Unite.AI

AUGUST 25, 2023

Can focusing on Explainable AI (XAI) ever address this? To engineers, explainable AI is currently thought of as a group of technological constraints and practices, aimed at making the models more transparent to people working on them. You can't really reengineer the design logic from the source code.

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Marktechpost

MAY 8, 2024

Among the main advancements in AI, seven areas stand out for their potential to revolutionize different sectors: neuromorphic computing, quantum computing for AI, Explainable AI (XAI), AI-augmented design and Creativity, Autonomous Vehicles and Robotics, AI in Cybersecurity and AI for Environmental Sustainability.

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Marktechpost

AUGUST 13, 2024

Integrating AI and human expertise addresses the need for reliable, explainable AI systems while ensuring that technology complements rather than replaces human capabilities. Automated AI Systems handle repetitive tasks within specific domains, like robotic process automation and forest management.

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ODSC - Open Data Science

JANUARY 29, 2025

Grounding Techniques Over the years, several innovative techniques have been developed to address the challenges of grounding inAI: Embodied AI Embodied AI integrates physical systems, such as robots or drones, to enable interaction with the environment.

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IBM Journey to AI blog

OCTOBER 16, 2023

They are used in everything from robotics to tools that reason and interact with humans. “Foundation models make deploying AI significantly more scalable, affordable and efficient.” Open-source projects, academic institutions, startups and legacy tech companies all contributed to the development of foundation models.

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Mlearning.ai

DECEMBER 31, 2023

Principles of Explainable AI( Source ) Imagine a world where artificial intelligence (AI) not only makes decisions but also explains them as clearly as a human expert. This isn’t a scene from a sci-fi movie; it’s the emerging reality of Explainable AI (XAI). What is Explainable AI?

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Aiiot Talk

JULY 7, 2023

Black-box AI poses a serious concern in the aviation industry. In fact, explainability is a top priority laid out in the European Union Aviation Safety Administration’s first-ever AI roadmap. Explainable AI, sometimes called white-box AI, is designed to have high transparency so logic processes are accessible.

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Towards AI

OCTOBER 3, 2024

Lifelong Learning Models: Research aims to develop models that can learn incrementally without forgetting previous knowledge, which is essential for applications in autonomous systems and robotics.

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IBM Journey to AI blog

SEPTEMBER 24, 2024

Can AI help mitigate the impending agricultural crisis we’ll be facing over the next few decades? Dr. Abhisesh Silwal, a systems scientist at Carnegie Mellon University whose research focuses on AI and robotics in agriculture, thinks so.

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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.

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NVIDIA

MARCH 1, 2024

Transparency in AI is a set of best practices, tools and design principles that helps users and other stakeholders understand how an AI model was trained and how it works. Explainable AI , or XAI, is a subset of transparency covering tools that inform stakeholders how an AI model makes certain predictions and decisions.

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ODSC - Open Data Science

SEPTEMBER 26, 2024

The NVIDIA AI Hackathon at ODSC West, Reinforcement Learning for Finance, the Future of Humanoid AI Robotics, and Detecting Anomalies Unleash Innovation at the NVIDIA AI Hackathon at ODSC West 2024 Ready to put your data science skills to the test? Where do explainable AI models come into play?

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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. Discussions about automation, additive manufacturing, robots, AI, and all the other technologies we’ve developed to enable a world without menial work can be found on the r/Automate subreddit.

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Pickl AI

FEBRUARY 17, 2025

AlphaGo) and robotics. Approximately 44% of organisations express concerns about transparency in AI adoption. The “black box” nature of many algorithms makes it difficult for stakeholders to understand how decisions are made, leading to reduced trust in AI systems. Notable applications include game playing (e.g.,

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Pickl AI

JULY 14, 2023

Reinforcement Learning and Robotics (2010s-2020s): Reinforcement Learning (RL) gained traction, focusing on training AI agents to make sequential decisions based on rewards and punishments. Researchers began addressing the need for Explainable AI (XAI) to make AI systems more understandable and interpretable.

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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

AI comprises Natural Language Processing, computer vision, and robotics. AI Engineer, Machine Learning Engineer, and Robotics Engineer are prominent roles in AI. Emerging Trends Emerging trends in Data Science include integrating AI technologies and the rise of Explainable AI for transparent decision-making.

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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. This is particularly useful in applications such as spam detection in emails, sentiment analysis of social media posts, and credit scoring in finance.

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Pickl AI

AUGUST 14, 2024

Robotics Neural networks are also applied in robotics, enabling machines to learn from their environments and perform complex tasks. Explainable AI (XAI): Efforts to make neural networks more interpretable, allowing users to understand how models make decisions.

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Ofemwire

MARCH 1, 2024

It’s like a robot that plays a game but doesn’t learn from its mistakes. Limited Memory AI : These AIs can remember some things from the past to help them make decisions in the present. It’s like a robot that remembers where it’s been before so it can figure out where to go next.

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Pickl AI

NOVEMBER 5, 2024

AI encompasses various subfields, including Machine Learning (ML), Natural Language Processing (NLP), robotics, and computer vision. Together, Data Science and AI enable organisations to analyse vast amounts of data efficiently and make informed decisions based on predictive analytics.

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ODSC - Open Data Science

FEBRUARY 25, 2025

This track brings together industry pioneers and leading researchers to showcase the breakthroughs shaping tomorrows AI landscape. This track is designed to help practitioners strengthen their ML foundations while exploring advanced algorithms and deployment techniques.

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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. Minimal technical jargon.

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Viso.ai

JANUARY 21, 2025

This tech is powering some of the biggest advancements in virtual reality, augmented reality, and robotics. Explainable AI (XAI) in Vision Systems Explainable Artificial Intelligence (XAI) focuses on making AI decision-making transparent and understandable. These methods create detailed 3D maps of environments.

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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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Pickl AI

SEPTEMBER 17, 2024

Reinforcement learning has applications in areas such as robotics, game playing, and resource allocation. What Is the Role of Explainable AI (XAI) In Machine Learning? Explainable AI (XAI) is a field of study that focuses on making Machine Learning models more interpretable and transparent.

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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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Mlearning.ai

MARCH 24, 2023

Nonetheless, the pursuit of artificial intelligence continued to drive progress in computer science and robotics. In present day, we are seeing AI systems that appear truly intelligent, machines that can not only mimic human behavior, but also learn, reason, and solve complex problems in ways that were once thought impossible.

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Pickl AI

AUGUST 1, 2024

Techniques such as explainable AI (XAI) aim to provide insights into model behaviour, allowing users to gain confidence in AI-driven decisions, especially in critical fields like healthcare and finance. Proficiency in programming languages like Python, experience with Deep Learning frameworks (e.g.,

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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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Marktechpost

JULY 11, 2023

AI agents, the computer programs that interact with the environment to make decisions operate autonomously, or interact with humans or other agents using natural language.

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The MLOps Blog

FEBRUARY 27, 2025

Transparency has become a key expectation in the AI industry, as highlighted by initiatives like the EU AI Act and guidelines from organizations such as the Partnership on AI, which emphasize the importance of explainable AI. Syntax of a robots-txt file to prevent agents from crawling a website.

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AI Impacts

DECEMBER 22, 2022

I explained AI risk to my therapist recently, as an aside regarding his sense that I might be catastrophizing, and I feel like it went okay, though we may need to discuss again.

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IBM Journey to AI blog

SEPTEMBER 3, 2024

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. Take action: Adopt explainable AI techniques. Explore explainable AI tools, such as IBM’s open source AI Explainability 360 toolkit.

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NVIDIA

MARCH 18, 2025

Japan : Leading cloud providers including GMO Internet, Highreso, KDDI, Rutilea and SAKURA internet are building NVIDIA-powered AI infrastructure to transform industries such as robotics, automotive, healthcare and telecom.

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