IBM Journey to AI blog

AUGUST 9, 2024

Generative AI (gen AI) is artificial intelligence that responds to a user’s prompt or request with generated original content, such as audio, images, software code, text or video. Gen AI models are trained on massive volumes of raw data.

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Unite.AI

JUNE 18, 2024

This fascinating fusion of creativity and automation, powered by Generative AI , is not a dream anymore; it is reshaping our future in significant ways. GANs gave rise to DALL-E , an AI model that generates images based on textual descriptions. Addressing these biases is essential for developing equitable AI systems.

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IBM Journey to AI blog

OCTOBER 16, 2023

It encompasses risk management and regulatory compliance and guides how AI is managed within an organization. Foundation models: The power of curated datasets Foundation models , also known as “transformers,” are modern, large-scale AI models trained on large amounts of raw, unlabeled data.

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IBM Journey to AI blog

AUGUST 17, 2023

Next, the teams trained a foundation model using watsonx.ai , a powerful studio for training, validating, tuning and deploying generative AI models for business. That’s why the US Open will also use watsonx.governance to direct, manage and monitor its AI activities.

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IBM Journey to AI blog

FEBRUARY 8, 2024

For industries providing essential services to clients such as insurance, banking and retail, the law requires the use of a fundamental rights impact assessment that details how the use of AI will affect the rights of customers. Higher risk tiers have more transparency requirements including model evaluation, documentation and reporting.

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IBM Journey to AI blog

MARCH 4, 2024

Where do you harness gen AI vs. predictive AI vs. AI orchestration? For instance, when automating password change requests, do you need a 175 billion parameter public foundation model, a fine-tuned smaller model, or AI orchestration to call APIs? Operations Incidents occur, even in an AI-first world.

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IBM Journey to AI blog

JANUARY 25, 2024

How might this insight affect evaluation of AI models? Model (in)accuracy To quote a common aphorism, all models are wrong. This holds true in the areas of statistics, science and AI. Models created with a lack of domain expertise can lead to erroneous outputs. How are you making your model explainable?

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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 12, 2024

The integration of generative AI, particularly LLMs, offers transformative potential to automate compliance processes, detect anomalies, and provide comprehensive insights into regulatory requirements. Financial institutions are prioritizing the integration of AI to address pressing challenges and enhance their competitive edge.

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IBM Journey to AI blog

SEPTEMBER 18, 2023

Siloed processes can become integrated by using intelligent workflows, which help enable seamless and automated exchange of financial, informational and physical supply chain data in one distributed network. It can help connect disparate and disconnected manual processes and platforms to a data-driven and connected trade ecosystem.

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Dlabs.ai

JULY 25, 2023

New York City has responded to these concerns by introducing new regulations on using Automated Employment Decision Tools (AEDTs) in 2023. Let’s start by explaining what exactly Automated Employment Decision Tools (AEDTs) are. How can we ensure these technological advances don’t harbor biases or injustices?

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Unite.AI

JULY 2, 2024

Administrative automation for education and other industries AI systems classified as high risk are subject to strict compliance requirements, such as establishing a comprehensive risk management framework throughout the AI system’s lifecycle and implementing robust data governance measures.

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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 It is the process of defining policies and establishing accountability to guide the creation and deployment of AI systems. billion by 2025.

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Unite.AI

DECEMBER 13, 2023

Organizations must showcase how AI-driven decisions are made, making explainable AI models important. These models clearly understand the decision-making process, facilitating regulatory compliance audits. Prepare for a new cybersecurity workforce training era as AI enters the scene.

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AWS Machine Learning Blog

NOVEMBER 15, 2024

If you are planning on using automated model evaluation for toxicity, start by defining what constitutes toxic content for your specific application. Automated evaluations come with curated datasets to choose from. Accuracy evaluation helps AI models produce reliable and correct outputs across various tasks and datasets.

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IBM Journey to AI blog

AUGUST 11, 2023

It advances the scalability of ML in real-world applications by using algorithms to improve model performance and reproducibility. MLOps aims to streamline the time and resources it takes to run data science models using automation, ML and iterative improvements on each model version. How the models are stored.

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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

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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Unite.AI

SEPTEMBER 20, 2024

Its real-time trend analysis, investment evaluations, risk assessments, and automation features empower financial professionals to make informed choices efficiently. This transition from static, rule-based systems to adaptive, learning-based models opened new opportunities for market analysis.

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Unite.AI

OCTOBER 25, 2024

SLK's AI-powered platforms and accelerators are designed to automate and streamline processes, helping businesses reach the market more quickly. In mortgage requisition intake, AI optimizes efficiency by automating the analysis of requisition data, leading to faster processing times.

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Marktechpost

AUGUST 13, 2024

However, the challenge lies in integrating and explaining multimodal data from various sources, such as sensors and images. AI models are often sensitive to small changes, necessitating a focus on trustworthy AI that emphasizes explainability and robustness.

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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.

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Marktechpost

JULY 22, 2024

Current AV models focus mainly on binary classification, which often lacks transparency. This lack of explainability is a gap in academic interest and a practical concern. Analyzing the decision-making process of AI models is essential for building trust and reliability, particularly in identifying and addressing hidden biases.

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ODSC - Open Data Science

FEBRUARY 18, 2025

This is not science fiction, as these are the promises of PhD-level AI agentshighly autonomous systems capable of complex reasoning, problem-solving, and adaptive learning. Unlike traditional AI models, these agents go beyond pattern recognition to independently analyze, reason, and generate insights in specialized fields.

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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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IBM Journey to AI blog

JULY 6, 2023

A major online media company uses data science to develop personalized content, enhance marketing through targeted ads and continuously update music streams, among other automation decisions. to learn more) In other words, you get the ability to operationalize data science models on any cloud while instilling trust in AI outcomes.

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Dlabs.ai

JULY 19, 2023

It’s essential to keep humans involved in AI decision-making processes, especially when these decisions can significantly impact people’s lives. While AI systems can automate many tasks, they should not completely replace human judgment and intuition.

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Unite.AI

JUNE 24, 2024

Having worn both hats, I am very aware of the importance of the software development lifecycle (especially automation and testing) as applied to machine learning projects. What are the biggest challenges in moving, processing, and analyzing unstructured data for AI and large language models (LLMs)?

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ODSC - Open Data Science

NOVEMBER 20, 2024

The Role of Creativity and Critical Thinking in Generative AI Generative AI, a powerful tool for automating and augmenting tasks, still requires a nuanced, human touch to deliver effective results. Yves Mulkers stressed the need for clean, reliable data as a foundation for AI success.

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DataRobot Blog

JUNE 23, 2022

Replacing these with a more accurate (and rational) AI/ML model in an existing system or process is generally straightforward, because there is a context and environment in which the new model can succeed. DATAROBOT AI CLOUD. Automate and Scale Your Decisions. Too often the new model fails the “so what” test.

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Pickl AI

SEPTEMBER 17, 2024

Summary : AI is transforming the cybersecurity landscape by enabling advanced threat detection, automating security processes, and adapting to new threats. Introduction In the rapidly evolving landscape of cybersecurity, Artificial Intelligence (AI) has emerged as a powerful tool in the fight against cyber threats.

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LevelAI

DECEMBER 5, 2023

Level AI stands out as a pioneer in this domain, setting new standards for enterprise-ready generative AI. AI Benchmarking for Impact and Effectiveness Level AI recognizes that traditional benchmarking methods fall short when evaluating generative AI models.

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LevelAI

DECEMBER 5, 2023

Level AI stands out as a pioneer in this domain, setting new standards for enterprise-ready generative AI. AI Benchmarking for Impact and Effectiveness Level AI recognizes that traditional benchmarking methods fall short when evaluating generative AI models.

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The MLOps Blog

JUNE 27, 2023

Experimentation and model development: Platforms should offer features for you to design and run experiments, explore different algorithms and architectures, and optimize model performance. This includes features for hyperparameter tuning, automated model selection, and visualization of model metrics.

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ODSC - Open Data Science

DECEMBER 17, 2024

The efficiency of AI is evident. Automating underwriting processes can reduce approval times and result in an overall faster underwriting process. Types of Data Used in AILending One of AIs greatest strengths is its capacity to handle diverse datasets. For one, AI models inherit biases from the data they are trainedon.

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ODSC - Open Data Science

FEBRUARY 25, 2025

Generative AI TrackBuild the Future with GenAI Generative AI has captured the worlds attention with tools like ChatGPT, DALL-E, and Stable Diffusion revolutionizing how we create content and automate tasks. This track will cover the latest best practices for managing AI models from development to deployment.

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Pickl AI

APRIL 8, 2024

Job displacement One of the biggest fears surrounding AI is that it will automate many jobs currently performed by humans, leading to widespread unemployment. While AI will undoubtedly change the job market, the extent of job displacement remains uncertain. How Can We Ensure the Transparency of AI Systems?

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