IBM Journey to AI blog

SEPTEMBER 20, 2023

Large language models (LLMs) are a class of foundational models (FM) that consist of layers of neural networks that have been trained on these massive amounts of unlabeled data. IBM watsonx.data is a fit-for-purpose data store built on an open lakehouse architecture to scale AI workloads for all of your data, anywhere.

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IBM Journey to AI blog

SEPTEMBER 24, 2024

” When Guerena’s team first started working with smartphone images, they used convolutional neural networks (CNNs). ” Guerena’s team is now working on integrating speech-to-text and natural language processing alongside computer vision in the systems they’re building.

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IBM Journey to AI blog

JULY 6, 2023

Deep learning teaches computers to process data the way the human brain does. Deep learning algorithms are neural networks modeled after the human brain. Machine learning engineers can specialize in natural language processing and computer vision, become software engineers focused on machine learning and more.

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NVIDIA

DECEMBER 13, 2023

Financial Services Firms Embrace AI for Identity Verification The financial services industry is developing AI for identity verification. Harnessing Graph Neural Networks and NVIDIA GPUs GNNs have been embraced for their ability to reveal suspicious activity.

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Pickl AI

AUGUST 1, 2024

Summary : Deep Learning engineers specialise in designing, developing, and implementing neural networks to solve complex problems. They work on complex problems that require advanced neural networks to analyse vast amounts of data. Hyperparameter Tuning: Adjusting model parameters to improve performance and accuracy.

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Pickl AI

JULY 24, 2024

The Golden Age of AI (1960s-1970s) Experts often refer to the 1960s and 1970s as the “Golden Age of AI.” ” During this time, researchers made remarkable strides in natural language processing, robotics, and expert systems. 2011: IBM Watson defeats Ken Jennings on the quiz show “Jeopardy!

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Pickl AI

SEPTEMBER 17, 2024

Machine Learning (ML) is a subset of AI that focuses on developing algorithms and statistical models that enable systems to perform specific tasks effectively without being explicitly programmed. Explain The Concept of Supervised and Unsupervised Learning. What Is the Purpose of The Activation Function in Artificial Neural Networks?

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Pickl AI

JULY 14, 2023

Machine Learning and Neural Networks (1990s-2000s): Machine Learning (ML) became a focal point, enabling systems to learn from data and improve performance without explicit programming. Techniques such as decision trees, support vector machines, and neural networks gained popularity.

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Ofemwire

MARCH 1, 2024

D – Deep Learning : A subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Deep learning networks can automatically learn to represent patterns in the data with multiple levels of abstraction.

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Viso.ai

MARCH 26, 2024

With advancements in machine learning (ML) and deep learning (DL), AI has begun to significantly influence financial operations. Arguably, one of the most pivotal breakthroughs is the application of Convolutional Neural Networks (CNNs) to financial processes. 2: Automated Document Analysis and Processing No.3:

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Pickl AI

JANUARY 29, 2024

AI comprises Natural Language Processing, computer vision, and robotics. ML focuses on algorithms like decision trees, neural networks, and support vector machines for pattern recognition. Skills Proficiency in programming languages (Python, R), statistical analysis, and domain expertise are crucial.

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Viso.ai

JULY 9, 2024

On the other hand, the generative AI task is to create new data points that look like the existing ones. Discriminative models include a wide range of models, like Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Support Vector Machines (SVMs), or even simpler models like random forests.

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Pickl AI

NOVEMBER 5, 2024

AI encompasses various subfields, including Machine Learning (ML), Natural Language Processing (NLP), robotics, and computer vision. Together, Data Science and AI enable organisations to analyse vast amounts of data efficiently and make informed decisions based on predictive analytics.

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Pickl AI

SEPTEMBER 17, 2024

Summary : AI is transforming the cybersecurity landscape by enabling advanced threat detection, automating security processes, and adapting to new threats. It leverages Machine Learning, natural language processing, and predictive analytics to identify malicious activities, streamline incident response, and optimise security measures.

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Pickl AI

NOVEMBER 18, 2024

Natural language processing ( NLP ) allows machines to understand, interpret, and generate human language, which powers applications like chatbots and voice assistants. Neural networks are powerful for complex tasks, such as image recognition or NLP, but may require more computational resources.

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The MLOps Blog

AUGUST 11, 2023

More specifically, embeddings enable neural networks to consume training data in formats that allow extracting features from the data, which is particularly important in tasks such as natural language processing (NLP) or image recognition. This is where embeddings come into play.

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The MLOps Blog

JUNE 27, 2023

For example, if your team works on recommender systems or natural language processing applications, you may want an MLOps tool that has built-in algorithms or templates for these use cases. Scale AI combines human annotators and machine learning algorithms to deliver efficient and reliable annotations for your team.

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Mlearning.ai

FEBRUARY 6, 2023

The classifier currently only works on English text, but not on other languages or on code [3]. Classifiers based on neural networks are known to be poorly calibrated outside of their training data [3]. This is why we need Explainable AI (XAI). There are plenty of techniques to help reduce overfitting in ML models.

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IBM Journey to AI blog

SEPTEMBER 3, 2024

One study estimates that training a single natural language processing model emits over 600,000 pounds of carbon dioxide; nearly 5 times the average emissions of a car over its lifetime. Many AI applications run on servers in data centers, which generate considerable heat and need large volumes of water for cooling.

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Pickl AI

APRIL 2, 2024

Deep Learning: Neural networks with multiple layers used for complex pattern recognition tasks. Tools and Technologies Python/R: Popular programming languages for data analysis and machine learning. Explainable AI (XAI): As AI models become more complex, there’s a growing need for interpretability.

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IBM Journey to AI blog

FEBRUARY 9, 2024

The incoming generation of interdisciplinary models, comprising proprietary models like OpenAI’s GPT-4V or Google’s Gemini, as well as open source models like LLaVa, Adept or Qwen-VL, can move freely between natural language processing (NLP) and computer vision tasks. The power of open models will continue to grow.

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