & Group VP, Toyota Sol Rashidi, Former Chief Analytics Officer, Estée Lauder Robert Chilvers, Chief Data & AI Officer, Newcross Healthcare Sanjeevan Bala, Group Chief Data and AI Officer, ITV Detlef Nauck, Chief Research Scientist for Data Science, BT Aymen Shabou, CTO & Head of Artificial Intelligence, Crédit Agricole Anna Zeiter, Chief Privacy Officer & Associate General Counsel, eBay Dara Sosulski, Head of Artificial Intelligence and Model Management, HSBC Manu Kumar, CDO, Richemont Group YNAP Tomas Navarro, Future Projects Engineer, European Space Agency Máuhan Zonoozy, Head of Innovation, Spotify Tim Fu, Founder, Studio Tim Fu Nikolay Burlutskiy, Director AI, AstraZeneca Fausto Artico, Global Head of Innovation and Data Science, GSK David Moore, Director of Emerging Technologies, Balfour Beatty Divita Vohra, Senior AI Product Manager, Spotify Wolfgang Hofmann, Group Head of Technology and Innovation, Fresenius Jim Edwards, Global Innovation Capabilities Leader, Kimberly Clark  Shruthi Velidi, Former Responsible AI, Visa Imre Szücs, Group Head of Data and Analytics, MOL Group Chanuki Illushka Seresinhe, Head of Data Science, Zoopla  Jean-Paul Paoli Michon, Generative AI Business Transformation Director, L'Oréal Hatim Abdulhussein, National Clinical Lead for AI and Digital Workforce, NHS England Daniel Hulme, Chief AI Officer, WPP David Stevenson, Chief Data Scientist, Leonardo  Andy Parsons, Senior Director Content Authenticity Initiative, Adobe Stefanie Khan, Director of Business Intelligence & Data Analytics, UPS Kazik Surala, Head of Data Governance & Analytics, Philip Morris Hina Dixit, Partner, M12 Emanuele Colonnella, Innovation Lead, Generali Mohamed Dhouib, Researcher, École Polytechnique’s Informatics Lab Jason Smith, CDO, Publicis Groupe Bilal Alani, Group Head IT & Data - R&D, Danone Jordi Escayola, Head of Advanced Analytics, Artificial Intelligence and Data, Sanofi CHC Francesco Federico, Global Head of Marketing, S&P Global Ratings Matt Kurleto, Founder & CEO, Neoteric Paul Cardno, Global Digital Automation & Innovation, 3M  Joe Reis, Fundamentals of Data Engineering (O'Reilly 2022) Sateesh Vidhyanadhan, Head of Digital Transformations and Technology, Centrica Djamel Mostefa, Head of Data and Artificial Intelligence, Adeo Jules Ferdinand Pagna Disso, Head of Cyber Risk, BNP Paribas Rebecca Swift, SVP Creative Content, Getty Images Matt Cosad, Head of Data & Analytics, Kraft Heinz  Nivedh R Iyer, Group Head of Data Management, Danske Bank Igino Fucci, Head of Experience Design for AI, AIG David Foster, Founding Partner, Applied Data Science Partners Igor Menghini, Senior Director of Product, Roche Jonathan Crowther, Head, Predictive Analytics, Pfizer

Snowflake and Synthetic Data: Snowflake, a prominent "data-as-a-service" company, has recognized the immense potential of synthetic data in the realm of artificial intelligence (AI). Synthetic data, generated by machines to emulate real-world data, presents a solution to the challenges associated with using actual data, such as privacy concerns and data collection expenses. Benefits of Synthetic Data for Snowflake and Others: Snowflake's data marketplace, encompassing a wide array of industries, now includes synthetic datasets crafted by generative AI algorithms. For instance, San Francisco-based Synthesis AI offers a synthetic human face dataset comprising diverse images. This synthetic data can be invaluable for training AI models without exposing sensitive information contained in actual datasets. Addressing Bias and Privacy Concerns: Synthetic data can address concerns about bias in datasets, particularly in applications like facial recognition. By generating synthetic datasets aligned with specific inclusiveness requirements, it mitigates biases and potential unfairness in AI algorithms. Scalability and Customization: The synergy of generative AI algorithms with synthetic data allows rapid scaling of datasets to meet diverse needs. Furthermore, customization for different global customers becomes easier. Future Role of Synthetic Data: Snowflake anticipates that AI-generated synthetic data will have a substantial impact on its business. As generative models, such as large language models (LLMs), advance, they will excel in creating synthetic data that increasingly mirrors the real world. This trend promises more cost-effective and efficient insights for businesses, enhancing their AI capabilities. Gartner research indicates that business leaders are turning to synthetic data due to issues related to real-world data's accessibility, complexity, and availability. AgileView's work in synthetic data serves as a nexus to Snowflake by providing valuable synthetic data assets that can be integrated into Snowflake's expansive data marketplace, enriching the offerings available to businesses and organizations seeking reliable, privacy-conscious data for their AI initiatives. Our past success in supporting businesses and defense entities with high-quality synthetic data underscores its capability to meet the rigorous demands of Snowflake's data marketplace, offering trustworthy and efficient data solutions for a wide range of industries and government applications. #syntheticdata #business #support #AI #dataasservice #datacollection #operations #privacy

🚀 Your AI is Only as Good as Your Data 🚀 As the Director of Innovation, I am constantly amazed by the transformative power of generative AI. These cutting-edge models have the ability to generate human-like text, images, code, and so much more. It's almost like magic! But here's the catch: the quality of your AI is directly dependent on the quality of your data. Artificial intelligence is no longer a technology of the future, it's a reality of today. We've already witnessed how AI-powered tools like ChatGPT and language models like BERT have revolutionized industries. But these developments are just the tip of the iceberg. AI is here to stay, and it's only going to become more powerful and pervasive. That's why partnering with artificial intelligence consulting companies is crucial. These companies specialize in harnessing the power of AI and can help you navigate the complex world of generative AI. They have the expertise to ensure that your AI models are trained on high-quality data, resulting in more accurate and reliable outputs. But it's not just about the consulting companies. The importance of open source in generative AI cannot be overstated. Open source technology has played a significant role in the rise of generative AI, allowing for collaboration and innovation. However, the question of open source is still "open" in generative AI. Sometimes the code is open, other times the training data and weights are open. It's a dynamic and evolving landscape. One company that has embraced the power of generative AI and open source is Microsoft. Their Fabric platform has recently introduced new generative AI technology, further pushing the boundaries of what is possible. This commitment to openness and innovation is commendable and sets an example for others in the industry. At Wolters Kluwer and Tagetik, we understand the importance of data in AI. We know that your AI is only as good as your data. That's why we invest heavily in data quality and integrity. [3] [2] We work closely with our clients to ensure that their AI models are trained on the right data, resulting in actionable insights and better decision-making. So, whether you're a business leader, a data scientist, or simply someone interested in the world of AI, remember that your AI is only as good as your data. Partner with AI consulting companies, embrace open source, and invest in high-quality data. Together, we can unlock the full potential of generative AI and shape a better future. #AI #GenerativeAI #DataQuality #OpenSource #Innovation References: [1] Why Partner with Artificial Intelligence Consulting Companies?: https://lnkd.in/eGXrim-j [2] The importance of open source in GenAI: https://lnkd.in/dDYZVfQ6 [3] Microsoft Fabric gets new generative AI tech, more openness: https://lnkd.in/dXPRjni6

In 2023, Artificial Intelligence (AI) stole the spotlight, setting the stage for a technological super-cycle comparable to the internet and smartphones. As we move into 2024, there are four crucial trends that businesses need to consider for successful AI implementation: 💡 Operationalizing AI and Machine Learning Enterprises must ensure access to high-quality data, automate data blending, ensure security, and maintain governance protocols to support various use cases. Rich, unbiased datasets at scale are essential for AI success. 💡 Cultural Shift in Data Literacy Generative AI is breaking down technical barriers, enabling millions of non-technical users to access data and insights easily. This shift will elevate the importance of data literacy across organizations. Companies investing in data training and understanding will harness higher returns on AI investments than those who don't. Teams understanding how data drives machine-generated recommendations will embrace data-integrated workflows powered by AI. This democratization of data interaction will raise the bar for data literacy. 💡 Cloud Cost Management for AI In 2023, companies focused on controlling cloud costs. In 2024, this attention will shift to infrastructure and applications supporting generative AI. Businesses will scrutinize cloud expenses, demanding justifications for their spending. Analysts will become more aware of the costs associated with their queries and dashboards. An emphasis on value will help organizations optimize data and analytics expenses, trimming resource-intensive reports while highlighting cost-effective analytic resources at the core of transformative business applications. 💡 Data & Analytics Leadership Challenges FP&A teams will face increased demand for integrating AI-based applications with existing organizational data. The ability to access data from numerous sources in a flexible, reliable, and governed manner will be crucial. Teams overly invested in complex data architectures may face scrutiny when the total cost outweighs the benefits. Streamlining data access and delivery of analytics applications targeting specific business processes will be key to success. I believe that organizations that embrace these trends will gain a significant advantage in the market as we enter this new phase of technological evolution in 2024. Connect with me and the eCapital Advisors team, and let's elevate and enhance AI's potential in your organization. 🔽 🔽 🔽 👋 Hi, I'm Lisa. Thanks for checking out my Post!   Here is what you can do next ⬇️   ➕ Follow me for more FP&A insights    🔔 Hit the bell on my profile to be notified when I post   💬 Share your ideas or insights in the comments ♻ Inform others in your network via a Share or Repost https://lnkd.in/edssF7B9 #digitaltransformation #finance #business #technology #cfo

From Good to Great: How Data Augmentation Elevates AI Performance Introduction: Artificial Intelligence (AI) has become an integral part of our lives, revolutionizing various industries such as healthcare, finance, and transportation. However, the performance of AI models heavily relies on the quality and quantity of data available for training. Data augmentation, a technique that artificially expands the dataset, has emerged as a powerful tool to enhance AI performance. In this article, we will explore the concept of data augmentation and its impact on AI performance. Understanding Data Augmentation: Data augmentation involves creating new training data by applying various transformations or modifications to existing data. These transformations can include rotations, translations, scaling, cropping, flipping, and adding noise to the images or text. The augmented data is then used to train AI models, enabling them to learn from a more diverse and comprehensive dataset. The Importance of Data Augmentation: 1. Increased Generalization: By augmenting the training data, AI models become more robust and capable of generalizing patterns. This is particularly important when dealing with limited or imbalanced datasets. Data augmentation helps prevent overfitting, where the model memorizes the training data instead of learning the underlying patterns. By exposing the model to a wider range of augmented data, it learns to recognize and generalize patterns more effectively. 2. Improved Robustness: Real-world data is often noisy, incomplete, or subject to various distortions. Data augmentation helps AI models become more resilient to such variations by simulating them during training. For example, in image classification tasks, augmentations like rotation, scaling, and flipping can mimic different angles, sizes, and orientations of objects. This enables the model to better handle variations in real-world scenarios. 3. Enhanced Performance on Unseen Data: AI models trained with augmented data tend to perform better on unseen or test data. By exposing the model to a diverse range of augmented examples, it becomes more adaptable and capable of handling novel situations. This is particularly useful in applications where the model needs to generalize well on unseen data, such as autonomous driving or medical diagnosis. 4. Reduced Dependency on Annotated Data: Annotated data, where each example is labeled or classified, is often expensive and time-consuming to obtain. Data augmentation allows us to generate additional labeled examples without the need for manual annotation. This reduces the dependency on annotated data and enables the training of AI models with larger and more diverse datasets. Popular Data Augmentation Techniques: 1. Image Augmentation: Image augmentation is widely used in computer vision tasks such as object detection, image classification, and segmentation. Techniques like rotation, scaling, flipping, cropping, and adding noise can

From Good to Great: How Data Augmentation Elevates AI Performance Introduction: Artificial Intelligence (AI) has become an integral part of our lives, revolutionizing various industries such as healthcare, finance, and transportation. However, the performance of AI models heavily relies on the quality and quantity of data available for training. Data augmentation, a technique that artificially expands the dataset, has emerged as a powerful tool to enhance AI performance. In this article, we will explore the concept of data augmentation and its impact on AI performance. Understanding Data Augmentation: Data augmentation involves creating new training data by applying various transformations or modifications to existing data. These transformations can include rotations, translations, scaling, cropping, flipping, and adding noise to the images or text. The augmented data is then used to train AI models, enabling them to learn from a more diverse and comprehensive dataset. The Importance of Data Augmentation: 1. Increased Generalization: By augmenting the training data, AI models become more robust and capable of generalizing patterns. This is particularly important when dealing with limited or imbalanced datasets. Data augmentation helps prevent overfitting, where the model memorizes the training data instead of learning the underlying patterns. By exposing the model to a wider range of augmented data, it learns to recognize and generalize patterns more effectively. 2. Improved Robustness: Real-world data is often noisy, incomplete, or subject to various distortions. Data augmentation helps AI models become more resilient to such variations by simulating them during training. For example, in image classification tasks, augmentations like rotation, scaling, and flipping can mimic different angles, sizes, and orientations of objects. This enables the model to better handle variations in real-world scenarios. 3. Enhanced Performance on Unseen Data: AI models trained with augmented data tend to perform better on unseen or test data. By exposing the model to a diverse range of augmented examples, it becomes more adaptable and capable of handling novel situations. This is particularly useful in applications where the model needs to generalize well on unseen data, such as autonomous driving or medical diagnosis. 4. Reduced Dependency on Annotated Data: Annotated data, where each example is labeled or classified, is often expensive and time-consuming to obtain. Data augmentation allows us to generate additional labeled examples without the need for manual annotation. This reduces the dependency on annotated data and enables the training of AI models with larger and more diverse datasets. Popular Data Augmentation Techniques: 1. Image Augmentation: Image augmentation is widely used in computer vision tasks such as object detection, image classification, and segmentation. Techniques like rotation, scaling, flipping, cropping, and adding noise can

Announcing new Microsoft AI Hub in London https://ift.tt/SE4g5jM Microsoft recently announced the creation of Microsoft AI, a newly formed organization to help advance our consumer AI products and research, including Copilot. Building on that news, I’m thrilled to share that Microsoft AI is opening a new AI hub in the heart of London. Microsoft AI London will drive pioneering work to advance state-of-the-art language models and their supporting infrastructure, and to create world-class tooling for foundation models, collaborating closely with our AI teams across Microsoft and with our partners, including OpenAI.  The new AI hub will be led by Jordan Hoffmann, an exceptional AI scientist and engineer. Prior to joining Microsoft AI, Hoffmann distinguished himself as an AI pioneer at Inflection and DeepMind, based in London. Hoffmann will be joined by a talented group of Microsoft AI team members based in our London Paddington office.    There is an enormous pool of AI talent and expertise in the U.K., and Microsoft AI plans to make a significant, long-term investment in the region as we begin hiring the best AI scientists and engineers into this new AI hub.   In the coming weeks and months, we will be posting job openings and actively hiring exceptional individuals who want to work on the most interesting and challenging AI questions of our time. We’re looking for new team members who are driven by impact at scale, and who are passionate innovators eager to contribute to a team culture where continuous learning is the norm.   This is great news for Microsoft AI and for the U.K.  As a British citizen, born and raised in London, I’m proud to have co-founded and built a cutting-edge AI business here. I’m deeply aware of the extraordinary talent pool and AI ecosystem in the U.K., and I’m excited to make this commitment to the U.K. on behalf of Microsoft AI. I know – through my close work with thought leaders in the U.K. government, business community and academia – that the country is committed to advancing AI responsibly and with a safety-first commitment to drive investment, innovation and economic growth. Our decision to open this hub in the U.K. reflects this ambition.   The Microsoft AI London hub adds to Microsoft’s existing presence in the U.K., including the Microsoft Research Cambridge lab, home to some of the foremost researchers in the areas of AI, cloud and productivity. At the same time, it builds off Microsoft’s recently announced £2.5 billion investment to upskill the U.K. workforce for the AI era and to build the infrastructure to power the AI economy, including our commitment to bring 20,000 of the most advanced GPUs to the country by 2026.  Stay tuned for more updates as we continue to push the boundaries of what’s possible with AI and extend the benefits to every person and organization across the U.K.   The post Announcing new Microsoft AI Hub in London appeared first on The Official Microsoft Blog. via The Official M...

In order to build a successful digital business In today's fast-paced digital business landscape, AI isn't just an option; it's a necessity. To harness its power, you need the right team. The article posted below dives into the core of building a proficient AI team, highlighting key roles and responsibilities. It really does take a village! Data Scientists, Machine Learning Engineers, Data Engineers, Domain Experts, Project Managers, and Legal Advisors each bring unique expertise, ensuring a harmonious blend of skills for AI success. This isn't just about AI; it's about how to secure a place in the future of business. Don't miss out on this insightful read: https://lnkd.in/gCA_MFRW #AI #DigitalBusiness #TeamBuilding #Innovation

From Good to Great: How Data Augmentation Elevates AI Performance Introduction: Artificial Intelligence (AI) has become an integral part of our lives, revolutionizing various industries such as healthcare, finance, and transportation. However, the performance of AI models heavily relies on the quality and quantity of data available for training. Data augmentation, a technique that artificially expands the dataset, has emerged as a powerful tool to enhance AI performance. In this article, we will explore the concept of data augmentation and its impact on AI performance. Understanding Data Augmentation: Data augmentation involves creating new training data by applying various transformations or modifications to existing data. These transformations can include rotations, translations, scaling, cropping, flipping, and adding noise to the images or text. The augmented data is then used to train AI models, enabling them to learn from a more diverse and comprehensive dataset. The Importance of Data Augmentation: 1. Increased Generalization: By augmenting the training data, AI models become more robust and capable of generalizing patterns. This is particularly important when dealing with limited or imbalanced datasets. Data augmentation helps prevent overfitting, where the model memorizes the training data instead of learning the underlying patterns. By exposing the model to a wider range of augmented data, it learns to recognize and generalize patterns more effectively. 2. Improved Robustness: Real-world data is often noisy, incomplete, or subject to various distortions. Data augmentation helps AI models become more resilient to such variations by simulating them during training. For example, in image classification tasks, augmentations like rotation, scaling, and flipping can mimic different angles, sizes, and orientations of objects. This enables the model to better handle variations in real-world scenarios. 3. Enhanced Performance on Unseen Data: AI models trained with augmented data tend to perform better on unseen or test data. By exposing the model to a diverse range of augmented examples, it becomes more adaptable and capable of handling novel situations. This is particularly useful in applications where the model needs to generalize well on unseen data, such as autonomous driving or medical diagnosis. 4. Reduced Dependency on Annotated Data: Annotated data, where each example is labeled or classified, is often expensive and time-consuming to obtain. Data augmentation allows us to generate additional labeled examples without the need for manual annotation. This reduces the dependency on annotated data and enables the training of AI models with larger and more diverse datasets. Popular Data Augmentation Techniques: 1. Image Augmentation: Image augmentation is widely used in computer vision tasks such as object detection, image classification, and segmentation. Techniques like rotation, scaling, flipping, cropping, and adding noise can

62% of workers say they don’t have the skills to use AI effectively. Meanwhile, 40% of the workforce will need to be retrained in the next 3 years. What better way to learn hands-on AI skills for free than Salesforce Trailhead? Salesforce is set to generate 11.6 million jobs from 2022 to 2028 by blending generative AI into their system. Here’s what you need to learn about Salesforce AI and validate your skills to earn the Salesforce AI Associate Credential: 1️⃣Artificial Intelligence and Machine Learning: At the heart of AI is machine learning. Learn how to transform data into models and the need for neural networks. This includes modules such as: -Artificial Intelligence Fundamentals -Natural Language Processing Basics -Large Language Models -Model Fine-Tuning. 2️⃣Generative AI: Generative AI allows machines to generate text or images by training itself on pre-existing work. The right prompts help you get the right set of outputs. And so, you should learn: -Generative AI Basics -Generative AI for Organizations -Prompt Fundamentals -Prompt Builder Basics -Generative AI for Images. 3️⃣Einstein AI and AI For Business: These modules will help you learn how to use AI in your business, especially with Einstein as your assistant. -Artificial Intelligence for Business -Artificial Intelligence for Customer Service -Artificial Intelligence for Marketing -Salesforce Einstein Basics -Sales Cloud Einstein -Einstein Bots Basics. 4️⃣Ethical Use of AI: Employers not only want skilled AI users but also responsible and ethical AI users. Learn fairness, transparency, and accountability while using AI with the following modules: -Responsible Creation of Artificial Intelligence -Ethical Data Use in Personalization -The Einstein Trust Layer -Artificial Intelligence and Cybersecurity. 5️⃣Data Management: This is where you will learn how to deal with low-quality data, make up for or find missing data, and how to extract usable data. Modules we recommend: -Data Analytics Fundamentals -Data Fundamentals for AI -Data Literacy Basics -Data Quality -Salesforce Data Cloud: Quick Look Salesforce AI Associate module on Trailhead gives you a clear understanding of AI. 🔗 Click here to begin your prep: https://lnkd.in/dGXATPmT Meanwhile, if you are looking for AI-certified integration architects to streamline your business, you can contact NexGen Architects via this form: https://lnkd.in/gX97Mgrh #mulesoft #mulesoftcommunity #Einstein #einsteinai #generativeai #einsteincopilot #salesforce #mulesoftdeveloper #integration #api #trailhead #trailblazer #trailblazercommunity #nexgenarchitects #mulesoftarchitect #salesforcecommunity