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

JULY 19, 2024

This extensive knowledge base allows for robust AI validation that makes Pythia ideal for situations where accuracy is important. Here are some key features of Pythia: With its real-time hallucination detection capabilities, Pythia enables AI models to make reliable decisions. Automatically detects mislabeled data.

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Unite.AI

JANUARY 9, 2024

There are three areas of AI in particular that will always require human involvement to achieve optimal outcomes. Building a strong data foundation. Building a robust data foundation is critical, as the underlying data model with proper metadata, data quality, and governance is key to enabling AI to achieve peak efficiencies.

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AWS Machine Learning Blog

NOVEMBER 15, 2024

At AWS, we are committed to developing AI responsibly , taking a people-centric approach that prioritizes education, science, and our customers, integrating responsible AI across the end-to-end AI lifecycle. What constitutes responsible AI is continually evolving. This is a powerful method to reduce hallucinations.

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IBM Journey to AI blog

MAY 8, 2023

Additionally, should the parameters of the AI algorithm model may be modified illegally by a cyber attacker, it will cause the performance deterioration of the AI model and lead to undesirable consequences. You should host the model on internal servers.

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Towards AI

FEBRUARY 11, 2025

Author(s): Richie Bachala Originally published on Towards AI. Beyond Scale: Data Quality for AI Infrastructure The trajectory of AI over the past decade has been driven largely by the scale of data available for training and the ability to process it with increasingly powerful compute & experimental models.

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Unite.AI

JANUARY 20, 2025

Headquartered in Oregon, the company is at the forefront of transforming how healthcare data is shared, monetized, and applied, enabling secure collaboration between data custodians and data consumers. Can you explain how datma.FED utilizes AI to revolutionize healthcare data sharing and analysis?

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IBM Journey to AI blog

DECEMBER 16, 2022

An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance.

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Unite.AI

OCTOBER 22, 2024

A staggering 71% of organizations have integrated AI and Gen AI into their operations, up from 34% in previous years. This shift marks a pivotal moment in the industry, with AI set to revolutionize various aspects of QE, from test automation to data quality management.

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Snorkel AI

MARCH 4, 2025

Alignment ensures that an AI models outputs align with specific values, principles, or goals, such as generating polite, safe, and accurate responses or adhering to a company’s ethical guidelines. LLM alignment techniques come in three major varieties: Prompt engineering that explicitly tells the model how to behave.

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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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Unite.AI

JUNE 10, 2024

It works with utility planners and operators, to model the grid into its “AI digital twin”, perform high speed and large scale analytics including in near real time, and make recommendations on grid operations, plans, and designs. AI by itself can’t learn such a complex system as the grid with measurement data only.

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Unite.AI

APRIL 23, 2024

At Aiimi, we believe that AI should give users more, not less, control over their data. AI should be a driver of data quality and brand-new insights that genuinely help businesses make their most important decisions with confidence. A typical enterprise uses hundreds of different systems to store data.

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The MLOps Blog

JUNE 27, 2023

Model governance and compliance : They should address model governance and compliance requirements, so you can implement ethical considerations, privacy safeguards, and regulatory compliance into your ML solutions. This includes features for model explainability, fairness assessment, privacy preservation, and compliance tracking.

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Towards AI

OCTOBER 3, 2024

.– Model Robustness: Ensuring that models can handle unforeseen inputs without failure is a significant hurdle for deploying AI in critical applications. Research focuses on creating algorithms that allow models to learn from data on local devices without transferring sensitive information to central servers.

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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. As per the official blog, Grok-1.5

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Unite.AI

JUNE 17, 2024

In the context of AI specifically, companies should be transparent about where and how AI is being used, and what impact it may have on customers' experiences or decisions. Thirdly, companies need to establish strong data governance frameworks. In the context of AI, data governance also extends to model governance.

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ODSC - Open Data Science

NOVEMBER 20, 2024

Understanding Prompt Engineering and the Evolution of Generative AI A particularly intriguing part of the conversation touched upon prompt engineering, a skill Yves believes will eventually phase out as generative AI models evolve. Yves Mulkers stressed the need for clean, reliable data as a foundation for AI success.

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Marktechpost

DECEMBER 8, 2024

Structured data is important in this process, as it provides a clear and organized framework for the AI to learn from, unlike messy or unstructured data, which can lead to ambiguities. Employ Data Templates With data quality, implementing data templates offers another layer of control and precision.

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Unite.AI

JUNE 24, 2024

Deep learning is great for some applications — large language models are brilliant for summarizing documents, for example — but sometimes a simple regression model is more appropriate and easier to explain. My own data team generates reports on consumption which we make available daily to our customers.

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AWS Machine Learning Blog

JUNE 3, 2024

In a single visual interface, you can complete each step of a data preparation workflow: data selection, cleansing, exploration, visualization, and processing. Custom Spark commands can also expand the over 300 built-in data transformations. Other analyses are also available to help you visualize and understand your data.

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Snorkel AI

MARCH 4, 2025

Alignment ensures that an AI models outputs align with specific values, principles, or goals, such as generating polite, safe, and accurate responses or adhering to a company’s ethical guidelines. LLM alignment techniques come in three major varieties: Prompt engineering that explicitly tells the model how to behave.

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Pickl AI

DECEMBER 16, 2024

This blog aims to help you navigate this growth by addressing key enablers of AI development. Key Takeaways Reliable, diverse, and preprocessed data is critical for accurate AI model training. GPUs, TPUs, and AI frameworks like TensorFlow drive computational efficiency and scalability.

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AWS Machine Learning Blog

SEPTEMBER 8, 2023

There are only 0.12% of anomalous images in the entire data set (i.e., Finally, there is no labeled data available for training a supervised machine learning model. Next, we describe how we address these challenges and explain our proposed method. First, we will describe the steps involved in the data processing pipeline.

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Defined.ai blog

AUGUST 10, 2023

It can quickly process large amounts of data, precisely identifying patterns and insights humans might overlook. 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.

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Snorkel AI

OCTOBER 4, 2023

ML can significantly reduce the time necessary to pre-process customer data for downstream tasks, like training predictive models. Supercharge predictive modeling. Instead of the rule-based decision-making of traditional credit scoring, AI can continually learn and adapt, improving accuracy and efficiency.

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Snorkel AI

DECEMBER 2, 2024

At its core, Snorkel Flow empowers data scientists and domain experts to encode their knowledge into labeling functions, which are then used to generate high-quality training datasets. This approach not only enhances the efficiency of data preparation but also improves the accuracy and relevance of AI models.

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Snorkel AI

OCTOBER 4, 2023

ML can significantly reduce the time necessary to pre-process customer data for downstream tasks, like training predictive models. Supercharge predictive modeling. Instead of the rule-based decision-making of traditional credit scoring, AI can continually learn and adapt, improving accuracy and efficiency.

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Defined.ai blog

AUGUST 23, 2023

Every piece of data adds to the picture, providing insights that can lead to innovation and efficiency. Among these puzzle pieces, marketing data stands out as a valuable component. But the influence of marketing data isn’t limited to shaping AI. Inaccurate data can lead to misleading AI insights.

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Snorkel AI

JANUARY 25, 2024

QBE Ventures has made a strategic investment in Snorkel AI , a company providing a leading platform for data-centric AI model development. Insurers need simple, scalable, and affordable ways to customise Machine Learning models and fine-tune foundation models.

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Snorkel AI

JANUARY 25, 2024

QBE Ventures has made a strategic investment in Snorkel AI , a company providing a leading platform for data-centric AI model development. Insurers need simple, scalable, and affordable ways to customise Machine Learning models and fine-tune foundation models.

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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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AWS Machine Learning Blog

AUGUST 4, 2023

After you create the API, we recommend registering the model endpoint in Salesforce Einstein Studio. For instructions, refer to Bring Your Own AI Models to Salesforce with Einstein Studio The following diagram illustrates the solution architecture. In the data flow view, you can now see a new node added to the visual graph.

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ODSC - Open Data Science

FEBRUARY 11, 2025

What is AI Engineering? Chip Huyen began by explaining how AI engineering has emerged as a distinct discipline, evolving out of traditional machine learning engineering. While machine learning engineers focus on building models, AI engineers often work with pre-trained foundation models, adapting them to specific use cases.

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ODSC - Open Data Science

OCTOBER 16, 2024

Increasingly powerful open-source LLMs are a trend that will continue to shape the AI landscape into 2025. A Focus on Explainability and Responsible AI: The session “ Causal Graphs: Applying PyWhy to Go Beyond Explainability ” underscores the growing importance of understanding the “why” behind LLM predictions.

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DataRobot Blog

DECEMBER 20, 2022

Datarobot enables users to easily combine multiple datasets into a single training dataset for AI modeling. City’s pulse (quality and density of the points of interest). The great thing about DataRobot Explainable AI is that it spans the entire platform. You can understand the data and model’s behavior at any time.

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O'Reilly Media

NOVEMBER 28, 2023

Few nonusers (2%) report that lack of data or data quality is an issue, and only 1.3% report that the difficulty of training a model is a problem. In hindsight, this was predictable: these are problems that only appear after you’ve started down the road to generative AI. Model degradation is a different concern.

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