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

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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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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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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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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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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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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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

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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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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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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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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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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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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Heartbeat

FEBRUARY 2, 2024

As AI strives to emulate human-like understanding, VQA plays a pivotal role by demanding systems to recognize objects and scenes in images and comprehend and respond to human-generated questions about those images. It's remarkable diversity and scale position it as a cornerstone for evaluating and benchmarking VQA algorithms.

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Heartbeat

SEPTEMBER 7, 2023

Let’s start by understanding why transparency in AI is not just an option but a necessity in today’s world. The Need for Model Interpretability and Explainability In the age of AI, models impact our lives in countless ways. Consider healthcare, where AI models are being used for disease diagnosis.

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ODSC - Open Data Science

FEBRUARY 22, 2024

Join this upcoming training session which will demonstrate how you can use low-code/no-code programming platforms to effectively integrate geospatial analysis with a variety of AI algorithms, including machine learning, deep learning, and Explainable AI.

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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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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

MAY 10, 2024

Feature engineering via singular value decomposition (SVD) — Source Step 3: Select a Model Architecture Now that we have a large enough dataset that has been cleaned and feature-engineered, we need to choose what type of model to use. text vs images) and (2) the desired output (e.g.

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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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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.

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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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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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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

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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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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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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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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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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Viso.ai

APRIL 25, 2024

Bias Detection in Computer Vision: A Guide to Types and Origins Artificial Intelligence (AI) bias detection generally refers to detecting systematic errors or prejudices in AI models that amplify societal biases, leading to unfair or discriminatory outcomes. This underscores the importance of bias detection in CV models.

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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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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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The MLOps Blog

JUNE 27, 2023

For example, if your team works on recommender systems or natural language processing applications, you may want an MLOps tool that has built-in algorithms or templates for these use cases. This includes features for hyperparameter tuning, automated model selection, and visualization of model metrics.

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AI Impacts

DECEMBER 22, 2022

Quick clarifications First, to fend off misunderstanding— I take ‘slowing down dangerous AI’ to include any of: reducing the speed at which AI progress is made in general, e.g. as would occur if general funding for AI declined.

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Pickl AI

MARCH 31, 2024

Summary: Generative AI isn’t magic, but it learns like one! This powerful technology utilizes deep learning algorithms to analyze massive amounts of data, be it text, images, or code. Through a multi-step process, the AI extracts patterns and relationships within the data. That’s the magic of generative AI.

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