IBM Journey to AI blog

OCTOBER 16, 2023

Foundation models are widely used for ML tasks like classification and entity extraction, as well as generative AI tasks such as translation, summarization and creating realistic content. The development and use of these models explain the enormous amount of recent AI breakthroughs.

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Unite.AI

JANUARY 1, 2025

For example, AI-driven underwriting tools help banks assess risk in merchant services by analyzing transaction histories and identifying potential red flags, enhancing efficiency and security in the approval process. While AI has made significant strides in fraud prevention, its not without its complexities.

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Unite.AI

DECEMBER 19, 2024

Better Analysis Before Taking the Plunge With more emphasis on improved ROI, businesses will be turning to AI itself to ensure they are spending wisely. One of the biggest problems to date is the haste to jump on the bandwagon especially since the introduction of generative AI and LLMs.

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Unite.AI

DECEMBER 27, 2024

Its because the foundational principle of data-centric AI is straightforward: a model is only as good as the data it learns from. For example, generative AI systems that produce erroneous outputs often trace their limitations to inadequate training datasets, not the underlying architecture.

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Aiiot Talk

JULY 7, 2023

Black-box AI poses a serious concern in the aviation industry. In fact, explainability is a top priority laid out in the European Union Aviation Safety Administration’s first-ever AI roadmap. Explainable AI, sometimes called white-box AI, is designed to have high transparency so logic processes are accessible.

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NVIDIA

MARCH 1, 2024

Trustworthy AI initiatives recognize the real-world effects that AI can have on people and society, and aim to channel that power responsibly for positive change. What Is Trustworthy AI? Trustworthy AI is an approach to AI development that prioritizes safety and transparency for those who interact with it.

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Unite.AI

JULY 1, 2024

Transparency and Explainability Enhancing transparency and explainability is essential. Techniques such as model interpretability frameworks and Explainable AI (XAI) help auditors understand decision-making processes and identify potential issues. This involves human experts reviewing and validating AI outputs.

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Marktechpost

DECEMBER 11, 2024

Image Source : LG AI Research Blog ([link] Responsible AI Development: Ethical and Transparent Practices The development of EXAONE 3.5 models adhered to LG AI Research s Responsible AI Development Framework, prioritizing data governance, ethical considerations, and risk management. model scored 70.2.

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Towards AI

APRIL 2, 2024

This is only clearer with this week’s news of Microsoft and OpenAI planning a >$100bn 5 GW AI data center for 2028. This would be its 5th generation AI training cluster. Hence, we are focused on making AI more accessible and releasing AI learning materials and courses! Why should you care?

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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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ODSC - Open Data Science

SEPTEMBER 26, 2024

Using AI to Detect Anomalies in Robotics at the Edge Integrating AI-driven anomaly detection for edge robotics can transform countless industries by enhancing operational efficiency and improving safety. Where do explainable AI models come into play? New Podcast Episode: AI for Robotics and Autonomy with Francis X.

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IBM Journey to AI blog

SEPTEMBER 3, 2024

These systems inadvertently learn biases that might be present in the training data and exhibited in the machine learning (ML) algorithms and deep learning models that underpin AI development. Those learned biases might be perpetuated during the deployment of AI, resulting in skewed outcomes. million in 2024.

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Pickl AI

DECEMBER 10, 2024

Businesses face fines and reputational damage when AI decisions are deemed unethical or discriminatory. Socially, biased AI systems amplify inequalities, while data breaches erode trust in technology and institutions. Broader Ethical Implications Ethical AI development transcends individual failures.

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ODSC - Open Data Science

OCTOBER 3, 2024

AI Development Lifecycle: Learnings of What Changed with LLMs Noé Achache | Engineering Manager & Generative AI Lead | Sicara Using LLMs to build models and pipelines has made it incredibly easy to build proof of concepts, but much more challenging to evaluate the models. billion customer interactions.

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IBM Journey to AI blog

FEBRUARY 28, 2024

They also found that, while the public is still wary about new technologies like artificial intelligence (AI), most people are in favor of government adoption of generative AI. All respondents had at least a basic understanding of AI and generative AI. However, trust is an issue.

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IBM Journey to AI blog

FEBRUARY 9, 2024

2022 was the year that generative artificial intelligence (AI) exploded into the public consciousness, and 2023 was the year it began to take root in the business world. The evolution of generative AI has mirrored that of computers, albeit on a dramatically accelerated timeline.

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