A few things we’re great at
Computer Vision Services
Image Annotation Services
Image annotation is the process of associating an entire image, or a section of an image, with an identifier label. Power your computer vision models with high-quality image data, meticulously tagged by our expert annotators.
Face Detection and Recognition
Deep learning algorithms are increasingly being applied to face recognition systems to make them more efficient and accurate systems in less ideal conditions. In these systems, using the features extracted in the image and learning the patterns contained therein, the human face is detected in the images and is recognized by matching the predefined patterns to the recognition operation. One of the most significant benefits of deep learning algorithms is the ability to accurately detect, even better than humans, the anti-interference capability and the ability to detect, extract and classify thousands of features from a person's face.
One of the most important data that is used daily on the Internet is images. Image annotation refers to the process of producing words that describe the content of the image. The purpose of annotating images is to produce words that are appropriate descriptors for images. The automated method attempts to completely annotate the image process by machine.
With the emergence of large data, monitoring large aria and buildings, and 24/7 is expensive, and it can be full of errors. Therefore security sectors can benefit from AI-driven surveillance technologies to tackle the mentioned problems. Our solutions are for Aerial object detection, indoor and outdoor monitoring, behavior detection, and traffic monitoring.
Object Segmentation implies understanding pixel wise location of an object in an image. This allows for a deeper understanding that is needed for many tasks.
the idea of classification could be extended to videos as well. Deep learning techniques make us able to detect, identify and do different analysis on video objects.
Medical Image Processing
Deep learning with convolutional neural networks (CNNs) is recently gaining wide attention for its high performance in recognizing images. It can help the healthcare team to come up with a better decision.
Computer-Aided Detection and Diagnosis
Today, GPUs are found in almost all imaging modalities, including CT, MRI, X-ray, and Ultrasound bringing more compute capabilities to the edge devices. (image credit: Nvidia)
Google Cloud AI Solutions
AI-based technologies through Google cloud. Services that we offer include but not limited to video processing, speech recognition, image processing, computer vision, and natural language processing applications.
Speech recognition applications include voice user interfaces, such as voice dialing, natural language processing, and speech-to-text for radiologic reporting (which has been proven to be a natural interaction modality and effective technology for medical reporting), particularly in the field of radiology.
Natural Language Processing Services
Keyword Extraction and Topic Modeling
Keyword extraction (also known as keyword detection or keyword analysis) is a text analysis technique that consists of automatically extracting the most important words and expressions in a text. It helps summarize the content of a text and recognize the main topics which are being discussed. Topic modeling, just as it sounds, is using an algorithm to discover the topic or set of topics that best describes a given text document.
Summarization is the task of producing a shorter version of one or several documents that preserves most of the input’s meaning.
Text Generation and Language Modeling
Text generation is the task of generating text with the goal of appearing indistinguishable to human-written text. Language modeling is the task of predicting the next word or character in a document.
The word semantic is a Linguistic term. It means something related to meaning in a language or logic. In a natural language, semantic analysis is relating the structures and occurrences of the words, phrases, clauses, paragraphs etc and understanding the idea of what’s written in particular text.
Question answering (QA) is a computer science discipline within the fields of information retrieval and natural language processing (NLP), which is concerned with building systems that automatically answer questions posed by humans in a natural language.
Text classification is the task of assigning a sentence or document an appropriate category. The categories depend on the chosen dataset and can range from topics.
Text Clustering and Word Clouds
Document clustering (or text clustering) is the application of cluster analysis to textual documents. It has applications in automatic document organization, topic extraction and fast information retrieval or filtering. A word cloud is an image made of words that together resemble a cloudy shape. The size of a word shows how important it is e.g. how often it appears in a text and its frequency.
Textual similarity deals with determining how similar two pieces of texts are. This can take the form of assigning a score from 1 to 5. Related tasks are paraphrase or duplicate identification.
Relation Prediction is the task of recognizing a named relation between two named semantic entities. The common test setup is to hide one entity from the relation triplet, asking the system to recover it based on the other entity and the relation type.
Named Entity Recognition
Named entity recognition (NER) is the task of tagging entities in text with their corresponding type.
Sentiment Analysis is a procedure used to determine if a chunk of text is positive, negative or neutral. In text analytics, natural language processing (NLP) and machine learning (ML) techniques are combined to assign sentiment scores to the topics, categories or entities within a phrase.
In natural language processing, language identification or language guessing is the problem of determining which natural language given content is in. Computational approaches to this problem view it as a special case of text categorization, solved with various statistical methods.
Translate given sentences from one language to another. Here, both the input and output are sentences.
Grammatical Error Correction
Grammatical Error Correction (GEC) is the task of correcting different kinds of errors in text such as spelling, punctuation, grammatical, and word choice errors.
In NLP, text preprocessing is the first step in the process of building a model. The various text preprocessing steps are: Normalization, Word and sentence tokenization, Informal to formal text converter, Multiple meaning words detector, Lower casing, Stop words removal, Stemming, Lemmatization • PoS Tagging
Thanks to great harmony between hardware capacity and opensource software, Machine Learning (ML) and especially Deep Learning (DL) is in its golden time. Companies compete on leveraging ML capabilities to make their products more accurate or offer new products that could not exist without ML. The essential components of an ML project are:
I. Data which needs tooling
II. Set of approaches and technologies which needs researchers who design the roadmap,
III. and great team of ML-developers to realize the idea
Best case would be undoubtedly to have all the above teams in your company. However, ML-technologies are expensive and building a Full-stack ML team is not always the best strategy for managers.
Here is where we at AI-Bridge define our role as a consulting agency. We build a bridge between your technical requirements and the world of Artificial Intelligence (AI). From the above-said components for ML projects, the data part (I.) is normally available in the field and could be acquired with some tooling. In the case of data confidentiality, we only need a very small subset of data to design models and perform all trainings on your authorized servers.
The second component (II.) is normally partly available inside the company. That is the company’s field-specific know-how for solving technical problems. On the other side of, let’s assume, the bridge there are ML technologies and solutions. An undeniably important component for building this bridge is how these field-specific problems could be translated to ML-problems and what set of technologies is the optimal set for problem-solving. We make this possible with our great access to researchers in the community of ML.
Finally, to process data at scale and make added-value for your company, it’s essential to have ML-developers with hands-on experience on delivering standard industrial code (based on iso 9126). AI-Bridge generates its problem-solving power by a great set of researchers and developers.
Here's how we do it
List of Services
•NLP, Sentiment analysis
•Car Plate Detection Recognition
•Motion2Motion (GAN based) generation
•Body Pose detection and estimation
•Medical Image Processing
•Clustering and Segmentation
•Classification and Labeling
•Deep neural Model analysis and improvement
•Deep Learning on Cloud: Amazon Web Services & Google Cloud Platform
•Consultant on Algortihm design and model architecture
•Big data end to end strategy
•Hadoop, Spark, Kafka and other big data technologies
•Traditional machine learning and deep learning
•AWS, Azure, and Google cloud
•Tensorflow, Scikit-learn, Keras, Caffe, MLlib, and other machine learning frameworks
we use the latest project management tools to make sure our (continuous) process of software development meets the latest standards that shape a high-quality software