IBM Journey to AI blog

JANUARY 9, 2024

Companies still often accept the risk of using internal data when exploring large language models (LLMs) because this contextual data is what enables LLMs to change from general-purpose to domain-specific knowledge. In the generative AI or traditional AI development cycle, data ingestion serves as the entry point.

Data Ingestion 222

Data Ingestion Data Integration Data Quality LLM 222

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.

LLM 279

LLM AI AI Data Quality 279

IBM Journey to AI blog

MAY 8, 2023

Risk and limitations of AI The risk associated with the adoption of AI in insurance can be separated broadly into two categories—technological and usage. Technological risk—data confidentiality The chief technological risk is the matter of data confidentiality.

AI 218

AI AI Algorithm Generative AI 218

Unite.AI

SEPTEMBER 12, 2024

Traditionally, AI research and development have focused on refining models, enhancing algorithms, optimizing architectures, and increasing computational power to advance the frontiers of machine learning. However, a noticeable shift is occurring in how experts approach AI development, centered around Data-Centric AI.

Data Quality 162

Data Quality Data Scarcity AI AI 162

Unite.AI

DECEMBER 20, 2024

This raises a crucial question: Are the datasets being sold trustworthy, and what implications does this practice have for the scientific community and generative AI models? These agreements enable AI companies to access diverse and expansive scientific datasets, presumably improving the quality of their AI tools.

Generative AI 130

Generative AI AI AI AI Modeling 130

Unite.AI

JUNE 12, 2024

The rapid advancement of AI raises questions about data protection, the integrity of AI models, and the safeguarding of proprietary information. Regulatory Needs : A substantial majority (88%) of respondents support increased government oversight of AI, particularly in areas related to security (72%) and privacy (64%).

Data Quality 147

Data Quality Automation AI AI 147

Unite.AI

OCTOBER 12, 2023

Another key takeaway from that experience is the crucial role that data plays, through quantity and quality, as a key driver of AI model capabilities and performance. Throughout my academic and professional experience prior to LXT, I have always worked directly with data.

Machine Learning 278

Machine Learning Deep Learning Conversational AI Data Quality 278

IBM Journey to AI blog

DECEMBER 20, 2023

This calls for the organization to also make important decisions regarding data, talent and technology: A well-crafted strategy will provide a clear plan for managing, analyzing and leveraging data for AI initiatives. Commit to ethical AI initiatives, inclusive governance models and actionable guidelines.

AI Strategy 224

AI Strategy Artificial Intelligence Artificial Intelligence AI 224

Unite.AI

DECEMBER 17, 2024

AI can be prone to false positives if the models arent well-tuned, or are trained on biased data. While humans are also susceptible to bias, the added risk of AI is that it can be difficult to identify bias within the system. A full replacement of rules-based systems with AI could leave blind spots in AFC monitoring.

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Automation AI AI Explainability 130

Marktechpost

JULY 22, 2024

Models are trained on these data pools, enabling in-depth analysis of OP effectiveness and its correlation with model performance across various quantitative and qualitative indicators. The sandbox’s compatibility with existing model-centric infrastructures makes it a versatile tool for AI development.

Generative AI 109

Generative AI AI Modeling Categorization AI 109

Unite.AI

JULY 24, 2024

Unfortunately, the process of achieving this kind of data is not always simple – companies need to capture and process data in a way that removes bias, and format it to be easily consumed by AI. Poor Data on AI Solutions AI models rely on data to learn patterns, make predictions, and perform tasks.

AI 147

AI AI AI Modeling Data Quality 147

Unite.AI

OCTOBER 22, 2024

There are major growth opportunities in both the model builders and companies looking to adopt generative AI into their products and operations. We feel we are just at the beginning of the largest AI wave. Data quality plays a crucial role in AI model development.

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Natural Language Processing Generative AI Computer Vision Automation 130

Marktechpost

AUGUST 27, 2024

The quantity and quality of data directly impact the efficacy and accuracy of AI models. Getting accurate and pertinent data is one of the biggest challenges in the development of AI. LLMs require current, high-quality internet data to address certain issues.

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Data Extraction Automation Linked Data AI 101

Marktechpost

DECEMBER 6, 2023

Training artificial intelligence (AI) models often requires massive amounts of labeled data. Annotating data is similar to finding a specific grain of sand on a beach. For example, it might look for data the model is unsure about or represent different parts of the overall dataset.

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Marktechpost

AUGUST 10, 2024

It is the world’s first comprehensive milestone in terms of regulation of AI and reflects EU’s ambitions to establish itself as a leader in safe and trustworthy AI development The Genesis and Objectives of the AI Act The Act was first proposed by the EU Commission in April 2021 in the midst of growing concerns about the risks posed by AI systems.

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Artificial Intelligence Artificial Intelligence AI AI 134

Unite.AI

MAY 3, 2024

Considering the Prolific business model, what are your thoughts on the essential role of human feedback in AI development, especially in areas like bias detection and social reasoning improvement? Human feedback in AI development is crucial. The importance of data quality cannot be overstated for AI systems.

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AI Researcher AI Research Data Quality AI Developer 130

Unite.AI

JUNE 10, 2024

Josh Wong is the Founder and CEO of ThinkLabs AI. ThinkLabs AI is a specialized AI development and deployment company. Its mission is to empower critical industries and infrastructure with trustworthy AI aimed at achieving global energy sustainability. Josh Wong attended the University of Waterloo.

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Continuous Learning AI AI Automation 130

Unite.AI

MARCH 14, 2024

The demand for high-quality training data is intensifying , with 66% of respondents anticipating an increase in their training data needs over the next two to five years. This underscores the critical role of data in training more sophisticated and accurate AI models.

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Computer Vision AI AI Artificial Intelligence 124

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. Why should you care?

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OpenAI LLM Explainable AI AI 103

Snorkel AI

DECEMBER 2, 2024

The integration between the Snorkel Flow AI data development platform and AWS’s robust AI infrastructure empowers enterprises to streamline LLM evaluation and fine-tuning, transforming raw data into actionable insights and competitive advantages. Here’s what that looks like in practice.

Data Ingestion 52

Data Ingestion Large Language Models LLM Machine Learning 52

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.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Neural Network Algorithm 52

Snorkel AI

DECEMBER 2, 2024

The integration between the Snorkel Flow AI data development platform and AWS’s robust AI infrastructure empowers enterprises to streamline LLM evaluation and fine-tuning, transforming raw data into actionable insights and competitive advantages. Heres what that looks like in practice.

Data Ingestion 52

Data Ingestion Large Language Models LLM Machine Learning 52

AWS Machine Learning Blog

NOVEMBER 15, 2024

The rapid advancement of generative AI promises transformative innovation, yet it also presents significant challenges. Concerns about legal implications, accuracy of AI-generated outputs, data privacy, and broader societal impacts have underscored the importance of responsible AI development.

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Responsible AI Prompt Engineer Prompt Engineering AI 120

Pickl AI

JUNE 23, 2023

The Importance of Data-Centric Architecture Data-centric architecture is an approach that places data at the core of AI systems. At the same time, it emphasizes the collection, storage, and processing of high-quality data to drive accurate and reliable AI models. How Does Data-Centric AI Work?

Continuous Learning 52

Continuous Learning Data Quality AI AI 52

Pickl AI

JULY 12, 2024

Introduction Artificial Intelligence (AI) transforms industries by enabling machines to mimic human intelligence. Python’s simplicity, versatility, and extensive library support make it the go-to language for AI development. It includes Python and a vast collection of pre-installed libraries and tools for AI development.

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Artificial Intelligence Artificial Intelligence Python Neural Network 52

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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Data Scientist AI AI Neural Network 64

NVIDIA

JULY 13, 2023

Researchers are now using generative AI models to read a protein’s amino acid sequence and accurately predict the structure of target proteins in seconds, rather than weeks or months. Financial Transformers , or “FinFormers,” can learn context and understand the meaning of unstructured financial data.

Generative AI 98

Generative AI AI AI Natural Language Processing 98

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.

Data Scientist 59

Data Scientist AI AI Neural Network 59

O'Reilly Media

NOVEMBER 28, 2023

People with AI skills have always been hard to find and are often expensive. While experienced AI developers are starting to leave powerhouses like Google, OpenAI, Meta, and Microsoft, not enough are leaving to meet demand—and most of them will probably gravitate to startups rather than adding to the AI talent within established companies.

Generative AI 111

Generative AI AI AI Data Analysis 111

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.

Data Scientist 52

Data Scientist AI AI Neural Network 52

AWS Machine Learning Blog

NOVEMBER 7, 2024

It contains services used to onboard, manage, and operate the environment, for example, to onboard and off-board tenants, users, and models, assign quotas to different tenants, and authentication and authorization microservices. Generative AI model components contain microservices for foundation and custom model invocation operations.

Generative AI 122

Generative AI AI AI Responsible AI 122

Snorkel AI

MARCH 14, 2023

Users are able to rapidly improve training data quality and model performance using integrated error analysis and model-guided feedback to develop highly accurate and adaptable AI applications. Alternately, Vertex AI Endpoints can be used to rapidly deploy models trained in Snorkel Flow.

Data Scientist 52

Data Scientist Data Drift AI AI 52

Snorkel AI

MARCH 14, 2023

Users are able to rapidly improve training data quality and model performance using integrated error analysis and model-guided feedback to develop highly accurate and adaptable AI applications. Alternately, Vertex AI Endpoints can be used to rapidly deploy models trained in Snorkel Flow.

Data Scientist 52

Data Scientist Data Drift AI AI 52

Viso.ai

DECEMBER 18, 2023

An example is a privacy-preserving solution for developing healthcare AI models. Healthcare professionals can use artificial patient data while keeping the statistical properties of real-world health data. Particularly, generative techniques for data that closely mirror authentic visual patterns.

Computer Vision 52

Computer Vision Neural Network Auto-complete Data Scarcity 52

Pickl AI

NOVEMBER 3, 2023

Data processing: In order to make well-informed forecasts, AI quickly analyses large datasets, including real-time information from social media and the news. Algorithms for ML: AI models employ ML to adjust and find correlations, gradually improving accuracy.

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Artificial Intelligence Artificial Intelligence Algorithm AI 52

John Snow Labs

OCTOBER 19, 2023

Without bias, these models would struggle to understand and interpret complex language patterns, hindering their ability to provide accurate insights and predictions. This happens when AI models generalize from biased data and make incorrect or harmful assumptions.

NLP 52

NLP Natural Language Processing AI AI 52

Topbots

SEPTEMBER 11, 2023

While each of them offers exciting perspectives for research, a real-life product needs to combine the data, the model, and the human-machine interaction into a coherent system. AI development is a highly collaborative enterprise. AI products need special treatment when it comes to design.

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