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

AI models in production. Today, seven in 10 companies are experimenting with generative AI, meaning that the number of AI models in production will skyrocket over the coming years. As a result, industry discussions around responsible AI have taken on greater urgency. In 2022, companies had an average of 3.8

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IBM Journey to AI blog

JANUARY 25, 2024

Yet many AI creators are currently facing backlash for the biases, inaccuracies and problematic data practices being exposed in their models. These issues require more than a technical, algorithmic or AI-based solution. How might this insight affect evaluation of AI models? How are you assessing fairness?

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Unite.AI

SEPTEMBER 23, 2024

Critics point out that the complexity of biological systems far exceeds what current AI models can fully comprehend. While generative AI is excellent at data-driven prediction, it struggles to navigate the uncertainties and nuances that arise in human biology. One major hurdle is the ‘black box’ nature of AI algorithms.

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Unite.AI

APRIL 9, 2024

The adoption of Artificial Intelligence (AI) has increased rapidly across domains such as healthcare, finance, and legal systems. However, this surge in AI usage has raised concerns about transparency and accountability. Composite AI is a cutting-edge approach to holistically tackling complex business problems.

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IBM Journey to AI blog

OCTOBER 12, 2023

The tasks behind efficient, responsible AI lifecycle management The continuous application of AI and the ability to benefit from its ongoing use require the persistent management of a dynamic and intricate AI lifecycle—and doing so efficiently and responsibly. Here’s what’s involved in making that happen.

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Unite.AI

SEPTEMBER 20, 2024

This transition from static, rule-based systems to adaptive, learning-based models opened new opportunities for market analysis. Key milestones in this evolution include the advent of algorithmic trading in the late 1980s and early 1990s, where simple algorithms automated trades based on set criteria.

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Unite.AI

MARCH 18, 2024

As generative AI technology advances, there's been a significant increase in AI-generated content. 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.

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Pickl AI

OCTOBER 8, 2024

This blog will explore the concept of XAI, its importance in fostering trust in AI systems, its benefits, challenges, techniques, and real-world applications. What is Explainable AI (XAI)? Explainable AI refers to methods and techniques that enable human users to comprehend and interpret the decisions made by AI systems.

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Unite.AI

JULY 1, 2024

For example, AI models used in medical diagnoses must be thoroughly audited to prevent misdiagnosis and ensure patient safety. Another critical aspect of AI auditing is bias mitigation. AI models can perpetuate biases from their training data, leading to unfair outcomes.

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Towards AI

JULY 22, 2023

Last Updated on July 24, 2023 by Editorial Team Author(s): Data Science meets Cyber Security Originally published on Towards AI. Now Algorithms know what they are doing and why! Let us go further into the enigmas of Artificial Intelligence, where AI is making waves like never before! SOURCE: [link] A.

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IBM Journey to AI blog

JUNE 26, 2023

What is generative AI? Generative AI uses an advanced form of machine learning algorithms that takes users prompts and uses natural language processing (NLP) to generate answers to almost any question asked. You can start by learning more about the advances IBM is making in new generative AI models with watsonx.ai

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Towards AI

OCTOBER 3, 2024

Ongoing Challenges: – Design Complexity: Designing and training these complex networks remains a hurdle due to their intricate architectures and the need for specialized algorithms.– These chips have demonstrated the ability to process complex algorithms using a fraction of the energy required by traditional GPUs.–

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AI Weekly

OCTOBER 12, 2023

coindesk.com Chorus of creative workers demands AI regulation at FTC roundtable At a virtual Federal Trade Commission (FTC) roundtable yesterday, a deep lineup of creative workers and labor leaders representing artists demanded AI regulation of generative AI models and tools.

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NVIDIA

MARCH 1, 2024

Developers of trustworthy AI understand that no model is perfect, and take steps to help customers and the general public understand how the technology was built, its intended use cases and its limitations. Privacy: Complying With Regulations, Safeguarding Data AI is often described as data hungry.

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Unite.AI

DECEMBER 13, 2023

Ultimately, staying updated empowers enthusiasts to leverage the full potential of AI and make confident decisions in their professional and personal pursuits. AI-Powered Threat Detection and Response AI takes the lead in making the digital world safer.

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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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Pickl AI

SEPTEMBER 17, 2024

This is where AI steps in, offering advanced capabilities in threat detection, prevention, and response. By leveraging Machine Learning algorithms and predictive analytics, AI-powered cybersecurity solutions can proactively identify and mitigate risks, providing a more robust and adaptive defence against cyber criminals.

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Unite.AI

SEPTEMBER 13, 2023

Increasingly though, large datasets and the muddled pathways by which AI models generate their outputs are obscuring the explainability that hospitals and healthcare providers require to trace and prevent potential inaccuracies. In this context, explainability refers to the ability to understand any given LLM’s logic pathways.

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IBM Journey to AI blog

FEBRUARY 8, 2023

Adherence to responsible artificial intelligence (AI) standards follows similar tenants. Gartner predicts that the market for artificial intelligence (AI) software will reach almost $134.8 Manual processes can lead to “black box models” that lack transparent and explainable analytic results. billion by 2025.

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Dlabs.ai

JULY 19, 2023

Example: Algorithmic Bias in the UK A-level Grading To illustrate, consider a real-world example that occurred during the COVID-19 pandemic in the UK. With the traditional A-level exams canceled due to health concerns, the UK government used an algorithm to determine student grades.

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Flipboard

JUNE 20, 2023

Understanding AI’s mysterious “opaque box” is paramount to creating explainable AI. This can be simplified by considering that AI, like all other technology, has a supply chain. AI models are also created and delivered via supply chains. What factors does it weigh?

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IBM Journey to AI blog

JULY 6, 2023

Machine learning works on a known problem with tools and techniques, creating algorithms that let a machine learn from data through experience and with minimal human intervention. Deep learning algorithms are neural networks modeled after the human brain. Some people worry that AI and machine learning will eliminate jobs.

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Unite.AI

JANUARY 12, 2024

Processing multiple data streams strains compute resources, demanding optimized model architectures. Advances in attention mechanisms and algorithms are needed to integrate contradictory multimodal inputs. Enhancing user trust via explainable AI also remains vital.

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IBM Journey to AI blog

SEPTEMBER 20, 2023

Foundational models (FMs) are marking the beginning of a new era in machine learning (ML) and artificial intelligence (AI) , which is leading to faster development of AI that can be adapted to a wide range of downstream tasks and fine-tuned for an array of applications.

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IBM Journey to AI blog

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects. What is MLOps?

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Towards AI

APRIL 2, 2024

However, the AI community has also been making a lot of progress in developing capable, smaller, and cheaper models. This can come from algorithmic improvements and more focus on pretraining data quality, such as the new open-source DBRX model from Databricks. X’s Grok Chatbot Will Soon Get an Upgraded Model, Grok-1.5

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Dlabs.ai

JULY 25, 2023

In an era where AI and machine learning have streamlined everything, including hiring processes, the balance between efficiency and equity comes into question. With algorithms, machine learning, and statistical modeling defining who gets hired or promoted, are these decisions genuinely fair? Who do AEDT Regulations address?

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Marktechpost

JULY 19, 2023

Nowadays, there are hardly any fields that do not make use of AI. For instance, AI is everywhere, from AI agents in voice assistants such as Amazon Echo and Google Home to using machine learning algorithms in predicting protein structure. Is that actually the case, though? Check out the Paper and Stanford Article.

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IBM Journey to AI blog

JULY 17, 2023

While traditional AI approaches provide customers with quick service, they have their limitations. Currently chat bots are relying on rule-based systems or traditional machine learning algorithms (or models) to automate tasks and provide predefined responses to customer inquiries. Watsonx.ai

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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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Unite.AI

OCTOBER 25, 2024

In mortgage requisition intake, AI optimizes efficiency by automating the analysis of requisition data, leading to faster processing times. Fraud detection has become more robust with advanced AI algorithms that help identify and prevent fraudulent activities, thereby safeguarding assets and reducing risks.

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Defined.ai blog

AUGUST 10, 2023

Businesses can transform raw numbers into actionable insights by applying AI. For instance, an AI model can predict future sales based on past data, helping businesses plan better. AI refers to computer systems capable of executing tasks that typically require human intelligence.

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Heartbeat

SEPTEMBER 13, 2023

Source: ResearchGate Explainability refers to the ability to understand and evaluate the decisions and reasoning underlying the predictions from AI models (Castillo, 2021). Algorithmic Accountability: Explainability ensures accountability in machine learning and AI systems.

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Viso.ai

DECEMBER 6, 2023

Computer vision (CV) is a rapidly evolving area in artificial intelligence (AI), allowing machines to process complex real-world visual data in different domains like healthcare, transportation, agriculture, and manufacturing. Edge AI involves processing data near the source. Get a demo here.

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Bugra Akyildiz

MAY 25, 2024

Articles OpenAI has announced GPT-4o , their new flagship AI model that can reason across audio, vision, and text in real-time. The blog post acknowledges that while GPT-4o represents a significant step forward, all AI models including this one have limitations in terms of biases, hallucinations, and lack of true understanding.

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Viso.ai

MARCH 26, 2024

4: Algorithmic Trading and Market Analysis No.5: Viso Suite is the Computer Vision Enterprise Platform Computer Vision Algorithms for Finance Models like YOLO (You Only Look Once) models and Faster R-CNN have set benchmarks in real-time processing as well. 1: Fraud Detection and Prevention No.2:

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