AWS Machine Learning Blog

FEBRUARY 21, 2025

There are various techniques of preference alignment, including proximal policy optimization (PPO), direct preference optimization (DPO), odds ratio policy optimization (ORPO), group relative policy optimization (GRPO), and other algorithms, that can be used in this process. Set up a SageMaker notebook instance.

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Heartbeat

SEPTEMBER 13, 2023

Source: ResearchGate Explainability refers to the ability to understand and evaluate the decisions and reasoning underlying the predictions from AI models (Castillo, 2021). Explainability techniques aim to reveal the inner workings of AI systems by offering insights into their predictions. What is Explainability?

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Unite.AI

JULY 8, 2024

Prior to Zingtree, Brandon led Product, User Experience and Analytics at SportsEngine, a B2B and B2B2C SaaS company which was acquired by NBC Sports in 2016. Could you explain the core function of Zingtree's AI-enabled support automation platform and how it differentiates itself from other solutions in the market?

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Unite.AI

AUGUST 5, 2024

I received my masters in Civil/Environmental Engineering from Stanford University in 2016. Can you explain the process of training AI models with field-tested data from vital infrastructure sites? We use three main different types of algorithms: image clustering, segmentation, and anomaly detection.

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Viso.ai

NOVEMBER 27, 2024

YOLO (You Only Look Once) is a family of real-time object detection machine-learning algorithms. Multiple machine-learning algorithms are used for object detection, one of which is convolutional neural networks (CNNs). Improved Explainability : Making the model’s decision-making process more transparent.

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Unite.AI

JUNE 17, 2024

However, the real turning point for me was around 2015-2016, when AI started making headline news with breakthroughs like AlphaGo defeating the world champion in the complex game of Go. Algorithms can analyze market data, news sentiment, and social media trends to predict stock prices and optimize portfolio allocation.

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Unite.AI

AUGUST 1, 2024

Can you explain the key features and benefits of Pimloc's Secure Redact privacy platform? These deep learning algorithms are trained on domain-specific videos from sources like CCTV, body-worn cameras, and road survey footage. Pimloc’s AI models accurately detect and redact PII even under challenging conditions.

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Viso.ai

MARCH 11, 2024

This was done by using a region proposal algorithm to generate potential bounding boxes (regions) in the image. The YOLO algorithm works by predicting three different features: Grid Division: YOLO divides the input image into a grid of cells. Timeline of YOLO Models What is YOLOX? How Does YOLOX Work?

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Pickl AI

APRIL 8, 2024

This blog explores 13 major AI blunders, highlighting issues like algorithmic bias, lack of transparency, and job displacement. From the moment we wake up to the personalized recommendations on our phones to the algorithms powering facial recognition software, AI is constantly shaping our world.

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Unite.AI

FEBRUARY 16, 2024

For example, see Face-to-Face Interaction with Pedagogical Agents, Twenty Years Later , a 2016 article that overviews the field and cites a lot of the relevant material. Children download the App and convince parents to pay for a subscription, explaining that Buddy is a teacher.

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AI Impacts

FEBRUARY 1, 2023

A faulty brake line on a car is not much of a concern to the public until the car is on public roads, and the facebook feed algorithm cannot be a threat to society until it is used to control what large numbers of people see on their screens. But this model, on its own, is inadequate for AI, for reasons I will explain in the next section.

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Topbots

JUNE 22, 2023

Also, since at least 2018, the American agency DARPA has delved into the significance of bringing explainability to AI decisions. Outstandingly, ChatPGT presents such a capacity: it can explain its decisions. Outperforming algorithmic trading reinforcement learning systems: A supervised approach to the cryptocurrency market.

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PyImageSearch

NOVEMBER 13, 2023

One of the most popular deep learning-based object detection algorithms is the family of R-CNN algorithms, originally introduced by Girshick et al. Since then, the R-CNN algorithm has gone through numerous iterations, improving the algorithm with each new publication and outperforming traditional object detection algorithms (e.g.,

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TransOrg Analytics

MAY 27, 2024

This article explores the transformative impact of LLM chatbots compared to traditional chatbots and explains how TranOrg provided an LLM chatbot for an Airline company. million US dollars in 2016 and is expected to grow to 1250 million US dollars in 2025. that can understand images and explain things.

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Marktechpost

FEBRUARY 17, 2025

The study’s bibliometric analysis revealed a steady increase in AI safety research since 2016, driven by advancements in deep learning. Research methods include applied algorithms, simulated agents, analysis frameworks, and mechanistic interpretability.

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ML Review

JUNE 4, 2018

These ideas also move in step with the explainability of results. Finally, one can use a sentence similarity evaluation metric to evaluate the algorithm. One such evaluation metric is the Bilingual Evaluation Understudy algorithm, or BLEU score. Source : Britz (2016)[ 62 ] CNNs can encode abstract features from images.

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AWS Machine Learning Blog

JULY 13, 2023

Another way can be to use an AllReduce algorithm. For example, in the ring-allreduce algorithm, each node communicates with only two of its neighboring nodes, thereby reducing the overall data transfers. Train a binary classification model using the SageMaker built-in XGBoost algorithm. alpha – L1 regularization term on weights.

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Mlearning.ai

MARCH 9, 2023

Automated algorithms for image segmentation have been developed based on various techniques, including clustering, thresholding, and machine learning (Arbeláez et al., Understanding the robustness of image segmentation algorithms to adversarial attacks is critical for ensuring their reliability and security in practical applications.

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Heartbeat

AUGUST 23, 2023

Consider a scenario where legal practitioners are armed with clever algorithms capable of analyzing, comprehending, and extracting key insights from massive collections of legal papers. Algorithms can automatically detect and extract key items. But what if there was a technique to quickly and accurately solve this language puzzle?

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Pickl AI

JULY 24, 2024

Turing proposed the concept of a “universal machine,” capable of simulating any algorithmic process. The development of LISP by John McCarthy became the programming language of choice for AI research, enabling the creation of more sophisticated algorithms. Simon, demonstrated the ability to prove mathematical theorems.

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AWS Machine Learning Blog

APRIL 18, 2023

To make things easy, these three inputs depend solely on the model name, version (for a list of the available models, see Built-in Algorithms with pre-trained Model Table ), and the type of instance you want to train on. learning_rate – Controls the step size or learning rate of the optimization algorithm during training.

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Snorkel AI

MARCH 1, 2023

It is based on GPT and uses machine learning algorithms to generate code suggestions as developers write. This can make it challenging for businesses to explain or justify their decisions to customers or regulators. Microsoft Microsoft launched its Language Understanding Intelligent Service in 2016. What are foundation models?

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Heartbeat

FEBRUARY 2, 2024

The significance of VQA extends beyond traditional computer vision tasks, requiring algorithms to exhibit a broader understanding of context, semantics, and reasoning. It's remarkable diversity and scale position it as a cornerstone for evaluating and benchmarking VQA algorithms.

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Viso.ai

JULY 17, 2024

Output from Neural Style Transfer – source Neural Style Transfer Explained Neural Style Transfer follows a simple process that involves: Three images, the image from which the style is copied, the content image, and a starting image that is just random noise. With deep learning, the results were impressively good. Gatys et al.

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TheSequence

DECEMBER 13, 2023

My path to working in AI is somewhat unconventional and began when I was wrapping up a postdoc in theoretical particle physics around 2016. Your background, current role and how did you get started in AI? Why another Transformers book, and what sets this one apart?

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deepsense.ai

APRIL 16, 2023

First, we will explain the MLP block. Source: [ 5 ] It runs GEMM with query ((W^Q)), key ((W^K)), and value weights ((W^V)) according to the previously explained partitioning in parallel. 2016 ), only the activations at the boundaries of each partition are saved and shared between workers during training. billion parameters.

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Dlabs.ai

AUGUST 10, 2020

In simple terms, intent detection is the process of algorithmically identifying user intent from a given statement. One of the first widely discussed chatbots was the one deployed by SkyScanner in 2016. Intent detection – what is it? That’s a lot of words to describe a rather simple process, so let’s take a look at an example.

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John Snow Labs

MAY 23, 2023

This is because NLP technology enables the VQA algorithm to not only understand the question posed to it about the input image, but also to generate an answer in a language that the user (asking the question) can easily understand. This explains why many practical applications have been discovered for VQA in just the last half decade.

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Heartbeat

MAY 11, 2023

The first version of YOLO was introduced in 2016 and changed how object detection was performed by treating object detection as a single regression problem. ✨ The algorithm for selecting layers in the model quantizes certain parts to minimize loss of information while ensuring a balance between latency and accuracy.

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AWS Machine Learning Blog

APRIL 18, 2023

To make things easy, these three inputs depend solely on the model name, version (for a list of the available models, see Built-in Algorithms with pre-trained Model Table ), and the type of instance you want to train on. learning_rate – Controls the step size or learning rate of the optimization algorithm during training.

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Mlearning.ai

MARCH 2, 2023

Moreover, the most important theoretical foundations for BERT are explained and additional graphics are provided for illustration purposes. In particular, the architecture of transformers and the attention mechanism, as well as the idea behind unsupervised transfer learning, especially with fine-tuning, are explained. 10] Rajpurkar, P.,

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Snorkel AI

MAY 3, 2023

The model will inevitably encounter some new contexts and data that were not taught to this learning algorithm in training time. A fun story that I want to share—I remember back in 2016-2017ish when we started working on this problem and submitted one of our first papers on OOD detection called Odin to the conference.

Machine Learning 52

Machine Learning Neural Network Explainability Data Scientist 52

Snorkel AI

MAY 3, 2023

The model will inevitably encounter some new contexts and data that were not taught to this learning algorithm in training time. A fun story that I want to share—I remember back in 2016-2017ish when we started working on this problem and submitted one of our first papers on OOD detection called Odin to the conference.

Machine Learning 52

Machine Learning Neural Network Explainability Data Scientist 52

The MLOps Blog

MARCH 14, 2023

To back this up, here is the Nature survey conducted in 2016. Also, this helped us improve the efficiency of our team as we did not have to spend time explaining the entire workflow to the new joiners and other developers as everything was just mentioned in the document. neptune.ai | Source Tools like neptune.ai , Comet , MLFlow , etc.

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Sebastian Ruder

MAY 22, 2020

2016 ), physics ( Cohen et al., From a research perspective, it allows you to practice communicating and explaining things clearly. Even an application of an existing algorithm can shed light on new and unsolved questions. The papers that draw such connections can often be insightful. 2014 ), neuroscience ( Wang et al.,

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Sebastian Ruder

JANUARY 24, 2022

  While pre-trained transformers will likely continue to be deployed as standard baselines for many tasks, we should expect to see alternative architectures particularly in settings where current models fail short, such as modeling long-range dependencies and high-dimensional inputs or where interpretability and explainability are required.

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Unite.AI

JANUARY 16, 2025

He is the co-author of the widely cited book, Healthcare Disrupted (Wiley, 2016). Scientists and clinicians are inherently skepticalas they should beand dont trust black boxes or algorithms they dont understand. Can you explain how ConcertAIs Digital Trial Solution works to match cancer patients with life-saving clinical trials?

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