Tag: Artificial Intelligence

How is AI Transforming Various Industry Sectors?

From Siri to self-driving cars, Artificial Intelligence has been breaking into new realms, including industries that are late to adopt technology or that heavily rely on manual labor. Gartner predicts that by 2020, AI will produce more jobs than it displaces. By 2022, one in five workers engaged in mostly non-routine tasks will rely on AI to do a job.

The future of AI only looks bright! 

According to experts, AI and the future of work will amplify human efficiency and productivity. AI may match or even surpass human intelligence and capabilities on tasks related to pattern recognition, complex decision making, sophisticated analytics, language translation, reasoning and learning, and speech recognition. 

This article discusses how five major industries are benefiting from AI and its innovations.

1. Healthcare

No surprises here. AI in healthcare always comes first on the list. From doctors, surgeons, nurses to desk receptionists at clinics, Artificial Intelligence is enabling process automation across the healthcare community and ecosystem.

AI tools enable medical professionals to diagnose symptoms, identify trends, analyze data or information that would predispose a person to a particular disease.

AI-powered bots assist surgeons with heart, thoracic, and colorectal surgeries. Using bots for surgeries helps lower the risk of infection and blood loss, reduce pain, ensure higher accuracy, shorten hospital stays, and expedite recovery. Digitized health records (EHRs) help patients access their information on a shared online health portal.

Along with other technologies, Artificial Intelligence is being widely used in the ongoing fight against the COVID-19 pandemic. Remote patient monitoring using AI-powered medical equipment or devices help doctors maintain a safe distance from the patients, while offering treatment. The massive amounts of data generated every second in the field of medicine can be utilized effectively to continuously train AI systems through which these systems acquire the capabilities to generate insights that can aid medical researchers.   

Read more: How Emerging Technology is Transforming the Healthcare Industry? 

The future of AI in healthcare could include everything from answering the phone to interpreting radiology images, and designing therapeutic drugs.

2. Manufacturing

AI plays a key role in helping achieve better productivity, efficiency, and visibility across manufacturing operations. AI systems can transform the way organizations run their production lines, enhance human capabilities, garner real-time insights, and facilitate the design and product innovation.

Read more: Digital Transformation in Manufacturing 

Following are some of the ways by which AI impacts the manufacturing sector:

• AI systems help monitor every stage of the production cycle and machine learning algorithms can be used to predict the fill rate, thereby optimizing the manufacturing processes and production planning.
• Small, lightweight “cobots” help create safer working environments. Manufacturers can adopt robotics to perform dangerous jobs, thus sparing their employees for crucial tasks, thereby avoiding occupational health hazards. Cobots are considerably less expensive and easy to program than the usual industrial robots. Soon, machine learning algorithms can improve their capabilities and help the cobots take instructions from humans and interact with them in a better way.

Read more: What Are Cobots and How Can They Benefit Industries? 

• Predictive maintenance helps companies understand when machines need to be attended and serviced. Using machine learning, predictive maintenance can generate valuable data that helps prevent unplanned downtime. Sensors and advanced analytics in manufacturing equipment allow manufacturers to respond to alerts and resolve machine issues on time.
• Engineers or designers can input design goals and other parameters into generative design software (a program that generates several outputs to meet specific criteria) to explore better designs. Using machine learning, designers can learn from each iteration and understand what works and what does not.

3. Finance

According to a report by Business Insider Intelligence, about 75% of bank respondents with assets worth over $100 billion said that they are using AI technologies compared to the 46% of banks with assets less than $100 billion. 

As much as $199 bn is saved for the front office and $217 bn for the middle office. AI technologies in banking can help generate over $250 billion in value. Considering the significant savings opportunities, more and more companies are implementing AI. Simply put, AI helps financial services companies mitigate risk, reduce overheads, and generate more revenue.

4. Education

Thanks to the numerous AI applications, the academic world is becoming more personalized. Today, a student can access study materials easily through computers and smart devices. AI helps automate administrative chores and minimizes the time required to complete complex tasks thereby allowing teachers to spend more time with each student. 

Teachers can assess both multiple choice tests as well as written responses easily. Robots are helping create smart content such as video lectures and simulations as well as digitized textbooks that can be customized to the learning requirements. Along with the learning aids, these digitized interfaces help students of all academic ages and grades. 

There is also a rising interest towards smart campus initiatives. A smart campus is a physical or digital set-up in which humans and technology- based systems come together to create and deliver automated experiences across higher education institutions.

AI is eliminating the boundaries of learning regardless of the physical locations. Today, students can learn any course from anywhere across the globe, at any time. AI-powered education helps nurture the fundamental IT skills of students and soon, there will be a wide range of highly interactive and personalized courses available online.

5. Retail

The retail and e-commerce industry has huge volumes of customer information, sales forecasting, stock and inventory to be tracked. Artificial Intelligence helps simplify data management to a large extent. For example, while searching for a product on an e-commerce application, AI recommends similar items according to your budget, color preference, purchase history, browsing data, online behavior, etc. 

Cart abandonment is a common issue in the e-commerce industry which occurs when a customer adds items to their shopping cart but does not purchase them. With the help of chatbots and predictive analysts, the likelihood of cart abandonment can be reduced. Chatbots can remind your customer of the items left in their cart before they choose to navigate away.

Previously, people had to rely on the FAQ section of the website to get their questions answered. However, this included unchangeable questions and static answers and most customers were not satisfied with the answers. Today, however, it is changing. A chatbot agent can respond to questions using Natural Language Processing or NLP in a much better way and ensure that potential customers don’t abandon your website. Integrating voice search features into e-commerce applications helps offer a seamless digital customer experience. 

Read more: How Will Artificial Intelligence Transform The World By 2030 

Final Thoughts

Artificial Intelligence and machine learning together are promising to help transform every industry by guiding, organizing, and automating work. AI is definitely here to stay! At Fingent, we have the expertise to help businesses of all sizes including startups as well as established enterprises to gain an edge over competitors. 

From suggesting products or providing basic customer service or running software tests, developing apps, and completing extensive problem-solving procedures for industries, we use AI technologies such as machine learning, natural language processing, and business rules that will provide you with optimal results. Get in touch with us to learn more. 

The Growing Application of AI in Insurance Leads to a Radical Transformation

• Introduction
• Why does the insurance industry require AI now?
• What are the benefits of AI in the insurance industry?
• Top 3 primary use cases for AI in the insurance industry
• Must-have AI technologies for the insurance industry
• Are you ready to ride on the wave of AI?

How Time Series Analysis Enables Businesses to Improve Their Decision Making

• Introduction
• Definition of Time Series
• The 5 Most Effective Time Series Methods for Business Development
• Time Series Regression
• Time Series Analysis in Python
• Time Series in Relation to R
• Time Series Data Analysis
• Deep Learning for Time Series
• Benefits of Using Deep Learning to Analyze Your Time Series
• Time Series is Valuable for Business Development

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. 

CX Solution to Improve Retail Growth

Nurturing communities and building loyalties is now more critical than ever for all retail brands. With instant access to the latest trends and technologies, customers demand better experiences in their interactions with retail brands across all touchpoints. Hence, Customer experience (CX) has become the most important facet of the retail marketing strategy. Retailers, therefore, have to focus on improving CX through every channel.

Importance of CX solutions

Companies can leverage authentic data and modern technology to transform customer experiences and positively impact their business’ future. While most organizations do have systems in place to track the performances of their CX strategies, few track the end-to-end customer journey. Using appropriate CX solutions, organizations can bridge the gap between expected and actual experiences. CX solutions help companies measure and understand the impact of their CX management strategies. 

By employing CX solutions, you can manage the interactions that current and potential customers can have with your brand, thus enabling you to meet or exceed their expectations. CX solutions leverage customer interactions to align the brand image according to the customer’s perceptions. This helps you foster strong and long term customer relationships.

Related Reading: 5 Ways to Enrich Customer Experience at Your Retail Store

Top Trends in CX

Staying abreast of the latest technologies and trends in Customer Experience will help you stay ahead of the competition. It’s time to hone your CX strategies by following these latest trends that rule the CX market.

• Omni-channel CX: Customer journeys have become more dynamic than ever. Based on convenience, customers constantly switch mediums. Since the line between physical and digital channels are blurring, customers expect seamless experiences in their interactions across all channels. It’s important for retailers to strike a proper balance between the “traditional” and “online” business models based on their customers’ preferences. Adopting omnichannel customer care strategies will help resolve complex issues quickly.

• Artificial Intelligence: CX enhancement requires comprehending vast amounts of chaotic and complex data in real-time at high speeds. This scenario is most suitable for AI-powered solutions. Using AI, you can replicate human-like engagements (chatbots for example), track customer-behavior and roll out customized campaigns on their preferred channel of operation. Thus you turn your data into valuable customer insights.

• Hyper personalization: Customers expect high levels of personalization and prefer to buy from brands that offer services/products that are fine-tuned according to their requirements. With a hyper-personalized approach, retailers can identify subtle customer traits and deliver highly targeted and relevant services. To develop this level of hyper-personalization, your data and analytics have to be aligned to paint a clear picture of your customers’ choices.   

• AR/VR: Augmented Reality (AR) and Virtual Reality (VR) technologies are touted as the “technologies of the future” since they provide highly immersive and engaging customer experiences. AR and VR provide customers with a hands-on experience which helps them make better choices. Many retailers are already reaping the benefits of implementing these futuristic technologies. For instance, Ikea allows customers to check how the furniture would look in their homes before buying using AR. Famous clothing brand Marks and Spencer uses virtual try-on mirrors to boost their store experiences.

• Virtual assistants and chatbots: Virtual assistants and chatbots enable companies to deliver faster and more efficient services at low costs. Some may argue that chatbots lack empathy and hence cannot replace human customer service representatives. However, you should not overlook the fact that advances in AI have given bots the ability to decipher human emotions. By combining the technologies of a virtual assistant and chatbots, you can provide your customers with personalized and empathetic experiences.

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

Future of CX

Customer Experience will continue to be crucial for brands to survive in a disruptive business environment. Retailers need to adopt agile models to retain customers and attract new ones. Going forward, CX will also depend on employee experiences. If your employees are empowered, they will in turn care for your customers. Your interactions, both with your customers as well as your employees across all channels need to be more meaningful and effective.

https://www.fingent.com/insights/portfolio/smart-product-ordering-system/

Gartner states that 64% of consumers give more importance to their experiences with a brand than to the price of a product or service. Fingent helps you implement the latest technological advancements to make your CX strategies fruitful. Contact us to know more. 

How is AI poised to transform our future?

“Artificial Intelligence is the new electricity. It has the potential to transform every industry and create huge economic value”, says Chinese-English scientist and entrepreneur, Andrew Ng. The impact of artificial intelligence on our daily lives cannot be overlooked. From smartphones to ride-sharing apps, smart home devices, Google search, and Social media- there is hardly any industry or sector that is left untouched by AI. 

There has been a huge surge in patenting of artificial intelligence in the last few years. PwC estimates that by 2030, AI would contribute a whopping $15.7 trillion to the global GDP. Analysis by the World Intellectual Property Organization (WIPO) states that the number of AI-related patent applications rose from 18,995 in 2013 to 55,660 in 2017. WIPO Director-General, Francis Gurry says that “We can expect a very significant number of new AI-based products, applications, and techniques that will alter our daily lives and also shape future human interaction with the machines we created”.

Industries such as healthcare, automotive, and financial services were the fastest to adopt AI.

Following are a few key domains that would be impacted most by AI in the coming years:

Related Reading: How AI Integration Helps Maximize Your Business ROI

AI will transform these areas in the coming years:

1. Transport

The general public would widely adopt self-driving vehicles. Apart from cars, self-driving vehicles would also include delivery trucks, autonomous delivery drones, and personal robots. Commutes may shift towards an on-demand approach like the Uber-style “cars as a service approach”. Commute-time would be viewed as a time to relax or just another way to work productively. People would live further away from their homes, reducing the need for parking space. This would change the face of modern cities. 

However, enhanced connectivity, real-time tracking, traffic gauging, route calculations, peer-to-peer ride-sharing, and self-driving cars would be impossible without personal user data. This calls for the need to implement more stringent measures to secure the data and privacy of citizens.

2. Home/ service robots

Robots have already entered our homes in the past fifteen years. Recent advances in mechanical and AI technologies substantiate the increasing safety and reliability of using home robots. In the foreseeable future, we can expect special-purpose robots to deliver packages to our doors, clean offices and enhance security. 

We are already familiar with the vacuum cleaning robot – Roomba, which has gained its place in millions of homes across the world. The AI capabilities of these kinds of robots are being increased rapidly with drastic improvements in the processing power and RAM capacity of low cost embedded processors. Low cost and safe robot arms are being used in research labs all over the world. Further advances enabled by deep learning will enable us to better interact with robots.

3. Healthcare

Healthcare is a promising domain for the use of AI technologies. AI-based applications have started gaining the trust of doctors, nurses, and patients. By revising the policies and other commercial regulations regarding the development and usage of such applications, AI can be used to improve health outcomes and quality of life for millions of people in the coming years. Patient monitoring, clinical decision support, remote patient monitoring, automated assists to perform surgeries, and healthcare management systems are some of the potential applications of AI in healthcare. 

4. Education

AI has the potential to enhance education at all levels, by providing personalization at scale. While computer learning will not replace human teachers, Massive open online courses (MOOCs) will help students learn at their own pace with techniques that work for them. AI technologies such as Natural language processing, machine learning, and crowdsourcing are giving an impetus to online learning. If these technologies can be meaningfully integrated with face-to-face learning, AI will find more applications in our classrooms. 

5. Entertainment

AI has already transformed this domain to a considerable extent. AI-driven entertainment is gaining huge traction and response from the masses with overwhelming enthusiasm. AI-enabled entertainment will become more interactive, personalized and engaging by 2030. However, the extent to which technology replaces or enhances sociability is debatable. More research is required to understand how to leverage these attributes of AI for the benefit of society.  

Related Reading: Building Incredible Mobile Experiences by Combining AR and AI

Concerns about AI

Advances in AI have already impacted our lives. However, you may also have heard of the dire predictions regarding AI made by some of the brightest minds such as the late scientist Stephen Hawking and Elon Musk (Tesla and SpaceX chief). Pew Research Centre surveyed some 979 technology experts to find out whether advancing AI and related technology would help or harm humanity. 63% of the respondents were hopeful of a better future in 2030. Many of them said that all would go well only if the concerned authorities paid close attention to how these tools, platforms, and networks are engineered, distributed and updated. 

Following were the concerns that were mentioned most often:

1. Individuals would lose control over their lives due to the use of AI
2. Surveillance and data systems that favor efficiency over human betterment would be dangerous.
3. AI would cause millions of people to lose their jobs leading to economic and social upheaval.
4. As people continue to depend on AI, their cognitive, social and survival skills would be diminished. 
5. Cybercrime, cyberwarfare and the possibility of essential organizations being endangered by weaponized information would open new facets of vulnerabilities. 

Overcoming the concerns

Following are a few solutions to take positive advantages of AI:

1. The global population should join hands and create cohesive approaches in tackling AI’s challenges.
2. The development, policies, regulation, and certification of autonomous systems should undergo essential transformations to ensure that any kind of AI development would be directed towards the common good.
3. Corporate and government organizations should shift their priorities towards the global advancement of humanity rather than profits and nationalism. AI advances should be aimed at human augmentation, regardless of economic class. 

Nicholas Beale rightly said, “AI done right will empower.” As artificial intelligence continues to be embedded in most human endeavors, let us make broad changes for the better. Let us be more thoughtful about how these technologies are implemented constructively.   

If you would like to know more about Fingent’s development and implementation approach on AI, give us a call. 

How AR and AI work together to build unique mobile experiences?

The intriguing partnership of Augmented Reality (AR) and Artificial Intelligence (AI) is a match made in the digital heaven. An AR application can become more beneficial when AI is incorporated into it. The natural bridging of AR and AI enables mobile app developers to build more interactive and intriguing apps. This article explores a few practical ways in which AR and AI can be combined to build incredible mobile experiences.

Awesome Ways AI and AR Complement Each Other

The partnership between AR and AI is likely to have a profound impact on customer experience. Companies are developing next-generation applications for mobiles that employ AR and AI technologies. In fact, AI is the heart of AR platforms.

Related Reading: How Top Brands Embrace Augmented Reality for Immersive Customer Experiences

Though Artificial Intelligence and Augmented Reality have distinct technologies, they can sync with one another on a variety of applications. They can leverage each other’s best features and aspects building incredible mobile experiences. AI enables AR to have a multidimensional interaction with the physical environment. It allows you to manipulate 2D and 3D virtual objects with your words, eyes, and hands. 

It is anticipated that the demand for AR apps is bound to soar in the next four to five years. Hence, the search for appropriate software development kits (SDK) and application program interfaces (API) for AI and AR is on.

Current State of SDKs and APIs For AR and AI

As the capabilities of current SDKs (Software Development Kits) and APIs (Application Programming Interfaces) rapidly expand, the number of commercial opportunities increase exponentially. Consider a few examples:

• • Vuforia: It is an Augmented Reality SDK that enables app developers to build mobile-centric, immersive AR experiences. It is capable of supporting both IOS and Android, allowing brands to develop apps with minimal commercial and technical risks. 
  • ARCore: It is Google’s proprietary AR SDK. It enables developers to get their AR apps up and running on mobile devices. ARCore supports IOS devices and allows developers to build rich and immersive AR experiences supported by mobile devices.  
  • Core ML: It is a Machine Learning framework used across Apple devices. This API allows you to perform real-time predictions of live images on your device. Its low latency and near real-time results are its biggest advantages. Core ML is an application that can be run without network connections. 
  • TensorFlow Lite: It is an open-source deep learning framework focused on mobile device inference. TensorFlow Lite enables developers to insert their own custom models.

Practical Ways to Combine AR and AI

The marriage of AR and AI opens up endless opportunities. Here are a few ways in which this combination is working to create digital miracles.

1. Speech recognition: As an AI model listens to what you say, AR effects appear in front of you. For example, if you say ‘pizza,’ a virtual pizza slice appears in front of your mouth. 

2. Image recognition and image tracking: It allows customers to see how an object would look and fit in a given space. Combining AR with AI technology allows users to move still photos of items into a still image of a room and assists them in making a decision. Example: IKEA Place. 

3. Human pose estimation: It is a technique that detects human figures and poses. It predicts the positions of a person’s joints in an image or video. This can be used in controlling AR content. Yopuppet.com is one example. 

4. Education: It allows students to have new perspectives through interaction with virtual reality. For example, it enables them to visualize and interact with a 3D life-size version of the human body. 

Related Reading: Impact Of Augmented Reality In Education Industry

5. Recognizing and labeling: When the camera is pointed to a scene or an image, the AR app displays a label that indicates the object or the item when it recognizes it. 

6. Car recognition: Using a smartphone camera, it allows its customers to sit inside the car and explore the car’s interiors. There isn’t even a need to download the application. 

7. Object detection: AR-AI combination can be applied to automatically learn and detect the position and extent of the objects within an image or a video. This mobile-friendly model facilitates interaction between physical and digital objects. 

Take Away

The bridging of AR and AI is offering businesses an opportunity to empower their customers more than ever before with information shared in captivating ways. Together, AR and AI continue to enhance mobile experiences.  It enables developers to design richer and more intuitive, relevant experiences for their diverse consumers. As we noted earlier, the applications of AR and AI are numerous. 

To know more about how Fingent can help you build incredible mobile experiences by combining AR and AI, get in touch with our experts today!

How AI is bringing change to the software testing practice

Artificial Intelligence is penetrating into multiple functions performed by the software industry. In software testing, the technology holds the potential to be a game-changer. Imagine the capability of your software to test and diagnose itself and make self-corrections! This will lead to huge savings on your resources. With this in mind, let’s try and understand exactly how AI will impact the traditional way of software testing. 

Before we proceed, let’s get one thing clear – Do we really need AI in software testing?

Do We Really Need AI in Software Testing? 

Software testing came into existence as a result of the evolution of development methodologies. It fed the need for robust, error-free software products. Testing was a laborious task for sure. However, automating software testing required traceability and versioning, both of which were critical and needed careful consideration. Something was needed to resolve this.

As businesses move towards digital transformation and the software market continues to grow, businesses expect a real-time risk assessment across all stages of the software delivery cycle. AI in software testing is the right response to these challenges. AI can develop error-free applications while enabling greater automation in software testing. This helps meet the expanded, critical demands for testing. It improves the quality of engineering and reduces testing time allowing the tester to focus on more important things. The verdict is clear then – We Really Need AI for Software Testing!

Five Impressive Ways AI Impacts Software Testing

1. Improves object application categorization

AI is widely used in object application categorization. When tools and testers are created, unique pre-train controls can be created. Once the hierarchy of the controls is categorized, testers can create a technical map to obtain labels for the different controls. 

In the near future, AI will become capable of observing users perform exploratory testing on the testing site. And once user behavior is assessed, it can assign, monitor, and categorize the risk preference.

2. Automation of test case writing 

Gone are the days of web crawlers. As automation is picking momentum, AI tools have become capable of learning business usage scenarios of test applications. 

Related Reading: Unconventional Ways Artificial Intelligence Drives Business Value

They can automatically collect insightful data such as HTML pages, screenshots and page loading time and eventually train ML models for expected patterns of the app. And as soon as they are executed, any variations are marked as potential issues. This makes it easier for the tester to find and validate differences and fix issues. 

3. Enhanced accuracy

To date, source analysis requires human resources to accomplish the task. Unfortunately, because of the enormity of the data, even the best experts could overlook, or miss out on observing certain critical defects. Human error and the tendency to lose focus further impairs the experts involved in software testing. It can be disastrous if bugs caused by these errors are caught by consumers before project stakeholders. Product positioning and brand reputation can be jeopardized. 

Thankfully, AI can teach systems to learn source analysis and, in the future, apply this acquired knowledge. This ensures that testers have greatly enhanced accuracy. It eliminates the probability of human error and also shortens the time to run a test and increases the possibility of finding defects or bugs. 

4. Automation without the user interface

AI-based techniques can be applied for non-functional tests such as performance, security and unit integration. It can also be applied on various application logs which assists in developing auto-scaling capabilities such as bug prediction.

AI algorithms can enhance UI testing, predict the next test, determine the outcomes for subjective and complex tests and much more. In other words, AI could increase the overall test coverage while it increases the depth and scope of the test itself.

5. Reduces cost and decreases time to market

The need for manually repeating a test is time-consuming and extremely expensive. But with AI, such tests can be automated to repeat several times over. Each time the software test is repeated automatically, the source code gets modified to correct any bugs. This eliminates the additional cost of repeating the test and increases the speed of the test from days to hours, which in turn saves more money.

Related Reading: Quality Assurance in Software Testing – Past, Present & Future

Allow AI to Revolutionize your Business

AI has proven to have a significant impact on software testing with its benefits ranging from optimization to extraordinary savings. It enables testers to move beyond the traditional route and dive toward precision-based testing processes. This can prove invaluable to your business. To find out how you can make this happen for your business, contact us. 

Knowledge Representation Models in Artificial Intelligence  

Knowledge representation plays a crucial role in artificial intelligence. It has to do with the ‘thinking’ of AI systems and contributes to its intelligent behavior. Knowledge Representation is a radical and new approach in AI that is changing the world. Let’s look into what it is and its applications. 

Understanding Knowledge Representation and its Use

Knowledge Representation is a field of artificial intelligence that is concerned with presenting real-world information in a form that the computer can ‘understand’ and use to ‘solve’ real-life problems or ‘handle’ real-life tasks.

The ability of machines to think and act like humans such as understanding, interpreting and reasoning constitute knowledge representation. It is related to designing agents that can think and ensure that such thinking can constructively contribute to the agent’s behavior. 

In simple words, knowledge representation allows machines to behave like humans by empowering an AI machine to learn from available information, experience or experts. However, it is important to choose the right type of knowledge representation if you want to ensure business success with AI. 

Four Fundamental Types of Knowledge Representation

In artificial intelligence, knowledge can be represented in various ways depending on the structure of the knowledge or the perspective of the designer or even the type of internal structure used. An effective knowledge representation should be rich enough to include the knowledge required to solve the problem. It should be natural, compact and maintainable. 

Related Reading: 6 Ways Artificial Intelligence Is Driving Decision Making

Here are the four fundamental types of knowledge representation techniques: 

1. Logical Representation

Knowledge and logical reasoning play a huge role in artificial intelligence. However, you often require more than just general and powerful methods to ensure intelligent behavior. Formal logic is the most helpful tool in this area. It is a language with unambiguous representation guided by certain concrete rules. Knowledge representation relies heavily not so much on what logic is used but the method of logic used to understand or decode knowledge.

It allows designers to lay down certain vital communication rules to give and acquire information from agents with minimum errors in communication. Different rules of logic allow you to represent different things resulting in an efficient inference. Hence, the knowledge acquired by logical agents will be definite which means it will either be true or false. 

Although working with logical representation is challenging, it forms the basis for programming languages and enables you to construct logical reasoning.

2. Semantic Network

A semantic network allows you to store knowledge in the form of a graphic network with nodes and arcs representing objects and their relationships. It could represent physical objects or concepts or even situations. A semantic network is generally used to represent data or reveal structure. It is also used to support conceptual editing and navigation. 

A semantic network is simple and easy to implement and understand. It is more natural than logical representation. It allows you to categorize objects in various forms and then link those objects. It also has greater expressiveness than logic representation. 

Related Reading: Understanding The Different Types Of Artificial Intelligence

3. Frame Representation

A frame is a collection of attributes and its associated values, which describes an entity in the real world. It is a record like structure consisting of slots and its values. Slots could be of varying sizes and types.  These slots have names and values. Or they could have subfields named as facets. They allow you to put constraints on the frames. 

There is no restraint or limit on the value of facets a slot could have, or the number of facets a slot could have or the number of slots a frame could have. Since a single frame is not very useful, building a frame system by collecting frames that are connected to each other will be more beneficial. It is flexible and can be used by various AI applications. 

4. Production Rules

Production rule-based representation has many properties essential for knowledge representation. It consists of production rules, working memory, and recognize-act-cycle. It is also called condition-action rules. According to the current database, if the condition of a rule is true, the action associated with the rule is performed. 

Although production rules lack precise semantics for the rules and are not always efficient, the rules lead to a higher degree of modularity. And it is the most expressive knowledge representation system. 

Gain the Benefits of Knowledge Representation

Used properly, knowledge representation enables artificial intelligence systems to function with near-human intelligence, even handling tasks that require a huge amount of knowledge. The increasing use of natural language also makes it human-like in its responses. Making the right choice in the type of knowledge representation you must incorporate is crucial and will ensure that you get the best out of your artificial intelligence system. If you need help with this, we’re here. Please reach out to us.