ODSC - Open Data Science

AUGUST 14, 2023

“I still don’t know what AI is” If you’re like my parents and think I work at ChatGPT, then you may have to learn a little bit more about AI. Funny enough, you can use AI to explain AI. And now that you understand what AI is, it’s all about using it, leading to the next point. “No

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Marktechpost

AUGUST 24, 2023

You will never miss any updates on ML/AI/CV/NLP fields because it is posted on a daily basis and highly moderated to avoid any spam. r/Automate The sub has more than 75k members participating in discussions and posts focused on automation. The subreddit has excellent computer vision and artificial intelligence content.

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ODSC - Open Data Science

FEBRUARY 18, 2025

Computer VisionAI agents in autonomous robotics interpret visual data to navigate complex environments, such as self-driving cars. Recent breakthroughs include OpenAIs GPT models, Google DeepMinds AlphaFold for protein folding, and AI-powered robotic assistants in industrial automation.

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How to Learn Machine Learning

JANUARY 21, 2023

Artificial intelligence (AI) is a term that encompasses the use of computer technology to solve complex problems and mimic human decision-making. At its core, AI relies on algorithms, data processing, and machine learning to generate insights from vast amounts of data. In the years to come, AI is expected to become even more powerful.

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Pickl AI

DECEMBER 10, 2024

Summary : Data Analytics trends like generative AI, edge computing, and Explainable AI redefine insights and decision-making. Key Takeaways Generative AI simplifies data insights, enabling actionable decision-making and enhancing data storytelling.

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Pickl AI

JANUARY 29, 2024

Opportunities abound in sectors like healthcare, finance, and automation. As we navigate this landscape, the interconnected world of Data Science, Machine Learning, and AI defines the era of 2024, emphasising the importance of these fields in shaping the future. AI comprises Natural Language Processing, computer vision, and robotics.

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Pickl AI

NOVEMBER 5, 2024

Summary: Data Science and AI are transforming the future by enabling smarter decision-making, automating processes, and uncovering valuable insights from vast datasets. AI automates processes, reducing human error and operational costs. Explainable AI (XAI) is crucial for building trust in automated systems.

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Pickl AI

SEPTEMBER 3, 2024

Autonomous Systems In robotics and autonomous vehicles, ANNs play a crucial role in enabling machines to perceive their environment and make decisions based on sensory input. ANNs are being deployed on edge devices to enable real-time decision-making in applications such as smart cities, autonomous vehicles, and industrial automation.

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Viso.ai

JANUARY 21, 2025

They are suitable for various advanced high-precision computer vision applications like medical imaging, autonomous vehicles, and industrial automation. Moreover, their ability to handle large datasets with fewer resources makes them a game-changer in AI development. ViTs offer better scalability and adaptability than CNNs.

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ODSC - Open Data Science

FEBRUARY 25, 2025

Generative AI TrackBuild the Future with GenAI Generative AI has captured the worlds attention with tools like ChatGPT, DALL-E, and Stable Diffusion revolutionizing how we create content and automate tasks.

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Pickl AI

JANUARY 13, 2025

It simplifies complex AI topics like clustering , dimensionality , and regression , providing practical examples and numeric calculations to enhance understanding. Key Features: Explains AI algorithms like clustering and regression. Key Features: Covers AI history and advancements. Discusses ethical challenges of AI.

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Pickl AI

AUGUST 1, 2024

Operational Efficiency Deep Learning can optimise healthcare operations by automating administrative tasks, predicting patient flow, and optimising resource allocation. Automated tuning methods like grid search and Bayesian optimization can systematically explore the hyperparameter space to find the best configuration.

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Pickl AI

JULY 24, 2024

The Golden Age of AI (1960s-1970s) Experts often refer to the 1960s and 1970s as the “Golden Age of AI.” ” During this time, researchers made remarkable strides in natural language processing, robotics, and expert systems. 2011: IBM Watson defeats Ken Jennings on the quiz show “Jeopardy! .”

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Viso.ai

DECEMBER 6, 2023

Real-Life Applications of Pose Estimation Pose estimation has many applications, some of which include: Computer vision robotics, where pose estimation models can help train robotic movements. Future Directions Explainable AI: Explainable AI (XAI) is one research paradigm that can help you detect biases easily.

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NVIDIA

MARCH 18, 2025

For AI factories, intelligence isnt a byproduct but the primary one. This intelligence is measured by AI token throughput the real-time predictions that drive decisions, automation and entirely new services.

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Pickl AI

NOVEMBER 18, 2024

It’s commonly used in robotics, gaming, and autonomous systems. Explainable AI (XAI) The demand for transparency in Machine Learning Models is growing. Explainable AI (XAI) focuses on making complex models more interpretable to humans. Let’s explore some of the key trends.

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The MLOps Blog

FEBRUARY 27, 2025

See in the app Full screen preview All metadata in a single place with an experiment tracker (example in neptune.ai) Integrate bias checks into your CI/CD workflows If your team manages model training through CI/CD, incorporate the automated bias detection scripts (that have already been created) into each pipeline iteration.

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AI Weekly

AUGUST 22, 2024

Here’s why that’s a problem Using generative large language models (LLMs) like those behind ChatGPT and other AI chatbots, the system can brainstorm, select a promising idea, code new algorithms, plot results, and write a paper summarising the experiment and its findings, complete with references. pdf, Word, etc.) into their platform.

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How to Learn Machine Learning

APRIL 12, 2025

Modern AI systems no longer merely respond to inputs; they initiate actions, set subgoals, and adapt strategies in real time. From Automation to Autonomy The evolution ofAI over time reveals a clear trajectory: from rule-based systems to machine learning, and now to autonomous agents. Early AI followed strict programming.

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IBM Journey to AI blog

SEPTEMBER 3, 2024

Job losses AI is expected to disrupt the job market, inciting fears that AI-powered automation will displace workers. According to a World Economic Forum report, nearly half of the surveyed organizations expect AI to create new jobs, while almost a quarter see it as a cause of job losses.

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AI Impacts

DECEMBER 22, 2022

Which might happen because different locales have similar interests (all rational governments should be similarly concerned about losing power to automated power-seeking systems with unverifiable goals), or because—as with individuals—there are social dynamics which support norms arising in a non-centralized way.

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