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Artificial Intelligence

What is the Difference between Artificial Intelligence and Machine Learning?



Artificial intelligence (AI) and machine learning (ML) are nowadays one of the hottest topics in the tech towns across the globe. China is heavily investing on R&D on AI and other developed nations like US, UK and Germany are also in the race to develop AI-base robots, machines, software, cars and business applications that can work without any human intervention.

Sometimes AI and ML are used interchangeably, making a kind of confusion among people who listen about the technological developments into this field. So, before we clarify the difference between AI and Machine Learning we need to simplify with the basic definition of both to make it more easily understandable while unfolding the dissimilarities between them.

What is Artificial Intelligence?

In a very simple and layman language, “Artificial intelligence is a theory and development of computer-based systems that can works behaving like human intelligence”. It is kind of study how to train computers so they can perform with their own thinking in different situations.

AI also sometimes called machine intelligence because it is demonstrated by machines. And nowadays automobiles, gadgets and equipment in healthcare or sector are using AI-enabled devices to reduce the repetitive tasks and minimize the human efforts.

What is Machine Learning?

Machine learning is a branch or you can say a subset of artificial intelligence in the field of computer science allowing machines to learn by its own without being explicitly programmed. Actually, with the help of an algorithm, it uses a machine learning training data to learn certain patterns and behaviours of the particular action to respond accordingly.

The prime aim of ML is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly. And the process of teaching initiates with observations of data such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we feed into them.

Difference between Artificial Intelligence and Machine Learning with examples

Artificial Intelligence is a wider concept that involves the research and development of machine learning based applications. ML is the sub-field of the AI and its main aim is to increase the accuracy by learning with high-quality training data while AI works as a computer program aimed to increase the chance of success and not the accuracy.

AI is you can say a final model that performs in decision making while ML allows the system to learn new things from the data. Machine learning process basically encompasses creating self-learning algorithms, whereas AI leads to develop a system to reposed like human intelligence and behave accordingly into different circumstances.

Self-driving cars, virtual apps like Google Assistant and Crotona, Alexa and Siri are nice examples of artificial intelligence. All these models have been trained with a huge amount of high-quality data with deep learning algorithms to work flawlessly and make human work easier. Cogito is the one the well-known AI training data companies, providing high-quality datasets for machine learning in various industries like healthcare, e-commerce and retail etc.

AI-enabled robotics and machines are various other best examples you can see around you or on the internet. But remember, artificial intelligence is a broad term that represents the general concept of machines being able to carry out smart tasks, and machine learning is a specific subset of algorithms for AI that helps to learn from data and perform accordingly.

To know more about AI and ML differences watch this video:



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Artificial Intelligence

How AI Can Help In Agriculture: Five Applications and Use Cases



AI in Agriculture

Artificial Intelligence (AI) is expanding its footprints at the ground level making a significant impact in the world’s most vital sector – Agriculture. After healthcare, automotive, manufacturing and finance sector now artificial intelligence in agriculture is providing cutting-edge technology for harvesting with better productivity and crop yield.

The Agriculture sector is the foundation of the world’s economy and with the increasing population, the world will need to produce 50% more food by 2050. AI-enabled technologies can help farmers get more from the land while using resources more sustainably. Here, we’ll learn how AI can be used in agriculture and its applications into farming.

AI Applications with Use Cases in Agriculture and Farming

ai in farming

Autonomous Tractors

With the heavy investment in developing autonomous vehicles for various needs, the agriculture sector will be also getting benefits with self-driving or you can say driverless tractors. With more quality training data for agriculture, the farm sector is going to be revolutionized by the large scale use of autonomous tractors for performing multiple tasks.

Video: Autonomous Tractor at Work

These self-driving or driverless tractors are programmed to independently detect their ploughing position into the fields or decide the speed and avoid obstacles like irrigation objects, humans and animals while performing various tasks.

Agricultural Robotics

Similarly, AI companies are developing robots that can easily perform multiple tasks in the farming field. Such robotics machines are trained to control weeds and harvest the crops at a much faster pace with higher volume compare to humans.

Video: AI Robots in Agriculture

These robots are well-trained to assist for checking the quality of crop and detect unwanted plants or weeds with picking and packing of crops at the same time capable to fight with other challenges faced by the agricultural labour force.

Companies like Blue River Technology and Harvest CROO Robotics are making such robotics machines that can control unwanted crops or weeds and help farmers in picking or packing of crops with higher volumes.

Controlling Pest Infestations

Pests are one of the worst enemies of the farmers damaging the crops globally before it is harvested and stored for human consumption. Popular insects like locusts, grasshoppers, and other insects are eating the profits of farmers and gobbling the grains meant for humans. But now AI in farming gives growers a weapon against such bugs.

ai in pest control

AI and data companies are helping farmers to get alert on his Smartphones about the grasshoppers likely to descend towards a particular farm. AI companies using the new satellite images against pictures of the same using historical data and AI algorithm detects that the insects had landed at another location and farmers use such information after confirmation and timely remove the costly pests from their fields.

Soil and Crops Health Monitoring

Continues deforestation and degradation of soil quality are becoming a big challenge for food producing countries. But now a German-based tech startup PEAT has developed a deep learning based application called Plantix that can identify the potential defects and nutrient deficiencies in the soil including plant pests and diseases. 

This app is working on image recognition based technology and you can use you your smartphone to capture the plant’s image and detect the defects into the plants. You will also get soil restoration techniques with tips and other solutions on short videos on this app.

Also Read: How Can Artificial Intelligence Benefit Humans?

Similarly, Trace Genomics is another machine learning based company provides soil analysis services to farmers. Such apps help farmers to monitor the soil and crop’s health conditions and produce a healthy crop with a higher level of productivity.

SkySquirrel Technologies acquired by another similar company VineView brought drone-based aerial imaging solutions for monitoring crops health. A drone is used to make a round of capturing the data from the vineyards field and then all the data is transferred via a USB drive from the drone to a computer and analyzed by the experts.

drone use in agriculture

The company uses the algorithms to analyze the captured images and provides a detailed report containing the current health of the vineyard, generally the condition of grapevine leaves as these plants are highly prone to grapevine diseases like molds and bacteria helping farmers to timely control using the pest control and other methods.  

Precision Farming with Predictive Analytics

AI applications in agriculture expanded into doing the accurate and controlled farming through providing proper guidance to farmers about optimum planting, water management, crop rotation, timely harvesting, nutrient management and pest attacks.

Video: What is Precision Farming?

While using the machine learning algorithms in connection with images captured by satellites and drones, AI-enabled technologies predict weather conditions, analyze crop sustainability and evaluate farms for the presence of diseases or pests and poor plant nutrition on farms with data like temperature, precipitation, wind speed, and solar radiation.

Also Read: How Does AI Detect Cancer in Lung Skin Prostate Breast and Ovary?

AI in agriculture not only helping farmers to automate their farming but also shifting to precise cultivation for higher crop yield and better quality while using less resources. Companies involved in improving the machine learning or AI-based products or services like training data for agriculture, drone and automated machine making will get technological advancement in future will provide the more useful applications to this sector helping the world deal with food production issues for the growing population.

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Artificial Intelligence

What is Deepfake: Know Everything About this AI-based Technology



What Is Deepfake

Technologies are not only born to provide the goodness to humans, as few people make misuse of it and try to create illegal things that can embarrass them or hurt their sentiments deeply. Deepfakes, an AI-backed technology, is one that comes into the limelight due to wrong reasons defamed many people in the last few years.

What is Deepfake Technology?

Deepfake is an AI-supported technique to create ultra-realistic fake videos of people by swapping their faces other person saying and doing things that they haven’t actually done. It includes images, videos and audios to combine with images and videos onto source images or videos. Porn videos having the mocked face of popular celebrities on the adult star are one of the well-known examples of deepfakes.

What Does Deepfake Mean?

Using the AI with high-powered computer a graphics processing unit (GPU) and time on their hands can create realistic fake videos is known as “Deepfakes”.

What Does Deepfake Mean

Deepfakes are not only limited to videos but the audio is also targeted to create such fake voice-based contents. Though, it is much more difficult to compare the face swapping but the voice of another person can be manipulated with any other person speaking something that is not originally said by him seems very original.

How Does Deepfake Technology Work?

Neural network based technology is used to make Deepfake videos. The deep learning process is used into deepfake creation that is kind of ultramodern application of neural net simulation to massive data sets. Primarily neural networks are capable to “learn” to perform tasks by considering, generally without being programmed with any task-specific rule.

Deepfakes use the human tendency through generative adversarial networks (GANs) in which two machine learning models are trained with the data sets to create fake videos at the same time also detect fake videos. The forger ML model keeps continuing creating fake videos until the second model fails to detect the forgery.

How Deepfake Videos are Created

The larger the quantity of training data sets it would be easier for a forger to create deepfakes that can be believed easily. Political celebrities and Hollywood’s popular actresses became the victims of Deepfake technology misrepresenting their personalities and pose a grave threat to women not prominent could have their reputations damaged by the depiction in involuntary deepfake pornography or revenge pornography.

What Software is Used to Make Deepfakes?

There are many software available in the market to create deepfakes. One of them is FakeApp that allows the normal person to make the deepfake content, though this website has been banned but still many apps are active in the market.

deepfake software

Samsung AI has recently developed a new artificial intelligence system that can generate a fake clip by feeding it as little as one photo. Usually requires big data sets of images in order to create a realistic forgery but here you don’t need a huge amount of images or videos to create deepfakes misusing the AI for humans.

Also Read: How Can Artificial Intelligence Benefit Humans?

Samsung has used this technology into famous Leonardo Da Vinci painting of Mona Lisa in which it has assigned a series of ‘facial landmarks’ to the portrait and then applied an algorithm that has access to metadata from a vast amount of image banks to form a different ‘movements’ of the Mona Lisa’s head that you can spot below.

Deepfake Mona Lisa painting using single image

Popular Deep Fake News

The most popular Deepfake news was a fake video of the former US president Barack Obama in which he was expressing a profanity-laced opinion on US president Donald Trump.

Similarly, a fake video of Steve Buscemi came into the market in which her face was superimposed onto Jennifer Lawrence while speaking at the Golden Globes award show.

Apart from this, many other celebrities also became the news headlines due to deepfakes porn videos of popular actresses gone viral making millions of people excited to watch such contents. However, knowing the truth all the leading porn sites have removed such fake videos from their websites that were embarrassing such celebrities.

Also Read: How Deepfake Technology Impact the People in Our Society?

Deepfake in Pornography

Actually, Deepfake is a major threat to popular celebrities by creating their deep fake porn videos. The images of such celebrities or popular actresses are freely available on the Internet at their social media pages or other entertainment sites.

deepfake celebrity

Many times such videos are created as revenge porn, in which deepfake creator needs a bunch of photos of the victim from various sources to superimposed the same on the body of a porn star and raunchy videos posted on adults websites.

deepfake celebrities

Female celebrities including Taylor Swift, Natalie Portman, Emma Watson, Gal Gadot, Michelle Obama, Daisy Ridley, Meghan Markle, Sophie Turner and Kate Middleton had become the victims of deepfake pornography.

How to Detect Deepfake Video?

Detecting deepfakes is not possible with the normal human eye but there are few activities and signs that you can spot deepfake videos. Quality of video, irregular blinking of eyes, inconsistent skin tone, unnatural movement of the body or facial expressions and unusual changes in the background suchlike lighting conditions or color etc. are the major clues you can detect the deepfakes.

Video: Why Detecting Deep Fake is Difficult?

However, AI-enabled applications can detect such loopholes or fake videos using the same level of technology that has been used to create such contents. While on the other hand, human-powered deepfake detection services are also offered by companies to check and find out the fake videos, audio and texts with better accuracy.

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Artificial Intelligence

The Main Purpose Of Image Annotations Is To Develop AI Model



The Main Purpose Of Image Annotations Is

The main purpose of image annotations is to highlight or capture the targeted object in a picture to make it recognizable for machines. Actually, image annotation is done for machine learning and AI to use the annotated images as a training data set while feeding with a deep learning algorithm and develop a fully functional AI model.

Objective Of Image Annotation

image annotation for object detection

The machine learning is the processes to develop the AI-based models that can work itself without human intervention. And to train such machines a huge amount of training data is required for computer vision to identify the objects in images from the particular industry helping machines to recognize the similar images when used in real-life.

Types of Image Annotations

types of image annotation

There are different types of image annotation techniques used to annotate such picture. And there are different types of free tools and software are available in the market to annotate the images as per the object dimension and requirement of the project. Actually, image annotation is important for machine learning and manual image annotation service is also available in the market you can choose as per your needs.

Also Read: Why Human Annotated Datasets is Important for Machine Learning?

Bounding boxes, Semantic Segmentation, 2D or 3D Cuboid, Polygons, Polylines or Splines, Point and Landmark annotations are popular types of image annotation techniques used in this field to make the machine learning and AI model development possible. is one of the companies, providing the best quality image annotation service with human-powered data labeling and image recognition service at best quality.

Applications of Image Annotation

The main purpose of image annotations is to make the object recognizable for machines or computer vision. And there are many industries adopting AI and integrating the related technology into their sub-fields for automation to work faster with more efficiency.

image annotation for object detection

A self-driving car is one of the best examples where annotated images have been used to train the model working into the auto functioning of such vehicles. The autonomous vehicles recognize the object on the street and move accordingly to avoid any crash. It is like making a child learn about new things with varied data sets so that it can be recognized easily when used in autonomous vehicles to drive on the road into real-world.

medical image annotation

Healthcare is another important filed where annotated medical imaging used to develop such machines that can detect various types of maladies including life-threatening diseases like cancer at the initial stage of developments with better accuracy. Medical images like X-ray, MRI and CT Scan reports are annotated manually by humans that are used to train the machine learning to detect such diseases without the help of humans.

Also Read: How Does AI Detect Cancer in Lung Skin Prostate Breast and Ovary?

Apart from autonomous vehicle driving, there are many other fields, like healthcare, retail, agriculture, security surveillance and sports analytics are other areas image annotation is playing an important role to make the images easily recognizable for machines allowing machine learning developers to build a right model at affordable pricing.

training data for retail

The main purpose and objective of image annotation are well-described here with a various set of examples showing the use and purpose of image annotation in machine learning and AI. The right applications of image annotation are only possible when annotations are accurate so that models can get accurate data sets to learn and give the right predictions.

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