Unite.AI

MARCH 18, 2025

Once the issue was explained – factories shutting down, shipping backlogs, material shortages – people understood. Instead of discussing qubits and error rates, companies should be explaining how quantum computing can optimize drug discovery, improve financial modeling, or enhance cybersecurity.

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Unite.AI

JANUARY 2, 2025

The Importance of Transparency in AI Transparency is essential for ethical AI development. Without it, users must rely on AI systems without understanding how decisions are made. Transparency allows AI decisions to be explained, understood, and verified. This is particularly important in areas like hiring.

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AI Developer AI Development Explainability AI 130

Unite.AI

JANUARY 23, 2025

AI systems are primarily driven by Western languages, cultures, and perspectives, creating a narrow and incomplete world representation. These systems, built on biased datasets and algorithms, fail to reflect the diversity of global populations. Bias in AI typically can be categorized into algorithmic bias and data-driven bias.

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Algorithm AI Developer AI Development AI 113

AI News

OCTOBER 8, 2024

As artificial intelligence systems increasingly permeate critical decision-making processes in our everyday lives, the integration of ethical frameworks into AI development is becoming a research priority. Canavotto and her colleagues, Jeff Horty and Eric Pacuit, are developing a hybrid approach to combine the best of both approaches.

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Artificial Intelligence Artificial Intelligence Explainability Big Data 315

Unite.AI

DECEMBER 27, 2024

As AI influences our world significantly, we need to understand what this data monopoly means for the future of technology and society. The Role of Data in AI Development Data is the foundation of AI. Without data, even the most complex algorithms are useless.

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AI AI Natural Language Processing AI Modeling 173

Unite.AI

JUNE 26, 2023

Who is responsible when AI mistakes in healthcare cause accidents, injuries or worse? Depending on the situation, it could be the AI developer, a healthcare professional or even the patient. Liability is an increasingly complex and serious concern as AI becomes more common in healthcare. Not necessarily.

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Black Box AI Explainable AI Explainability AI 297

Unite.AI

DECEMBER 27, 2024

Its because the foundational principle of data-centric AI is straightforward: a model is only as good as the data it learns from. No matter how advanced an algorithm is, noisy, biased, or insufficient data can bottleneck its potential. Another promising development is the rise of explainable data pipelines.

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Data Quality Data Drift Explainability Big Data 130

Unite.AI

JANUARY 20, 2025

Transparency = Good Business AI systems operate using vast datasets, intricate models, and algorithms that often lack visibility into their inner workings. This opacity can lead to outcomes that are difficult to explain, defend, or challengeraising concerns around bias, fairness, and accountability.

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Explainability Explainable AI AI AI 113

AI News

JANUARY 3, 2024

Developed by researchers at MIT, Tsinghua University, and Canadian startup MyShell, OpenVoice uses just seconds of audio to clone a voice and allows granular control over tone, emotion, accent, rhythm, and more. Today, we proudly open source our OpenVoice algorithm, embracing our core ethos – AI for all.

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Big Data AI AI Explainability 349

AI News

MAY 3, 2024

. “Our AI engineers built a prompt evaluation pipeline that seamlessly considers cost, processing time, semantic similarity, and the likelihood of hallucinations,” Ros explained. It’s obviously an ambitious goal, but it’s important to our employees and it’s important to our clients,” explained Ros.

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Big Data Generative AI Explainability AI 324

Unite.AI

JANUARY 28, 2025

This shift has increased competition among major AI companies, including DeepSeek, OpenAI, Google DeepMind , and Anthropic. Each brings unique benefits to the AI domain. DeepSeek focuses on modular and explainable AI, making it ideal for healthcare and finance industries where precision and transparency are vital.

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OpenAI Natural Language Processing Explainable AI Explainability 147

Unite.AI

MARCH 5, 2025

While many organizations focus on AIs technological capabilities and getting one step ahead of the competition, the real challenge lies in building the right operational framework to support AI adoption at scale. This requires a three-pronged approach: robust governance, continuous learning, and a commitment to ethical AI development.

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Emotion AI AI AI Automation 208

Unite.AI

JULY 21, 2023

In order to protect people from the potential harms of AI, some regulators in the United States and European Union are increasingly advocating for controls and checks and balances on the power of open-source AI models. The AI Bill of Rights and the NIST AI Risk Management Framework in the U.S.,

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Data Drift Large Language Models Algorithm Data Scientist 241

Unite.AI

DECEMBER 1, 2023

Machine learning , a subset of AI, involves three components: algorithms, training data, and the resulting model. An algorithm, essentially a set of procedures, learns to identify patterns from a large set of examples (training data). This obscurity makes it challenging to understand the AI's decision-making process.

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LLM Machine Learning Explainability Algorithm 264

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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Responsible AI Generative AI AI AI 283

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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Composite AI Explainability Black Box AI Explainable AI 182

Unite.AI

MARCH 6, 2025

It analyzes over 250 data points per property using proprietary algorithms to forecast which homes are most likely to list within the next 12 months. Top Features: Predictive analytics algorithm that identifies 70%+ of future listings in a territory. which the AI will immediately factor into the Zestimate.

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AI AI Automation Algorithm 182

Unite.AI

JULY 1, 2024

Transparency and Explainability Enhancing transparency and explainability is essential. Techniques such as model interpretability frameworks and Explainable AI (XAI) help auditors understand decision-making processes and identify potential issues. This involves human experts reviewing and validating AI outputs.

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Explainability BERT AI AI 182

Towards AI

OCTOBER 31, 2024

If you are excited to dive into applied AI, want a study partner, or even want to find a partner for your passion project, join the collaboration channel! Golden_leaves68731 is a senior AI developer looking for a non-technical co-founder to join their venture. If this sounds like you, reach out in the thread!

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LLM NLP BERT Large Language Models 116

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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Natural Language Processing AI AI Algorithm 278

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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Algorithm AI AI Generative AI 218

Unite.AI

JULY 19, 2024

These tools help identify when AI makes up information or gives incorrect answers, even if they sound believable. These tools use various techniques to detect AI hallucinations. Some rely on machine learning algorithms, while others use rule-based systems or statistical methods.

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LLM AI AI Data Quality 279

IBM Journey to AI blog

JANUARY 10, 2024

But even with the myriad benefits of AI, it does have noteworthy disadvantages when compared to traditional programming methods. AI development and deployment can come with data privacy concerns, job displacements and cybersecurity risks, not to mention the massive technical undertaking of ensuring AI systems behave as intended.

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Unite.AI

JULY 1, 2024

Seekr’s approach to AI is to ensure the user has full transparency into the content including provenance, lineage and objectivity, and the ability to build and leverage AI that is transparent, trustworthy, features explainability and has all the guardrails so consumers and businesses alike can trust it.

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Explainability Software Engineer AI Modeling Responsible AI 173

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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Artificial Intelligence Artificial Intelligence Neural Network Deep Learning 142

Flipboard

APRIL 1, 2025

Through logic-based algorithms and mathematical validation, Automated Reasoning checks validate LLM outputs against domain knowledge encoded in the Automated Reasoning policy to help prevent factual inaccuracies. This hybrid architecture allows users to input policies in plain language while maintaining mathematically rigorous verification.

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Automation Generative AI AI AI 164

AWS Machine Learning Blog

DECEMBER 5, 2024

Technical standards, such as ISO/IEC 42001, are significant because they provide a common framework for responsible AI development and deployment, fostering trust and interoperability in an increasingly global and AI-driven technological landscape.

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Responsible AI AI Tools AI AI 102

AWS Machine Learning Blog

NOVEMBER 15, 2024

The rapid advancement of generative AI promises transformative innovation, yet it also presents significant challenges. Concerns about legal implications, accuracy of AI-generated outputs, data privacy, and broader societal impacts have underscored the importance of responsible AI development.

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Responsible AI Prompt Engineering Prompt Engineer AI 105

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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Data Drift AI AI AI Modeling 246

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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AI AI Data Integration Generative AI 189

Marktechpost

JUNE 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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Artificial Intelligence Artificial Intelligence Neural Network Deep Learning 117

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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Black Box AI Algorithm Explainability Explainable AI 130

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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AI AI Algorithm Artificial Intelligence 134

Dlabs.ai

AUGUST 16, 2022

This is the challenge that explainable AI solves. Explainable artificial intelligence shows how a model arrives at a conclusion. What is explainable AI? Explainable artificial intelligence (or XAI, for short) is a process that helps people understand an AI model’s output. Let’s begin.

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Explainability Explainable AI AI Modeling Artificial Intelligence 52

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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Generative AI Large Language Models AI AI 248

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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Artificial Intelligence Artificial Intelligence Prompt Engineering Prompt Engineer 109

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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