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In an interview ahead of the Intelligent Automation Conference , Ben Ball, Senior Director of Product Marketing at IBM , shed light on the tech giant’s latest AI endeavours and its groundbreaking new Concert product. IBM’s current focal point in AI research and development lies in applying it to technology operations.
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ExplainableAI(XAI) ExplainableAI emphasizes transparency and interpretability, enabling users to understand how AI models arrive at decisions. Techniques such as embodied AI, multimodal learning, knowledge graphs, reinforcement learning, and explainableAI are paving the way for more grounded and reliablesystems.
Understanding AI’s mysterious “opaque box” is paramount to creating explainableAI. This can be simplified by considering that AI, like all other technology, has a supply chain. How does it weigh those factors? And so you are really left unable to make a case for your client in those circumstances.”
Deep learning is great for some applications — large language models are brilliant for summarizing documents, for example — but sometimes a simple regression model is more appropriate and easier to explain. What are some future trends in AI and data science that you are excited about, and how is Astronomer preparing for them?
InsightsAct’s AIengine works continuously in the background to surface relevant insights. Our AI technologies meticulously sift through Big Data, capturing valuable nuggets often overlooked by traditional dashboards and reports. This report not only ranks your insights but deciphers them too, courtesy of eXplainableAI.
Top ODSC East 2023 Virtual Sessions Available to Watch for Free With topics ranging from explainableAI to delivering data-driven presentations, these are the top virtual sessions from ODSC East that you can watch for free. Learn more about how you can speak and present at ODSC West here!
Robotics also witnessed advancements, with AI-powered robots becoming more capable in navigation, manipulation, and interaction with the physical world. ExplainableAI and Ethical Considerations (2010s-present): As AI systems became more complex and influential, concerns about transparency, fairness, and accountability arose.
Topics Include: Advanced ML Algorithms & EnsembleMethods Hyperparameter Tuning & Model Optimization AutoML & Real-Time MLSystems ExplainableAI & EthicalAI Time Series Forecasting & NLP Techniques Who Should Attend: ML Engineers, Data Scientists, and Technical Practitioners working on production-level ML solutions.
AIEngineer, Machine Learning Engineer, and Robotics Engineer are prominent roles in AI. ML Engineer, Data Scientist, and Research Scientist are typical roles in Machine Learning. Job Roles Data Scientist, Data Analyst , and Business Analyst are typical roles in Data Science.
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