Artificial Intelligence

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artificial-intelligence

Drawbacks of Artificial Intelligence

Artificial Intelligence is a terminology used in machines as simulated or artificially generated intelligence. These machines are totally programmed to think as human think and to mimic in the same manner a person acts. The best out of artificial intelligence is that has the ability to rationalize and react as per the situations because it totally works on the command and commands are programmed in these machines.

Artificial Intelligence can be best utilised in order to achieve specific goals. Scientists are using their minds to exhibit traits in these machines in order to understand problems and resolve it. Although, this intelligent technology has some drawbacks also, which you must know upfront.

Malfunction of Artificial Intelligence

The aim of artificial intelligence include learning, reasoning and perception and machine are wired using of a cross-disciplinary approach based in science, maths & psychology. As technology advances, previous benchmarks that defined artificial intelligence become outdated.

Examples of artificial intelligence are self-driven cars; computers that play chess are the new development in this field. Computers need to predict the situations and react accordingly like in chess; the end result is winning the game. Same as in the case of self-driven cars, a computer needs to consider all the external data and analyse it in order to get prevented from any disaster or accident.

Disputes on Artificial Intelligence

One common phenomenon is that machines will become so highly developed that human civilisation won’t be able to keep up with it. Another is that these machines can actually hack into human’s privacy and could use it as a weapon against humanity. It came to notice that people are arguing for making code of conduct or ethics for artificial intelligence also or otherwise they should also be treated same like as humans.

Self-driven cars are a subject matter of controversy, as their machines are designed for lowest possible risks and least casualties. While they remove the incidence of human error, it means if they were put in a situation in which they had to decide between a crash with one person and a crash with another then, they would calculate which crash would lead to fewer amounts of damage and the risks but yes, it would need to choose one. People are disagreeing with the fact that lives of humans should not be put at the mercy of a machine.


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Artificial Intelligence

3 Driving Factors of Artificial Intelligence

Artificial Intelligence is no doubt the next generation technology everyone is looking forward to. China who wants to be the world leader in Artificial Intelligence has added AI in the school curriculum of high-school students. Now, you can imagine the importance of AI in the coming future.

AI has already put his legs on various verticals of the industry including automobile, healthcare, finance, manufacturing and retail to name a few. From robotic surgery to self-driving cars, AI has proven its implications on each and every application. But what actually is driving the Artificial Intelligence?

Well, companies like Amazon, Facebook, Apple, Google, IBM as well as Microsoft are investing in the research and development of AI. However, there are 3 factors which are accelerating the growth of Artificial Intelligent.

Next-Generation Computing Architecture (GPU)

Machine Learning and Artificial Intelligence require speedy processors. The traditional microprocessors and CPUs are not meant for Machine Learning. They require a new breed of processors. This made the rise of GPU. The Graphics Processing Units which used to be the part of high-end gaming PC’s and workstations have seen a tremendous growth after the evolution of AI.

GPU’s come with numerous cores that speed up the ML training process. Seeing the growth of AI, it might happen that down the line each CPU will come paired-up with a GPU.

Availability of Cloud

Data Scientists require large data sets and historic data for ML models to predict with more accuracy. In order to predict the weather or solve problems like detecting cancers, data scientists need a large amount of data to analyze. The efficiency of ML models depends merely on the size of data. The more the data, the better is the accuracy.

With the use of cloud and businesses, government and academia (the major data holders) unlocking their data on cloud, helped the Artificial Intelligence to grow by leaps and bounds.

Advancement in Deep Learning



Artificial Neural Network is replacing the conventional Machine Learning system. The advancement in Machine Learning Models with the new technologies like Capsule Neural Network and Transfer Learning have changes the way ML models are trained and implemented. These techniques are so advanced that they are able to produce the most accurate results with a limited data.

Conclusion

Various companies like IBM through IBM Watson, Microsoft with Corona, Amazon with its Alexa and major players like China are investing billions in Artificial Intelligence to make the world mobile. With selecting the clothes to turning off lights through voice and self driving cars, they want the automation to rule the world by 2030.

 

 


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AI and the Future of Food

The role of AI in the Future of Food

The growing world population facing the scarcity of resources is not new to the world. According to Malthusian Theory of Population, the human population grows exponentially in geometric progressions (2,4,8,16, 32…) while that of food production grows arithmetically (1, 2,3,4…). Thus, defining a wide gap between the food requirements for the growing population.

According to UN projections, the global population will reach 8.5 Billion in 2030 and 9.5 Billion by 2050. Presently, we are not even capable of feeding the 7.3 Billion people in the world. The FAO has estimated that the food production will have to grow by 70% in order to meet the requirement.

However, his theory (1798 and revised 1803) was a thing of the past. He wasn’t having any idea of the Artificial Intelligence which incepted in 1940 with the dawn of modern age. He didn’t included Artificial Intelligence and its use cases in the agriculture. The growth of AI in agriculture is remarkable in the past few years. And if this progress continues, we could be able to meet that 70% food production growth.

Smart Agriculture

Credit-Statista

Growth of AI in Agriculture

The use of AI has made the agriculture smarter than ever. Smart agriculture is used to enhance the food productivity and address the issue of food supply and make the farms more connected and intelligent. According to a report by Statista, the share of US agricultural retailers that offer yield monitor data analysis is expected to grow from 51% in 2015 to 62% by 2020.

Smart agriculture employs a range of AI and IoT based applications such as precision farming, variable rate technology, smart irrigation as well as smart green houses.

The modern times is having many companies which are utilizing AI for increased food productivity.There are many applications, use of image recognition to identify weeds, assess plant help, predict weather patterns, to name a few.

Investment in Smart Agriculture

This smart agriculture has seen a tremendous growth in investment collectively for the production of drones for farming and other applications of AI in agriculture. According to an article published in CNN News, the US-based company NatureSweet has raised 15 million investment from investors like Qualcomm Ventures and Cisco Investment for the adoption of AI in their greenhouse.

Not only this, Sweden-based AI focussed company, Imagimob has received 400 K Euro in funding for its Edge AI research in Farming.

On the basis of these facts, we can clearly depicts that with the dawn of AI, there is a significant room for agricultural productivity to improve. However, AI is still in its infancy and there is much more to come that could provide a solution for future food shortages.