Tag: Artificial Intelligence

How Artificial Intelligence Is Simplifying Business Decision Making?

Technology in 2019 is moving with the speed of light. Immense breakthroughs in the field of deep learning and machine learning have allowed machines to process and analyze information in ways that we could never have imagined. 

The role of Artificial Intelligence (AI) is noteworthy in this regard. One definition of AI is that it is “a collective term for computer systems that can sense their environment, think, learn, and take action in response to what they are sensing and their objectives.” This makes it a powerful tool, which when used the right way can radicalize decision making and completely changes the way we do business. This article discusses how AI achieves that. 

AI – A Boon to Business

AI includes the automation of cognitive and physical tasks. It helps people perform tasks faster and better and make better decisions. It enables the automation of decision making without human intervention. AI can enhance automation thus reducing intensive human labor and tedious tasks. There are many more ways in which AI is making a difference. With smart weather forecasting, for example, AI is bridging the gap between data scientists and climate scientists. This gives companies the opportunity to fight disaster with algorithms. 

The world is about to witness a great impact of AI on the economy and humans. According to McKinsey Global Institute’s research, AI could deliver an additional output of $13 trillion to the world economy by 2030, which would boost global GDP by nearly 1.2 percent a year. Acting as a capital-hybrid, AI can aid the growth of both the economy and humans. It will definitely have a revolutionary impact on the decision-making process. 

Top Artificial Intelligence Trends to Watch Out for In 2019

AI the Game Changer

From tarot cards to time machines and more, the quest of man to know the future has been relentless. The ability to make decisions based on a knowledge of the outcome is no more fantasy, however. AI has brought this to the realm of reality and has revolutionized business decision making.  

In the recent past, we have embraced analytics-driven decision making. Along with ever-increasing data storage and computing power, AI has the potential to augment human intelligence and enable smarter decision-making. AI could eliminate the huge costs of a wrong decision because it can practically eliminate human biases and errors. This could in turn speed up the decision-making process. The focus of the next few points is to highlight the ways in which AI can make a difference in business.

1. Marketing Decision-Making

In today’s customer-driven market complexities involved in decision making is increasing every day. This includes understanding customer needs and desires and aligning products to those needs and desires. A handle on changing customer behavior is vital to make the best marketing decisions. 

AI simulation and modeling techniques provide reliable insight into the consumers’ persona. This will help predict consumers’ behavior. Through real-time data gathering, trend analysis and forecasting, an AI system can help businesses make insightful marketing decisions.

2. CRM

Organizations can identify a consumer’s lifetime value with the help of AI’s buyer persona modeling. It can help organizations manage multiple inputs. During a complex decision-making process, AI can efficiently manage and control different factors at the same point in time. It can source and process large amounts of data within minutes while providing valuable business-based insights. While we humans face decision fatigue, algorithms do not have such limitations, which make AI-based decisions faster and better.

3. Recommender System

Recommender system (engine) is a technology that recommends products or other items to users. Although recommendation systems were initially used for music content sites, now it’s used has expanded to various industries. In this, an AI system learns a consumer’s preference based on ‘explicit’ or ‘implicit’ feedbacks. This information can help the organization reduce bounce rate and craft better customer-specific targeted content. 

4. Problem Solving 

An expert system is a kind of problem-solving software which tries to replicate the knowledge and reasoning methods of the experts. This system uses expert thinking processes to provide data, which includes assessment and recommendations for your problem. This makes it easier to make the right decision and respond swiftly when faced with issues and problems.

5. Opinion Mining

AI has been able to provide reliable insight to decision-makers. For example, in marketing, AI has provided businesses invaluable insight about consumers, which helps them enhance their communication with the consumers. It also helps retailers predict product demand and respond to it quickly. 

To that end, opinion mining helps businesses understand why people feel the way they feel. Most often a single customer’s concerns might be common among others. When sufficient opinions are gathered and analyzed correctly, the information gleaned will help organizations gauge and predict the concerns of the silent majority.  AI has improved this mining process through automation, which is quicker and more reliable, helping organizations in making critical business decisions. 

6. Augmented Analytics

According to a recent press release by Gartner, Augmented Analytics is going to be the next big trend which will transform the way analytics content is advanced, expended and shared. VP analyst at Gartner, Rita Sallam said, “The story of data and analytics keeps evolving, from supporting internal decision making to continuous intelligence, information products and appointing chief data officers.”

Wise business decisions are made when business executives and decision-makers have reliable data and recommendations. AI not only improves the performance of both the individual members of the team but also the competitive edge of the business.

Make Bigger, Faster, Better decisions with AI

Common sense and experience are no longer enough to anticipate the risks and consequences of critical business choices. AI with its varied applications helps businesses make informed and effective decisions which will have a positive impact on their business. 

Implementing AI in your business isn’t as tall an order as you may imagine. It can be incorporated with your existing business applications to enhance them and make them invaluable.  To learn how your business can avail the best of AI, get in touch with our experts today!

Related Reading: You might also like to take a look at this guide to help you enhance your existing business app with AI.

What Are Cobots?

Cobots have been around since the 1990s. Cobots operate in conjunction with humans to perform given tasks. They are built to interact physically with humans in a shared workspace. 

In other words, cobots or collaborative robots can be defined as the hardware version of Augmented Intelligence. Rather than replacing humans with their autonomous counterparts, collaborative robots augment artificial intelligence technologies to physical bots. According to Barclays, cobots can revolutionize production. This is effective, especially for smaller companies, that account for 70% of the manufacturing industries, globally.

Cobots help in improving human capabilities in performing tasks with greater strength, accuracy, and data capabilities. The first collaborative robot was a device used to directly interact physically with a manipulator that was computer-controlled’. It was invented by J Edward Colgate and Michael Peshkin in the year 1996. Later, Kuka Robotics launched its first collaborative robot in the year 2004, named LBR 3. 

How Are Cobots Useful In Various Industries?

Since Cobots are capable of performing tasks alongside humans instead of replacing them, there is a multitude of ways in which Cobots are used in different industries for varying purposes. The major ones are as follows:

1. Hand–guiding: This cobot has an additional hand – a pressure-sensitive device at the end of its arm. With this arm, the human operator can teach the collaborative robot how to hold an object or how to move, or how fast to move, and so on. It also ensures that nothing gets damaged.

2. Speed and Separation Monitoring Cobots: This particular cobot operates in safety zones. Instead of stopping to perform its task when it senses an outsider’s presence in the safety zone, this cobot slows down and then tracks the location of the human. It stops as the human gets too close.

3. Power And Force Limiting Cobots: These collaborative robots are designed to frequently interact with humans. This specific cobot stops or reverses its movement on encountering any abnormality.

4. Safety Monitored Stop Cobots: These cobots are designed to work independently, but stop whenever a human needs to intervene. This cobot senses human presence and stops all movement until the human has left the safety zone. 

Related Read: Check out how Robotic Process Automation Is Revolutionizing Industries

How Collaborative Robots Offer Game-Changing Benefits

Cobots have been brewing in the web-space for quite some time. In general, robots have replaced human labor from the industrial workforce since the industrial revolution. Robots, but operated within safe environments. Cobots, on the other hand, help in putting away some of the major spatial and environmental dangers that robots may cause, whilst working alongside humans!

Cobots can also be easily reprogrammed. Many businesses and industries are skeptical about falling behind their competitors, especially in situations where irrecoverable disasters are likely to take place. Here is when cobots can be an effective solution. Let us walk through the major benefits that collaborative robots offer in various industries:

1. Increased and efficient Human-Robot Interaction

In any given industry utilizing bots for performing tedious tasks, time, cost, and floor space are the three major critical factors to be considered. This is because the operator can work alongside the cobot, without having to leave the workspace. Cobots are known to reduce idle time of human workforce by 85%. 

Consider a traditional assembly line that is set up in a workspace. Here the human workforce sets up the mechanical robots with required parts to perform the rest of the tasks. The entire production will be put to halt for a long time or stopped from its current operations, in case of any required human intervention. While on the other hand, a collaborative robot works along with the human workforce, which increases the efficiency significantly.  

2. Applicable To Small And Mid-Sized Industries

Strategies to optimize costs are a major concern in every industry. This is because larger industries that have a higher production volume prefer a robot to perform tedious tasks. On the other hand, smaller industries prefer manual labor. 

Implementing cobots can be beneficial across a range of industry sizes, as these bots do not require a heavy set up process. 

3. Safety In Handling Dangerous And Tedious Tasks

Preventing human error is critical in every industry. For instance, steadying the movement of tools used in surgeries is complex. The process needs to be highly accurate as well. Cobots ensure a safer working environment by preventing human errors that can hinder operation accuracies.

4. Increased ROI

Cobots can be conveniently relocated whenever required. This makes it easy to eliminate any non-productive activities during working hours. In addition to being highly efficient and flexible, cobots ensure increased ROI due to significantly reduced labor and maintenance costs. This also results in an increased profit margin as well. 

Related Reading: Read on to learn how you can accelerate your business growth with Robotic Process Automation. 

Future Of Cobots

By the year 2020, cobot sales are expected to cross $3.1 billion. The sales of cobots are increasing every year. According to Barclays Equity Research, analysts state that the global sales of cobots have crossed US$120 million in the year 2015. This figure is forecasted to grow to $12 billion by the year 2025!

The figures above illustrate that the sales of cobots are just 5% of the total robot market. This figure is forecasted to grow exponentially as more industries start to explore the multitude of possibilities of these bots. 

To know more about how cobots can be leveraged for your business, drop a call to our strategists right away!

Five Ways AI Is Accelerating Mobile App Technology

Artificial Intelligence (AI) has permeated the tech world. It is enhancing everything from your car to your toothbrush.  It is influencing the decisions that affect your life. Artificial Intelligence is the term used to define a machine’s ability to simulate human intelligence. Actions which were once considered unique to humans are now being stimulated by technology and used in every industry. This includes mobile app technology. 

Mobile phones have been using AI for some time now. The earlier generation of phones was cloud-based and Internet-dependent. The difference today is that the latest smartphones integrate cloud-based AI along with built-in AI. The rate at which AI is expanding is accelerating. 

Let us now discuss how AI is enhancing mobile app technology.

Related Reading: Take a look at the top AI trends of 2019.

AI is the Catalyst in Mobile Apps

As per a study by McKinsey Global Institute, AI expansion brought in an investment of $39 billion back in 2016 which was three times the amount invested in AI three years prior to that. Acting as a catalyst, AI is continuing to enhance mobile apps. It empowers the evolution of mobile apps by making them intelligent pieces of software that can predict user behavior and make decisions. AI allows mobile apps to learn from data generated by the user.

Mobile developers are adapting quickly to changing innovations. There are over 5 million apps in the leading app stores, which stands to show how AI is creating personalized app experiences for users and is adapting to various situations due to automated learning capabilities. Here are five specific real-life benefits of AI on mobile apps. 

1. The Wrapping of Artificial Intelligence with the Internet of Things (IoT)

The combination of AI and IoT is powerful in creating a personalized experience for users. A large amount of varied data is collected from the customer in real-time as he uses the device. Each usage has commands or interactions that are being used with mobile apps. AI can then leverage this real-time data to deliver an enhanced personalized experience.

IoT reduces app development time significantly. In mobile app development, IoT along with Artificial Intelligence can lead to better utilization of resources and higher efficiency. Together they free up a good part of employee bandwidth. It delivers modified and more efficient apps and strengthens data security measures, which is vital because the future of mobile apps will always be revolving around connected devices.

2. Enhances Search Engines

Text and voice have been the traditional search modes. But say, you saw something you would love to buy, but you don’t know what it is called or how to find it. Visual search helps you find what you want even when you don’t know the words to describe it. The smartphone is the best launchpad for visual search technology. An example of this is Google Lens. 

In some cases, visual search is more accurate and faster than a voice or a text search. Due to integrated AI in mobile applications, android developers are bound to develop an image recognition system and a voice recognition system. To increase conversion rates AI will provide localization of applications.

Related Reading: Learn more on how to build an intelligent app ecosystem with AI.

3. Empowers Real-Time Translation

There are so many translation apps which enable translation. However, most of these apps do not work without the internet. AI could enable your smartphone to translate different languages in real-time without the need for an internet connection.

Much like a digital version of what interpreters do, Artificial Intelligence can provide a simultaneous translation tool which allows sentences to be translated almost instantly without a time lag. AI allows the translation tool to be adjusted for latency. This would mean that a user can set the lag between a spoken word and its translation. This would be especially useful for certain languages which would require a longer time lag for better translation. Example: Baidu.

4. Improved Security with Face Unlock 

Face Unlock was launched in September 2017 by Apple. Combined with Apple’s elaborate hardware, Apple iPhone X uses an AI-based algorithm for its face unlocking system. Using AI processing, the phone can easily identify the user’s face even with facial changes like specs or beard.

Recently, Google announced radar-based, hands-free gestures to face unlock. With its hands-free system, unlocking the phone would be easier, faster and secure. It claims that it can unlock the phone almost in any orientation.

5. Enhanced Mobile App Authentication

As AI is becoming easily available as a commercial technology, both criminals and organizations are taking full advantage of it. There are predictions by cyber-security experts that the world might witness many AI-powered cyber-attacks in the future. This necessitates the development of more sophisticated cyber defense systems. Also, with the increasing use of smartphones, we all need an advanced level of data security. Security has been one of the biggest concerns for Android developers. 

Thanks to enhanced artificial intelligence with machine learning and deep learning algorithms we can look forward to a time when authentication becomes a smooth experience, which allows users to enjoy security without trading convenience. AI can be enabled to give alerts to users about possible threats. AI can also add a level of augmentation to biometric authentication which makes it almost hack-proof. Another benefit of AI algorithms is that they can find and alert potentially compromised accounts in real-time.

A Guide for AI-Enhancing Your Existing Business Application

AI With You Now and Into the Future

AI presents numerous possibilities for innovation in the mobile app industry. AI is the future of mobile app development. Artificial Intelligence is changing how users interact with app services and products. Mobile app users will be linked to an ecosystem of intelligent applications and will work together to deliver a personalized user experience. 

Businesses developing AI-enhanced mobile apps will benefit from the predictive analysis these apps can create. According to a study conducted by Callsign, the user preference for authentication is shifting. Such changing preferences would mandate further enhancements in the development of mobile apps.

An intelligent ecosystem will gather a large pool of social data and behavioral interest, which can be used to further increase revenue and improve user experience. It is not an exaggeration to say that the smartphone industry is being revolutionized by AI. This makes it important to enable AI in your business and mobile applications. Give us a call to discuss how this can be done.

Key Differences Between Machine Learning And Deep Learning Algorithms

Artificial Intelligence is on the rise in this digital era. According to IDC’s latest market report, global investment of businesses in AI and cognitive systems is increasing and will mount to $57.6 billion by the year 2021. 

Artificial Intelligence holds a high-scope in implementing intelligent machines to perform redundant and time-consuming tasks without frequent human intervention. AI’s capability to impart a cognitive ability in machines has 3 different levels, namely, Active AI, General AI, and Narrow AI. Artificially intelligent systems use pattern matching to make critical decisions for businesses.

Related Reading: Know the different types of Artificial Intelligence.

Categories Of Artificial Intelligence

Machine learning and Deep learning are 2 categories of AI used for statistical modeling of data. The paradigms for the 2 models vary from each other. Let us walk through the key differences between the two:

• Machine Learning: Process Involved

Machine learning is a tool or a statistical learning method by which various patterns in data are analyzed and identified. In machine learning, each instance in a data set is characterized by a set of attributes. Here, the computer or the machine is trained to perform automated tasks with minimal human intervention. 

To train a model in a machine learning process, a classifier is used. The classifier makes use of characteristics of an object to identify the class it belongs to. For instance, if an object is a car, the classifier is trained to identify its class by feeding it with input data and by assigning a label to the data. This is called Supervised Learning. 

To train a machine with an algorithm, the following are the standard steps involved:

• Data collection  
• Training the Classifier
• Analyze Predictions 

While gathering data, it is critical to choose the right set of data. This is because it is the data that decides the success or failure of the algorithm. This data that is chosen to train the algorithm is called feature. This training data is then used to classify the object type. The next step involves choosing an algorithm for training the model. Once the model is trained, it is used to predict the class it belongs to. 

For instance, when an image of a car is given to a human, he can identify it belongs to the class vehicle. But a machine requires to be trained via an algorithm to predict that it is a car through its previous knowledge. 

Various machine learning algorithms include Decision trees, Random forest, Gaussian mixture model, Naive Bayes, Linear regression, Logistic regression, and so on. 

Machine Learning- Deciphering the most Disruptive Innovation : INFOGRAPHIC

• Deep Learning: Process Involved

Deep learning can be defined as a subcategory of machine learning. Inspired by ANN (Artificial Neural Networks), deep learning is all about various ways in which machine learning can be executed. Deep learning is performed through a neural network, which is an architecture having its layers, one stacked on top of the other.

A neural network has an input layer that can be pixels of an image or even data of a particular time series. The next layer comprises of a hidden layer that is commonly known as weights and learns while the neural network is trained. The final layer or the third layer is that predicts the result based on the input fed into the network. 

The neural network thus makes use of a mathematical algorithm to predict the weights of the neurons. Additionally, it provides an output close to the most accurate value. 

Automate Feature Extraction is a way in which process performed to find a relevant set of features. It is performed by combining an existing set of features using algorithms such as PCA, T-SNE, etc. For instance, to extract features manually from an image while processing it, the practitioner requires to identify features on the image such as nose, lips, eyes, etc. These extracted features are fed into the classification model. 

The process of feature extraction is performed automatically by the Feature Extraction process in Deep Learning by identifying matches. 

Related Reading: AI and ML are revolutionizing software development. Here’s how!

Key Differences Between Machine Learning And Deep Learning Algorithms

Though both Machine Learning and Deep Learning are statistical modeling techniques under Artificial Intelligence, each has its own set of real-life use cases to depict how one is different from the other. Let us walk through the major differences between the modeling techniques.

1. Data Dependencies

Machine learning algorithms are employed mostly when it comes to small data sets. Even though both machine learning and deep learning can handle massive amounts of data sets, deep learning employs a deep neural network on the data as they are ‘data-hungry’. The more data there is, the more will be the number of layers, that is the network depth. This increases the computation as well and thus employs deep learning for better performance when the data set sizes are huge.

2. Interpretability

Interpretability in Machine Learning refers to the degree to which a human can understand and relate to the reason and rationale behind a specific model’s output. The major objective of Interpretability in machine learning is to provide accountability to model predictions. 

Certain algorithms under machine learning are easily interpretable, such as the Logistic and Decision Tree algorithms. On the other hand, Naive Bayes, SVM, XGBoost algorithms are difficult to interpret. 

Interpretability for deep learning algorithms can be referred to as difficult to nearly impossible. If it is possible to reason about similar instances, such as in the case of Decision Trees, the algorithm is interpretable. For instance, the k-Nearest Neighbors is a machine learning algorithm that has high interpretability.

3. Feature Extraction

When it comes to extracting meaningful features from raw data, deep learning algorithms are the most suitable method. Deep learning does not depend on binary patterns or a histogram of gradients, etc., but it extracts hierarchically in a layer-wise manner. 

Machine learning algorithms, on the other hand, depend on handcrafted features as inputs to extract features. 

4. Training And Inference/ Execution Time

Machine learning algorithms can train very fast as compared to deep learning algorithms. It takes a few minutes to a couple of hours to train. On the other hand, deep learning algorithms deploy neural networks and consumes a lot of inference time as it passes through a multitude of layers. 

5. Industry-Readiness

Machine learning algorithms can be decoded easily. Deep learning algorithms, on the other hand, are a black box. Machine learning algorithms such as linear regression and decision trees are made use of in banks and other financial organizations for predicting stocks etc. 

Deep learning algorithms are not fully reliable when it comes to deploying them in industries. 

Both machine learning and deep learning algorithms are used by businesses to generate more revenue. To know more about how your business can benefit from artificially intelligent systems and which algorithms can be leveraged for a positive business outcome, call our strategists right away!

Chatbot or Chatbaby: Why Chatbot Technology Needs Time To Mature?

Automation is at its height in this digital era and artificial intelligence is the core technology behind successful industries today. A chatbot is a computer program that interacts with human users via the Internet. These auto-operating conversational bots, artificially replicate the patterns of human interaction. This is made possible via Machine Learning algorithms.

Amazon’s Alexa and Apple’s Siri are well-known examples of chatbots. They respond to queries based on the data they are provided with.  Since a chatbot is programmed to perform tasks independently, it can respond to queries based on predefined scripts and machine learning applications as well. 

Enterprises that have enhanced IT infrastructure, implement chatbots in varying departments. For instance, ERP, on-premises to cloud, etc., improve customer experiences and increase business value. 

Chatbots As Conversational Tools In The Workplace

The potential benefits that chatbots provide to companies are immense. On one hand, when chatbots provide personalized and efficient search results, on the other hand, companies find cost savings a prominent feature on implementing chatbots. 

Enterprises enhance their workflows and operations increasingly with the help of chatbots as co-workers. This helps companies in serving a larger market segment. Chatbots automate redundant tasks that sap the productivity and efficiency of the workforce and enable them to focus on their core functions. Chatbot technology enhances customer experience by providing them with personalized content. 

Chatbots: Influence In Industries Today

• Enhances Customer Service 

According to a recent survey, 83% of customers need online assistance to complete their shopping. Customers will need online real-time assistance from a real store. The assistance required by customers include areas like during registration, logging in, adding products into the cart, payment, checkout, etc. This is where chatbots serve as a salesperson by interactively communicating with customers via text, voice and so on and provide them with rich content. 

Additionally, chatbots provide automated answers to redundant queries of customers. It also forwards the queries to real sales personnel when the need arises. This saves the time of human customer service resources, avoiding the need for customers to wait for responses. This scales up operations of enterprises to new markets globally as well. 

Customer service that chatbots offer is proactive. This means that chatbots facilitate a 24/7 service for interacting with its customers real-time. Initiating communication with the customer periodically, is another benefit of the prevailing chatbot technology, thus enhancing your brand perception considerably.

• Monitors Data To Provide Critical Insights

An enterprise benefits from gaining traction of customers on its website’s landing page. But it is equally important to ensure that the landing page also generates enough organic traffic. Chatbots reaches out to the customers who visit the landing page and gathers critical data as to why the customer left the page without a purchase and so on. 

Online customer behavior and buying patterns are tracked effectively via monitoring the data thus derived. 

• Generate Leads Effectively

Chatbots ensure a better lead generation by helping in determining leads via KPIs such as timeline, budget, etc. Chatbots thus ensure higher conversion rates.

• Saves Costs 

Implementation of a fully functioning chatbot is much faster than hiring individual employees for each task. With great speed, chatbots also ensure error-free operations. It is also easy to implement and maintain. This reduces costs and other overheads significantly. 

Related Reading:  Read on to reveal the top Chatbot Security measures you need to consider.

Chatbots In Its Chatbaby Phase: Conversational Limitations In Current Chatbots

Chatbots have evolved into a phase where it can integrate Natural Language Processing or NLP technology to support the workforce in performing extensive searches. That being said, chatbots currently face a limitation. AI-based chatbots, for instance, are restricted to duplicate results. For example, a flaw in a chatbot can result in the chatbot responding to a high engagement level content such as an office party album instead of retrieving business-related documents. 

Related Reading:  Can ChatBots redefine your real estate business? Read on to know more!

Why Do Chatbots Need To Mature?

Though chatbots support customer services, they lack a human element in them. A full-fledged implementation of a matured chatbot requires addressing numerous technological gaps. Only then the services of these chatbots can be extended from a chatbaby level, offering just employee assistance to a serious enterprise level. 

Even when chatbots interact in an automated manner, they sometimes cannot answer even simple queries. For instance, chatbots lack becoming conversational in a detail-oriented manner. Thus, it is necessary to learn the complex machine learning technology and its bottlenecks initially before you proceed to implement it in your business.  

Around-the-clock support is what customers look forward to while they search via queries online. This 24/7 ability to converse with the customers real-time is what is yet to be achieved. 

In 2019, chatbots are still in their chatbaby phase and need to mature. 75% of global consumers prefer human interaction rather than a chatbot or any other automated service at the time being. 

The data required to establish a fully conversational chatbot is immensely huge. This explains the need for a much higher understanding of machine learning and challenges around natural language processing technique implementation. 

Can There Be An Alternative To Chatbots?

Chatbots can handle simple tasks to help the human workforce. But when it comes to complicated tasks, assistants that can proactively work to streamline interactions with customers, are required. For this, employees must be able to access from a centralized location.

Employee Intranets, for instance, are hubs that allow organizations to access data in real-time and whenever required. This not only allows them to store data in an easily accessible manner, but also help in connecting with resources and tools at a fast pace. This now becomes a perfect collaboration system. 

https://www.fingent.com/uk/insights/portfolio/using-chatbots-to-create-an-enhanced-and-engaging-learning-experience/

As more and more employees become tech-savvy and with the growing requirement of companies to expand their mobile technologies and strategies, connecting remotely is a necessity. With the growing remote working, it is important to implement social media features into various communication channels as well. This enables a human element to be present and can act as an alternative to chatbots.

2019 can be seen as a year to understand machine learning technology and to develop the digital workplace. With evolving technology, it is also important that an organization ensures a perfect environment where resources and tools support these digital assistants and help in making critical data simple and accessible. Call our strategists right away to learn more about how chatbots can improve your business effectively and productively.

Understanding The Types Of AI Systems To Better Transform Your Business

In this digital era, industries are witnessing the ability of multifaceted artificially intelligent systems performing tasks that mimic intelligent human behavior or even beyond. Artificial Intelligence today, manage large chunks of data and perform redundant tasks, allowing the human workforce to focus on core tasks. This saves cost and time and improves productivity significantly. 

According to Gartner, the number of industries adopting AI has grown over 270% in the last 4 years. Technology giant, Google pledges $25  million USD in a new AI challenge named ‘AI For Social Good’. Understanding Artificial Intelligence Types, is important to get a clear picture of its potential. 

Related Reading: Check out how Artificial Intelligence is revolutionizing small businesses.

Types Of Artificial Intelligence Calculation: Two Main Kinds Of AI Categorization

AI makes systems imitate human capabilities. Though AI can be classified into different types, the 2 main categories are defined as Type-1 and Type-2 and are based on AI capabilities and functionalities. Let us walk through the major classifications of AI types. 

Type 1: AI-Based On Capabilities

1. Weak or Artificial Narrow Intelligence (ANI) 

Weak or Narrow AI is a type of AI which performs assigned tasks using intelligence. This is the most common form of AI available in today’s industries. The Narrow AI cannot function beyond what is assigned to the system. This is because it is trained to perform only a single specific task.

ANI represents all AI machines, created and deployed till date. All artificially intelligent systems that can perform a dedicated task autonomously by making use of human-like abilities, fall under this category. As the name suggests, these machines have a narrow range of responsibilities. 

Apple’s Siri, for instance, is an example for Narrow AI. Siri is trained to perform a limited pre-defined set of functions. Some other examples include self-driving cars, image and speech recognition systems.  

The category of complex artificially intelligent systems that make use of deep learning and machine learning, fall under the category of Artificial Narrow Intelligence systems. These machines are categorized under the ‘Reactive’ and ‘Limited Memory’ machines, which is discussed in detail going forward in this article. 

Know more about the key difference between deep learning and machine learning.

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2. Artificial General Intelligence (AGI)

General Artificial Intelligence is a type of AI which can perform any intellectual tasks as humans. AGI machines are intended to perceive, learn and function entirely like humans. Additionally, the objective of devising AGI systems is to build multiple competencies which can significantly bring down the time needed to train these machines. 

In a nutshell, AGI systems are machines that can replicate human multi-function capabilities. Currently, researchers around the globe are trying to design and develop such AI. Since there is no example as of now, it is termed, General AI. 

3. Artificial Super Intelligence (ASI)

Artificial Super Intelligent systems can be best described as the zenith of AI research. ASI is intended not only to replicate multi-faceted human intelligence, but also possess faster memory, data processing, and analytical abilities. 

This is a hypothetical concept of AI where researchers are trying to develop machines that can surpass humans. This is an outcome of General AI. 

Top Artificial Intelligence Trends to Watch Out for In 2019

Type 2: AI-Based On Functionalities

1. Reactive Machines

The reactive machines perceive the real world directly and react according to the environment. The intelligence of Reactive Machines focuses on perceiving the real-world directly and reacting to it. An example of reactive machines is Google’s AlphaGo. AlphaGo is also a computer program that plays the board game. It involves a more sophisticated analysis method than that of DeepBlue. AlphaGo uses neural networks for evaluating game strategies. 

2. Limited Memory

Limited memory machines are those that can retain memory for a short span of time. These machines have the capabilities as that of purely reactive machines. Additionally, limited memory machines can learn from previous experiences to make decisions. For instance, self-driving cars are limited memory machines that can store data such as the distance of the car with nearby cars, their recent speed, speed limit, lane markings, traffic signals, etc.

The observations from previous experiences are preprogrammed to the self-driving car’s system. This data, but is transient. That is, it is stored only for a limited period of time. This is because it is not programmed to be a part of the self-driving car’s library of experience, compared to the experience of human drivers.

Nearly every artificially intelligent system today uses limited memory technology. For instance, machines that make use of deep learning is a prime application of limited memory. These machines are trained with huge volumes of data sets which are stored in their memory as a reference model. An example of this is the AI that recognizes images. Image recognition AI is trained using a multitude of pictures along with their labels, as data sets. 

Artificial intelligent systems such as chatbots and virtual assistants are also examples of limited memory machines. 

3. Theory Of Mind Machines

Theory of Mind can be defined as a simulation. To be crisp, when a person considers himself in another person’s shoes, his brain tends to run simulations of the other person’s mind. Theory of mind is critical for human cognition. Additionally, it is crucial for social interaction as well. A breakdown of the theory of mind concept, for instance, can be illustrated as a case of autism.

Instead of a pre-programmed engine, AI scientists are looking forward to developing a series of neural networks. This series will be used to develop the ‘Theory Of Mind’. 

‘Theory Of Mind’ machines are aimed at figuring out someone else’s intentions or goals.  

4. Self-Awareness Machines

Self-Awareness machines exist hypothetically today. As the name suggests, these machines are supposed to be self-aware, like of the human brain. The machines can be described as the ultimate objective of AI scientists. 

The goal of developing self-awareness machines is to make these capable of having emotions and needs as of humans.

Related Reading: You may also like to read about building an Intelligent App Ecosystem with AI.

To learn more about AI capabilities and how it can benefit your organization, drop a call to us right away and gather strategies to implement AI for positive business outcomes!

Ways Small Businesses Can Benefit From Artificially Intelligent Systems

It is quite obvious how Artificial Intelligence has become a part of almost every industry verticals. In addition to enhancing intelligence and productivity to operations, AI is widely used by businesses to predict online customer behavior, manage supply chains, automate various difficult and redundant tasks.

According to a recent survey by Vistage, about three-quarters of businesses will invest in software applications. No wonder the AI revolution is also termed as the fourth revolution!

Starting from voice search mobile app development to self-driving cars being able to prompt the driver with possible destinations, most small businesses are jumping into the AI bandwagon. The early adopters of AI implementation have seen a considerable improvement in business turnover and in achieving an increased ROI.

Businesses have improved their turnover and overall performance by making use of data. Small businesses can make use of the AI Revolution to leverage the available data. This can bring a change in their overall business processes. Below are the major benefits that small businesses can reap out of AI:

1. Leveraging Artificially Intelligent Solutions

The fact that any business process can involve AI implementation proves the importance of large chunks of data being generated. To stay competitive, it is essential for businesses to follow the latest trends in the market. This is made possible by deriving insights from data available to predict business outcomes. The possible benefits of AI enabled systems are as follows:

a. Small businesses can use Artificial Intelligence to understand customer segmentation. AI is used by businesses to gather data and then perform market analytics. They can use AI to advertise as AI helps in providing insights to target the right customer base. This helps in determining market-fit customers rather than having to target clients blindly.

b. Small businesses can also make use of AI strategies. For instance, businesses can use third-party AI tools that involve easy interfaces and machine learning algorithms. This will help businesses to couple the critical data and workflows into business intelligence. This helps businesses to achieve practical insights. It also helps in saving time and costs that arise due to data review.

2. Creating Artificially Intelligent Customer Service Solutions

AI-powered chatbots steal the show when it comes to enabling Digital Customer Services. Estimates show that 67 percent of businesses use chatbots for rendering customer services. This number is forecasted to reach 85 percent by 2020.

Voice-search assistants are no exception to customer service solutions. The number of electronic assistants is expected to reach 7 billion by 2020. Chatbots and voice-search assistants can definitely help small businesses increase their productivity and efficiency.

Related Reading: Check out how capitalizing on chatbots will help in redefining your business.

3. Artificial Intelligence For Building A Positive Workplace Culture

Small businesses often find it difficult to hire talents. Artificial Intelligence has made it simpler for the HR Departments of small businesses to recruit talents by eliminating the need for HR managers to manually set and select through the large pile of candidate profiles. AI helps in building a positive workplace culture in the following ways:

a. Hiring is made easier by Machine learning algorithms. The process of hiring is streamlined by AI. The algorithms do so by the process of learning hiring practices of the past. AI applications help in finding good leads by learning an applicant’s history of work and studies and makes the hiring process easier.

b. Sales can be made simpler for small businesses with the help of AI applications. AI-enabled CRM platforms help businesses to derive insights. CRM systems compile data from different customers via phone, email, etc. This is done for automated lead generation. Sales folks of small businesses can make use of AI enabled CRM systems to adjust their leads by analyzing customer ideas through different channels like recording phone calls, emails, phone messages, online behavior, customer reviews, social media posts, etc. This helps the sales team of businesses to personalize their business, according to customer intentions.

c. Small businesses can use AI applications to manage back-end operations cost-effectively. Financial accounting and scheduling daily tasks to employees can be managed more effectively with the help of AI enabled tools. This helps small businesses save time from performing redundant and manual tasks.

Related Reading: Here’s an ultimate guide for you to enhance your existing business application with AI.

4. AI For Effective Data Collection And Competitive Analysis

Identification of competitors is crucial for any business. AI helps in building a competitive analysis that searches for competitors in particular fields. This helps small businesses to collect critical data regarding different competitor strategies.

Businesses learn about the latest trends from the collected data. This is performed via advanced AI methods such as statistical regression analysis. AI has made it cost-effective for small businesses to use sophisticated AI tools that help them in determining how to keep clients interested in their business.

Machine learning algorithms make use of customer sentiments to capture and track customer preferences. This data collection mechanism is made available to small businesses affordable by AI.

Competitive Intelligence implies being able to react to market trends rapidly and accurately. It is therefore essential for small businesses to be up-to-date about the current market trends. Tracking competitors, changing business processes to suit customer requirements, analyzing cost changes and business metrics is required for a business to succeed. This is made possible by AI-powered competitive intelligence tools.

Related Reading: Read on to know how artificial intelligence can enhance intelligent app ecosystem.

5. AI Enabled Tools To Enhance Marketing Strategies

Any business requires deep know-how in technology to enhance its marketing functions. Small businesses find it difficult and costly when it comes to hiring top-notch professionals to handle large marketing campaigns.

Small businesses can make use of AI enabled tools to manage marketing activities. AI helps in reaching out to a large audience online by making use of advertising platforms. For instance, Facebook and Google have implemented advertising platforms that are AI implemented. This targets specific customers making it easier to collect and analyze data that is critical for lead generation.

CPC (Cost Per Click) can be considerably reduced by implementing AI tools. In addition to being able to find the best marketing strategies, AI enabled tools also analyze consumer engagement via marketing campaigns.

Artificial Intelligence is an effective budget monitoring source for marketing management activities for a business.

The Artificial Intelligence Revolution can jumpstart your small business to its height. Get in touch with our tech-breathing IT consultants now to learn tips on how to start!

You may also be interested in reading Top AI Technology Trends To Watch Out For In 2019

Key Features And Benefits Of An Artificially Intelligent Ecosystem For App Development

Artificial Intelligence is omnipresent in this digital era. Artificial Intelligence and human intelligence work together to deliver numerous solutions at the technological forefront. The global artificial intelligence market size is expected to reach $169,411.8 million in 2025. The leading application development trendsetters such as IBM, Microsoft, and many more global leaders have adopted Artificial Intelligence as an inevitable part of their advancements in bringing up the intelligent app ecosystem.

Artificial Intelligence has its key segments in the market based on Technology and they are Machine Learning, Speech Recognition, Image Processing, and Natural Language Processing along with various other industry verticals.

What is an AI application?

Putting it in simple words, AI is the reason why your smartphone when connected to your car’s Bluetooth gives a beep with a message about the traffic conditions and in what time you are likely to reach your destination you often travel to. Artificial Intelligence makes this possible via its pattern recognition skills which it formulates over a period of time, known as machine learning!

For instance, Microsoft has developed a productivity AI application known as ‘Office Graph and Delve’ of Microsoft Office 365. It collects data and shows only relevant content to users according to their priority. Other examples include:

• Voice recognition apps with GPS.
• Search engine applications.
• Chatbots or AI-enabled assistants.
• Personalized shopping applications that make use of Google Analytics.
• Financial applications that are both accurate and efficient in calculations and providing results, such as automated advisors, powered by AI.
• Autonomous vehicles, drones.
• Transport apps powered by AI, Google Maps, etc.
• Social Media applications.
• Smart home devices that make use of data science like smart voice assistants.
• Creative arts such as Watson Beat, yet another powerful AI implemented the app.
• Security apps that implement Facial Recognition and image processing technologies.

Making Mobile Apps Intelligent With AI

The first quarter of 2019 witnessed over 2.1 million apps available in Google’s Play store for Android users. Apple’s Appstore was the second largest to have over 1.8 million apps ready for download.

Related Reading: Read on to learn how voice app can enhance your business. 

With such intense competition, app developers are looking for ways to create personalized applications. The solution to this is AI implementation. The role of AI in an intelligent app ecosystem is to make the applications learn the intention of users. Machine learning and Data analysis features form the Intelligent Ecosystem for these applications.

The AI ecosystem increases the efficiency of programs and machines and also allows users to predict user intentions online like their purchasing trends and other social behaviors when they are online. The following are some of the roles played by Artificial Intelligence in an intelligent app ecosystem:

• Multi-faceted technology that incorporates machine learning algorithms and deep learning.
• Implements Big Data, Neural Networks in its programming.
• Predicts user communication and intention online in real-time.
• Automates functions.
• Can make informed decisions for specific users.
• Can create analytics from a history of searches.
• Interprets tasks using preassigned commands.
• Provides personalization.
• Accurate Insights
• Perfection In App Development Features
• Efficient Ways to Complete Redundant Tasks
• Enhanced User Satisfaction
• Notifies Important Details making operations easier

AI integrated applications help in improving success rates and also increase app performance. Along with helping machines to mimic humans and its problem-solving capacity, AI has become an inevitable part of app development with its ability to learn, perceive and manipulate data.

AI protects an application user’s data. It performs an analysis of the data for identifying user patterns and behavior online in real-time. This data can be used to provide better customer experience and for user engagement.

Role of Artificial Intelligence is also in aiming at generating revenue through efficient user interfaces.

Related Reading: Are you planning on enhancing your business with AI? Here is a guide just right for you.

The Intelligent App Ecosystem: How It Functions

Applications are becoming more intelligent each day. Staying ahead of the game is crucial for app developers with users wanting a personalized experience and much-improved experiences. The following are the latest trends in AI intelligent app ecosystem:

• Wider use of machine learning techniques.
• Wider adoption of microservices for app development.
• A rapid increase of numerous platforms to develop applications.
• A rise in the adoption of various machine learning models.

Various layers of technology are involved in the development of artificially intelligent applications. Artificial Intelligence plays a major role in modeling the market dynamics of businesses to a large extent. In addition to creating new opportunities, be it for startups or even for large industrial giants, artificial intelligence provides numerous implications on the development side including the following key features:

1. Customer Experience with machine learning defines an intelligent app ecosystem

Every business needs high-quality data that will suit their specific application needs. So the machine learning models are required by user data specific companies. For instance, for Google, it was the ‘search’ function, ‘entertainment’ was defined by Netflix, Facebook for the ‘social’ amenities and many more such specific intelligent applications.

Many more categories include healthcare, personal assistants, retail, agriculture, sales, security, automation and many more industry verticals. Machine learning along with user critical data have formed the basis of these applications on the artificial intelligence vertical.

Related Reading: Here’s how you can redefine your business with AI chatbots.

2. Cross-platform services With The Intervention of New Platforms

There are many cross-platform mobile applications developed on cross-platforms such as hybrid and native platforms. Consider Facebook Messenger and Amazon’s Alexa. The added features make it more user-friendly and even beneficial on the sales front. This also becomes simpler for industries to perform when they can deliver their products/ services across such platforms as it is all about adding a new layer of API that connects with the entire existing microservices for authentication and other critical functions. These interfaces, thus revamp the existing applications into macro-services.

3. Machine Learning Models And Intermediate Services For Business-Specific Platforms

Pre-trained machine learning models can be used as plug-and-play for performing functions such as natural language processing, image processing, etc. These are the intermediate services. On the other hand, there are raw intelligence providers that form the building blocks. These two are the major 2 types of industries that provide value to the intelligent app ecosystem.

4. The Big Data Technology Analysis And The Intelligent App Ecosystem

Do you know what Hadoop has contributed to enabling an artificially intelligent ecosystem of app development? The hardware companies that store large chunks of IoT data and other transactional data perform? Well! Big Data Analysis is how businesses keep up with the intelligent app ecosystem.

According to the latest IDC forecast, 75% of the commercial enterprise applications will make use of AI by 2021. This shows that deploying AI for app development will lead your business to have an intense advantage over your competitors in the near future.  To learn more about how you can capitalize AI for your business benefits, get in touch with our experts today!

According to The Wall Street Journal, consumer spending is the primary driver of economic growth in the U.S. Manufacturers, suppliers and logistics companies are the industries that benefit from retail profits. Retailers now prefer artificial intelligence to protect profits. This improves customer service as well!

Next-Gen Solutions To Solve Retail Profit Shrinkage – Why Would You Choose AritificiaI Intelligence?

In the year 2018, the global AI market was expected to be worth 7,35 billion U.S. dollars. Also, the global AI market is expected to grow from 150 percent from 2016 numbers, reaching a forecast size of 4.8 billion U.S. dollars. These statistics show that AI is a new factor of production that can help skyrocket profitability for retailers!

Related Reading: Check out the latest trends in AI. Find 7 reasons why AI is expected to play out in 2019.

Why would you choose AI to protect retail profits?

By 2035, AI technologies will have the potential to boost productivity by 40% or even more! This means AI will increase economic growth at an average of 1.7% across 16 industries by this period.

These numbers show a straight 59% increase in retail profits alone! For this, advanced analytics are used to design and develop models. These models are then used to fetch possible outcomes from a wide spectrum of data given to a computer to analyze. From these outcomes, future decisions and actions are fed to the system which learns it. The system can now perform without human intervention! It can make decisions in real-time.

Machine learning, deep learning, and natural language processing are a few examples of AI. Marketing, pricing, logistics, risk management, store management, fraud detection are some inevitable areas where the largest retailers have used AI for decades now!!

AI – How it uses advanced analytics to solve a wide spectrum of retail problems

Manufacturers, Logistics companies, and suppliers are the major industries that support retail sales. Consumer spending depends on fluctuation in these sectors. This drives the economic growth in the U.S! Since these sectors benefit from strong retail profits, “retailers are turning to artificial intelligence to help protect profits” says a report from Forbes!

AI replaces redundant and individual-driven analysis. This is a more convenient method and ensures consistency across the retail chain. Thus AI can replace 1000 people performing the same tasks by answering spontaneously with just the same analysis!

AI can also enhance customer experience and drive sales. Online retailers use chatbots and product suggestions, while stores can enjoy real-time, targeted marketing messages. Retailers have the need to generate chunks of data on a daily basis. With predictive analytics, this data can predict trends and thus reduce the chance of failures by determining necessary changes to improve profitability. Thus the employees can have proper information faster and deliver more consistent results!

Related Reading: AI and Robotics carry the power to enhance customer experience. Here’s a CTO Guide to it.

Solving Retail Problems With AI – Steps to Overcome Challenges And Improve Efficiency To Boost Sales

All retail companies (physical and online), face four fundamental challenges. These can be solved with predictive analytics and an effective data production plan. They include:

• Siloed and Static Customer Views

Retailers can adopt a complete, real-time strategy by combining traditional data sources with the non-traditional like social media or other external data sources to create valuable insight, resulting in robust fraud detection systems, more effective marketing campaigns, more accurate and targeted churn prediction, and better customer service. This helps them encounter the problems faced by siloed data, where transaction data are separated from web pages, which is again separate from CRM data.

• Time Consuming Vendor and Supply Chain Management

Retailers can adopt real-time analytics and unstructured data sets to combine structured and unstructured data to create more accurate forecasts or automatic reordering, resulting in optimized pricing strategies and more efficient inventory management.

• Analysis Based on Historical Data

Retailers can use prediction and machine learning in real time to create predictions based on current behaviors and trends. This helps them predict the client’s next move. Thus AI helps to adapt automatically to customer behaviors.

• Single-Time Data Projects

It is high time retailers turned towards automated and scalable data workflows. This helps them improve their overall efficiency. This is made possible with predictive analytics using AI.

Related Reading: Enrich customer experience at your retail store. Read along to reveal five secret to win your retail customers.

How AI can Generate Additional Revenue

IDC analysts predict that by 2019, 40 percent of retailers will have developed a customer experience architecture supported by an AI layer! In a nutshell, the major ways in which AI can generate additional revenue and help avoid additional overheads and losses are the following:

• AI can replace redundant and individual-driven analysis for a retailer. This process is far more efficient and it ensures consistency across the retailer’s stores.
• Additionally, employee theft and paperwork errors contribute a high figure of shrink to retail profits. For instance, the National Retail Federation conducted a survey and it turned out that the reason for more than 50% of retail shrink, ie, (the difference between the real ‘on-hand’ inventory and the inventory level recorded in the computer system) is a result of employee theft and manual errors! AI models yield better and efficient results.
• Also, retailers can use AI to predict solutions benefits and functionalities prior to buying it. This way, AI can help reduce risks in retail profit shrinking.
• Choosing a consultant that can address the needs of your company can be another major benefit to reap out of AI to reduce retail profit risks. The following concerns can be addressed with the consultant:

1. How to address constraints like budget, time and personnel? Ask your consultant to provide explanations for the outcomes.
2. Consider the consultant’s skill sets from their previous projects and work experience before you decide to hand over the responsibilities and contract

Related Reading: Does AI have the potential to drive business value across industries? Read through to find how AI is revolutionizing various industries.

AI adopts next-generation solutions that use predictive analytics to capitalize on their data and knowing that 80% of this data is untapped and unstructured is the winning solution! Want to know more reasons why you can undoubtedly bet your company on AI?

Stay tuned to our latest articles and blogs to learn how AI has managed to perform a quantum shift in computing and in generating revenue to successful retail companies!!

Most of us, technology geeks or not, are eager to stay first in line to catch up with the latest game-changing technology trends. Here we are to know which technologies will thrive in 2019!

The Potential Technology Trends You Need To Explore In 2025

Have you ever looked up at the sky and clapped your eyelids on a bat? This is commonplace. But what if it was a drone. Or would it be a flying fleet? Since we don’t belong to the Jetsons family, the latter is not expected but we are close to it! 2019 is definitely a transformative year for technological innovation!

According to Gartner, the Top 10 Strategic Technology Trends for 2019 are Block chain, Artificial Intelligence, Empowered Edge, Privacy and Digital Ethics, Quantum Computing, Immersive Experiences, Augmented Analytics, Autonomous Things, and Digital Twins!

This is just the tip of the iceberg. Following are the emerging technology trends and catalyzing technical innovation that we can expect to see more of in 2019!!

Related Reading: Find how digital innovation is transforming today’s business world.

1. Blockchain Technology – The ‘New Internet’

Some call Blockchain technology the ‘New Internet’. The blockchain is the brainchild of a person or group of people known by the pseudonym, Satoshi Nakamoto. It permits digital information to be distributed but not duplicated.

It was first devised for the digital currency, Bitcoin.  It is also called the “digital gold”. To this day, the total value of the currency is nearly $112 billion US!

“Blockchain solves the manipulation problem”, says Vitalik Buterin, inventor of Ethereum.

2. Artificial Intelligence (AI)

Apart from AI-powered chatbots, 2019 will witness chip manufacturers such as Intel, NVIDIA, AMD, ARM, and Qualcomm shipping specialized chips that speed up the execution of AI-enabled applications.

2019 will also be the year for hyperscale infrastructure companies like Amazon, Microsoft, Google, and Facebook.

Related Reading: Check out the top AI trends of 2019.

3. Cloud-independent edge computing

The study from IDC illustrates that 45 percent of the entire data created by IoT devices will be stored, processed, analyzed and acted upon close to or at the edge of a network by 2020! Edge computing is a mesh network of data centers that process and store data locally before being sent to a centralized storage center or cloud.

4. Privacy and Digital Ethics

Facebook, recently witnessed the biggest security breach in which 50 million accounts were compromised. Facebook, later clarified that data of 30 million accounts were stolen.

People are becoming more nervous about how organizations and third-parties are using their personal data.

5. Quantum Computing

The world is behind building the first fully-functional quantum computer. Also called the supercomputer, this is expected to be a cloud service rather than an on-prem service. IBM is already offering cloud-based quantum computing services. For instance, the automotive, financial, insurance, pharmaceuticals, military, and research industries have the most to gain from the advancements in Quantum Computing.

6. Immersive Experiences

Conversational platforms are changing the way in which people communicate with the digital world. Virtual reality (VR), augmented reality (AR) and mixed reality (MR) are changing their approaches to know more about people’s perception.

7. Augmented Analytics

Augmented analytics relies on augmented intelligence. This uses machine learning (ML) to transform how analytics content can be developed, consumed and shared.

“Through 2020, the number of data scientists will grow five times faster than the number of experts”, says David Cearley!

8. Autonomous Things

Autonomous things, such as robots, drones, and autonomous fleet, use Artificial Intelligence techniques to automate their functions that were previously performed by humans.

9. Digital Twins

A digital twin is a digital representation of real-world items that are interlinked. Cearley states that there can be digital twins of people, processes, and things!

A DTO is an aspect of the Digital Twin evolution that is a dynamic software model that relies on operational or other data. DTOs help drive efficiencies in business processes.

Apart from these, there are other key technology trends that organizations need to explore in 2019. These include:

10. Cybersecurity and Risk Management

According to the estimates from the firm Gemalto, the data breaches were 4.5 billion in the first half of 2018! The University of Maryland study found that hackers attack computers every 39 seconds.

In 2019 we will be facing a more sophisticated array of physical security and cybersecurity challenges.

Cybersecurity is thus the digital glue that has held IoT, Smart Cities, and the world of converged machines, sensors, applications, and algorithms operational throughout!

11. Smart Spaces

A smart space is a physical or digital environment in which humans and technology-enabled systems interact in an increasingly open, connected, coordinated and intelligent ecosystems, according to Gartner! The world of technology is to enter accelerated delivery of smart spaces in 2019.

 12. Self-powered data centers

Data centers grow every minute with the implementation of virtual servers and storage, energy-efficient buildings. In 2019, the data centers are expected to run on its own self-contained power plants!

13. IoT integration

2019 will witness more IoT implementation. An International Forrester IT survey that said among a recent group study, 82% of respondents were unable to identify all of the devices connected to their networks. Of this lot, 54% were nervous about device security, and 55% were concerned about integration!

Related Reading: Find the role of Data Analytics in Internet of Things (IoT)

14. More self-service IT kiosks for business users

2019 will be a year of IT innovation designed to build better communication between IT and end users. The self-service IT kiosks to be set up would enable users to log on and choose what they want for the apps that they build.

15. The Internet of Things and Smart Cities

50 billion equipment, including smartphones, and others are expected by the IoT to be wirelessly connected via a network of sensors to the internet by 2020!

The term “Smart City” means creating a public/private infrastructure to conduct activities that protect and secure citizens. It integrates communications (5-G), transportation, energy, water resources, waste collections, smart-building technologies, and security technologies and services!

To upgrade your business with the latest technology trends on the table, contact the experts at Fingent today! Also, read through our latest blogs to learn more about accelerated technological development!!