In recent years we have seen some exciting advances in machine learning, which has raised its significance across various applications. With these advances, systems that performed below human-level tasks have surpassed humans in some specific tasks.

Many people interact with machine learning based systems almost everyday – image recognition, voice recognition, and virtual assistants. As social and economic area develops, machine learning continues to support, promising potential transformation. Machine learning is also providing effective and accurate diagnoses in the healthcare sector helping doctors in certain conditions.

Further, it is helping the existing transport networks, public services to be more effective. Since a broader field artificial intelligence is the technology that is making machines smart, machine learning is the technology that learns directly through experiences, examples, and data to perform specific tasks intelligently. Machine learning is developed to manage the potential risk assuring a full range of benefits.

UK has researched broadly in these technology fields – AI and Machine Learning has established a test to check whether a person could differentiate between the answers given by a machine or a human, which is in progress and enhancing with time.

Machine learning has the potential to carry out the desired outputs of complex tasks into step-by-step process. Further, it uses computer science, statistics, and data science to improve the accuracy in the results, predict future activity, or make decisions.

The following are the sections of some applications that describe machine learning’s contribution to everyday life –

1) Recommender systems – Amongst the most widely used applications, recommender systems are the system that provides a recommendation of the products and services on the basis of previous choices/purchases. Amazon and Netflix are among the applications using such a system.

2) Organizing Information – This system of machine learning provides information or predict the right page as a result of the query entered on the search engine. This system also helps in filtering emails, distinguish them according to the sender, specific words, spam emails, and other characteristics.

3) Voice recognition – This system is in trend but less accurate than other systems. With recent advances, voice recognition systems have developed accuracy in identifying speech and translating the data patterns into text.

ML is thoroughly related to data science and statistics that consist of a wide range of tools and methods. Processing and data analysis technique feed into machine learning whereas statistical approach informs ML to deal with uncertainty in decision making. The greatest example of machine learning use today is – AlphaGo, DeepBlue, IBM’s Watson, Libratus – these machines showed remarkable ability of a computer system that plays with humans beating them in different games.

Significant opportunities through machine learning analyzed –

  • Avoid cases of human errors and be more objective.
  • Accuracy in featuring medical images and diagnoses.
  • Efficiency in shaping public sector resources delivery.
  • Opportunity and economic growth for new businesses across a wide variety of sectors.
  • Play a great role in addressing societal challenges such as climate change or pressure of an ageing population.

Machine learning can serve as a great tool for collaborating humans and machine network in a new data environment. But who should be accountable when machine learning goes wrong? Whether or not, a human is in the loop.

Emerging technologies show that early adoption does not guarantee continual support of the maximum public. There is still a lack of awareness and public support to benefit from the full potential of ML.

The Royal Society carried out a public exercise over 978 people to learn their views and awareness on machine learning and only 9% of people have heard of it. This study was meant to demonstrate how practically machine learning could be used in the future.

How does machine learning affect society?

Shaping our lives machine learning is deployed in a wide range of systems. ML can be effective in predicting flood levels over time by combining large datasets to find relevant information. It can also draw effective responses to such situations anticipating how situations might develop and how to decide for the best resources. It uses current and past meteorological and environmental data to predict. ML could deliver benefits that could be unavoidable when taken care of its capabilities to advance society.

Machine Learning also brings new challenges to society with its potential offering to business areas. Its capabilities enable new uses of data which is a challenge to the existing governance system. People still today consider robots very less in personal and caring roles fearing the loss of human-to-human contact.

While humans only understand the way other human thinks, behave and analyse situations and circumstances to deliver the appropriate judgement or solution thus, it is difficult to trust the artificial algorithm that may lack the absolute judgement or decisions required.

 

Limitations of existing approaches-

 

While it is not clear that machine learning will impact employment, but definitely will affect the world of work. It is expected to see the increased adoption of ML methods for increased productivity.

With significant progress and impressive advances comes along some restrictions and limitations –

  • Machine learning uses a large amount of training data for the algorithm to work for accuracy and lucrative solution, but acquiring this approach requires resource-intensive and time-consuming.
  • Certain algorithms require a simple common-sense that humans inherit that machine lack when machines fail, they fail in a brittle manner.
  • Humans inherit the stratagem to transfer ideas of one problem to another which still remains a challenge for a machine learning system.
  • There are areas where machines aren’t able to match the understanding of human needs especially when collaborating environments.
  •  Humans understand constrains of natural, mathematical laws for data accuracy and efficiency that machines lack.
  • The canonical problems that machine learning systems still seek to solve are related to – classification, regression, clustering, semi-supervised learning, and reinforcement learning.

Machine learning has the ability to disrupt the value created and its economic benefits- on the social, political, ethical, and legal environment – depending on the nature, scale, and duration.

Potential concerns associated with the increased use of machine learning-

Sharing personal experiences with society was a primary method to resolve things but, with the advances in technology, there has been a reduction in the way people interact, individual’s awareness, the ability to understand issues due to more reliance on systems.

48% of the experts show concern over the impact on employment till 2025 whereas 52% of the experts are optimistic about the future scenarios of automation.

The enhanced analytical capabilities of ML can form some of these concerns mentioned below –

  • Concerns have been shown about the potential of the machine learning systems that can cause harm in certain areas likewise – accidents may occur in autonomous vehicles.
  • A constant threat hovers to humans of the possibility to be replaced or become over-reliant on machines in the workplace.
  • Generalized predictions may overpower the experiences of people.
  • Machines lack the behavior present in humans providing consumers to explore a wide variety of products and services.
  • ML is a challenge to our privacy as well as consent as it requires large data for any solution.
  • Biases are one major issue embedded in datasets by social structures that reflect in training data resulting in lacking accuracy.
  • Interpretability and transparency can be difficult in ML to know the accurate methods to generate results.

There are other concerns that might occur from ML-like taking actions without recourse of human agency which might result in less confidence over the system.

In the Nutshell –

We have learned some unresolved issues of the applications of machine learning such as – does algorithms have to be interpretable in use cases, should human decisions be involved, when should the algorithms be held higher than the human decision-makers standards for accuracy or interpretability.

Machine learning will feature in the near future in our personal as well as professional life. Estimation of jobs lost or created may vary in future, but machine learning will have a significant impact on the way we work and will force us to think about our occupation and skills. Its effects will become pervasive across the economy and effect everyone.

By Ashley Marsh, who is a senior content writer at Maan Softwares Inc. She has been writing for various companies for over four years on a variety of topics. Ashley specializes in technical writing with an emphasis in mobile app development, web design, and technology trends. Ashley finds covering the tech world to be an exciting and engaging experience as each day brings new and groundbreaking technologies to explore and write about. When she’s not writing about tech, she enjoys walking her two chihuahua-poodle mixes, Ginger and Pepper.

 

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