In today’s era, mechanization taking place everywhere with a new age of development in more automated systems, applications, and robots, etc. Machine learning and AI are the leading cutting-edge technologies giving automation a new dimension with more tasks performed by machines itself.
Though, nowadays many tasks can be independently performed by AI-enabled devices, systems or machines without the help of humans. But developing such machines is not possible without the help of humans. So, Human-in-the-Loop or HITL is a model or concept require human interaction.
What is Human-in-the-Loop?
Human-in-the-loop (HITL), basically you can say, is the process of leveraging the power of the machine and human intelligence to create machine learning-based AI models. HITL describes the process when the machine or computer system is unable to solve a problem, needs human intervention like involving in both the training and testing stages of building an algorithm, for creating a continuous feedback loop allowing the algorithm to give every time better results.
Humans annotate or label data then give to the machine learning algorithm to learn from such and take decisions from such predictions. And then humans also involve in tuning the model to improve its accuracy. And finally, these people test and validate the model by scoring its outputs, when machine learning algorithms not able to make the right decisions or gives incorrect decisions.
Why Human-in-the-Loop Machine Learning is Used?
If you have a sufficient amount of datasets, an ML algorithm can easily make decisions with accuracy just learned from these datasets. But before that machine needs to get learn from the certain amount and quality of data sets, how to properly identify the right criteria and thus comes to the right results.
This where Human-in-the-Loop machine learning is used to the combination of human and machine intelligence creating a continuous circle where ML algorithms are trained, tested, tuned, and validated. In this loop, with the help of humans, the machine becomes smarter as well as more trained and confident to take the quick and accurate decisions when used in real-life and also help to train the algorithms.
How Human-in-the-Loop Machine Learning is Used Today?
Human-in-the-loop is basically integrated through two machine learning algorithm processes – supervised and unsupervised learning. In supervised machine learning, labeled or annotated data sets are used by ML experts to train the algorithms, so that it can make the right predictions when used in real-life.
While on the other hand, in unsupervised machine learning there is no labels are given to the learning algorithm, leaving it on its own to find structure in its input and memorize the data in its own ways.
In HITL, initially, humans label the training data for the algorithm which is later fed into the algorithms to make the various scenarios understandable to machines. Later humans also check and evaluate the results or predictions for ML model validation and if results are inaccurate humans tune the algorithms or data is re-checked and again fed into the algorithm to make the right predictions.
Why Human-In-The-Loop Computing is the Future Of Machine Learning?
Doing a machine learning process without human inputs is not possible. Algorithms cannot learn everything unless provided as per its compatibility. For example, a machine learning model cannot understand raw data unless humans explain and make it understandable to machines.
Here, the data labeling process is the first step in creating a reliable model trained through algorithms, especially when data is available in an unstructured format. Actually, an algorithm cannot understand the unstructured data like texts, audio, video, images and other contents that are not properly labeled.
Hence, the human-in-the-loop approach is required to make such data comprehensible to machines. These data are labeled as per the desired instructions like what is seen in the images, what is spoken in the audio or video using the data labeling or image annotation techniques to label such data.
When Human-in-the-loop Machine Learning is used?
Human-in-the-loop is not the concept you can implement in every machine learning project. Mainly HITL approach is used, when there is not much data available yet, human-in-the-loop is suitable because, at this stage, people can initially make much better judgments than machines are capable of.
And using this, humans produce machine learning training data sets helping the machine to learn from such data. And human in the loop deep learning is used when humans and machine learning processes interact to solve one or more of the following scenarios:
- Algorithms are not understanding the input.
- When data input is interpreted incorrectly.
- Algorithms don’t know how to perform the task.
- To make humans more efficient and accurate.
- To make the machine learning model more accurate.
- When the cost of errors is too high in ML development.
- When the data you’re looking for is rare or not available.
Human-in-the-Loop for Different Types of Data Labeling
As per the algorithms, different types of datasets in machine learning training are required. And the human-in-the-loop approach is used for such different types of data labeling process. If you want to train your model to identify or recognize the shape of objects like an animal on the road or other objects, then bounding box annotation is best suitable to make them recognizable to machines.
While, on the other hand, if you have to classify the objects in a single class, you have to use the semantic segmentation annotation suitable for computer vision to train the visual perception based ML model.
Similarly, to create facial recognition training data sets, landmark annotation is used. In language or voice-recognition machine learning training, text annotation, NLP annotation, audio annotation, and sentiment analysis is used to understand what humans are trying to say in different scenarios.
And when such data is labeled, annotated or make usable to machines, chatbot or virtual assistant like AI devices are developed to communicate with humans. Humans-in-the-loop can create different types of training data sets for different types of machine learning models built for different fields.
AI is getting integrated almost every field around the world, but we still required Human-in-the-Loop, especially to produce and feed the training data into the algorithms at the initial stage of model development. Here, Cogito provides wide-ranging services for human-in-the-loop machine learning and human in the loop AI comprising text, videos, data and image annotation services for AI development.
This article was originally written for Cogito Tech
What is the Difference Between AI, Machine Learning & Deep Learning?
Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) are the most widely used interchangeable words creating confusion among many people globally.
Although, these three terminologies are typically used interchangeably, but they all are different from each other especially in terms of their applications, capabilities, and results.
Understanding the difference between AI, ML, and deep learning is important to utilize the precise applications of these jargons and take the right decision while dealing with AI, ML, or DL related projects.
Before we start, I would like to show you few images (see below) that will give an overview, how AI, ML, and DL are different from each other or how these three terminologies are related to each other.
The easiest way to understand their relationship is to visualize them as concentric circles with AI – which is a broader area, then ML – which is the branch or subset of AI, and finally deep learning – which is a part of the subset of ML, fitting inside both or you can say – DL is driving today’s AI explosion due to more complex inputs and outputs.
I think these highly illustrative images cleared some doubts and misconceptions about these jargons. But you need to go through more definitions with a few sets of useful examples and use cases that will help you understand these concepts better.
What is Artificial Intelligence?
As the name denotes, AI is a broader concept used to create an intelligent system that can act like human intelligence. The terms – “Artificial” and “intelligence” means “a human-made thinking power”.
Basically, AI is the field of computer science used to incorporate human intelligence into machines, so that such machines or systems can think (not exactly) and take sensible decisions like humans.
And such AI-enabled machines can perform specific tasks very well and sometimes even better than humans – though they are limited in scope. And to develop such machines AI training data sets are processed through machine learning algorithms.
To be more precise, AI-enabled systems don’t need to be pre-programmed, instead such algorithms are used, that can work with their own intelligence. And machine learning algorithms such as reinforcement learning algorithms and deep learning neural networks are used to create such systems.
Example of AI in Daily Life
Smart Home Devices, automated mail filters in our Gmail, Self-driving cars, Chatbots, AI Robots, Drones and AI Security Cameras are the popular examples where AI in integrated. Though, there are many more other applications, devices, systems and machines works on AI principles helping humans in various areas across the globe.
What is Machine Learning?
As the name suggests, machine learning empowers the computer system to learn from past experiences earned through training data. As of now, you got to know machine learning is the subset of artificial intelligence, in fact, it is the technique used to develop AI-enabled models.
Machine Learning is used to create various types of AI models that learn by themselves. And as much as it gets more data, it gets better at learning and gives more accurate results.
Let’s take an example of how machine learning and algorithms work while making predictions. ML is actually a process of training the algorithms to learn and make the decisions as per the learning.
While training an ML-based model, we need certain machine learning training data sets to feed into the algorithm allowing it to learn more about the processed information.
Machine Learning Examples in Real Life
Recommendation on your Mobile or Desktop based on your web search history, Virtual Assistance, Face & Speech Recognition, Tag or Face Suggestion on Social Media Platforms, Fraud Detection, Spam Email Filtering, are the major examples of machine learning in our daily life. Most of the AI devices are developed through machine learning training.
Machine learning is most prevalent in the development of fraud detection software. It has made the job of fraud analysts more efficient because it allows them to devote their time to more strategic tasks. If you want to learn more about the companies that offer Fraud detection software in your country, do a quick Google search for ‘Internal Fraud Prevention in New York‘ or whatever geographic region you are in.
What is Deep Learning?
It is the subset of machine learning that allows computers to solve more complex problems to get more accurate results by far out of any type of machine learning.
Deep learning uses the Neural Network to learn, understand, interpret and solve crucial problems with a higher level of accuracy.
DL algorithm-based neural networks are roughly inspired by the information processing patterns that are mainly found in the human brain.
While learning, understanding, and predicting just like we use our brains to recognize and understand certain patterns to classify various types of information, deep learning algorithms are mainly used to train machines for performing such crucial tasks easily.
Whenever we try to perceive new information, the brain tries to compare it with the items known to the brain before making sense of it. In deep learning – neural network algorithms employ to perceive new information and give results accordingly.
Actually, the brain usually tries to decode the information it receives and archives this through classification and assigning the items into various categories.
Let’s take an example – As we know DL uses a neural network which is a type of algorithms aiming to emulate the way human brains make decisions.
The notable difference between machine learning and deep learning is that the later can help you to understand the subtle differences. Because DL can automatically determine the features to be used for classification, while ML needs to make understandable these features manually.
Finally, the point is compared to ML, DL requires high-end machines and a substantially huge amount of deep learning training data to give more accurate results.
Deep Learning Examples in Real Life
Automated Translation, Customers Shopping Experience, Language Recognition, Autonomous Vehicles, Sentiment Analysis, Automatic Image Caption Generation & Medical Imaging Analysis are the leading examples of deep learning in our daily life.
Machine learning is already being used in various areas, sectors, and systems but deep learning is more indispensable for the healthcare sector where the accuracy of results can save the lives of humans. Though, countless opportunities lie for machine learning and deep learning to make the machines more intelligent and contribute to developing a feasible AI model.
In the healthcare and medical field, AI can diagnosis disease using the medical imaging data that are fed into deep learning algorithms to learn the tumors or other life-threatening diseases. Now deep learning is giving excellent results, even performing better than radiologists.
Finally, in all types of AI, ML or DL models working on computer vision-based technology needs a huge amount of training data for object detection. These datasets help them to learn the patterns and utilize similar information for predicting the results when used in real-life.
Image Annotation for Applying the Machine Learning in Agriculture
Artificial Intelligence (AI) is getting integrated into vital fields making human life more efficient and productive. Similarly, AI in agriculture is making agriculture and farming easier with computer vision-based crop monitoring and production system.
AI Robots, drones and automated machines are playing a big role in harvesting, ripping, and health monitoring and improving the productivity of the crops. But do you know how these AI-enabled machines help in precise agriculture and farming?
Actually, these AI machines work on computer vision technology and AI models are trained through annotated images fed using the right machine learning algorithms. Image annotation is the process that helps machines to detect or recognize various objects or things in the agricultural fields, so that machine can easily identify and take the right action.
IMAGE ANNOTATION FOR MACHINE LEARNING IN AGRICULTURE
Image annotation in agriculture helps to detect and perform various actions like detecting the crops, weeds, fruits and vegetables. And when a huge amount of such annotated data is feed into the deep learning algorithm, the AI model becomes enough to recognize similar things like picking the plants, checking the health of the crops.
Image annotation is playing a crucial role in applying machine learning to agricultural data created through the data labeling process. Let’s find out how and what are the applications of machine learning in agriculture possible through image annotation services.
Robots for Precision Agriculture
Robots are nowadays widely in use across the fields. In the agriculture sector, it is performing various actions with the help of machine vision algorithms to operate successfully. It can perform actions like plowing, seeds planting, weeds handling, monitoring of productivity growth, fruits, and vegetable picking, packaging, sorting and grading, etc.
Robots can also detect weeds, check the fructify level of fruits or vegetables, and monitor the health condition of plants. Apart from that, using the computer vision camera, robots can classify the various fruits at high speed with better accuracy.
And deep learning algorithms can identify defects from any angle with large color and geometric variation. The algorithms are set to perform the first object detection to locate the fruits and, after that, the classification is done accordingly.
To train robots annotated images of such plants, crops and floras are feed into the algorithms. Bounding box annotation is one of the most popular image annotation techniques used to make the crops, weeds, fruits and vegetables recognizable to robots.
Sorting of Fruits and Vegetables
After collecting the fruits and vegetables at the time of packing at processing plants, a sorting task is performed by the robots to separate the healthy and rotten fruits or vegetables from each other send them to the right place. These robots can also detect existing features and defects, to predict which items will last longer to ship away and which items can be retained for the local market.
Sorting and grading tasks can be performed based on deep learning using the huge quantity of training data of annotated images. Making the sorting and grading process accurate is possible when precisely annotated images are used to train the robots.
Similarly, robots can sort the flowers, buds, and stems of different breeds, sizes and shapes, making them usable as per the strict standards and rules in use in the international flower markets. And with the help of first-class image annotation techniques, AI-enabled machines save time and reduce wastage promising more precise agriculture and farming.
Anolytics provide the image annotation service to help robots detect the different types of fruits and vegetables with the right accuracy. And as much as similar data will be used, the robots will become more efficient to detect such things agro field.
Monitoring the Health of Soil, Animals & Crops
Using Geosensing technology, drones and other autonomous flying objects can monitor the health condition or soils and crops. This helps farmers to make sure what the right time for sowing is and what action should be taken to save the crops. Right soil conditions and timely insecticides are very important for better production and high crop yield.
Similarly, AI-enabled technology makes it possible to detect the health of animals. Yes, Body Condition Score for bovines is the technique that helps to accurately measure the body of the cow, buffalo and other similar animals. Such a score is actually given by the veterinarian. As the body condition of such animals affects reproductive health, milk production, and feeding efficiency, and AI-based knowing the score helps the animal husbandry business more profitable.
Crop Yield Prediction Using Deep Learning
AI in agriculture is possible with deep learning datasets that help to predict the crop yield through portable devices like smartphones and tablets. Collecting and developing deep learning platforms requires expert knowledge for their training in order to provide reliable yield forecasts using the ample amount of training data used to train such models.
AI in Forest Management
Using aerial images taken by drones, planes, or satellites, AI in forest management is possible. Yes, images that are taken from such sources help to detect illegal activities like cutting trees that leads to deforestation affecting the Eco-system of our planet.
Actually, aerial images taken by drone, plane or satellite, in the field of forestry is automating the process of forest management through huge amounts of data to produce accurate measures, assessing the health and the growth of trees and enabling forest management professionals to make more accurate decisions while controlling the deforestation.
Other Projects in Precise Agriculture
Apart from the above-discussed use cases, image annotation offers various other object detection efficiencies in agricultural sub-fields irrigation, weed detection, soil management, maturity evaluation, detection of foreign substances, fruit density, soil management, yield forecasting, canopy measurement, land mapping, and various others.
Image Annotation in Deep Learning for Agriculture
Acquiring high-quality machine learning training data for computer vision-based AI models is a challenging task for the companies working on such projects. But dedicated data annotation companies like Anolytics are providing the right solution for AI companies to get the computer vision training data in large volumes at the lowest cost with the best accuracy.
Anolytics is known for providing training datasets for various fields like Healthcare, Agriculture, Retail, Self-driving cars, Autonomous Flying, AI Security Cameras, Robotics and Satellite Imagery.
Working with world-class annotators, Anolytics ensure the precision levels of data labeling at every stage making sure the machine learning project can get the right data for giving accurate results by AI models especially when it is used in the real life.
This blog was originally written and submitted for anolytics.ai
Artificial Intelligence in High-Quality Embryo Selection for IVF
IVF treatment is becoming a common practice in today’s reality, where 12% of the world population struggle to conceive naturally. But thanks to artificial intelligence in IVF, the whole process is going to help the embryologists to select the best quality embryos for in-vitro fertilization improving the success of conception through artificial insemination.
As per the latest study published in eLife, a deep learning system was able to choose the most high-quality embryos for IVF with 90% accuracy. Compared to trained embryologists, the deep learning model performed with an accuracy of approximately 75% while the embryologists performed with an average accuracy of 67%.
As per the research stated, the average success rate of IVF is 30 percent. The treatment is also expensive, costing patients over $10,000 for each IVF cycle with many patients requiring multiple cycles in order to achieve successful pregnancy.
Risk Factors in IVF Treatment
While multiple factors determine the success of IVF cycles, the challenge of non-invasive selection of the highest available quality embryos from a patient remains one of the most important factors in achieving successful IVF outcomes.
Currently, tools available to embryologists are limited and expensive, leaving most embryologists to rely on their observational skills and expertise. As selection of quality embryo increases the pregnancy rates, that is now possible with AI.
Researchers from Brigham and Women’s Hospital and Massachusetts General Hospital (MGH) set out to develop an assistive tool that can evaluate images captured using microscopes traditionally available at fertility centers.
There is so much at stake for our patients with each IVF cycle. Embryologists make dozens of critical decisions that impact the success of a patient cycle. With assistance from our AI system, embryologists will be able to select the embryo that will result in a successful pregnancy better than ever before,” said co-lead author Charles Bormann, PhD, MGH IVF Laboratory director.
AI in Embryo Selection through Machine Learning
The team trained the deep learning system (sub branch of machine learning) using images of embryos captured at 113 hours post-insemination. Among 742 embryos, the AI system was 90% accurate in choosing the most high-quality embryos.
The investigators further assessed the system’s ability to distinguish among high-quality embryos with the normal number of human chromosomes and compared the system’s performance to that of trained embryologists help in healthy baby growth in the womb.
The results showed that the system was able to differentiate and identify embryos with the highest potential for success significantly better than 15 experienced embryologists from five different fertility centers across the US.
However, the deep learning system is meant to act only as an assistive tool for embryologists to make judgments during embryo selection but going to benefit clinical embryologists and patients. Actually, a major challenge in the field is deciding on the embryos that need to be transferred during IVF and such AI models can make right decisions.
Machine Learning Training Data for AI Model
The research stated that deep learning model has potential to outperform human clinicians, if algorithms are trained with more qualitative healthcare training datasets. Advances in AI have promoted numerous applications that have the potential to improve standard-of-care in the different fields of medicine.
Though, few other groups use to evaluate different use cases for machine learning in assisted reproductive medicine, this approach is novel in how it used a deep learning system trained on a large dataset to make predictions based on static images.
Such findings could help the couples become parents through IVF with higher chances of conceptions with right embryos selections. And further with more improvement in training development of AI systems will be used in aiding embryologists to select the embryo with the highest implantation potential, especially amongst high-quality embryos.
Watch Video: Future of AI in Embryo Selection for IVF
Source: Health Analytics
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