Revolutionizing the consumer goods industry with integrated business planning

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The world of business demands the right decisions to succeed. For Al Rabie—a prominent juice manufacturing company in the Middle East—their reality was no different. However, their manual planning and budgeting process in spreadsheets posed several challenges, including lack of control, delayed data, poor execution, and the need for continuous follow-up with IT for actual …

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Beyond automatic differentiation

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Posted by Matthew Streeter, Software Engineer, Google Research Derivatives play a central role in optimization and machine learning. By locally approximating a training loss, derivatives guide an optimizer toward lower values of the loss. Automatic differentiation frameworks such as TensorFlow, PyTorch, and JAX are an essential part of modern machine learning, making it feasible to …

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Angler: Helping Machine Translation Practitioners Prioritize Model Improvements

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*=Authors contributed equally Machine learning (ML) models can fail in unexpected ways in the real world, but not all model failures are equal. With finite time and resources, ML practitioners are forced to prioritize their model debugging and improvement efforts. Through interviews with 13 ML practitioners at Apple, we found that practitioners construct small targeted …

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5 takeaways from the early days of IBM Partner Plus

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The past three months have been transformative for the IBM Ecosystem. Our investment in the ecosystem is deeper than ever before, and momentum is accelerating across all partner types as they continue to scale and innovate. This demonstrates their commitment to clients, deep expertise and hunger to collaborate and co-create with us. Thanks to each …

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Robotic deep RL at scale: Sorting waste and recyclables with a fleet of robots

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Posted by Sergey Levine, Research Scientist, and Alexander Herzog, Staff Research Software Engineer, Google Research, Brain Team Reinforcement learning (RL) can enable robots to learn complex behaviors through trial-and-error interaction, getting better and better over time. Several of our prior works explored how RL can enable intricate robotic skills, such as robotic grasping, multi-task learning, …

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Announcing New Tools for Building with Generative AI on AWS

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The seeds of a machine learning (ML) paradigm shift have existed for decades, but with the ready availability of scalable compute capacity, a massive proliferation of data, and the rapid advancement of ML technologies, customers across industries are transforming their businesses. Just recently, generative AI applications like ChatGPT have captured widespread attention and imagination. We …

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Rapidly deploy PyTorch applications on Batch using TorchX

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Whether you are performing image recognition, language processing, regression analysis, or other machine learning demonstrations, the importance of rapid prototyping and deployment remains of utmost importance. These computations tend to have steps with various CPU and GPU requirements. Google Cloud introduced Batch, which is a fully managed service that handles infrastructure lifecycle management, queuing, and …

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Feedback Effect in User Interaction with Intelligent Assistants: Delayed Engagement, Adaption and Drop-out

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With the growing popularity of intelligent assistants (IAs), evaluating IA quality becomes an increasingly active field of research. This paper identifies and quantifies the feedback effect, a novel component in IA-user interactions: how the capabilities and limitations of the IA influence user behavior over time. First, we demonstrate that unhelpful responses from the IA cause …

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Addressing the network data monetization complexities

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In our previous blog, we identified the three layers to network data monetization. These were the data layer, the analytics layer and the automation layer. To address the network data value tree successfully, we must address the complexities of these three layers, which are essential for automated operations in telco. In the next part we …

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