IBM Journey to AI blog

JUNE 26, 2023

What is generative AI? Generative AI uses an advanced form of machine learning algorithms that takes users prompts and uses natural language processing (NLP) to generate answers to almost any question asked. According to Precedence Research , the global generative AI market size valued at USD 10.79

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NYU Center for Data Science

JUNE 16, 2023

Home to both a thriving tech ecosystem and pioneering efforts on regulating algorithm decision-making systems, New York City provides a vibrant research environment and a plethora of interdisciplinary collaborators,” said Umang. For these reasons, I am excited to start my academic journey at NYU. His work has been covered in press (e.g.,

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Dlabs.ai

JULY 19, 2023

Example: Algorithmic Bias in the UK A-level Grading To illustrate, consider a real-world example that occurred during the COVID-19 pandemic in the UK. With the traditional A-level exams canceled due to health concerns, the UK government used an algorithm to determine student grades.

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How to Learn Machine Learning

JUNE 9, 2024

billion in 2023 and is expected to grow and reach $503.40 This technology streamlines the model-building process while simultaneously increasing productivity by determining the best algorithms for specific data sets. 2) Explainable AI Explainability AI and interpretable machine learning are the different names of the same things.

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Dlabs.ai

MAY 22, 2022

It’ll help you get to grips with the fundamentals of ML and its respective algorithms, including linear regression and supervised and unsupervised learning, among others. That’s why it helps to know the fundamentals of ML and the different learning algorithms before you do any data science work.

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Dlabs.ai

JULY 25, 2023

In an era where AI and machine learning have streamlined everything, including hiring processes, the balance between efficiency and equity comes into question. With algorithms, machine learning, and statistical modeling defining who gets hired or promoted, are these decisions genuinely fair?

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IBM Journey to AI blog

JULY 17, 2023

While traditional AI approaches provide customers with quick service, they have their limitations. Currently chat bots are relying on rule-based systems or traditional machine learning algorithms (or models) to automate tasks and provide predefined responses to customer inquiries. Watsonx.ai

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Pickl AI

JANUARY 29, 2024

Data Science extracts insights, while Machine Learning focuses on self-learning algorithms. The collective strength of both forms the groundwork for AI and Data Science, propelling innovation. Key takeaways Data Science lays the groundwork for Machine Learning, providing curated datasets for ML algorithms to learn and make predictions.

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Unite.AI

JANUARY 12, 2024

The field of artificial intelligence (AI) has seen tremendous growth in 2023. Generative AI, which focuses on creating realistic content like images, audio, video and text, has been at the forefront of these advancements. Advances in attention mechanisms and algorithms are needed to integrate contradictory multimodal inputs.

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Towards AI

OCTOBER 7, 2023

Last Updated on October 9, 2023 by Editorial Team Author(s): Lye Jia Jun Originally published on Towards AI. Balancing Ethics and Innovation: An Introduction to the Guiding Principles of Responsible AI Sarah, a seasoned AI developer, found herself at a moral crossroads. If you were Sarah, which algorithm would you choose?

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Pickl AI

JULY 24, 2024

The pivotal moment in AI’s history occurred with the work of Alan Turing in the 1930s and 1940s. Turing proposed the concept of a “universal machine,” capable of simulating any algorithmic process. During this period, optimism about AI’s potential led to substantial funding and research initiatives.

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Heartbeat

SEPTEMBER 13, 2023

Through several " Explainable Models ," explainability enables users to achieve insights into the inner workings of the models and understand the factors that influence their output. Algorithmic Accountability: Explainability ensures accountability in machine learning and AI systems. Russell, C. &

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ML @ CMU

MARCH 31, 2023

However, while numerous explainable AI (XAI) methods have been developed, XAI has yet to deliver on this promise. The stages of evaluation are adapted from Doshi-Velez and Kim (2017); we introduce an additional stage, use-case-grounded algorithmic evaluations, in a recent Neurips 2022 paper [ 2 ]. Our contributions. Vaughan, J.

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Heartbeat

FEBRUARY 2, 2024

As AI strives to emulate human-like understanding, VQA plays a pivotal role by demanding systems to recognize objects and scenes in images and comprehend and respond to human-generated questions about those images. It's remarkable diversity and scale position it as a cornerstone for evaluating and benchmarking VQA algorithms.

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ML @ CMU

MARCH 30, 2023

However, while numerous explainable AI (XAI) methods have been developed, XAI has yet to deliver on this promise. The stages of evaluation are adapted from Doshi-Velez and Kim (2017); we introduce an additional stage, use-case-grounded algorithmic evaluations, in a recent Neurips 2022 paper [ 2 ]. Our contributions. Vaughan, J.

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IBM Journey to AI blog

SEPTEMBER 3, 2024

Artificial intelligence (AI) has enormous value but capturing the full benefits of AI means facing and handling its potential pitfalls. Here’s a closer look at 10 dangers of AI and actionable risk management strategies. Bias Humans are innately biased, and the AI we develop can reflect our biases.

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Unite.AI

NOVEMBER 29, 2023

Likewise, a report by Byline Times on November 20, 2023 , highlights significant concerns surrounding the election oversight bodies in the United Kingdom. AI watchdogs employ state-of-the-art technologies, particularly machine learning and deep learning algorithms, to combat the ever-increasing amount of election-related false information.

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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. In December of 2023, Mistral released “Mixtral,” a mixture of experts (MoE) model integrating 8 neural networks, each with 7 billion parameters.

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