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The term Artificial intelligence was first coined in 1956 by John McCarty at a conference. At that time, the researchers came together to clarify and develop the concepts around “thinking machines”.In a sense Artificial intelligence is the simulation of Human intelligence into machines or computers. Almost all businesses today employ some type of AI. However many AI applications are not perceived as AI because we tend to think of artificial intelligence as robots daily course.Truth is that artificial intelligence has found its way into our daily life. Like Siri, Google assistant, music recommender systems.
However, the confusion between the terms artificial intelligence, machine learning, and deep learning remains. People often think they are the same, but that is not the case. Let’s clear up this confusion.
Artificial intelligence: Artificial Intelligence is the ability of machines to function like the human brain. It studies ways to build intelligent programs and machines that can creatively solve problems, which has always been considered a human prerogative.
Machine learning: It is a subset of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Deep learning: It is a subset of artificial intelligence (ML) where systems can learn hidden patterns from data by themselves, combine them together, and can make decisions based on rules.
So, we can say all three are interconnected and a lot of AI systems are powered by Machine Learning and Deep Learning algorithms. AI is achieved through Machine Learning, and Deep Learning and they are not the same. And as AI is no longer confined to innovation labs and is being hailed for its immense transformational possibilities for business. However, businesses need to overcome certain challenges before they can realise the true potential of this emerging technology.
Challenges in AI Implementation:
First biggest challenge is Computation Power. Artificial Intelligence needs lots of calculations and it has to be done quickly (in milliseconds). To achieve this, it needs lots of computational resources. AI experts suggest there are lots of ideas and scope to implement AI like in HealthCare, Farming, Data Analysis. But again the hardware or computing power required doesn’t come cheap.
Also, AI implementation does not have a big market as of now. So, not many organizations want to invest the resources and time into it. Hence small groups of people have knowledge of it. There are a small number of groups/people, who have knowledge to train the machines to train which can learn by themselves. As a short term solution, people are utilizing the Data scientist. They analyze the big data solutions and give recommendations over it.
A big problem with AI systems is that biased algorithms, their level of goodness or badness depends on the amount of data they are trained on. Bad data is often associated with, ethnic, communal, gender or racial biases. In future, such biases will be more highlighted as many AI systems will continue to be trained to utilize bad data. Hence, the real change can be brought only by defining some algorithms that can efficiently track these problems.
Another problem that should be taken into account is that most of the AI implementations are highly specialized. It is built just to perform a single task and keep learning to become better and better at it. The process that it follows is to look at the inputs given and results produced. It looks at the best result produced and notes down those input values. It is difficult to prove that the AI system’s decision-making process is fine. And it can be done by making AI explainable, provable and transparent.
Data Security or theft is also a big challenge. Most of the AI applications are based on massive volumes of data to learn and make intelligent decisions. Machine learning systems depend on the data which is often sensitive and personal in nature.As we mentioned above, these systems learn from the data and improve themselves. Due to this self learning, these systems can become prone to data breach and identity theft.
As of now organizations like google,amazon, facebook have access to a large amount of data. However, datasets that are applicable to AI applications to learn are really rare And countries like India and Europe are putting stringent laws that data can not go outside of the country or can not be used to alter/influence the results of elections or any other campaigns.
Opportunities for Artificial Intelligence in Business:
As we have many challenges in the AI but we also have many opportunities in different fields/sectors. With the help of AI/ML/DL, we can build great solutions, which can help us to achieve the next generation of automation. Some industries which are already incorporating AI in their business, which are helping them to grow their businesses.
One of them is the Automobile industry. Many companies are working on autonomous vehicles, people are also buying cars that are self driven. Like Tesla, they have AI into the car so efficiently that Car can drive itself for hours.
There are different levels of driving. Mostly cars at level 2or3 where ADAS(Advance driving assistant system) helps you drive and prevent accidents. But some companies are working on fully autonomous vehicles, which will not require a human assistant to take you from one place to another.
RPA, It is also primarily based on automating business processes and repetitive tasks in the back office. Companies are enhancing the automation process with the use of AI to automate repetitive processes at scale, eliminating inefficiencies. And to reduce cost and time.
It has huge market potential, and has already implemented some solutions for warehouse management. Where small bots take the stuff from one place to another and manage the inventory without the help of humans. It is already a billion dollar market, but still lots of scope for automation.
Chatbots have been trained and automated for years. But now, we have AI chatbots that are programs to simulate human-like conversations using natural language processing (NLP). AI chatbots are becoming increasingly valuable to organizations for automating business processes such as customer service, sales, and human resources.
For digital marketing as well you need the right content, audience to be reached at. Today’s algorithms are advanced to write everything on their own. But it doesn’t mean it can’t help you in creating the content. AI can also be used for content curation and personalization for which tools are already available in the market. As companies have lots of data, on which they evolve their system and train them. So, now the system can shortlist the audience itself by their interest and history. Like Twitter, FB, and AdSense that’s what they do.
Robotic manufacturing line, many have heard of it. Many companies are using it. Like SpaceX, they are trying to achieve 100% automation in building a rocket. So, they increase efficiency and speed as they plan to put lots of humans into space. In some of the industries, Covid-19 also accelerated the process of automation, which is helping AI industries to achieve better goals.
To summarize, It is clear that AI has a lot of challenges to implement but the usability list is still growing. There are lots of opportunities in the field of AI as it will provide a lot of solutions and services to software and other elements of IT. Paired with data analytics, it will assist a lot of companies and individuals offering it a lot of support.
The above article is authored by Akshay Chouhan – Co-Founder and Technical evangelist at SpeckyFox Technologies India Pvt Ltd.