IBM Journey to AI blog

APRIL 18, 2024

Most experts categorize it as a powerful, but narrow AI model. Current AI advancements demonstrate impressive capabilities in specific areas. A key trend is the adoption of multiple models in production. This multi-model approach uses multiple AI models together to combine their strengths and improve the overall output.

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Viso.ai

DECEMBER 18, 2023

An example is a privacy-preserving solution for developing healthcare AI models. Visual synthetic data involves artificially generated images to enhance ML models’ training by providing diverse and privacy-conscious datasets – source. Neural networks can also synthesize unstructured data like images and video.

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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). Present the model’s predictions to stakeholders.

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TensorFlow

MARCH 14, 2023

Today’s post will show you how to use these features, and give you examples of when you might want to use them and how they connect the work of AI developers and engineers to enable domain-specific AI system design. Simulink users have expressed interest in the ability to bring in AI models and simulate entire systems.

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Becoming Human

MAY 12, 2023

Since it lays the groundwork for AI applications, it is also often referred to as the ‘core of AI and machine learning.’ The 1950s saw the development of neural networks that were trained by using hand-labeled images. As early as the dawn of artificial intelligence, image annotation was used for machine learning.

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IBM Journey to AI blog

JANUARY 10, 2024

But even with the myriad benefits of AI, it does have noteworthy disadvantages when compared to traditional programming methods. AI development and deployment can come with data privacy concerns, job displacements and cybersecurity risks, not to mention the massive technical undertaking of ensuring AI systems behave as intended.

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Marktechpost

MAY 10, 2024

SageMaker Canvas may then make predictions and put the trained model into production. Data Robot Data Robot is an artificial intelligence platform that streamlines the entire lifecycle of machine learning model development, deployment, and management.

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Marktechpost

DECEMBER 10, 2023

SageMaker Canvas may then make predictions and put the trained model into production. Data Robot Data Robot is an artificial intelligence platform that streamlines the entire lifecycle of machine learning model development, deployment, and management.

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Unite.AI

MAY 29, 2023

This far outstrips its predecessor models by a factor of 500, opening the door for developers to construct complex language models for next-generation AI chatbots, craft advanced algorithms for recommender systems, and build sophisticated graph neural networks, vital for fraud detection and data analytics tasks.

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ODSC - Open Data Science

FEBRUARY 29, 2024

The Stanford AI Lab Founded in 1963, the Stanford AI Lab has made significant contributions to various domains, including natural language processing, computer vision, and robotics. Similar to the MIT lab, its approach is multidisciplinary to enhance human-robot interactions using the power of AI.

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Cold Takes

NOVEMBER 29, 2022

I’ll argue that if today’s AI development methods lead directly to powerful enough AI systems, disaster is likely 1 by default (in the absence of specific countermeasures). I assume the world could develop extraordinarily powerful AI systems in the coming decades. I call this nearcasting.)

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Unite.AI

MARCH 8, 2024

Tiny AI operates discreetly within our devices, orchestrating smart homes and propelling advancements in personalized medicine. Tiny AI excels in efficiency, adaptability, and impact by utilizing compact neural networks , streamlined algorithms, and edge computing capabilities.

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Pickl AI

JULY 31, 2023

General AI would have the ability to understand, learn, and apply knowledge across various domains, demonstrating true cognitive abilities. AI encompasses various techniques, including machine learning, natural language processing, computer vision, robotics, expert systems, and neural networks.

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