APPIT Software Solutions – Machine Learning:
The modern era is heading towards implementing artificial intelligence (AI) technologies in business decision makings and leveraging their strength and computing power for the maximum benefits.
Technologies such as advanced Machine learning, deep learning, natural language processing, and business rules help software developers and testers understand the business requirements better and faster.
These techniques have enabled businesses to think smarter and reap the benefits of the first-mover advantage.
APPIT Software Solutions Machine Learning, Machine learning was not autonomous or smart in the past as it largely depended upon the patterns, formulas, and algorithms fed by the developers.
Modern machine learning relies on big data or cloud platforms, and hence it’s able to analyze the data and provide intelligent decisions.
For example, self-driving cars analyze the situations in their surroundings in the same way as pedestrians do while walking on the road. Based on the situations, they take decisions as to where to slow down, where to speed up, and when to take turns – right or left.
Websites like Amazon and Facebook use machine learning applications to study the trends of their visitors, such as what they are looking at, what type of content they like, what they are interested in, and how frequently they get connected with the community.
In the software industry, AI has brought a revolutionary effect for developers and testers.
Usually, developers debug the program with the help of a debugger, but AI helps them to interrogate the intelligence. i.e., probing questions where the developer’s intent was ambiguous and inviting developers for clarifying the queries.
Secondly, AI embeds state-of-the-art into their software development which needs a shift in focus from algorithm development to data development. For instance, while developing an image transformation, the focus should shift from defining the algorithm to creating a good training set.
Machine learning works fantastically in optimizing a large array of parameters, for instance, a neural architecture can have billions of tunable parameters. To work system properly, all these parameters must be well set. This task can be a nightmare for humans.
Machine learning has changed the classical way of coding. Developers now train the system and provide tips and advice to carry out the task to achieve various actions.
The computer then filters the chunk of available data, analyze, and carry out the data processing.
Azure Machine Learning is a predictive analytics service that is equipped with a ready-to-use library of algorithms. The data analysts can use this library to build creative models and web services as ready-to-use solutions.
Though machine learning is good at data analysis and other related tasks, but it fails to understand the complexity and context of the code in the same way as humans can do.
In some cases, a machine learning algorithm needs a lot of training data, and gathering such huge data becomes cumbersome. Furthermore, it’s not guaranteed that machine learning algorithms will work in all situations. A clear understanding of the problem is required to implement the right machine language.
As nothing is perfect, even machine learning technologies face considerable challenges.
The machine should be tested first to check for the correctness of its outputs before taking the output forward for further processing. Secondly, the data should be checked thoroughly that has been fed to the computer based on the query asked from it to execute.
Another challenge could be to convert the data into a more useable form.
The data scientists and analysts could be employed to sort these trends and patterns but there is a shortage of such highly knowledgeable professionals in the market.
Machine learning has enabled strong computing power that was not feasible for many companies before.
It has leveraged Amazon Web Services (AWS), Oracle Cloud(saas), Google Cloud Platform, and Microsoft Azure to assist the developers to build their optimized infrastructure at the least possible cost.
Cyber security is the greatest threat companies are facing these days. Machine learning techniques are the only techniques that can help companies to adapt to security countermeasures.
For example, IBM has trained its AI-based Watson in security protocols presented to customers.
Another example could be Amazon which acquired AI-based cyber-security company Harvest.ai to identify the important documents and intellectual property of a business in a way to prevent data theft.
Machine learning can not only help companies like Google, Microsoft, and Facebook that spend huge budget on research and development, but it has already started benefitting almost every Fortune 500 company that runs efficiently and aims at expanding business year by year.
- How AI is Changing Software Development, By Esther Shein, January 26, 2017. https://cacm.acm.org/news/212583-how-ai-is-changing-software-development/fulltext
- Connecting expert communities to the Forbes audience. What is this?, Tech#NewTech Aug 31, 2017. https://www.forbes.com/sites/forbestechcouncil/2017/08/31/how-will-ai-impact-software-development/#31efb5d4264d
- How Machine Learning is Impacting the Way We Test Software, Kayla Matthews June 05, 2017. http://www.techzone360.com/topics/techzone/articles/2017/06/05/432557-how-machine-learning-impacting-way-we-test-software.htm#
- Machine Learning Top Trends in 2017, James Ovenden, https://channels.theinnovationenterprise.com/articles/machine-learning-top-trends-in-2017
- Introduction to Machine Learning in the Azure cloud, July 12, 2017, https://docs.microsoft.com/en-us/azure/machine-learning/studio/what-is-machine-learning
- Reasons Machine Learning Is the Future of Marketing, November 5, 2017, https://www.entrepreneur.com/article/300713
Many other companies (Oracle, SAP, etc.) have some kind of offering. A lot of companies are now learning how to use Machine Learning to generate revenue.
In particular, Demand Media was based on ML technology that allowed it to become the first Billion Dollar IPO since Google.