AI News

MARCH 7, 2024

Indeed, as Anthropic prompt engineer Alex Albert pointed out, during the testing phase of Claude 3 Opus, the most potent LLM (large language model) variant, the model exhibited signs of awareness that it was being evaluated. In addition, editorial guidance on AI has been updated to note that ‘all AI usage has active human oversight.’

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Unite.AI

DECEMBER 19, 2024

Thats why explainability is such a key issue. People want to know how AI systems work, why they make certain decisions, and what data they use. The more we can explain AI, the easier it is to trust and use it. Large Language Models (LLMs) are changing how we interact with AI. Thats where LLMs come in.

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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

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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Marktechpost

JUNE 8, 2024

Despite performing remarkably well on various tasks, these models are often unable to provide a clear understanding of how specific visual changes affect ML decisions. In conclusion, the proposed framework enhances the explainability of AI models in medical imaging. If you like our work, you will love our newsletter.

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Towards AI

AUGUST 31, 2023

Last Updated on September 1, 2023 by Editorial Team Author(s): Louis Bouchard Originally published on Towards AI. An introduction to explainable AI This member-only story is on us. Powerful artificial intelligence models like DALLE or ChatGPT are super useful and fun to use. Upgrade to access all of Medium.

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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

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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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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Dlabs.ai

AUGUST 16, 2022

One of the major hurdles to AI adoption is that people struggle to understand how AI models work. This is the challenge that explainable AI solves. Explainable artificial intelligence shows how a model arrives at a conclusion. What is explainable AI? Let’s begin.

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IBM Journey to AI blog

JUNE 26, 2023

Concerns to consider with off the shelf generative AI models include: Internet data is not always fair and accurate At the heart of much of generative AI today is vast amounts of data from sources such as Wikipedia, websites, articles, image or audio files, etc. What is watsonx.governance?

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Unite.AI

JUNE 18, 2024

GANs gave rise to DALL-E , an AI model that generates images based on textual descriptions. Looking further ahead, one critical area of focus is Explainable AI , which aims to make AI decisions transparent and understandable. On the other hand, VAEs are used primarily in unsupervised learning.

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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.

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IBM Journey to AI blog

MAY 9, 2023

A lack of confidence to operationalize AI Many organizations struggle when adopting AI. According to Gartner , 54% of models are stuck in pre-production because there is not an automated process to manage these pipelines and there is a need to ensure the AI models can be trusted.

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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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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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Unite.AI

AUGUST 13, 2024

Likewise, The US Department of Justice (DOJ) initiated two distinct inquiries into Nvidia due to rising antitrust concerns surrounding their AI-centric business operations. Nvidia commands a 70% to 95% market share in the chips essential for training AI models.

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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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Marktechpost

JULY 6, 2023

It is crucial to distinguish between IAI and XAI models because of their increasing popularity in the ML field in order to assist organizations in selecting the best strategy for their use case. In other words, it is safe to say that an IAI model provides its own explanation. Situations of this nature can be interpreted.

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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

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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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

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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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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Towards AI

OCTOBER 19, 2024

Yet, for all their sophistication, they often can’t explain their choices — this lack of transparency isn’t just frustrating — it’s increasingly problematic as AI becomes more integrated into critical areas of our lives. What is Explainability AI (XAI)?

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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

MARCH 4, 2024

Operations Incidents occur, even in an AI-first world. However, an AI+ enterprise uses AI not only to delight customers but also to solve IT problems. The scale and impact of next-generation AI emphasize the importance of governance and risk controls.

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AI News

MAY 29, 2024

. “It’s using AI to figure out actually how your application works, and then provides recommendations about how to make it better,” Ball said. Upcoming AI opportunities According to Ball, a current opportunity is organising the unstructured data that feeds into AI models.

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Unite.AI

DECEMBER 17, 2024

Humans can validate automated decisions by, for example, interpreting the reasoning behind a flagged transaction, making it explainable and defensible to regulators. Financial institutions are also under increasing pressure to use Explainable AI (XAI) tools to make AI-driven decisions understandable to regulators and auditors.

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Unite.AI

JANUARY 12, 2024

Enhancing user trust via explainable AI also remains vital. Addressing these technical obstacles will be key to unlocking multimodal AI's capabilities. Meta-learning Meta-learning, or ‘learning to learn', focuses on equipping AI models with the ability to rapidly adapt to new tasks using limited data samples.

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Unite.AI

DECEMBER 13, 2023

The need for explainability in AI algorithms becomes important in meeting compliance requirements. 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.

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IBM Journey to AI blog

SEPTEMBER 18, 2023

Although AI is what will unleash productivity for banks and corporate buyers, it will require responsible, transparent and explainable AI to transform the workflows and provide cross-border assurance.

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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.

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IBM Journey to AI blog

MARCH 7, 2023

Using IBM Watson Studio , a service available on IBM Cloud Pak for Data , to train and monitor the AI solution’s machine learning models, Innocens BV could help doctors by providing data driven insights that are associated with a potential onset of sepsis.

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Marktechpost

AUGUST 24, 2024

EXplainable AI (XAI) has become a critical research domain since AI systems have progressed to being deployed in essential sectors such as health, finance, and criminal justice. The intrinsic complexity—the so-called “black boxes”—given by AI models makes research in the field of XAI difficult.

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Unite.AI

JULY 2, 2024

GDPR's stringent data protection standards present several challenges for businesses using personal data in AI. Similarly, the California Consumer Privacy Act (CCPA) significantly impacts AI by requiring companies to disclose data collection practices to ensure that AI models are transparent, accountable, and respectful of user privacy.

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IBM Journey to AI blog

AUGUST 12, 2024

Regulatory insights: Current AI regulations in financial services Existing AI regulations in financial services are primarily focused on ensuring transparency, accountability, and data privacy. Regulators require financial institutions to implement robust governance frameworks that ensure the ethical use of AI.

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