Tag: AI

Understanding the concept and significance of Deep Reinforcement Learning

The field of reinforcement learning has exploded in recent years with the success of supervised deep learning continuing to pile up. People are now using deep neural nets to learn how to use intelligent behavior in complex dynamic environments. Deep reinforcement learning is one of the most exciting fields in artificial intelligence where we combine the power of deep neural networks to comprehend the world with the ability to act on that understanding.    

In deep learning, we take samples of data and supervise the way we compress and code the data representation in a manner that you can reason about. Deep reinforcement learning is when we take this power and apply it to a world where sequential decisions are to be made. 

We use deep reinforcement learning to solve tasks where an agent or an intelligent system has to make a sequence of decisions that directly affect the world around the agent. While trial-and-error is the fundamental process by which reinforcement learning agents learn, they do use neural networks to represent the world.  

Read more: Key Differences Between Machine Learning And Deep Learning Algorithms

Types of learning

All types of machine learning– supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning are supervised by a loss function. Even in unsupervised learning, there is some kind of human intervention required to determine and provide inputs on what is good or bad. Only the cost of human labor required to obtain this supervision is low. Thus, the challenges and the exciting opportunities of reinforcement learning lie in how we get that supervision in the most efficient way possible.  

In supervised learning, you take a bunch of data samples and use them to learn patterns to interpret similar samples in the future. However, in reinforcement learning, you teach an agent through experience. So the essential design step in reinforcement learning is to provide the environment in which the agent has to experience and gain rewards. In other words, a designer has to design not only the algorithm but also the environment where the agent is trying to solve a task. 

The most difficult element in reinforcement learning is the reward – good vs bad. For example, when a baby learns to walk, success is the ability to walk across the room and failure is the inability to do so. Simple! Well, this is reinforcement learning in humans. How we learn from so few examples through trial-and-error is a mystery. It could be the hardware – 230 million years of bipedal movement data that is genetically encoded in us or it could be the ability to learn quickly through the few minutes or hours or years of observing other humans walking. So the idea is if there was no one around to observe, we would never be able to walk. Another possible explanation is the algorithm that our brain uses to learn which has not yet been understood. 

The promise of deep learning is that it converts raw data into meaningful representations whereas the promise of deep reinforcement learning is that it builds an agent that uses this representation to achieve success in the environment.     

Deep Q learning

Q-learning is a simple and powerful algorithm that helps an agent to take action without the need for a policy. Depending on the current state, it finds the best action on a trial-and-error basis. While this works for practical purposes, once the problem size starts increasing, maintaining a Q-value table becomes infeasible considering the amount of memory and time that would be required. This is where neural networks come in. 

From a given input of action and state, a neural network approximates the Q-value function. Basically, you feed the initial state into the neural network to get the Q-value of all possible actions as the output. This neural network is called Deep Q-Network. However, DQN is not without challenges. The input and output undergo frequent changes in reinforcement learning with progress in exploration. The concepts of experience replay and target network help control these changes.

Read more: Top 10 Machine Learning Algorithms in 2020

Deep Reinforcement Learning Frameworks

Here are the three Deep Reinforcement Learning frameworks:

1. Tensorflow reinforcement learning

RL algorithms can be used to solve tasks where automation is required. However actual implementation is easier said than done. You can ease your pain by using TF-Agents, a flexible library for TensorFlow to build reinforcement learning models. TF-Agents makes it easy to use reinforced learning for TensorFlow. TF-Agents enables newbies to learn RL using Colabs, documentation, and examples as well as researchers who want to build new RL algorithms. TF-Agents is built on top of TensorFlow 2.0. It uses TF-Eagers to make development and debugging a lot easier, tf.keras to define networks and tf.function to make things faster. It is modular and extensible helping you to pick only those pieces that you need and extend them as required. It is also compatible with TensorFlow 1.14.

2. Keras reinforcement learning

Keras is a free, open-source, neural network Python library that implements modern deep reinforcement learning algorithms. Using Keras, you can easily assess and dabble with different algorithms as it works with OpenAI Gym out of the box. Keras offers APIs that are easy and consistent, thus reducing the cognitive load. These APIs can handle the building of models, defining of layers or implementation of multiple input and output models. Keras is fast to deploy, easy to learn, and supports multiple backends. 

3. PyTorch Reinforcement learning

PyTorch is an open-source machine learning library for Python based on Torch and is used for applications such as natural language processing. It consists of a low-level API that focuses on array expressions. This framework is mostly used for academic research and deep learning applications that require optimized custom expressions. The PyTorch framework has a high processing speed with complex architecture.  

All these frameworks have gained immense popularity and you can choose the one that suits your requirements.

While deep reinforcement learning holds immense potential for development in various fields, it is vital to focus on AI safety research as well. This is going to be fundamental in the coming years in order to tackle threats like autonomous weapons and mass surveillance. We should, therefore, ensure that there are no monopolies that can enforce their power with the malignant use of AI. So international laws need to keep up with the rapid progress in technology. 

We have tried to brush across the basics of deep reinforcement learning and the top 3 frameworks that are in use currently. Want to know more about this amazing technology? Reach out to us at Fingent! 

What is Transfer Learning and how can it help you?

Have you ever tried teaching a baby to recognize objects?

That’s an example of Transfer Learning at work in its most elementary form.  Babies as young as eight months old can transfer learning from images to objects. As we grow, we continue to use the same method to learn things. We continue to use the knowledge we gain from one domain to learn other things faster in another domain. This is the concept in Artificial Intelligence that has come to be known as Transfer Learning. This blog discusses transfer learning and its vital role in the future of AI.

What is Transfer Learning?

Transfer Learning is a method in which a model developed for a particular task is used as a building block to solve a different problem. It is a domain of AI, which uses machine learning algorithms to improve learning capacities in one domain through previous exposure to another domain.

Currently, Transfer Learning is gaining much popularity because it can train deep neural networks with lesser data. The goal of transfer learning is to build a model that can be applied to different, yet related problem areas.

It is interesting to note that the pre-trained AI models are called “teacher” models, and fine-tuned AI models are called “student” models. For example, we need not learn and remember that a bus has wheels on four ends. Why? Because we are capable of relating it to what we already know: that a vehicle generally has four wheels. On the other hand, a computer needs to develop such logic by learning all the attributes of a bus. That is the reason why a computer needs much more data than we do. This is where transfer learning comes into play. Transfer Learning aims to reduce the need to use huge amounts of data, by using data available from related domains.

It is important to note that Transfer Learning is different from Traditional Machine Learning. Traditional learning works in isolation, in the sense that it is based on specific tasks and datasets, and separate isolated models are trained through this. The knowledge gained is not retained or transferable to other models. Transfer Learning, on the other hand, ensures that this knowledge is retained and leveraged to train newer models to perform different yet related tasks.

Related Reading: Classifying Knowledge Representation In Artificial Intelligence

Transfer Learning Collaborates Perfectly With AI

Being a fast-evolving frontier of data science, transfer learning can be used by data scientists to tap into statistical knowledge that is gained from previous projects. Its benefits are manifold. 

• Boosts productivity: Deep Learning and Machine Learning projects address solution domains for which huge amounts of data have already been collected, used and stored. The same work can be used by data scientists to develop and train fresh neural networks. This boosts productivity and accelerates the time required to gain insight into new modeling projects. 

Transfer Learning also enhances productivity when there are close parallels between the source and target domains. For example, deep learning knowledge gained from training a computer to translate from English to Arabic can also be partially applicable to help it learn to translate from English to Hindi.

Related Reading: Why Time Series Forecasting Is A Crucial Part Of Machine Learning

• Risk Reduction: At times, underlying conditions of the phenomenon that has been modeled might change radically. That will render the previous training data set inapplicable. On such occasions, data scientists can use Transfer Learning to leverage useful subsets of that previous training data from related domains as they now build a fresh model.

Transfer Learning can be used to predict certain problems in domains that are susceptible to highly improbable events. For example, a stock-market crash might be useful to predict political catastrophes. This way, Transfer Learning can stand at the forefront of data science by gaining and applying fresh contextual knowledge through various forms of AI.

• Improves learning: Transfer Learning can use the knowledge gained from source models to improve learning in the target model. This improves baseline performance. It also saves time because it does not have to learn from scratch.

Transfer learning allows the use of small datasets to solve complex problems.  If a new domain lacks sufficient labeled training data, transfer data can assist in leveraging relevant data from older modeling projects. Applications of deep learning generate enormous amounts of complex data. Managing such data manually would require a lot of human resources. Hence, Transfer Learning is critical for the success of IoT and deep learning applications. 

https://www.fingent.com/insights/portfolio/achieve-higher-business-growth-profits-with-artificial-intelligence/

Transfer Learning for Future Innovation

As machine learning and deep learning continue to accelerate, transfer learning will accomplish things with improved efficiencies that were unimaginable in the past. Transfer learning will support deep neural networks in running businesses more efficiently. 

In a tutorial called Nuts and bolts of building AI applications using Deep Learning, renowned professor and data scientist Andrew Ng predicted that “after supervised learning — Transfer Learning will be the next driver of ML commercial success.” We are seeing that happen right in front of our eyes. Explore this revolutionary tool with Fingent’s experts and see if this is something that could help your business. 

How AI and Voice Search Will Impact Your Business in 2020

“It is common now for people to say ‘I love you’ to their smart speakers,” says Professor Trevor Cox, Acoustic engineer, Salford University. 

The Professor wasn’t exactly talking about the love affair between robots and humans, but his statement definitely draws attention to the growing importance of voice search technology in our lives. AI-driven voice computing technology has drastically changed the way we interact with our smart devices and it is bound to have a further impact as we move into 2020. 

In this blog, we will consider six key predictions for AI-Driven voice computing in 2020.

How Essential Is AI-Driven Voice Search For Businesses?

Voice search is becoming increasingly popular and is evolving day after day. It can support basic tasks at home, organize and manage work, and the clincher – it makes shopping so much easier. No doubt about it, AI-driven voice search and conversational AI are capturing the center stage. 

Related Reading: Why you can and should give your app the ability to listen and speak

Voice-based shopping is expected to hit USD 40 billion in 2022. In other words, more and more consumers will be expecting to interact with brands on their own terms and would like to have fully personalized experiences. As the number of consumers opting for voice-based searches keeps increasing, businesses have no option than to go all-in with AI-driven voice search. With that in mind, let’s see where this is going to be leading businesses in 2020. 

Six key predictions for AI-driven voice search and conversational AI in 2020

1. Voicing a human experience in conversational AI

Chatbots are excellent, but the only downside is that most of them lack human focus. They only provide information, which is great in itself, but not enough to provide the top-notch personalized experience that consumers are looking for.  This calls for a paradigm shift in conversational design where the tone, emotion, and personality of humans are incorporated into bot technologies. 

Statista reports that by 2020, 50% of all internet searches will be generated through voice search. Hence, developers are already working on a language that would be crisp, one that is typically used in the film industry. Such language could also be widely used on various channels such as websites and messaging platforms. 

Related Reading: Capitalizing on AI Chatbots Will Redefine Your Business: Here’s How

2. Personalization

A noteworthy accomplishment in voice recognition software enhancing personalization is the recent developments in Alexa’s voice profiling capabilities. Personalization capabilities already in place for consumers are now being made available to skill developers as part of the Alexa Skills Kit. This will allow developers to improve customers’ overall experience by using their created voice profiles.

Such personalization can be based on gender, language, age and other aspects of the user. Voice assistants are building the capacity to cater even to the emotional state of users. Some developers are aiming to create virtual entities that could act as companions or councilors. 

3. Security will be addressed 

Hyper personalization will require that businesses acquire large amounts of data related to each individual customer. According to a Richrelevance study, 80% of consumers demand AI transparency. They have valid reasons to be concerned about their security. This brings the onus on developers to make voice computing more secure, especially for voice payments.

4. Natural conversations

Both Google and Amazon assistants had a wake word to initiate a new command. But recently it was revealed that both companies are considering reducing the frequency of the wake word such as “Alexa.” This would eliminate the need to say the wake word again and again. It would ensure that their consumers enjoy more natural, smooth and streamlined conversations.

5. Compatibility and integration

There are several tasks a consumer can accomplish while using voice assistants such as Amazon’s Alexa or Google’s Assistant. They can control lights, appliances, smart home devices, make calls, play games, get cooking tips, and more. What the consumer expects is the integration of their devices with the voice assistant. 2020 will see a greatly increased development of voice-enabled devices.

6. Voice push notifications

Push notification is the delivery of information to a computing device. These notifications can be read by the user even when the phone is locked. It is a unique way to increase user engagement.  Now developers of Amazon’s Alexa and Google Assistant have integrated voice push notifications which allow its users to listen to their notifications if they prefer hearing over reading them.

What Does It Mean for Your Business In 2020?

AI-driven voice computing and conversational AI is going to change all aspects of where, when and how you engage and communicate with your consumers. By 2020, IDC  estimates a double-digit growth in the smart home market. Wherever they are and whatever channel they are using, you will be required to hold seamless conversations with your customers across various channels. 

“Early bird catches the worm.” Be the first in your industry to adopt and gain the benefits of voice search and conversational AI.  Call us and find out how we can make this happen for you.

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.

This video is made using InVideo.io

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!

Artificial Intelligence (AI) is considered to be one of the most significant disruptive technologies today. More and more businesses are already realizing its benefits. Gartner’s 2019 CIO survey revealed that the percentage of companies implementing AI increased by about 270 percent over the last four years, and 37 percent in 2018 alone.

Leveraging the power of AI to enhance your existing business applications isn’t nearly as complicated as you might think. You don’t need a billion-dollar budget to implement AI-powered applications. In fact, small and midsize businesses (SMBs) today are cutting costs and delivering great customer experiences with AI-powered applications—and they are competing with giant companies at scale.

Here’s a look at how you can enhance your existing business applications with AI:

Enhance CRM Apps with AI

Incorporating AI into your current Customer Relationship Management (CRM) system, for instance by using chatbot or automated live chat support, will allow your company’s helpdesk to provide better, faster and more dynamic responses. It will also help you reduce the man-hours needed to resolve queries and help you build better engagement and customer trust. And because the AI-powered CRM system provides predictive insights, you can automatically recommend similar products or services a customer may be interested in.

Related Reading: Unconventional Ways Artificial Intelligence Drives Business Value

Streamline Supply Chain with Machine Learning

Machine learning (ML) allows your system to discover patterns in the supply chain data using algorithms that automatically identify the factors that contribute to the success of your supply networks, while constantly learning in the process. ML algorithms and the applications running them can analyze large, varied data sets in no time, improving accuracy in forecasting supply and demand. If applied correctly within your SCM work tools, ML could revolutionize the agility and optimization of your supply chain planning.

AI-Powered Recruitment Apps

Artificial Intelligence is expected to replace 16 percent of Human Resource (HR) jobs within the next 10 years, according to Undercover Recruiter. Integrating AI into your existing recruitment processes or tools could help your company’s HR department find the right candidate or the best fit faster and easier, thereby saving you time and money. AI-powered video interview tools, for instance, can utilize biometric and psychometric analysis to evaluate your applicants’ tone of voice, micro-expressions, and body language.

Related Reading: AI To Solve Today’s Retail Profit Problems

Improving Cybersecurity System with AI

Given the data breaches and cyber-attacks that have hit headlines in recent years, integrating AI into your current security system is vital to protect consumer data, improve trust and deliver true business value. About 71 percent of companies in the US plan to spend more budget on AI and machine learning in their cybersecurity software this year.

AI not only improves your company’s existing detection and response capabilities but also allows new abilities in preventive defense. It enhances and streamlines your security operating model by reducing complex, laborious and time-consuming manual inspection and intervention processes. Because the AI-powered cybersecurity system can self-adjust and learn data over time, you can automatically detect and block cyber-attacks and fraud.

Enhancing Space Exploration with AI

Another area where the application of AI has great potential is exploring outer space. NASA has plans to look for life on other planets, such as Mars, in the very near future. In their Mars 2020 initiative, they will use AI to explore Mars in greater depth, which includes looking for alien lifeforms. Most of us are at least slightly familiar with or aware of NASA’s Opportunity rover, which wrapped up a 14-year Mars mission when it quietly went dark in February 2019. Opportunity, also known as “Oppy,” found evidence that Mars at some point was home to water — a huge discovery.

Going forward with Mars 2020, NASA’s Mars Exploration Program will continue its use of AI for space exploration. In ongoing efforts to evaluate whether Mars is (or was at some point) habitable for humans and other animals, the Mars 2020 rover is equipped with a drill it will use to collect samples of rock and soil. It will store these samples in special tubes that will be collected by a later NASA mission. Read more about the artificially-intelligent robotic arm that will make it all happen.

Related Reading: Industry experts weigh in on the adoption of AI and ML in software development

Taking Your Existing Business Applications to the Next Level with AI

New AI frameworks and tools make provisioning AI capabilities more feasible than ever before. Working with a development partner who has the data science and AI technology experience, creating or updating a business application with AI can be started rapidly, take less time to code, and the resulting application placed into service sooner. Nor would it be necessary to staff for these hard-to-find resources for the long term.

Related Video: Artificial Intelligence – How to navigate AI

6 Chatbot Security Practices You Need To Implement

According to a survey by Oracle, regarding the benefits of using chatbots for their consumer-facing products, which included responses from 800 decision-makers, including chief marketing officers, chief strategy officers, senior marketers, and senior sales executives from France, the Netherlands, South Africa, and the UK, it was found out that “80 percent of companies wanted to have some type of chatbots implemented by 2020!

It is also forecasted that 90% of bank-related interactions will be automated by 2022.  Moreover, 80% of businesses will have chatbot automation implemented by 2020. Also, 47% of consumers would buy items from a chatbot when 28% of top-performing companies are already using AI for marketing! With chatbots turning into the trend, it is vital to implement chatbot security measures. 

A Back Door Open To Hackers

Chatbots are nowadays mostly used in industries such as retail, banking, financial services, and travel that handles very crucial data such as credit/debit cards, SSN, bank accounts, and other Sensitive PII (Personally identifiable information).

The aggregation of such data is crucial for the chatbot to perform. Thus, it is required that chatbots are not vulnerable to be exploited by any hackers.

A recently released report from MIT Technology Review and Genesys showed that 90% of companies are already using AI strategies to increase revenue. The research also found that on average, between 25% and 50% of all customer queries can be solved through automated techniques. This has made it easier than before to handle complex tasks.

Related Reading: Read on to know more about the top AI trends of 2019.

The HTTPS Protocol For Security Of Chatbots

HTTPS protocol is the basic and default setting required for a good security system. The data that is being transferred over the HTTP via encrypted connections are secured by Transport Layer Security (TLS) or Secure Sockets Layer (SSL).

Related Reading: Check out how Fingent helped create an enhanced and engaging learning experience through chatbots.

Types of Security Issues

Security Issues fall into two main categories:

• Threats

Threats are usually defined as different methods by which a system can be negotiated or compromised. Threats can include incidents such as Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privileges, and many other threats.

• Vulnerabilities

Vulnerabilities are defined as methods that a system is compromised and cannot be identified and solved correctly and on time. A system becomes open to attack when it has poor coding, lax security, or because of human errors. The most effective way to solve the issues of a possible vulnerability is to implement SDL (Security Development Lifecycle) activities into the development and deployment methods.

As per the study by the Ponemon Institute, In 2017, the average total cost of a successful cyber-attack was over $5 million, or $301 per employee!

Here are 6 chatbot security issues that you need to consider right away:

1. Encryption

Data while transit can also be misused.  There exist different protocols that provide encryption, while addressing these problems of misuse and tampering.

According to article 32 (a) of the General Data Protection Regulation (GDPR), “it is specifically required that companies take measures to de-identify and encrypt personal data. So, chatbots have access only to encrypted channels and communicate through those”.

For instance, Facebook Messenger introduced the new feature called “Secret Conversations” that enabled end-to-end encryption based on Signal Protocol.

2. Authentication and Authorization

Authentication is performed when the user needs to verify their identity. This is often used for bank chatbots.

Generated authentication tokens verify data that are requested through a chatbot. On completing the verification of the user’s identity, the Application produces a secure authentication token, along with the request.

Another step of security measures is an authentication timeout. The token generated is used for only a certain amount of time, after which the application has to process a new one.

Two-way verification is another process where the user is asked to authorize their email address or to receive a code via SMS. This is a crucial process which is necessary to verify that the user of that account is the real user that is using the chatbot.

3. Self-destructing Messages

When Sensitive PII (Personally identifiable information) is being transferred, the message with this data is deleted after a definite period of time.  

Personally identifiable information (PII) is any data which can be used to identify a particular person. It includes records such as a person’s medical, educational, financial and employment information. Examples of data elements that can identify and locate an individual include their name, fingerprints or other biometric (including genetic) data, email address, telephone number or even their social security number.

This kind of security measure is crucial when working with banking and other financial chatbots.

4. Personal Scan

When working with personal data, it is necessary to take security precautions and measures.

Apple was the first company that added finger authentication to their iPhones. This technology is now being used widely to verify an individual’s identity. This is performed when initiating a transaction or when you want to access your bank account using a chatbot that a personal scan is required.

5. Data Storage

Chatbots are effective because they retrieve and store information from users.

For instance, if you have a chatbot that performs online payments, this can mean that your clients are providing their financial information to a chatbot.

The best solution in this situation is to store such information in a secure state for a required amount of time and to discard these data later on.

Some other concerns are the following:

• Biometric authentication: Iris scans and fingerprint scans are popular and robust.
• User ID: User IDs involve processing secure login credentials.
• Authentication Timeouts: A ‘ticking clock’ for correct authentication input. This prevents giving hackers an opportunity to guess more passwords.
• Other strategies could include 2FA, behavior analytics, and kudos to the ever-evolving AI trends.

6. Tackling Human Causes

The one and only other factor or cause that cannot be altered is the human factor. With commercial applications in specific, that chatbot security and end-user technique have to be resolved. This will ensure the chatbots from being vulnerable to threats.

Related Reading: Find how artificial intelligence can drive business value.

To know more about secure bot building, get in touch with our IT consultants today!

Disclaimer: This is an opinion piece. The views expressed in this article are mine and does not represent my employer.

Smart, sentient machines! The latest (well, not really) hype! Look back a week or two, and think about the number of days you went without hearing about how AI is going to change your career, health, medicine, food, travel or whatever. Television, newspapers, and blogs remain constantly flooded with announcements about the imminent disruption <insert field here> that is going to witness due to using AI.

Let me show you some, ahem, examples.

We have here (in the order of increasing horror):

• AI-powered Air Conditioners

• AI-powered Washing Machines

Source – Gizmodo

• AI-powered Suitcases

Source – Indiegogo

• AI-powered Phones

• AI-powered Toilet

Source – The Verge

• AI-powered Underwear!

Okay, I made that last one up. But for a second there, you guys did believe me, right? RIGHT?

That is the sad state of affairs. We are all techies here, and might think “wait, WHAT?”. But the vast majority of the not so technical audience out there sees AI as magic. They see it as something beyond their cognitive ability to process and accept any BS branded as “AI-powered” without questions. Thus, we have this article!

Source – Mashable

So what is the truth with AI? If you dig deep enough, or if you peel off enough layers(pun intended), what is happening?

Before we move on to taking the buzz off of buzzwords, let’s look at some core concepts.

Related Read: Top Artificial Intelligence Trends to Watch Out for In 2019

What is AI?

From wiki, Artificial intelligence is intelligence demonstrated by machines. It is the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.

But Really, What Is Artificial Intelligence?

IM[not so H]O, AI is just a buzzword. Really, it is just meaningless jargon. Okay, maybe not meaningless, but it’s still jargon. Don’t believe me? Let me give you some examples:

• Computers playing checkers and beating the best human players was considered AI. Until it was not when it was accomplished around 1994 by Chinook, the checkers-playing computer program.
• Computers playing chess and beating the best human players was considered AI. Until it was not when it was accomplished around 1997 when IBM’s Deep Blue defeated the then world champion, Garry Kasparov.
• Cruise control was considered AI. Until it was not when it started being available in production cars in 1990+(partial) and 2010+(full speed range).
• Automatic parking was considered AI. Until it was not when it started being available in production cars somewhere around 2006.
• Human speech recognition was considered AI. Until it was not when it started being available as Google Assistant, Cortana, Siri, etc. Now we have a real-time speech translation!

Obligatory XKCD.

I could go on, there are quite a few examples of this phenomenon, formally known as(yes, it is so well known that it has a name) the AI effect [wiki].

So a much better definition of AI was put forth by Douglas Hofstadter.

“AI is whatever hasn’t been done yet.”   

 – Douglas Hofstadter

Just Computation

“Every time we figure out a piece of it, it stops being magical; we say, ‘Oh, that’s just a computation’.”

– Rodney Brooks

So, if it’s all just computation, why was it not, well, “computed” earlier?

Yes, computation, or rather, the capacity for computation is the key. A lot of problems were characterized as AI because, at the time, algorithms for solving that were not known yet, or because the resources to compute those were not available yet.

• Availability of Computation Power

Eg. Chess/other games, etc.

Moore’s law and the explosion in storage availability have played a major role in turning the tables. [It is important to note that the tables have not turned completely. Yet. There is so much more ground to cover.]

• Availability of Unbiased Data

Eg. Natural language processing (NLP).

Okay, now you may be thinking “Enough data was not available for speech recognition? This guy is full of BS”, but hear me out. With the explosion of social networks, so much content is created and made freely available that finding huge swaths of unbiased(this is the key here) voice/video of natural speech is available, which in turn has helped the advances in NLP.

• Availability of Infrastructure

I guess I don’t have to mention the improvement in internet speed that happened over the decade. This has accelerated content creation, real-time processing, etc.

So, What is All the Current Hype About?

The hype is not current. There has been huge interest around AI from the time it was first proposed around the 1950s. The sheer number of films about it tells us about how much.

But the current wave of hype and buzz surrounding AI comes from the recent advances made in, drumroll please, Machine Learning.

What is Machine Learning?

Machine learning is

• giving computers the ability to learn
• to find patterns in data
• from experience
• without explicit programming.

ML is essentially about classifying and predicting stuff.

The typical operation is something like:

1. Take some data
2. Learn patterns in the data
3. When presented with new data, classify it for the best guess of what it probably is, based on the “learning” that happened in [2].

Related Read: Machine Learning- Deciphering the most Disruptive Innovation

Meh! So what is the big deal?

Once trained for one purpose, the same ML system can be reused(with additional training) to learn new concepts. This can be done without rewriting the code. Now that is a big deal.

Let’s look at a simple example: Classifying emails.

Traditional programming:

if the email contains "it's never a job, its always a career"

then send to trash;

if the email contains ...

then ...

if the email contains ...

then ...

ML programs:

try to classify some emails;

change self to reduce errors;

repeat;

That was a two-minute primer on Machine Learning. So next time someone starts talking about Artificial I, I hope you feel the pang and say “Excuse me, I think you mean Machine Learning, not AI”.

Source – HubSpot

What To Watch Out For In 2019 On ERP Market Trends

It’s true that we can never bid adieu to last year’s trend-setting technologies like blockchain, AI, IoT, and multi-cloud. But, neither can we not talk about the progress where technology is likely to echo around the ERP space.

According to a study by MarketWatch, “The ERP software market is expected to rise globally to $47B by 2022”. Let us find out what are the possible market trends in ERP for 2019.

Drastic Rise Of The IoT Market

According to Statista’s latest research, “the global IoT market will rise exponentially from $2.9 trillion in 2014 to over $7 trillion in 2020”.  This leads to a situation where  ERP systems will need a new level of intelligence and automation to make platforms collect data and insights.

Earlier, only a small percentage of data were created and processed outside a traditional data center. But in the future, there will be a situation where nearly all data will be generated from outside of the data center.

IoT is expected to drive the design and deployment of many operational analytics solutions. Industries such as retail, banking, and telecommunications will also adopt operational analytics to enhance customer experience and quality.

Related Reading: Get answers to where and why should you invest in IoT.

Accelerating Transition To Cloud Computing

According to Statista Reports, “the global market revenue of public cloud services will surpass 278.3 U.S. Dollars in 2021”.

The benefits of this would be greater data security, minimal dependency on hardware, speedy results, and high customer satisfaction. As per the research report by IDC, spending on cloud computing is anticipated to rise at a rate of six times the rate of the current IT spending through 2020 which upsurges at 4.5 times the IT spending rate since 2019.

Cloud concepts, Content Delivery Network (CDN), DevOps, Big Data and Artificial Intelligence, are going to be the key players in the future of Cloud Computing.

Contribution Of AI And Emerging Disruptive Technologies

The three main trends behind the huge adoption of ERP services are the integration of artificial intelligence (AI), deployment in the cloud, and improved IoT technology.

Big data analytics and the various kinds of AI, including predictive analytics, machine learning, and deep learning, form the catalysts for industries to improve customer experience.

The third wave of technology evolution is based on systems of intelligence (Cloud, IoT, AI, VR, AR). For instance, Chatbots and messaging apps are examples for a rapid expansion in the implementation and adoption of AI.

By implementing advanced solutions such as AI-based chat-bots, IoT sensors and more, businesses will streamline and thereby accelerate their functions. This can tackle productivity issues and also it is an opportunity for businesses to realize the value and utility of new disruptive technologies.

Related Reading: Watch out for the top AI trends in 2019.

ERP and SaaS – Differences In Overheads

The traditional applications based on ERP were stored on servers. This meant overheads as a result of increased hardware costs as well as costs associated with backup, recovery, and maintenance. The difference between traditional and SaaS applications is that SaaS applications are stored on cloud-based servers.

The benefits of SaaS are that these applications do not demand high maintenance costs, or rather are they expensive. The additional overhead costs that are reduced when it comes to SaaS are that they differ in terms of per-seat licensing costs as well as the total cost of ownership etc.

So, since Saas is a cloud-based model, SaaS-based applications are neither costly, neither are they difficult to maintain.

ERP transition has taken place rapidly. The new SaaS model for ERP is very flexible and useful.

Inclusion Of Social Media Channels

ERP systems in 2019 will need to be able to include direct marketing and data links across multiple social media channels to make their presence felt in the market.

HR managers frequently use social media to search for and hire new employees and also as a background check and even as performance management indicators. These changing trends in business operations are reflected in any competitive ERP platform.

The modules that address are becoming social-media savvy in 2019. This is mainly due to the high use of customer base that accounts for 2.77 billion customers (according to eMarketer research).

The other engaging trends in ERP adoption are as follows:

• Focus on Business Intelligence

Organizations are trying to make forecasts with ERP software for business intelligence.

• Good Integration architecture

An increasing need for ERP software system has resulted in the act of ERP consultants being shifted towards a better integration architecture for ERP software.

• Two-Tier ERP

Two-tiered ERP is very useful for enterprises since they run in different processes at different places. These tend to match the needs of all locations with a better cost structure. It best suits when the enterprise is large.

• The Personalization Advantages

Today’s ERP systems are built for personalization. Some systems offer tools to help make it easy and fast in customizing the application to their needs.

• Large Organizations Acquiring Small Startups

Large organizations try acquiring smaller startups in that regards. This increases ERP implementation.

• Partnering With Firms That Break The Traditional Rules

With the onset of the cloud, the traditional role of technology partners will no longer be enough. Finding an ERP partner that utilizes the latest technology and analyzing how they deliver service with your needs is required for a successful business.

• More focus on profit from ERP

As ERP becomes more and more successful in the market, firms blindly implement them now. They are sensitive to the return of investment.

These above trends in ERP to be witnessed in 2019 are beneficial for an organization as well as provide to business growth. ERP software is used in various fields for work and that is the reason why the demand for an  ERP software system is increasing every day.

Related Reading: Check out these tips to get your business the best out of your ERP system.

Watch out for more market trends and highlights showcased for 2019 in our latest blogs!!

Also, empower your business with trending technologies. Contact our tech-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!!