Up until two years ago, chatbots were hailed as the next big trend. Thousands of chatbots flocked the market as they are relatively easy to build and could be controlled by a predefined flow. However, this trend began to wane when the chatbot could meet complex customer demands. It could only work for completing simpler commands like ordering or booking something.

Conversational AI was sought out in order to deal conversations that require a level of comprehension and cognition that goes far beyond the predefined flow of today’s chatbots. This is a form of Artificial Intelligence that allows people to communicate with applications, websites and devices in everyday, humanlike natural language via voice, text, touch or gesture input. Ram Menon, CEO of Awaamo, writes that “these platforms offer more than a natural language interface (NLI): they demonstrate true advancements in combining a variety of emerging technologies — everything from speech synthesis to natural language understanding (NLU) to cognitive and machine learning technologies — and are capable of replacing humans in a variety of tasks.”

Understanding the customer is the key.  Using advanced AI-driven conversational platforms such as Teneo, can result not only in an increase in customer satisfaction, but in the actionable data that conversational interfaces generate. Such conversational chatbots can understand the context and the sentiment behind the conversation. When conversational AI solution integrates with back-end data and third-party databases, a deeper personalization can take place. It also needs to be capable of creating detailed analysis of the chat logs in real-time to feedback into the conversation, improve and maintain the system and deliver actionable insights to the business.

The benefits of using Intelligent Conversational Interface

Intelligent conversational interfaces are the simplest way for businesses to interact with devices, services, customers, suppliers and employees everywhere. There are lots of companies that provide AI-driven conversational platforms specifically focused on high impact use cases, including IBM’s Watson and KAI.

Analysts predict rapid and sustained growth of Virtual Digital Assistants in the coming years. This growth underlines the strongly defined benefits that both consumers and enterprises see in conversational AI.

Giving the customers the best experience and analyzing the data garnered can increase the profits of the company.  Businesses can also cut down on costs by using AI-driven chatbots for automating many tasks such as customer service. As cost benefits continue to pile up, the trend will accelerate in 2018.

This blog was written by our Content Writing Intern – Rona Sara George. Click on the name to view her LinkedIn profile.

Author: Xaltius (Rona Sara George)

This content is not for distribution. Any use of the content without intimation to its owner will be considered as violation.


There are many factors that spike up the production costs of a company. In manufacturing, ongoing maintenance of production line machinery and equipment represents a major expense, having a crucial impact on the bottom line of any asset-reliant production operation. Manufacturing companies are finding it increasingly harder to maintain high levels of quality. Bringing out the best product takes time as well as large human resources. But all that is set to change.

Introducing – The Fourth Industrial Revolution

The fourth industrial revolution, powered by technology is remolding the industrial sector, helping businesses achieve more profits and more efficiency. It has had a massive impact on the manufacturing sector. The sector is entering its next phase – Industry 4.0 – which is driven by automation, Internet of things and cloud computing. The big players are already investing millions in computer intelligence, so that they can save time, money and resources while maximizing their production. The Manufacturer’s Annual Manufacturing Report 2018 found that 92% of senior manufacturing executives believe that ‘Smart Factory’ digital technologies – including Artificial Intelligence – will enable them to increase their productivity levels and empower staff to work smarter.

How is the Manufacturing Sector using Artificial Intelligence?

Through computer vision, machines can be powered to pay attention to the tiniest of details, far beyond a man’s potential. Landing.ai, a startup formed by Silicon Valley veteran Andrew Ng, has developed machine-vision tools to find microscopic defects in products such as circuit boards, using a machine-learning algorithm trained on remarkably small volumes of sample images. If it spots a problem or defect, it sends an immediate alert, an AI process known as “automated issue identification.”

Artificial intelligence can also be used to monitor the whole process of manufacturing. Siemens, one of the leading manufacturing companies on the planet, did just that. They embarked on a digitalization strategy of which one of the major goals was Overall Equipment Efficiency. In late 2017, the company announced the latest version of its IoT operating system, MindSphere. Physical machines can be connected to Mindsphere cloud environment, enabling it to build the application that visualizes the various metrics that plant managers need to monitor in 2018. It also gives the resources needed to build an industrial IoT system in a fraction of the time it would take to set up a physical environment.

General Electrics is yet another leader in the manufacturing sector that has adopted the ai strategy. In 2015 GE launched its Brilliant Manufacturing Suite for customers, which it had been field testing in its own factories. The system takes a holistic approach of tracking and processing everything in the manufacturing process to find possible issues before they emerge and to detect inefficiencies. GE claims it improved equipment effectiveness at this facility by 18 percent.

It is powered by Predix, their industrial internet of things platform. In the manufacturing space, Predix can use sensors to automatically capture every step of the process and monitor each piece of complex equipment. You can view a short video of how its done here.

Another application of Ai is the use of generative design. Designers or engineers input design goals into generative design software, along with parameters such as materials, manufacturing methods, and cost constraints. Software explores all the possible permutations of a solution, quickly generating design alternatives. It tests and learns from each iteration what works and what doesn’t. From the many solutions that are put forward, the designers or engineers filter and select the outcomes that best meet their needs. This can lead to major reductions in cost, development time, material consumption and product weight. Airplane manufacturer Airbus used generative design to reimagine an interior partition for its A320 aircrafts and came up with an intricate design that ultimately shaved off 45 percent (30kg) of the weight off the part.

Such applications bring down labor costs, reduce product defects, shorten unplanned downtimes, improve transition times and increase production speed. It all comes down to an optimal manufacturing performance. If manufacturers do not invest in the long term and ignore advancing technologies, then their profits would be affected, as prices of products as well as the raw materials would only go up. However, it is not late yet as the field artificial intelligence is constantly evolving.

This blog was written by our Content Writing Intern – Rona Sara George. Click on the name to view her LinkedIn profile.

Author: Xaltius (Rona Sara George)

This content is not for distribution. Any use of the content without intimation to its owner will be considered as violation.


R is a tool which has been used over a long period of time and still continues to be used by both students and industries in the fields of statistics and data science. R continues to be important because of its versatality in these fields, especially in data exploration.

Xaltius conducted an R Bootcamp for the students of Yale-NUS to prepare them for their internal hackathon, Datathon. More than a 150 students were trained on R over the span of three workshops.

Through this R bootcamp, the students learnt how to do the following:

  • Webscraping using various R libraries and cleaning the scraped data.
  • Relational data cleaning and exploration through Exploratory data analysis.
  • Data Visualization using ggplots and plotly.
  • How business questions should be asked and given data how to approach the solution?

The students learnt the above through intensive hands-on during the sessions and self-practice. We received tremendous positive feedback and response from Yale-NUS for taking up these sessions.

If you are interested conduct such workshops and talks for your institution or be part of one, please get in touch with us.


Data Science and Python, though not a lot of people may realize it, go hand in hand with each other. Businesses today, especially the higher level management, require to see accurate and efficient depictions of various data science solutions and projects. Knowing both bridges that gap considerably.

Xaltius took to imparting the fundamentals of both these areas to over 150 students at NUS Business School over an 8 hour, hands-on intensive seminar and workshop.

Through the workshop the keys takeaways for the students were:

  • Understand the fundamentals of Python and working with small datasets.
  • How to create basic data visualizations through seaborn in python.
  • How to tell a story about your data, which is one of the most important lessons.
  • Basics of HTML, CSS and JavaScript which would help users learn about how to create basic web pages.

The end of the workshop ended by doing a small hack with the students where they were given data and had to tell a story around it. They were given an opportunity to present their findings and many of did amazingly well in such a short period!

If you are interested conduct such workshops and talks for your institution or be part of one, please get in touch with us.


Deep learning is an aspect of artificial intelligence (AI) that is concerned with emulating the learning approach that human beings use to gain certain types of knowledge. It is fast becoming a technology to reckon with and has been quite in vogue.

Xaltius held a deep learning session at Informatics Academy in Singapore, to help students and corporate professionals understand the fundaments of deep learning through an intensive hands-on session on tensorflow and keras

The key takeaways for the participants from this session was to get an understanding of the subtle differences between machine and deep learning, to understand how to build neural network models using tensorflow and keras on particular use cases, the parameters involved and how to monitor and understand how the models work.

If you are interested to be part of such workshops and talks for your institution, please get in touch with us.

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