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?

Automation 130

Automation Chatbots Algorithm Large Language Models 130

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.

Machine Learning 147

Machine Learning Artificial Intelligence Artificial Intelligence Computer Vision 147

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.

Automation 147

Automation Artificial Intelligence Artificial Intelligence Generative AI 147

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.

Natural Language Processing 173

Natural Language Processing AI AI UX Design 173

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?

Explainability 52

Explainability Convolutional Neural Networks Neural Network Computer Vision 52

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.

Machine Learning 147

Machine Learning Deep Learning Automation Computer Vision 147

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.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Algorithm AI 52

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.

AI 130

AI AI OpenAI Large Language Models 130

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.

Machine Learning 107

Machine Learning ChatGPT Natural Language Processing Categorization 107

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.,

Convolutional Neural Networks 115

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 115

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.

LLM 52

LLM Chatbots Continuous Learning Natural Language Processing 52

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.

Algorithm 89

Algorithm Deep Learning Neural Network Machine Learning 89

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.

Neural Network 52

Neural Network Convolutional Neural Networks Computer Vision Deep Learning 52

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.

Large Language Models 88

Large Language Models LLM NLP Deep Learning 88

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.

Computer Vision 52

Computer Vision Deep Learning Machine Learning Algorithm 52

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?

NLP 52

NLP Natural Language Processing Algorithm Categorization 52

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.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Natural Language Processing Neural Network 52

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.

Computer Vision 52

Computer Vision Natural Language Processing Artificial Intelligence Artificial Intelligence 52

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?

BERT 83

BERT Natural Language Processing Large Language Models Neural Network 83

Bugra Akyildiz

NOVEMBER 17, 2024

Interleaving Algorithm: DoorDash uses an algorithm that can be likened to team captains drafting players, where each "captain" represents a list to be interleaved. 2020) EBM : Explainable Boosting Machine (Nori, et al. Instead, they use experiment objects to control the interleaving flow. 2019; Lou, et al.

Convolutional Neural Networks 52

Convolutional Neural Networks Metadata Computer Vision Python 52

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.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 52

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?

LLM 64

LLM Natural Language Processing Machine Learning Deep Learning 64

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.

Large Language Models 52

Large Language Models Neural Network Deep Learning Explainability 52

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.

Natural Language Processing 52

Natural Language Processing Chatbots Neural Network Machine Learning 52

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.

Natural Language Processing 40

Natural Language Processing Deep Learning NLP Convolutional Neural Networks 40

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.

Computer Vision 75

Computer Vision Deep Learning Data Scientist ML 75

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.

LLM 52

LLM NLP Deep Learning ML 52

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.,

BERT 52

BERT NLP Deep Learning Neural Network 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

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.

ML 52

ML Machine Learning Metadata ML Engineer 52

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.,

Natural Language Processing 52

Natural Language Processing NLP Deep Learning ML 52

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.

NLP 52

NLP ML BERT Computational Linguistics 52

AI Impacts

OCTOBER 20, 2023

I hope to explain my intuition, and provide enough historical evidence to make this intuition at least plausible. The concerns are that research might shift to locations with fewer regulations, and that algorithmic progress will make AGI possible on a personal computer. This is a plausible future for AI.

Algorithm 147

Algorithm AI AI AI Researcher 147

ML Review

MAY 3, 2018

2016) — “ LipNet: End-to-End Sentence-level Lipreading.” [17] 17] “ LipNet ” introduces the first approach for an end-to-end lip reading algorithm at sentence level. 27] LipNet also makes use of an additional algorithm typically used in speech recognition systems — a Connectionist Temporal Classification (CTC) output.

Neural Network 52

Neural Network Computer Vision Deep Learning Convolutional Neural Networks 52

Probably Approximately a Scientific Blog

AUGUST 14, 2018

It’s a challenge to explain deep learning using simple concepts and without the caveat of remaining at a very high level. A machine learning algorithm expects as input a set of properties pertaining to each word (called “feature vector”), and predicts a label for this word, out of a set of possible labels (e.g.

Deep Learning 52

Deep Learning NLP Neural Network Machine Learning 52

Unite.AI

NOVEMBER 28, 2024

The invention of the backpropagation algorithm in 1986 allowed neural networks to improve by learning from errors. 2000s – Big Data, GPUs, and the AI Renaissance The 2000s ushered in the era of Big Data and GPUs , revolutionizing AI by enabling algorithms to train on massive datasets.

Robotics 290

Robotics AI AI Algorithm 290