Upcoming AI based Technologies in Research

Upcoming AI based Technologies in Research

Reinforcement Learning


What is Deepfake?

AI-generated fake videos are becoming more common. Before going in detail, Lets know about what is deepfake.

The 21st century’s answer to Photoshopping, deepfakes use a form of artificial intelligence called deep learning to make images of fake events, hence the name deepfake. Want to put new words in a politician’s mouth, star in your favourite movie, or dance like a pro? Then it’s time to make a deepfake. Deepfakes are synthetic media in which a person in an existing image or video is replaced with someone else’s likeness.

How DeepFakes are made?

One way to make deepfakes uses what’s called a generative adversarial network, or Gan. A Gan pits two artificial intelligence algorithms against each other. The first algorithm, known as the generator, is fed random noise and turns it into an image. This synthetic image is then added to a stream of real images – of celebrities, say – that are fed into the second algorithm, known as the discriminator. At first, the synthetic images will look nothing like faces. But repeat the process countless times, with feedback on performance, and the discriminator and generator both improve. Given enough cycles and feedback, the generator will start producing utterly realistic faces of completely non-existent celebrities.

Uses of DeepFake

1.This deep fake can be used for Investment purpose and Commercial use i.e., it can be used for training courses with different languages.

2. Aids in Future conversation with Grandchildren.


Worldwide, agriculture is a $5 trillion industry, and now the industry is turning to AI technologies to help yield healthier crops, control pests, monitor soil and growing conditions, organize data for farmers,and help with workload. It also helps to improve a wide range of agriculture-related tasks in the entire food supply chain.

AI in plant disease detection

AI systems are also helping to improve harvest quality and accuracy and it is known as precision agriculture. Precision agriculture uses AI technology to aid in detecting diseases in plants, pests, and poor plant nutrition on farms.  AI sensors can detect and target weeds and then decide which herbicides to apply within the right buffer zone. This helps to prevent over application of herbicides and excessive toxins that find their way in our food.


Phytophthora infestans is a water mold, a microorganism that causes the serious potato disease known as late blight. Once infected, entire fields can be wiped out in under a week. University researchers partnered with IBM to develop AI-powered drones that can quickly and accurately detect potato blight. With accuracy of 75 percent to 97 percent, the drone system can process images ten-times faster than human observers. Using these drone images, late blight in potato fields can be detected earlier using AI. This can reduce pesticides cost and save the potato plants.

AI in Robotic Harvesting

Harvesting these crops require a lot of labour and time. If a farmer is unable to harvest in time, the crop will go to waste. So, Robots urges here to help farmers. The robotic system utilizes soft-touch robotics and a lidar sensing system to detect ripe apples, leaving out unripe fruits during the picking process. The goal so far is not to replace human pickers on the farm, but to enable 24 hour harvesting.

The technology employed in agricultural robots is machine learning. Machine learning provides an advanced method of identifying collision paths that can help autonomous vehicles learn to adapt and avoid new or unexpected hazards in their paths. It also enables picking and quality control robots to learn as they go, and to develop the best methods for identifying and executing their tasks. The machines are equipped with delicate suction cups or padded grabbers are just one way growers are protecting their fruit during autonomous harvests.


There are multiple benefits for the increased adoption of AI in the banking sector.

Some of them are payment fraud, loan fraud, and customer onboarding fraud. Detecting these frauds using AI, raises 26% of the venture funding in the banking. In credit card and loan sections, banks get a clear image of risks and danger and possible return for every individual and other past practices.

How AI helps?

Banks could be beneficial from a machine learning-based fraud detection solution in that they would be able to instrument it across more than one channel of data to be analyzed. This would mean the model could be trained to detect fraud within more than one type of transaction or application, or both of these at the same time.

For example, a given customer may be doing his ATM and online transactions from within a given geographical radius. There are services available which can map IP addresses to a physical address. If a transaction happens suddenly from a new IP address far away, this may be classified as an outlier and the transaction may not be allowed to happen.

Client support

Chatbots in banking allow heavily automated customer service, in a highly scalable way. It helps 24/7 service to consumers. Chatbot integration in mobile banking apps will be the dominant channel for chatbot-driven customer communications, accounting for 79% of successful interactions in 2023. sAlso, it provide accounting & money management services to users like updation about their transfer and expense limits. Helps in data management (managing huge volume of data).

How AI helps?

AI, including chatbots, will have a highly disruptive impact on insurance claims management, leading to cost savings of almost $1.3 billion by 2023, across motor, life, property and health insurance, up from $300 million in 2019.

Chatbots can automate post-incident data collection, with AI used to analyse the details/images provided using computer vision. These methods will not only save money for insurers, they will also reduce time to claim settlement, improving customer loyalty.

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