ARTIFICIAL INTELLIGENCE

Artificial intelligence is “Augmentation of Human and Machine Intelligence”. The ideal characteristic of artificial intelligence is its ability to rationalize and take actions that have the best chance of achieving a specific goal. It is based on the principle that human intelligence can be defined in a way that a machine can easily mimic it and execute tasks, from the most simple to complex. AI systems can go beyond knowledge; they understand, reason, learn and interact.

AI enables partnership between human and technology. Artificial Intelligence is a Machine learning that involves Prediction, which can be done through the following ways:

· Regression: Regression is a set of statistical processes for estimating the relationships between a dependent and independent variables

It is of two types-Linear and Logistic Regression- Where Linear Regression is the linear approach to modeling the relationship between scalar response and variable. Logistic Regression- Statistical model that uses logistic function to binary dependent variable.

· Classification: When data are not related to each other and hence are classified according to their shared qualities or characteristics, this is called Classification method.

· Clustering: When data are grouped such that data of same set of objects are grouped together, this is called Clustering.

Regression and Classification are parts of Supervised Learning. Clustering is Unsupervised Learning.

1. Evolution of AI in the past 40 years (1980–2020) and driving forces of this evolution:

Evolution of AI started as early as in 1906. And here we will be discussing about the last 40 years of AI Evolution:

1974–1980 (First AI Winter): This era was the First AI winter that means Government had stopped giving funds to universities for AI work, and it caused faltering.

1985 (AI Renaissance): Several researchers independently invented back propagation Training of neural nets. The algorithm successfully learned in many areas, which Minsky and Papert had predicted, would be impossible, helping launch a revival of interest in neural nets. So here the third network neural information processing system a-livened a little bit.

1992 (TD-Gammon): Gerald Tesauro IBM researcher came up with a self-teaching neural net that learns to play backgammon. So this impacted in a way that both math and algorithm were becoming good but were slow.

1987–1993 (Second AI winter): There was lull in research and science.

1993 (LeNet 1): Yann Lecun demonstrated a Convolutional Neural Network, which was capable of recognizing handwritten digits quickly and accurately.

1997 (Deep Blue): This was the second challenge, when IBM deep Blue capable of analyzing 200 million moves per second defeats world chess champion Garry Kasparov.

2006 (Deep Learning): Deep Learning means network has more than one layer, otherwise it is machine learning. Hinton and colleagues demonstrated “Deep Belief Nets”.

2009 (ImageNet): This is data set for teaching deep learning algorithms that can see, and researchers unveiled a massive collection of human-annotated images.

2011(Watson): Known for Natural Language Processing.

2015(Open Source AI): Open Source Frameworks are data available to everyone.

2016 (Self-Driving Cars): capable of transit through city traffic without a driver.

2018(Deep Learning as a Service): This means it allows anyone with an internet connection to take advantage of sophisticated AI Algorithm, rich data platforms and immense computing power.

AI Evolution Factors:

· Data: Exponential growth of available data, with the introduction of the Internet, social media, proliferation of sensors and smart devices, and data storage became cheaper.

· Algorithms: The development of more advanced algorithms has helped AI become more powerful and efficient.

· Computing: Back when AI was just beginning to be developed, the computing power was minimal. Computers nowadays can take much more data and heavier algorithms than in the1950s

2. Few significant AI achievements/contributions in the 21st Century:

AI is a computer that can mimic or simulate human thought or human behavior, within that there is subset called Machine Learning, which is underpinning of what is most exciting about AI. Many breakthroughs have been made which was considered impossible earlier. Like- Computers can understand our voice, spot a friend’s face in a photo and steer car. By 2012, neural network and Machine learning started popping up on front page of New York Times. Now it is everywhere, our phone is fully packed with it. Machine is learning fast, this can be known by many factors. Earlier in 1997, after IBM’s deep blue beat Garry Kasparov using Brute-force approach to explore the set of candidate moves. In 2016, AlphaGo built be Google beat Lee Sedol. AlphaGo came up with moves that people playing that game for decades had never seen them before. Now a days, AI monitors our every move, knows our songs, movies, reads CT Scan. It is part of our life.

3. Common and popular AI applications in Agriculture in the past 10 years:

Agriculture plays a significant role in the economic sector. The automation in agriculture is the main concern and the emerging subject across the world. The population is increasing tremendously and with this increase the demand of food and employment is also increasing. Artificial Intelligence (AI) has begun to play a major role in daily lives, extending our perceptions and ability to modify the environment around us. The technologies which are AI-based help to improve efficiency in all the fields and also manage the challenges faced by various industries including the various fields in the agricultural sector like the crop yield, irrigation, soil content sensing, crop- monitoring, weeding, crop establishment. The various ways in which AI has contributed in the agricultural sector are as follows:

· Image recognition and perception: recognition and surveillance, human body detection and geo-localization, search and rescue, forest fire detection

· Skills and workforce: AI in agriculture can be applied which would automate several processes, reduce risks and provide farmers with a comparatively easy and efficient farming.

· Robots in agriculture: The robots are performing various agricultural operations autonomously such as weeding, irrigation, guarding the farms for delivering effective reports, ensuring that the adverse environmental conditions do not affect the production, increase precision, and manage individual plants in various unfamiliar ways.

· Irrigation: The technology of smart irrigation is developed to increase the production without the involvement of large number of manpower by detecting the level of water, temperature of the soil, nutrient content and weather forecasting.

· Drones in agriculture: Drones are being implemented in agriculture for crop health monitoring, irrigation equipment monitoring, weed identification, herd and wildlife monitoring, and disaster management.

4. Major challenges in adopting and implementing AI for Agriculture:

Even though AI has such great effects on Agriculture industry, there are many challenges faced by AI, to be fully acceptable in this industry. Farmers may not consciously see the problem or they may not know it is solvable. Meanwhile, AI engineers know little about agriculture, the problems and the opportunities in this field. Unpredictable weather, changes in soil quality, and crop disease can all affect statistical analysis. The majority of farmers don’t have the time or digital skills experience to explore the AI solutions space by them.

5.Future extension and implementation of AI for Agriculture:

The Third Eye project that is shaping the direction that agriculture will take. Small-scale farmers were affected by armyworms because of late detection if there was a way that we could be able to monitor and then map out the affected areas. Experts in the UK are testing cutting-edge technology like fitting cows with collars that control a robotic milking system. Artificial intelligence experts at Carnegie Mellon University are teaming up with agricultural leaders and plant scientists to improve plant breeding and crop management practices using AI technology.