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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Unite.AI

OCTOBER 20, 2023

However, only around 20% have implemented comprehensive programs with frameworks, governance, and guardrails to oversee AI model development and proactively identify and mitigate risks. Given the fast pace of AI development, leaders should move forward now to implement frameworks and mature processes.

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Marktechpost

APRIL 28, 2024

The course covers how AI is used in real-world applications like recommender systems, self-driving cars, etc., and also allows the students to build an understanding of machine learning algorithms, including supervised, unsupervised, reinforcement, etc. It also covers the potential opportunities and risks that generative AI poses.

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Unite.AI

MARCH 27, 2024

Back then, people dreamed of what it could do, but now, with lots of data and powerful computers, AI has become even more advanced. Along the journey, many important moments have helped shape AI into what it is today. Today, AI benefits from the convergence of advanced algorithms, computational power, and the abundance of data.

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NVIDIA

MARCH 1, 2024

Trustworthy AI initiatives recognize the real-world effects that AI can have on people and society, and aim to channel that power responsibly for positive change. What Is Trustworthy AI? Trustworthy AI is an approach to AI development that prioritizes safety and transparency for those who interact with it.

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Unite.AI

MARCH 18, 2024

This content often fills the gap when data is scarce or diversifies the training material for AI models, sometimes without full recognition of its implications. While this expansion enriches the AI development landscape with varied datasets, it also introduces the risk of data contamination.

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Aiiot Talk

JULY 7, 2023

AI is today’s most advanced form of predictive maintenance, using algorithms to automate performance and sensor data analysis. Aircraft owners or technicians set up the algorithm with airplane data, including its key systems and typical performance metrics. Black-box AI poses a serious concern in the aviation industry.

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Bugra Akyildiz

JULY 29, 2023

Since the algorithms to train the above models are usually modality agnostic, one might wonder why there aren’t strong foundational models for these additional modalities. For these tasks, humans often start with a high-level algorithmic design and implement each part gradually.

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Marktechpost

JULY 14, 2023

From organizations to AI researchers, everyone is discovering the vast potential Generative AI holds to produce unique and original content, and that too in a wide range of fields. What is Generative AI? Generative AI is a common term for any type of process that uses an algorithm to generate, manipulate, and synthesize data.

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IBM Journey to AI blog

MAY 8, 2023

Risk and limitations of AI The risk associated with the adoption of AI in insurance can be separated broadly into two categories—technological and usage. Technological risk—security AI algorithms are the parameters that optimizes the training data that gives the AI its ability to give insights.

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AI Weekly

APRIL 20, 2023

nytimes.com OpenAI is not currently training GPT-5 Experts calling for a pause on AI development will be glad to hear that OpenAI isn’t currently training GPT-5. scitechdaily.com How AI is matching drugs to patients Dr Talia Cohen Solal sits down at a microscope to look closely at human brain cells grown in a petri dish.

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IBM Journey to AI blog

JANUARY 15, 2024

The differences between generative AI and traditional AI To understand the unique challenges that are posed by generative AI compared to traditional AI, it helps to understand their fundamental differences. It helps to ensure consistent outputs.

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Towards AI

JANUARY 24, 2024

What happened this week in AI by Louie This week, Meta’s AI strategy was in focus, with Mark Zuckerberg boasting of Meta’s GPU hoard and outlining his open-source-focused AI vision. As Zuckerberg explains, Meta’s new, broader focus on AGI was influenced by the release of Llama 2, its latest large language model, last year.

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Unite.AI

JULY 25, 2023

An AI feedback loop is an iterative process where an AI model's decisions and outputs are continuously collected and used to enhance or retrain the same model, resulting in continuous learning, development, and model improvement. Stages Of AI Feedback Loops A high-level illustration of feedback mechanism in AI models.

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Pickl AI

APRIL 8, 2024

Summary: AI’s immense potential is undeniable, but its journey riddle with roadblocks. This blog explores 13 major AI blunders, highlighting issues like algorithmic bias, lack of transparency, and job displacement. 13 AI Mistakes That Are Worth Your Attention 1.

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IBM Journey to AI blog

SEPTEMBER 28, 2023

As a testament to the rigor IBM puts into the development and testing of its foundation models, IBM will indemnify clients against third party IP claims against IBM-developed foundation models. It can also help autocomplete code, modify code and explain code snippets in natural language.

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NYU Center for Data Science

DECEMBER 12, 2023

In an era where algorithms determine everything from creditworthiness to carceral sentencing, the imperative for responsible innovation has never been more urgent. The Center for Responsible AI (NYU R/AI) is leading this charge by embedding ethical considerations into the fabric of artificial intelligence research and development.

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Becoming Human

APRIL 19, 2024

The rise of AI consulting services AI consulting services have emerged as a key player in the digital transformation landscape. Businesses are leveraging the expertise of AI consultants to navigate the complexities of implementing AI solutions, from developing custom algorithms to integrating off-the-shelf AI tools.

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IBM Journey to AI blog

FEBRUARY 23, 2024

Machine learning (ML) and deep learning (DL) form the foundation of conversational AI development. ML algorithms understand language in the NLU subprocesses and generate human language within the NLG subprocesses. This will likely lead to increased regulation and the development of ethical guidelines for AI development and use.

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Towards AI

OCTOBER 7, 2023

Balancing Ethics and Innovation: An Introduction to the Guiding Principles of Responsible AI Sarah, a seasoned AI developer, found herself at a moral crossroads. One algorithm could maximize efficiency but at the cost of privacy. If you were Sarah, which algorithm would you choose?

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Towards AI

FEBRUARY 14, 2023

Generative AI is a fascinating field that has gained a lot of attention in recent years. It involves using machine learning algorithms to generate new data based on existing data. In this article, we will explore what generative AI is, how it is being used today, and what the future holds for this exciting field.

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Unite.AI

APRIL 12, 2024

This level of conversational intelligence feels like a true leap forward, enabling seamless multi-turn dialogues without the frustration of constantly re-explaining context or repeating yourself.

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Pickl AI

APRIL 26, 2024

From automating data analysis in finance to streamlining factory assembly lines, AI streamlines processes and boosts productivity at an unprecedented scale. Eradicating Errors Humans are prone to mistakes, but AI algorithms can learn from vast amounts of data with incredible precision.

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IBM Journey to AI blog

APRIL 18, 2024

Achieving these feats is accomplished through a combination of sophisticated algorithms, natural language processing (NLP) and computer science principles. The skills gap in gen AI development is a significant hurdle. NLP techniques help them parse the nuances of human language, including grammar, syntax and context.

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Pickl AI

JANUARY 29, 2024

Data Science extracts insights, while Machine Learning focuses on self-learning algorithms. The collective strength of both forms the groundwork for AI and Data Science, propelling innovation. Key takeaways Data Science lays the groundwork for Machine Learning, providing curated datasets for ML algorithms to learn and make predictions.

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Snorkel AI

JANUARY 30, 2024

Through a combination of programmatic data development techniques, we fine-tuned every component of the RAG system. The result was a significant accuracy boost, with only a minimal amount of time required from subject matter experts (SMEs) to explain how to interpret document language and where to look for key pieces of information.

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Snorkel AI

JANUARY 30, 2024

Through a combination of programmatic data development techniques, we fine-tuned every component of the RAG system. The result was a significant accuracy boost, with only a minimal amount of time required from subject matter experts (SMEs) to explain how to interpret document language and where to look for key pieces of information.

LLM 52

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ODSC - Open Data Science

APRIL 4, 2023

This is a promising shift for AI developers, and many organizations have realized impressive benefits from the technology, but it also comes with significant risks. AI’s rapid growth could lead more companies to implement it without fully understanding how to manage it safely and ethically.

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Becoming Human

MAY 12, 2023

Image annotation is at the core of artificial intelligence and machine learning, and this note provides an overview of the various approaches and methods required to achieve AI and develop AI-enabled models. It takes creativity, intuition, and problem-solving skills to develop artificial intelligence.

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Unite.AI

APRIL 9, 2024

As organizations strive for responsible and effective AI, Composite AI stands at the forefront, bridging the gap between complexity and clarity. The Need for Explainability The demand for Explainable AI arises from the opacity of AI systems, which creates a significant trust gap between users and these algorithms.

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Towards AI

FEBRUARY 15, 2023

To overcome this limitation, computer scientists are developing new techniques to teach machines foundational concepts before unleashing them into the wild. This article delves into the details of these emerging approaches and their potential impact on AI development. This article explains why. […]

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Google Research AI blog

MARCH 16, 2023

The Impact Lab team, part of Google’s Responsible AI Team , employs a range of interdisciplinary methodologies to ensure critical and rich analysis of the potential implications of technology development. We examine systemic social issues and generate useful artifacts for responsible AI development.

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AWS Machine Learning Blog

MAY 9, 2024

Additionally, pay special attention to the changing nature of the risk and cost that is associated with the development as well as the scaling of AI. To provide ethical integrity , an AI/ML CoE helps integrate robust guidelines and safeguards across the AI/ML lifecycle in collaboration with stakeholders.

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Viso.ai

DECEMBER 18, 2023

ONNX (Open Neural Network Exchange) is an open-source format that facilitates interoperability between different deep learning algorithms for simple model sharing and deployment. This compatibility ensures that AI developers can leverage the strengths of diverse platforms while maintaining model portability and efficiency.

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Marktechpost

MARCH 28, 2024

Python: Advanced Guide to Artificial Intelligence This book helps individuals familiarize themselves with the most popular machine learning (ML) algorithms and delves into the details of deep learning, covering topics like CNN, RNN, etc. The book prepares its readers for the moral uncertainties of a world run by code.

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Viso.ai

FEBRUARY 12, 2024

True to its name, Explainable AI refers to the tools and methods that explain AI systems and how they arrive at a certain output. Artificial Intelligence (AI) models assist across various domains, from regression-based forecasting models to complex object detection algorithms in deep learning.

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Viso.ai

DECEMBER 18, 2023

The algorithm then generates new data points that follow the same statistical patterns. Then, we implement algorithms such as iterative proportional fitting (IPF) or combinatorial optimization. They can handle much richer data distributions than traditional algorithms, such as decision trees. Rapid AI Development.

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