It is said that human brain is capable of doing and achieving whatever it can think or imagine of. So what stops it from doing it? There are just two factors, access/availability of resources and time available in one’s life span in completing what can be imagined. AI is an attempt to finish tasks in a shorter span which brain may take longer to complete. This is possible by teaching machines over a period of time because life of a human is finite while of a machine is infinite hence continuity can be achieved by making machines learn.

Stephen Hawking once said that AI (Artificial Intelligence) could be the biggest event in the history of our civilization. We hear everywhere about work being done on artificial intelligence but is our understanding of the same is right?

Artificial Intelligence can be simply understood as:

• A method which can help machines & its creators to function in an intelligent manner
• Making machines intelligent so that they can think which is not natural hence artificial
• Analyse data and share information for the decision makers to save take and take informed decisions

As some of the explanations suggest AI is typically any process/method which helps humans in acting efficiently and saves on time by eliminating mundane/routine job. Each one of us knowingly or unknowingly has adapted Artificial Intelligence in daily life.

Understanding AI

Let us understand in detail about AI and its layers:

Machine Learning provides system the ability to learn from data, automates the process of building analytical models. The learning from data could be of supervised, semi-supervised or unsupervised. So what does it mean? It means that machines categories the data into various sets (having some correlation, pattern, similar nature etc.). Supervised machine learning always has an end objective and it is suggestive in nature i.e. typical prices of a commodity may be used as raw data to predict the future prices basis historical trends and events.

Deep Learning may be called is a subset or next steps of machine learning which may be based on neural networks. Deep learning is capable of learning in unsupervised manner the data which may be unstructured or unlabelled.

The fundamental difference between machine learning and deep learning is the manner in which data is presented to the system. Machine learning algorithm always needs structured data while deep learning can handle unstructured data which relies on neural networks.  

If you are still not clear the below diagram shall clarify it:

Machine learning can make decisions bases on what it has learned. Deep learning has capability to create artificial networks to make decisions on its own without any past learning.

Natural Language Processing as the name suggest is capability of a computer program to understand languages spoken by humans as it is.

Computer Vision is a science field and integral part of AI which deals with dealing of computers with digital images and videos. This fields extracts all existing information from any image or videos and correlates it with real world situations. A typical example of the same is LBW decision making by third umpire in cricket where the projection of the ball helps in determining whether ball was to hit stumps or not. A 3D imaging from a 2D image, completion of a torn picture/scene recreation are other most used applications of this field.

Neural Network is series of algorithm to identify, analyse find out relationship between data in the manner human brain operates. The brain has neurons which have and transmit information to next neuron. Neural networks are mostly used in pattern recognition due to inherent ability respond unexpected inputs.

What’s happening today?

Most of the business across the industries i.e. retail, energy, financial service, automobile, technology, marketing & manufacturing have already started adopting AI in one or other business functions.  AI today is critical function to stay ahead of competition and succeed in business. This is evident from a research data published by Statista in 2017 enquiring businesses about the reasons they adapt AI. As per their survey 84% of the respondents answered that they are adopting AI to stay ahead or have competitive advantage.

Source: Statista

The large corporates are investing heavily in AI across the world. The underlying assumption is that it shall help in various tasks which shall help in reducing costs, provide humans and machines the efficiency, boost customer experience, boost returns from the business.

According to the market research firm Tractica, the global artificial intelligence software market is expected to experience massive growth in the coming years, with revenues increasing from around 9.5 billion U.S. dollars in 2018 to an expected 118.6 billion by 2025. Between 2018 and 2019, the artificial intelligence market is expected to grow by 154 percent, reaching a $14.7 billion market size.

While all the countries are focusing on AI, China and US are leading the terms of number of AI companies present and working of research and development in this sector. China is leading in deep learning reach as per a report published by White House, National Artificial Intelligence Research and Development Strategic Plan.

In terms of investment in the sector from 2013 to the first quarter of 2018, the amount of investment and financing in AI technology in China itself accounts for 60% in the world, valued at $27 billion in 2017. The numbers are as per a report published by Tsinghua University in September 2018.

In terms of investment in the sector from 2013 to the first quarter of 2018, the amount of investment and financing in AI technology in China itself accounts for 60% in the world, valued at $27 billion in 2017. The numbers are as per a report published by Tsinghua University in September 2018.

As per the study by EY and Nasscom it is predicted that around 46% of the workforce will be engaged in entirely new jobs that do not exist today, or will be deployed in jobs that have radically-changed skill sets by 2022.

In February 2019, it was announced by a minister that Indian Government is taking investment in AI seriously and planning to launch a national program on AI soon.

The critical factors:

All efforts being put in AI is dependent on data. A confidence level of an outcome of artificial engine is completely dependent on integrity and accuracy of the data used by the engine. Adaptability of AI is also completely dependent on availability of big data. Hence collection of data by all the business is priority today. The firms also need to focus equally on quality and integrity of data.

Apart from data the availability of skill set amongst people to develop, supervise and control AI research and development is essential. Large corporates are making it possible by hiring the best of talent in the market, re-skilling their existing employees.

How the future looks like?

While answer of the same is whatever can be thought be achieved as well. The movies have even shown extremes of AI where machines start controlling humans. While the same cannot be denied but at the same time cannot be digested so easily.

In today’s life we see usage of AI mobile assist, voice assist, auto computation of distance vehicle can run on fuel etc. These application with continuous development may take user experience/interaction from initial search levels to the next level in future.

Some of the typical examples may be as below:

Smart home devices may soon become integral part of our daily life. It is capable of monitoring our routine, respond to our queries and even integrate with our shopping needs. Recent generations (including mine) have already adapted smart watches, google home, Alexa which are tracking us 24×7. Amazon has already started the above application of AI through Alexa, where an item can be added to e-shopping cart by just telling Alexa. Though order placement is limited to adding orders in an app managed within Amazon’s ecosystem, it may soon integrate with popular local application for shopping.

Smart automobile can respond to riders on their queries. It can also connect with users emotionally basis historical behaviours. The precautionary measures can also be suggested by the devices.

The devises may be able to run diagnostic tests on itself and report malfunction in a particular component/part.

The above represents application of AI, where chat bots are able to understand language, interact with customer, accept applications, process basis client profile, accept/reject basis assessment result and adhere to policy requirement. It can also reduce lot of back end operations by completing routine tasks and also pitch for next level of relationship by assessing client profile more than what’s required for the product to be processed.

In financial sector application of AI has huge scope. Already many Banks, Insurance firms are deploying AI methods across compliance, operations, customer acquisition, fraud management etc.

AI in financial sector frees up humans from robotic tasks and enables them in focusing on better valued, complex and creative work.

Growth & the Control

Success of all the efforts in AI is possible when Governments, Corporate, Social Institutions focus on long term investment of data, develop a common understanding for data which is essential for development of AI. The future looking organizations need to promote skill development needed for them to grow. The re-skilling of existing human capital is critical for growth and to say competitive but knowing potential risk and its mitigation are equally important.

Majority AI programs suffer with a bias of their source who is developing them. These biases can be specific to individual, culture, organizations. All for profit firm shall always focus investment in AI which shall ultimately results in higher returns.

The sectors where governments may need to focus more are on healthcare, education, defence. Whether private or public whatever is the source of investment in AI, critical role here is to be played by Government in developing a culture, developing enabling policies which is conducive as well as adaptive. Stringent laws related to application of researches is needed. From time to time it is required that Government keeps assessing the existing policies to gain from the true potential of AI while keeping humans and humanity at the centre of decision-making.

Tagged : Artificial Intelligence