Tag: AI

Supercharge Your Business with These 11 Hot Tech Trends

Technology is having an ever-greater impact on our personal lives and most importantly on the way we do business. The business world has transformed rapidly in the past few years and it will evidently continue to change the pace of business in the years to come. Whether it is the production of goods or the computing devices used at the office, new technologies have helped businesses run smoother and more effectively. As information travels more quickly and reliably, businesses are realizing how easy it is to grow globally and across multiple sectors. To gain benefits, however, businesses must keep up with technology and adopt new trends. This article discusses 11 tech trends to look forward to in the next couple of years. First, let us understand what drives these tech trends, and then we will consider their impact on businesses.

What Will Drive the Evolution of Tech Trends?

Two major reasons for the evolution of all kinds of tech trends are our day-to-day business challenges and the passion for innovation. As an intelligent being, it is normal for human beings to innovate to live better. However, from a business standpoint, it is about optimizing all that humans are capable of accomplishing and that includes making profits. 

1. Greater predictive levels and programmability will reshape cybersecurity

Cybersecurity will be one of the prominent IT functions to mature. New technologies will bring about a fundamental shift in cybersecurity as they enable greater predictive capabilities and programmability. It will become more predictive with the use of large-scale data along with AI, analytics, and machine learning. As a general rule, the more we have, the more difficult it becomes to protect it from thieves. That is not the case with immense data accumulation. Even as data increases it will become easier to determine and match patterns to both predict and shut down any attack vectors. 

As every element of infrastructure becomes programmable, you can put a firewall inside the software of all virtual machines in your architecture, thus limiting the flow of data within the software. This will eliminate the need of storing data on a network from where the data can easily be hacked or stolen. Businesses will continue to see this capability emerge as programmability improves. 

Read more: Safeguarding IT Infrastructure From Cyber Attacks – Best Practices

2. Promise of serverless computing 

According to IDC’s prediction, “from 2018 to 2023 — with new tools/platforms, more developers, agile methods, and lots of code reuse — 500 million new logical apps will be created, equal to the number built over the past 40 years.” Currently, we are witnessing a distinct change in the application infrastructure with most businesses moving to cloud-native applications. 

There are four distinct computing models that are evolving simultaneously such as virtual servers, physical servers, container-based computing, and serverless computing. Virtual servers and container-based computing make it easy to move applications. Whereas the promise of serverless computing offers greater agility and cost savings as the applications do not need to be deployed on a server. Alternatively, functions can be run from a cloud provider platform. It can return outputs and instantly release the associated resources.  As businesses see a change in the infrastructure, they will have to make choices about how they approach development. 

Read more: 5 Trends That Will Transform Cloud Computing in 2020 

Accelerated in part by the long-term shutdown due to COVID-19, industries that design and manufacture products will quickly adopt cloud-based technology trends to aggregate and intelligently transform. In a couple of years, these intelligent algorithms will allow manufacturing assembly lines to optimize towards increased levels of output and enhanced product quality thereby reducing the overall waste in manufacturing by half.

3. IoT and Edge

IoT and Edge can rightly be termed as superpowers of the tech world. The developing, managing, and running of widespread IoT and Edge applications will grow in complexity with numerous endpoints.  For example, audiovisual technologies are being used to achieve the same input as you would get when you connect numerous IoT sensors. When compared to individual sensors, these tech trends provide reasonable mass coverage at a minimum cost. This is only possible when AI and ML are integrated into the IoT platform. Powering and maintaining thousands of sensors is a daunting prospect. Audiovisual solutions can thus make this a significant growth area. As a result, these changes will have a profound impact on how the businesses get value out of new data that they are able to collect and process.

Read more: Gearing up for IoT in 2020 

4. Everything will revolve around data 

Enormous amounts of data collected from IoT devices and digital platforms can now be made available through application programming interfaces for business insights, analysis and to develop other applications. Collecting information from a sufficient amount of data-points enables you to model behavior and understand patterns and come to more accurate conclusions quickly with minimum cost. With abundant data from multiple touchpoints and new analytic tools, businesses are able to customize products and services by creating ever-finer consumer microsegments. Businesses that do not build around data will find themselves swamped by its enormity. 

5. Voice control is the next evolution of human-machine interaction

The advent of voice technology such as Apple’s Siri, Google’s Assistant, and Amazon’s Alexa is disrupting businesses as it creates a three-way interaction between devices, services, and people. It has completely changed the way consumers interact with smart devices. 

According to recent research, by 2024, the global voice-based smart speaker market could be worth $30 billion. This technology trend has a huge impact on how online searches will happen. Businesses will have to adapt their way of promoting their products and services. It will also affect the way companies are organized as internal knowledge can be shared more easily which improves the possibility of multitasking. This will result in increased productivity. 

In the next couple of years, we will see a transformation of voice technology from being an information tool to a transaction tool. It offers the possibility to directly order from brands and perhaps even pay.

Read more: 6 Key Predictions for AI-Driven Voice Computing in 2020 

6. Blockchain platform market

Blockchain started as an offshoot of the cryptocurrency movement. Now, it is evolving to find use cases beyond just the international settlement areas. According to Gartner, Blockchain will grow to slightly more than $176 billion by 2025 and continue to exceed $3.1 trillion by 2030. The marriage of Blockchain and Artificial Intelligence can significantly change the nature of transactional businesses. This is possible as Blockchain is a decentralized unchangeable space for encrypted data and AI will assist you in analyzing and interpreting that data quickly and reliably to drive actionable insights. There is great potential for this technology to have an immense impact on cybersecurity. 

Read more: How Blockchain Enables the Insurance Industry to Tackle Data Challenges 

7. Seamless blend of the digital twin

Applied technology will intersect between the physical and digital worlds, the digital twin. For example, the digital twin will have a perfect digital copy of the physical world.  Applied technology will allow you to blend these two worlds seamlessly. The resulting immersive environments will have a pervasive impact on the industry. This twin will allow you to collaborate virtually, simulate conditions quickly, understand what-if scenarios clearly, predict results more accurately, and more. Most businesses are already aware of the benefits of applying the digital world to enhance the physical world. They are digitizing physical processes to reduce inconsistencies, redundancies, and human error.

Explore: Learn more about Digital Twin Technology 

This pandemic has shown us that communication is not just for work but is required to form real emotional connections. In the next couple of years, AI technology will be used to connect people at a human level and drive them closer to each other. There has been a lot of concern over the security of video conferencing companies. However, these concerns will move companies to ensure that they provide secure digital connectivity for their consumers. 

Being a secure video conferencing software, InfinCE has been a game-changer for enterprises of all sizes. Click here to explore

8. 5G will be the game-changer

5G has innumerable use cases beginning from healthcare to more reliable security. With 5G, the audio-video experience will be faster and clearer than it has ever been. On the flip side, 5G will enable businesses to provide remote opportunities for their employees with work experience that would be similar to that inside the office. It will bolster recruitment and retention efforts for top talent. 

As more businesses move their critical tasks to the cloud, employees will become increasingly productive from wherever they work and with whatever device they use. Though currently, 5G coverage is limited, according to Ericson’s Mobility report, 5G subscriptions could cover up to 65% of the world’s population by 2025. Businesses that anticipate and embrace these emerging technology trends will see a positive impact in the years to come. Low latency 5G networks can help resolve the challenges caused by the absence of reliable networks and can facilitate more high-capacity services. Private 5G networks can offset the high cost of mobility with economy-boosting activities. 

Read more: From Remote Work to Virtual Work, 5G is Reinventing the Way We Work 

9. Data lakes enable new analytic models

Data lakes are storage repositories that contain quantitative and qualitative data. Data lakes enable new models of predictive analytics and help unlock the potential of digital twins. Since they can hold enormous amounts of data, organizations can leverage the insights, including discrete data points to create a ‘digital twin’ of each customer. You can gain access to customer details such as demographic data, browsing behavior, purchasing patterns, and payment preferences. The ability to gauge qualitative data will increase the demand for robust ERP systems and AI-driven automation. This would mean that businesses should acquire the skills to set up, manage, and secure their data lakes and build data models that will help extract the insights they require for ongoing innovation. 

Read more: 7 Key Differences Between Data Lake and Data Warehouse 

10. Sophisticated sentiment analysis for real-time insights

Sentiment analysis uses techniques to interpret and classify the ‘mood’ of your customers. Sophisticated sentiment analytical tools allow businesses to recognize the customers’ sentiment towards a product, a service, or a brand. It can also be used by the businesses to respond to the feedback with a proactive approach. It allows businesses to understand how people are feeling in real-time and proactively position products, services, and visual merchandise. In the future, this technology will be used in addition to tools such as conversation intelligence, text analysis, and natural language processing. It can enable innovation on demand. Businesses will find it advantageous to incorporate sentimental analysis into their data analysis in the areas of customer feedback, marketing, CRM, and e-commerce.

Read more: CTOs Guide – How Robotics and AI Can Improve Customer Experience 

11. Micro-fulfillment for e-commerce fulfillment 

Robotics has turned around numerous industries except for a few sectors such as grocery retail. With the new robotic application termed micro-fulfillment, grocery retailing will no longer remain the same. Micro fulfillment allows you to convert personal garages into storage spaces and can operate 5-10% more economically than a brick and mortar store. This rising trend is captured in tiny, urban warehouses that leverage high-end automated systems to complete online orders with greater efficiency. These centers are used to deliver goods rapidly, in as fast as an hour. Robotic arms can be used to pick up items. The application of robotics downstream at a ‘hyper-local’ level will disrupt the grocery retail industry. This technology trend will unlock wider access to food and a better customer proposition such as product availability, speed, and cost. 

Read more: 5 Transformative Trends Ushered by B2B E-commerce in Healthcare and Life Sciences 

How Technology will Continue to Disrupt Businesses?

The transformative potential of innovative technology trends is exciting businesses today. It will change the way businesses plan, start, manage, operate, market, and make a profit. The next couple of years will see profound improvements in addressing most business challenges as organizations develop and deploy solutions that will deliver tangible results. Driverless cars, 3d printing, artificial and business intelligence tools, robotics, and IoT are just a few examples of how technology has transformed or disrupted the business world and has the potential to continue to disrupt. 

The COVID-19 pandemic has necessitated worldwide collaboration, transparency of data, and speed at the highest levels to navigate the human and business impacts. Now is the time to recognize and support the opportunities for technology trends that can best and most rapidly address business challenges. Partner with us to capitalize on these trends and scale your business quickly. 

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 top custom software development company, 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?

Robotics in Logistics: Dawn of a New Era

Since the advent of e-commerce, getting goods to the customer’s door or stores from the factories or warehouses has become a mammoth task for logistics companies. Forecasts say that “worldwide warehousing and logistics robot unit shipments will increase to an estimated 620,000 units annually by 2021.” The solution for this herculean task of transporting goods far and wide thus becomes apparent: the dawn of robotics in logistics. The use of robotics in logistics offers far greater levels of uptime over manual labor, bolstering productivity in a vast array of professional environments. 

Retail giants like Amazon and Walmart that have already deployed robots in their warehouses and fulfillment centers will only expand their deployments, especially in the wake of current situations. Leveraging robotics in logistics cuts around 70% of warehouse labor costs and helps businesses function day and night with minimal costs. Want to find out how? This post will help you understand the importance of utilizing warehouse robotics in the supply chain.

A Surge in Warehouse Robotics in Supply Chain

The first robot in the supply chain was capable of moving material about a dozen feet. For several years, robots were used only in industrial manufacturing because it was not safe for people to be around them. However, over the last few decades, innovative logistic robotic companies have worked hard to mesh AI and machine learning, better sensors and response capabilities, warehouse management software or logistics management software. 

Recently, warehouse robotics in the supply chain has picked up pace exponentially. There has been huge funding and investment in the industry. For example, Alibaba invested $15 billion into robotic logistics infrastructure and Google invested $500 million into automated logistics for JD. It is also estimated that the global market for warehouse robotics in the supply chain is projected to reach a market value of $22.4 billion by the end of 2021.

Evidently, the dawn of robotic logistics is right here now!

Read More: How Robotic Process Automation Is Revolutionizing Industries?

What is Robotic Logistics?

The logistics industry is what is holding our modern world together. It includes a huge amount of different processes. Ordering, transportation, warehousing, picking, packing, delivery, inventory, and routing are just a few of those processes. 

So, robotic logistics means the application of robotics to one or more of these processes. A few common robotic applications are robotic palletizing, robotic packaging, robotic picking commonly used in warehousing or any other logistics software solutions. 

So, what kind of robots could be useful for your warehouse?

Warehouse Robotics in Supply Chain

1. Autonomous Mobile Robots (AMR)

AMRs use sophisticated sensor technology to deliver inventory all over the warehouse. They do not require a set track between locations. They can understand and interpret their environment through the use of maps, computers, and onboard sensors.

These warehouse robots are small and nimble with the ability to identify the information on each package and sort it with impeccable accuracy. They cut down on the redundant manual process which is prone to human error. 

2. Aerial Drones

Aerial drones aid in optimizing warehouse inventory processes. They can quickly scan locations for automated inventory. They can scan inventory much faster than a human can and send an accurate count immediately to your warehouse inventory management software.  

These drones do not need markers or lasers to guide them. They don’t take up valuable space in your warehouse. They can travel quickly and assist in hard-to-reach areas. 

3. Automated Guided Vehicles 

Automated guided vehicles and carts (AGVs and AGCs) transport inventory around your warehouse following a track laid in your warehouse. These warehouse robots are perfect for larger warehouses because it reduces the time spent by workers just moving from one area to the next. 

4. Automated Storage and Retrieval System (AS and RS)

Automated Storage and Retrieval Systems are robot-aided systems that can place or retrieve loads from set storage locations. AS and RS differ depending on the system needed, the type of task, or the goods that they will be working with. They can be programmed to work as a craft that moves and works on a well-defined path or a crane that retrieves goods between aisles. There are also aisle climbing robots that retrieve customer orders. 

These free up the time of workers who can then concentrate on more complicated processes such as packing and posting the goods. 

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

What is Driving the Need for Collaborative Robots in Logistics?

Although there has been a boom in logistic robotics, there are two specific factors that are driving the current need for collaborative robots in logistics. 

• The growth of e-commerce: When products are directly shipped to customers, there is a huge variety of different packing requirements. 
• The lack of available workforce: Shortage of skilled workers can affect logistics.

What are the Benefits of Adopting Robotic Logistics? 

The logistics industry can see many tangible and clear benefits of adopting robotic logistics. 

• By reducing human errors, robotic logistics can bring in significant profits and can also reduce warehouse costs. 
• Robotics can allow for workforce adaptability. 
• Robotic logistics improve safety for workers by taking over dangerous jobs such as getting items from high racks or storage spaces.
• Reduced human error and increased delivery speed brought about by robotic automation will increase customer satisfaction. 

Read more:  Open source robotics process automation

Enjoy the Freedom To Do More

Robots are being used rather extensively in logistics. Due to the complexity of supply chain processes robots will be increasingly used for dull, dirty, and dangerous tasks freeing your workers for more complex tasks. This means cost-effective, fast, and error-free operations. If you’re seeking to revolutionize your business with the incredible potential of robotics, Fingent, a top custom software development company, is here to assist you.

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

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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 custom software development experts and see if this is something that could help your business. 

How AI and Voice Search Will Impact Your Business

“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 coming years. 

In this blog, we will consider six key predictions for AI-Driven voice computing in 2024.

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 future. 

Six key predictions for AI-driven voice search and conversational AI in 2024

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. Coming years 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 2024?

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. In coming years, 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 top custom software development company 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.

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2. Artificial General Intelligence (AGI)

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

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

3. Artificial Super Intelligence (ASI)

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

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

Top Artificial Intelligence Trends to Watch Out for In 2024

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, Contact Fingent, top custom software development company, right away to explore strategies for implementing AI in your business. Unlock the potential of AI and achieve positive outcomes for your organization.

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.

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