IBM Journey to AI blog

AUGUST 11, 2023

The advancement of computing power over recent decades has led to an explosion of digital data, from traffic cameras monitoring commuter habits to smart refrigerators revealing how and when the average family eats. Both computer scientists and business leaders have taken note of the potential of the data. What is MLOps?

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IBM Journey to AI blog

SEPTEMBER 3, 2024

And using AI ethically isn’t just the right thing for businesses to do—it’s also something consumers want. In fact, 86% of businesses believe customers prefer companies that use ethical guidelines and are clear about how they use their data and AI models, according to the IBM Global AI Adoption Index.

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Marktechpost

SEPTEMBER 16, 2023

In the paper titled “ Considering Biased Data as Informative Artifacts in AI-Assisted Health Care ,” three researchers argue that we should see biased medical data as valuable artifacts in archaeology or anthropology. This means recognizing how social and historical factors influence data collection and clinical AI development.

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Pickl AI

DECEMBER 26, 2024

Introduction In recent years, two technological fields have emerged as frontrunners in shaping the future: Artificial Intelligence (AI) and Quantum Computing. A study demonstrated that quantum algorithms could accelerate the discovery of new materials by up to 100 times compared to classical methods.

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Towards AI

FEBRUARY 15, 2023

Announcing the launch of the Medical AI Research Center (MedARC) Medical AI Research Center (MedARC) announced a new open and collaborative research center dedicated to advancing the field of AI in healthcare. This article delves into the details of these emerging approaches and their potential impact on AI development.

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Pickl AI

OCTOBER 2, 2024

AI Techniques Used in Genomic Analysis AI encompasses a range of techniques that can be applied to genomic Data Analysis. Some of the most prominent AI techniques used in this field include: Machine Learning Machine Learning algorithms are designed to learn from data and make predictions or decisions based on that data.

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Viso.ai

MARCH 26, 2024

In this article, we present 7 key applications of computer vision in finance: No.1: 4: Algorithmic Trading and Market Analysis No.5: Privacy-preserving Computer Vision with TensorFlow Lite Other significant contributions include works by Andrew Ng. Applications of Computer Vision in Finance No.

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Snorkel AI

MAY 18, 2023

Hi, I’m excited to tell you about some of our work on responsible data-centric AI and applications in healthcare and medicine. So my group here at Stanford develops machine learning algorithms for biomedical and healthcare applications. So here are a couple of examples of things we’ve done recently.

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Snorkel AI

MAY 18, 2023

Hi, I’m excited to tell you about some of our work on responsible data-centric AI and applications in healthcare and medicine. So my group here at Stanford develops machine learning algorithms for biomedical and healthcare applications. So here are a couple of examples of things we’ve done recently.

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Pickl AI

OCTOBER 9, 2024

This blog explores the innovations in AI driven by SLMs, their applications, advantages, challenges, and future potential. What Are Small Language Models (SLMs)? Small Language Models (SLMs) are a subset of AI models specifically tailored for Natural Language Processing (NLP) tasks.

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NVIDIA

DECEMBER 5, 2024

Now, hear from company experts driving innovation in AI across enterprises, research and the startup ecosystem: IAN BUCK Vice President of Hyperscale and HPC Inference drives the AI charge: As AI models grow in size and complexity, the demand for efficient inference solutions will increase.

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Viso.ai

DECEMBER 21, 2023

Over the past decade, the field of computer vision has experienced monumental artificial intelligence (AI) breakthroughs. Andrej Karpathy: Tesla’s Renowned Computer Scientist Andrej Karpathy, holding a Ph.D. from Stanford, has made substantial contributions to three of the world’s leading AI projects.

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Flipboard

AUGUST 1, 2023

Algorithmic bias : In part because they draw upon datasets that inevitably reflect stereotypes and biases in humans’ writing, legal decisions, photography, and more, AI systems have often exhibited biases with the potential to harm women, people of color , and other marginalized groups.

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