Six Trends in Mobile Application Development To Watch

It is predicted that there are 7 billion mobile users around the world in 2021. It’s projected that this figure will continue to rise as technology becomes more accessible each passing day. Mobile phones have integrated into our lives seamlessly over the past decade.

Regardless of your industry, the mobile application development industry has been genuinely altering and redefining business for quite a while now. Almost every company needs to amalgamate the latest mobile app development trends and extend its marketing strategy to gain traction towards optimum growth and reach its targeted market effectively.

Kodak, Compaq, Blockbuster Video – what do these companies have in common? Despite being widely popular in the past, they failed to keep up with technology and, therefore, ended up closing or selling out. All this at a time when technology was not changing as fast as it is today. Technology truly is in a constant state of flux. When businesses fail to keep up, they usually tend to go the Blockbuster way. 

The only way to stand out in such an environment is through constant innovation. Whether you are a developer or a business with a mobile app, you must be updated with the latest mobile app trends. Without incorporating these trends into your apps, your mobile might become obsolete.

Apps are being developed faster than ever to meet the rising demand for new content. Consumers today expect services to come with apps with friendly, clean user interfaces. The presentation of your brand through your app can go a long way with the tech-savvy customers of today. Let’s look at the emerging trends in mobile application development that you must keep an eye out for. 

What are the New Trends in Mobile Application Development?

1.Wearables

Wearables have taken the world by storm; whether it’s on the Subway or at the gym, you can see everybody decked out with the latest wearables. Then, the Apple Watch and AirPods paved the way for more development in space. Today, every manufacturer offers its version of the smartwatch and smart earbuds. These are capable of doing everything from helping you navigate to your destination, and some might even let you make a call without your phone being around! With the wearable industry being valued at over $44 billion, it’s safe to say wearables are one of the top mobile application trends.

Wearable Trends in 2021

• Fitness-based tech to stay at the forefront.
• Move to make wearables more independent of the smartphone.

2.On-Demand Development Apps

On-demand development apps were created to fill a void in the mobile app development industry. Building apps required technical expertise and knowledge of coding, but today, the on-demand development model has made building apps so much more accessible. Are you running a business and looking to scale using apps? Chances are, you won’t have to build it yourself at all. There’s a good chance that there’s an on-demand app that will do everything that you expect from it. This statistic says 42% of adults have used on-demand services. The on-demand development model is likely to grow as the demand for simplified app development increases.

On-Demand Apps in 2021

• More industries adopting the on-demand model
• B2B transactions are emphasised

3.Mobile Wallets

The pandemic changed our lifestyles and forced us to adopt a digital-first alternative. Today, everything from buying groceries to paying people for their services is done online. Mobile wallets have simplified online payments and made them accessible to everyone.

As we embrace transferring money online, service providers will push to make their products better and more secure. Security of funds and transactions is one of the primary concerns when it comes to mobile wallet development. Social distancing is the new norm post the pandemic, so contactless payment solutions like Apple Pay and Google Pay solve the problem. Going forward, security and ease of payment will drive innovation in this sector to emerge as a critical mobile application trend.

Mobile Wallet Trends in 2021

• 2 billion users worldwide and counting
• Secure and convenient wallets

4.Cloud-based Apps

Cloud technology has grown so much over the past few years. Cloud storage is growing to become inexpensive as service providers invest in more efficient cloud infrastructure. Cloud technology is the backbone of mobile app development in 2021.

Many things we do on apps today leverage cloud technology, like booking a cab or ordering food. Cloud has made web hosting inexpensive, more load efficient and accessible. This has prompted the quick adoption of the mobile technology trend.

Cloud Trends in 2021

• Efficient cloud infrastructure
• Hybrid cloud solutions
• Quantum computing

5.Smart Things / IoT

The Internet of Things is an ecosystem of intelligent devices that can communicate with other devices over the Internet. Everything from the lights in our homes to the ovens in our kitchens can be controlled through Alexa, Siri, or Google Assistant. This is the future envisioned by the IoT, and we’ve warmed up to it well so far. Manufacturers like Samsung, Xiaomi, Nest, and Honeywell build a solid platform at accessible price points. Some of the intelligent critical mobile application technology trends are:

IoT trends in 2021

• More affordable IoT tech
• Self-driving cars
• Smart home and appliances

6.Augmented Reality (AR) and Virtual Reality (VR)

Who isn’t familiar with Pokemon Go? The game took the world by storm and brought augmented reality into the mainstream. While augmented reality superimposes artificial objects on real-world objects, virtual reality offers an entirely artificial environment.

But games aren’t the only area of application of AR and VR. These technologies can be used to improve the efficacy of training and educational apps. They can give the student a true sense of performing the job at hand. 

Interior designing and marketing are other areas where AR and VR apps are creating game-changing experiences. The app can let the user see how the product will look in a particular space or give you a better idea about its size and shape.  

AR/VR Trends in 2021

• AR/VR in marketing, healthcare, education, and other industries
• Mobile AR technology is going to be at the forefront

Future of Mobile Application Development: 2021 and Beyond

Technology is constantly evolving, with new iterations of technology hitting the shelves every year. These mobile technology trends in the market provide plenty of new opportunities for app developers.

Keeping up with trends is vital to stay on top of the mobile app development space. In 2021, technologies like wearables, the Internet of Things, and cloud computing will continue to catch steam. 

This article was originally published here

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Dominant Data Science Developments in 2021

There’s nothing constant in our lives but change. Over the years, we’ve seen how businesses have become more modern, adopting the latest technology to boost productivity and increase the return on investment. 

Data analytics, big data, artificial intelligence, and data science are the trending keywords in the current scenario. Enterprises want to adopt data-driven models to streamline their business processes and make better decisions based on data analytical insights. 

With the pandemic disrupting industries around the world, SMEs and large enterprises had no option but to adapt to the changes in less time. This led to increasing investments in data analytics and data science. Data has become the center point for almost every organization. 

As businesses rely on data analytics to avoid and overcome several challenges, we see new trends emerging in the industries. AI trends 2021 by Gartner are an example of development. The trends have been divided into three major heads- accelerating change, operationalizing business value, and distribution of everything (data and insights). 

In this blog, we’ll look at the dominating data science developments in 2021 and understand how big data and data analytics are becoming an inherent part of every enterprise, irrespective of the industry. 

1. Big Data on the Cloud 

Data is already being generated in abundance. The problem lies with collecting, tagging, cleaning, structuring, formatting, and analyzing this huge volume of data in one place. How to collect data? Where to store and process it? How should we share the insights with others? 

Data science models and artificial intelligence come to the rescue. However, storage of data is still a concern. It has been found that around 45% of enterprises have moved their big data to cloud platforms. Businesses are increasingly turning towards cloud services for data storage, processing, and distribution. One of the major data management trends in 2021 is the use of public and private cloud services for big data and data analytics. 

2. Emphasis on Actionable Data 

What use is data in its raw, unstructured, and complex format if you don’t know what to do with it? The emphasis is on actionable data that brings together big data and business processes to help you make the right decisions. 

Investing in expensive data software will not give any results unless the data is analyzed to derive actionable insights. It is these insights that help you in understanding the current position of your business, the trends in the market, the challenges and opportunities, etc. Actionable data empowers you to become a better decision-maker and do what’s right for the business. From arranging activities/ jobs in the enterprise, streamlining the workflows, and distributing projects between teams, insights from actionable data help you increase the overall efficiency of the business. 

3. Data as a Service- Data Exchange in Marketplaces 

Data is now being offered as a service as well. How is that possible? 

You must have seen websites embedding Covid-19 data to show the number of cases in a region or the number of deaths, etc. This data is provided by other companies who offer data as a service. This data can be used by enterprises as a part of their business processes. 

Since it might lead to data privacy issues and complications, companies are coming with procedures that minimize the data risk of a data breach or attract a lawsuit. Data can be moved from the vendor’s platform to the buyer’s platforms with little or no disturbance and data breach of any kind. Data exchange in marketplaces for analytics and insights is one of the prominent data analytics trends in 2021. It is referred to as DaaS in short. 

4. Use of Augmented Analytics 

What is augmented analytics? AA is a concept of data analytics that uses AI, machine learning, and natural language processing to automate the analysis of massive data. What is normally handled by a data scientist is now being automated in deliver insights in real-time.    

It takes less time for enterprises to process the data and derives insights from it. The result is also more accurate, thus leading to better decisions. From assisting with data preparation to data processing, analytics, and visualization, AI, ML, and NLP help experts explore data and generate in-depth reports and predictions. Data from within the enterprise and outside the enterprise can be combined through augmented analytics. 

5. Cloud Automation and Hybrid Cloud Services

The automation of cloud computing services for public and private clouds is achieved using artificial intelligence and machine learning. AIOps is artificial intelligence for IT operations. This is bringing a change in the way enterprises look at big data and cloud services by offering more data security, scalability, centralized database and governance system, and ownership of data at low cost. 

One of the big data predictions for 2021 is the increase in the use of hybrid cloud services. A hybrid cloud is an amalgamation of a public cloud and a private cloud platform.

Public clouds are cost-effective but do not provide high data security. A private cloud is more secure but expensive and not a feasible option for all SMEs. The feasible solution is a combination of both where cost and security are balanced to offer more agility. A hybrid cloud helps optimize the resources and performance of the enterprise. 

6. Focus on Edge Intelligence 

Gartner and Forrester have predicted that edge computing will become a mainstream process in 2021. Edge computing or edge intelligence is where data analysis and data aggregation are done close to the network. Industries wish to take advantage of the internet of things (IoT) and data transformation services to incorporate edge computing into the business systems. 

This results in greater flexibility, scalability, and reliability, leading to a better performance of the enterprise. It also reduces latency and increases the processing speed. When combined with cloud computing services, edge intelligence allows employees to work remotely while improving the quality and speed of productivity. 

7. Hyperautomation 

Another dominant trend in data science in 2021 is hyper-automation, which began in 2020. Brian Burke, Research Vice President of Gartner, has once said that hyper-automation is inevitable and irreversible, and anything and everything that can be automated should be automated to improve efficiency. 

By combining automation with artificial intelligence, machine learning, and smart business processes, you can unlock a higher level of digital transformation in your enterprise. Advanced analytics, business process management, and robotic process automation are considered the core concepts of hyper-automation. The trend is all set to grow in the next few years, with more emphasis on robotic process automation (RPA). 

8. Use of Big Data in the Internet of Things (IoT)

Internet of Things (IoT) is a network of physical things embedded with software, sensors, and the latest technology. This allows different devices across the network to connect with each other and exchange information over the internet. By integrating the Internet of Things with machine learning and data analytics, you can increase the flexibility of the system and improves the accuracy of the responses provided by the machine learning algorithm. 

While many large-scale enterprises are already using IoT in their business, SMEs are starting to follow the trend and become better equipped to handle data. When this occurs in full swing, it is bound to disrupt the traditional business systems and result in tremendous changes in how business systems and processes are developed and used. 

9. Automation of Data Cleaning 

For advanced analytics in 2021, having data is not sufficient. We already mentioned in the previous points how big data is of no use if it’s not clean enough for analytics. It also refers to incorrect data, data redundancy, and duplicate data with no structure or format. 

This causes the data retrieval process to slow down. That directly leads to the loss of time and money for enterprises. On a large scale, this loss could be counted in millions. Many researchers and enterprises are looking for ways to automate data cleaning or scrubbing to speed up data analytics and gain accurate insights from big data. Artificial intelligence and machine learning will play a major role in data cleaning automation.

10. Increase in Use of Natural Language Processing 

Famously known as NLP, it started as a subset of artificial intelligence. It is now considered a part of the business processes used to study data to find patterns and trends. It is said that NLP will be used for the immediate retrieval of information from data repositories in 2021. Natural Language Processing will have access to quality information that will result in quality insights. 

Not just that, NLP also provides access to sentiment analysis. This way, you will have a clear picture of what your customers think and feel about your business and your competitors. When you know what your customers and target audience expect, it becomes easier to provide them with the required products/ services and enhance customer satisfaction. 

11. Quantum Computing for Faster Analysis 

One of the trending research topics in data science is Quantum computing. Google is already working on this, where decisions are not taken by the binary digits 0 and 1. The decisions are made using quantum bits of a processor called Sycamore. This processor is said to solve a problem in just 200 seconds. 

However, Quantum computing is very much in its early stages and needs a lot of fine-tuning before it can be adopted by a range of enterprises in different industries. Nevertheless, it has started to make its presence felt and will soon become an integral part of business processes. The aim of using Quantum computing is to integrate data by comparing data sets for faster analysis. It also helps in understanding the relationship between two or more models. 

12. Democratizing AI and Data Science 

We have already seen how DaaS is becoming famous. The same is now being applied to machine learning models as well. Thanks to the increase in demand for cloud services, AI and ML models are easier to be offered as a part of cloud computing services and tools. 

You can contact a data science company in India to use MLaaS (Machine Learning as a Service) for data visualization, NLP, and deep learning. MLaaS would be a perfect tool for predictive analytics. When you invest in DaaS and MLaaS, you don’t need to build an exclusive data science team in your enterprise. The services are provided by offshore companies. 

13. Automation of Machine Learning (AutoML)

Automated machine learning can automate various data science processes such as cleaning data, training models, predicting results and insights, interpreting the results, and much more. These tasks are usually performed by data science teams. We’ve mentioned how data cleaning will be automated for faster analytics. The other manual processes will also follow suit when enterprises adopt AutoML in their business. This is yet in the early stages of development.

14. Computer Vision for High Dimensional Data Analytics 

Forrester has predicted that more than 1/3rd of the enterprises will depend on artificial intelligence to reduce workplace disruptions. The advent of the covid-19 pandemic has forced organizations to make some drastic changes to the business processes. The remote working facility has become necessary for most businesses. Similarly, automation is being considered a better option than relying on workers and the human touch. 

Using computer vision for high-dimensional data analytics is one of the data science trends in 2021 that helps enterprises detect inconsistencies, perform quality checks, assure safe practices, speed up the processes, and perform more such actions. Especially seen in the manufacturing industry, CV is making it possible to automate production monitoring and quality assurance. 

Conclusion 

Data science will continue to be in the limelight in the coming years. We will see more such developments and innovations. The demand for data scientists, data analysts, and AI engineers is set to increase. The easiest way to adopt the latest changes in the business is by hiring a data analytics company. 

Stay relevant in this competitive market by adopting the data-driven model in your enterprise. Be prepared to tackle the changing trends and make the right decisions to increase returns.

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How to Connect Android with Firebase Database

Databases are an integral part of all of our projects. We store, retrieve, erase, and update our data in the database developed by the application or software. Things get more difficult because you try to download and save all of the data in real-time, which means that if you update the data, the updated data can be reflected in the application at that same moment. But don’t worry, Firebase Real-time Database streamlines things. In this article, we will learn how to connect Firebase Real-time Database with our Android application

 

What is Firebase Real-time Database?

Firebase Real-time Database is a cloud-hosted database that works on Android, IOS, and the web. All data is stored in JSON format, and any modifications to the data are automatically reflected by executing sync through all platforms and devices. This enables us to create more flexible real-time applications with limited effort.

 

Advantages of Using Firebase Real-time Database

1) Real-time

The data stored in the Firebase Real-time Database will be reflected in real-time, which means if the values in the database change, the change will be reflected back to all users at that point only, with no delay.

2) High Accessibility

 You can access Firebase Real-time Database from different devices such as Android, IOS, and the Web. So for other platforms, you don’t have to write the same code several times.

3) Offline Mode

The most significant advantage of real-time is that if you are not connected to the internet and make some changes in your application, then those changes will be reflected in your application at the time but only on the firebase database. Changes will be updated once you are online. As a result, though there is no internet, the customer feels as if he or she is using the facilities as if there is the internet.

4) Control Access to data

By default, no one is authorized to change the data in the Firebase Real-time Database, but you can control data access, i.e., you can specify which users are entitled to access the data. 

 

How to Connect Android with Firebase Database

You can connect your Android app firebase using two methods. 

Method1: Use the Firebase Console Setup Workflow

Method2: Use Android Studio Firebase Assistant

Step 1: Create a Database

Open the Firebase console and go to the Real-time Database portion. You’ll be asked to choose a Firebase project that already exists. Follow the steps for creating a database.

1) Test Mode: While this is an excellent way to get started with the mobile and web client libraries, it allows anyone to read and delete your data. After checking, go through the Understand Firebase Real-time Database Rules section. To get started with the web, IOS, or Android SDK, select test mode.

2) Locked Mode: Denies both reads and writes from mobile and web clients. Your authenticated application servers can still access your account.

• Select a location for the database. The database namespace will be of the form databaseName>.region>.firebaseio.com or databaseName>.region>.firebase database.app, depending on the region selection.  Select locations for your project for more details.
• Click Done

Step 2: Register Your App with Firebase

You must register your Android app with your Firebase project to use Firebase with it. Registering your app is often called “adding” your app to your project.

1) Go to the Firebase Console

2) To start the setup workflow, press the Android icon (plat android) or Add an app in the center of the project overview page.

3) Enter your app’s package name in the Android package name field.

4) Enter some additional app information: App nickname and SHA-1 signature certificate for debugging.

5) Click Register App

Step 3: Add a Firebase Configuration File

1) Add the Firebase Android configuration file to your app:

1. Click on Download google-services. Json to obtain your Firebase Android config file (google-services. Json).
2. Move your config file into the module directory of your app.

2) Add the google-services plugin to your Gradle files to support Firebase products in your application.

1. Include the Google Services Gradle plugin in your root-level (project-level) Gradle file (build. gradle). Check if you already have Google’s Maven repository installed.
2. In your module (app-level) Gradle file (usually app/build.gradle), apply the Google Services Gradle plugin

Step 4: Add Firebase SDKs to Your App

1) Declare the dependencies for the Firebase products you want to use with your app using the Firebase Android BoM. Declare them in your module (app-level) Gradle file (typically app/build.gradle).

2) Sync your app to ensure that all dependencies have the necessary versions.

Method2: Use Android Studio Firebase Assistant

Step 1: Update the Android Studio.

Step 2: Create a new project in the Firebase by clicking on the add project.

Step 3: Now open Android Studio and navigate to the Tools menu in the upper left corner.

Step 4: Now click on the Firebase option in the drop-down menu.

Step 5: On the right side of the screen, a menu will appear. It will show the different services provided by Firebase. Select the appropriate service.

Step 6: Now, In the menu of the desired service, choose Connect to Firebase.

Step 7: Add your service’s requirements by selecting the Add [YOUR NAME] to the app option.

 

The Final Verdict

So, In this article, we have seen two ways of connecting Firebase to an Android app. We hope you enjoyed reading this article. If you would like help programming your app, please Latitude Technolabs Contact us.  If you have any queries or suggestions about this blog, then feel free to ask them in the comment section.

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Exploring BERT Language Framework for NLP Tasks

As artificial intelligence apes the human speech, vision, and mind patterns, the domain of NLP is buzzing with some key developments in place.

NLP is one of the most crucial components for structuring a language-focused AI program, for example, the chatbots which readily assist visitors on the websites and AI-based voice assistants or VAs. NLP as the subset of AI enables machines to understand the language text and interpret the intent behind it by various means. A hoard of other tasks is being added via NLP like sentiment analysis, text classification, text extraction, text summarization, speech recognition, and auto-correction, etc.

However, NLP is being explored for many more tasks. There have been many advancements lately in the field of NLP and also NLU (natural language understanding) which are being applied on many analytics and modern BI platforms. Advanced applications are using ML algorithms with NLP to perform complex tasks by analyzing and interpreting a variety of content.

About NLP and NLP tasks

Apart from leveraging the data produced on social media in the form of text, image, video, and user profiles, NLP is working as a key enabler with the AI programs. It is heightening the application of Artificial Intelligence programs for innovative usages like speech recognition, chatbots, machine translation, and OCR or optical character recognition. Often the capabilities of NLP are turning the unstructured content into useful insights to predict the trends and empower the next level of customer-focused product solutions or platforms.

Among many, NLP is being utilized for programs that require to apply techniques like:

Machine translation: Using different methods for processing like statistical, or rule-based, with this technique, one natural language is converted into another without impacting its fluency and meaning to produce text as result.

Parts of speech tagging: NLP technique of NER or named entity recognition is key to establish the relation between words. But before that, the NLP model needs to tag parts of speech or POS for evaluating the context. There are multiple methods of POS tagging like probabilistic or lexical.

Parts of speech tagging: NLP technique of NER or named entity recognition is key to establish the relation between words. But before that, the NLP model needs to tag parts of speech or POS for evaluating the context. There are multiple methods of POS tagging like probabilistic or lexical.

Information grouping: An NLP model which requires to classify documents on the basis of language, subject, type of document, time or author would require labeled data for text classification.

Named entity recognition: NER is primarily used for identifying and categorizing text on the basis of name, time, location, company, and more for content classification in programs for academic research, lab reports analysis, or for customer support practices. This often involves text summarization, classification, and extraction.

Virtual assistance: Specifically for chatbots and virtual assistants, NLG or natural language generation is a crucial technique that enables the program to respond to queries using appropriate words and sentence structures.

All about the BERT framework

An open-source machine learning framework, BERT, or bidirectional encoder representation from a transformer is used for training the baseline model of NLP for streamlining the NLP tasks further. This framework is used for language modeling tasks and is pre-trained on unlabelled data. BERT is particularly useful for neural network-based NLP models, which make use of left and right layers to form relations to move to the next step.

BERT is based on Transformer, a path-breaking model developed and adopted in 2017 to identify important words to predict the next word in a sentence of a language. Toppling the earlier NLP frameworks which were limited to smaller data sets, the Transformer could establish larger contexts and handle issues related to the ambiguity of the text. Following this, the BERT framework performs exceptionally on deep learning-based NLP tasks. BERT enables the NLP model to understand the semantic meaning of a sentence – The market valuation of XX firm stands at XX%, by reading bi-directionally (right to left and left to right) and helps in predicting the next sentence.

In tasks like sentence pair, single sentence classification, single sentence tagging, and question answering, the BERT framework is highly usable and works with impressive accuracy. BERT involves two-stage applications – unsupervised pre-training and supervised fine-tuning. It is pre-trained on MLM (masked language model) and NSP (next sentence prediction). While the MLM task helps the framework to learn using the context in right and left layers through unmasking the masked tokens; the NSP task helps in capturing the relation between two sentences. In terms of the technical specifications required for the framework, the pre-trained models are available as Base (12 layers, 786 hidden layers, 12 self attention head, and 110 m parameters) and Large (24 layers, 1024 hidden layer, 16 self attention head, and 340 m parameters).

BERT creates multiple embeddings around a word to find and relate with the context. The input embeddings of BERT include token, segment, and position components.

Since 2018, reportedly the BERT framework is in extensive usage for various NLP models and in deep language learning algorithms. As Bert is open source, there are several variants that have also been in usage, often delivering better results than the base framework such as ALBERT, HUBERT, XLNet, VisualBERT, RoBERTA, MT-DNN, etc.

What makes BERT so useful for NLP?

When Google introduced and open-sourced the BERT framework, it produced highly accurate results in 11 languages simplifying tasks such as sentiment analysis, words with multiple meanings, and sentence classification. Again in 2019, Google utilized the framework for understanding the intent of search queries on its search engine. Following this, it is being widely applied for tasks like answering questions on SquAD (Stanford question answering dataset), GLUE (Generational language understanding evaluation), and NQ datasets, for product recommendation based on product review, deeper sentiment analysis based on user intent.

By the end of 2019, the framework was adopted for almost 70 languages used across different AI programs. BERT helped solve various complexities of NLP models built with a focus on natural languages spoken by humans. Where previous NLP techniques were required to train on repositories of large unlabeled data, BERT is pre-trained and works bi-directionally to establish contexts and predict. This mechanism increases the capability of NLP models further which are able to execute data without requiring it to be sequenced and organized in order. In addition to this, the BERT framework performs exceptionally for NLP tasks surrounding sequence-to-sequence language development and natural language understanding (NLU) tasks.

Endnote:

BERT has helped in saving a lot of time, cost, energy, and infrastructural resources by emerging as the sole enabler in place of building a distinguished language processing model from scratch. By being open-source, it has proved to be far more efficient and scalable than previous language models Word2Vec and Glove. BERT has outperformed human accuracy levels by 2% and has scored 80% on GLUE score and almost 93.2% accuracy on SquAD 1.1. BERT can be fine-tuned as per user specification while it is adaptable for any volume of content.

The framework has been a valuable addition to NLP tasks by introducing pre-trained language models while proving as a reliable source to execute NLU and NLG tasks through the availability of its multiple variants. The BERT framework definitely provides the opportunity to watch out for some exciting New Development in NLP in the near future.

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Analytics Maturity: from Descriptive to Autonomous Analytics

In Chapter 8 of my new book “The Economics of Data, Analytics, and Digitalization Transformation”, I discuss the 8 Laws of Digital Transformation.  My goal for chapter 8 was to push folks out of their comfort zones, especially with respect to how they are defining Digital Transformation success. Why? Because too many folks don’t really understand “Digital Transformation.”  For example, from the Forbes article “100 Stats On Digital Transformation And Customer Experience”, we get the following factoid:

“21% of companies think they’ve already completed digital transformation”

To that factoid, I say BS!  I think those 21% of companies are confusing Digitalization with Digital Transformation. Digitalization is the conversion of human-centric analog tasks into digital capabilities. For example, digitalization is replacing human meter readers, who manually record home electricity consumption data monthly, with internet-enabled meter readers that send a continuous stream of electricity consumption data to the utility company.

But Digital Transformation is something bigger, harder, and much more valuable:

Digital Transformation is where organizations have created a continuously learning and adapting culture, both AI‐driven and human‐empowered, that seeks to optimize AI-Human interactions to identify, codify, and operationalize actionable customer, product, and operational insights to optimize operational efficiency, reinvent value creation processes, mitigate new operational and compliance risk, and continuously create new revenue opportunities.

Digital Transformation is about predicting what’s likely to happen, prescribing recommended actions and continuously-learning and adapting (autonomous) faster than your competition.

Digital Transformation is about creating an organization that continuously explores, learns, adapts, and re-learns. Wash, rinse, repeat. Every customer engagement is an opportunity to learn more about the preferences and behaviors of that customer. Every product interaction or usage is an opportunity to learn more about the performance and behaviors of that product. Every employee, supplier and partner engagement provide an opportunity to learn more about the effectiveness and efficiencies of your business operations.

To create a continuously learning intelligent organization, organizations need to master the transition from reporting to predicting to prescribing to autonomous analytics. Now I know that most analytics maturity models stop at prescriptive analytics (descriptive to predictive to prescriptive), but that’s old school thinking.  The world is changing, and the new analytics maturity goal is autonomous analytics (Figure 1).

Figure 1: Analytics Maturity Curve: From Descriptive to Autonomous Analytics

This Analytics Maturation Curve provides a guide to help organizations transition through the three levels of analytics maturity—from reporting to autonomous:

• Level 1: Insights and Foresight. This is the foundational level of advanced analytics. Level 1 includes statistical analysis, data mining, and descriptive and exploratory analytics (e.g., clustering, classification, regression) to quantify cause-and-effect, determine confidence levels, and measure goodness of fit with respect to the predictive insights.
• Level 2: Optimized Human-decision Making. The second level of advanced analytics seeks to uncover and quantify the customer, product, and operational insights (predicted behavioral and performance propensities) buried in the data. Level 2 leverages Predictive and Prescriptive analytics that can uncover and codify individualized trends, patterns, and relationships; determine the root causes of the trends, patterns and relationships, and deliver dynamic, prescriptive recommendations and actions.
• Level 3: The Learning and Intelligent Enterprise. The third level of advanced analytics includes artificial intelligence, reinforcement learning and deep learning/neural networks. Level 3 leverages Autonomous analytics that continuously learn and adapt with minimal human intervention. These analytics seek to model the world around them—based upon the objectives as defined in the AI Utility Function—by continuously taking action, learning from that action, and adjusting the next action based upon the feedback from the previous action. Think of this as a giant game of “Hotter and Colder” where the analytics are continuously learning from each action and adjusting based upon the effectiveness of that action with respect to the operational goals (maximize rewards while minimizing costs) with minimal human intervention.

There are a couple of different ways or uses cases for exploiting autonomous analytics.  One is the creation of autonomous devices and the other is the creation of autonomous processes.  Let’s review each.

Use Case #1: Autonomous Devices

Okay, I have certainly beaten this topic to death, but it bears repeating because it is such a game changer for any organization that sells products (or products as a service).  Tesla is exploiting its ever-growing body of operational and driving data to continuously train the autonomous Tesla Artificial Intelligence-based Full Self-Driving (FSD) brain powers the Tesla semi-autonomous car.  Tesla mines this data to uncover and codify operator, product, and operational insights then get propagated back to each individual car resulting in continuously-refining and adapting capabilities such passing cars on the highway, navigating to the off ramp, maneuvering around traffic accidents and debris on the roads, and parking.  

Tesla autonomous cars are exploiting the capabilities of AI to create continuously learning autonomous cars that get more reliable, more efficient, safer, more intelligent, and consequently more valuable through usage…with minimal human intervention (Figure 2).

Figure 2: How AI is Creating Autonomous Devices

Tesla is not alone in building autonomous products.  John Deere is building autonomous farm tractors, Caterpillar is building autonomous construction equipment, and Nuro is building autonomous delivery vehicles because you gotta get that pizza delivered on time. Heck, one can’t be considered a serious industrial company if you don’t have a plan for creating autonomous products.

Use Case #2: Autonomous Policies

As operations become more complicated and more real-time, it’s becoming harder for organizations to ensure that their operating policies and procedures are evolving as fast as their business and operating environments. Digitalization provides a golden opportunity to improve operational effectiveness by replacing human-centric analog tasks with digital capabilities. That not only reduces human time and expense but allows organizations to capture more real-time, granular data about customer usage patterns and product performance characteristics.

This is the perfect time for leveraging AI to create autonomous policies, and procedures that can evolve at the speed of the business…with minimal human intervention. This evolution to autonomous policies and procedures starts by replacing code-based procedures and policies with AI-based learning-based procedures and policies (Figure 3).

Figure 3: “Leverage AI to Create Autonomous Policies”

Using AI, we can transition from static policies to autonomous policies that learn how to map any given situation (or state) to an action to reach a desired goal or objective with minimal human intervention. These autonomous policies would dynamically learn and update in response to constantly changing environmental factors (such as changes in weather patterns, economic conditions, price of commodities, trade and deficit balances, global GDP growth, student debt levels, fashion trends, Cubs winning the World Series, etc.).

Autonomous policies and procedures not only can ensure that the organization is making informed business and operational decisions, but can also combat bias, prejudice, and discrimination in making decisions. For example, Malcolm Gladwell’s “Talking to Strangers” highlights how AI-informed decisions can lead to equitable decisions in the judicial system.

Economist Sendhil Mullainathan examined 554,689 bail hearings conducted by judges in New York City between 2008 and 2013. Of the more than 400,000 people released, over 40% either failed to appear at their subsequent trials or were arrested for another crime. Mullainathan applied an ML program to the raw data available to the judges and the computer made decisions on whom to detain or release that would have resulted in 25% fewer crimes.

However, AI-driven policy decisions have their own challenges. As I discussed in “Ethical AI, Monetizing False Negatives and Growing Total Addressabl…”, AI model confirmation bias is the tendency for an AI model to identify, interpret, and present recommendations in a way that confirms the AI model’s preexisting assumptions. AI model confirmation bias feeds upon itself, creating an echo chamber effect with respect to the biased data that continuously feeds the AI models. Overcoming AI model confirmation bias starts by 1) understanding the costs associated with False Positives and False Negatives and 2) building a feedback loop where the AI model can continuously-learn and adapt from the False Positives and False Negatives.

Transitioning from Descriptive to Autonomous analytics is a game-changer but must be framed against the Data & Analytics Business Maturity Index to help organizations become more effective at leveraging data and analytics to power their business (Figure 4).

Figure 4: Data & Analytics Business Maturity Index

By the way, Jeff Frick does a marvelous job grilling me on the 8 Laws of Digital Transformation.  The video is a lot more interesting than this blog. Grab some Cap’n Crunch and enjoy the conversation!

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DSC Weekly Digest 20 July 2021

COVID-19 has been in the news again lately, for several reasons. In many parts of the world, the delta variant of the virus has been hitting hard in those areas where vaccination rates are low. Not surprisingly, these are also the areas where there is a broad mistrust of science and where local leaders have sewn that mistrust for their own political gain. This is in turn breeding resentment and denialism in those regions that have the potential to turn into a vicious cycle, exacerbating geopolitical tensions and widening economic inequality.

It is still possible, with the vaccine, to still get COVID-19 Delta. though the likelihood is much smaller and the effects (and transmissibility) of the virus considerably tempered. However, this does not mean that the virus (regardless of variant) has become less dangerous to those who haven’t been inoculated, and even those who have had COVID-19 are not necessarily immune to the delta variant, though at least some of the vaccines seem to be better at provoking a full spectrum response.

This is raising the specter of a forever virus, one that may very well take years to fully recede, as potentially dangerous mutations continue to build up in broad pockets of “low-science” regions. The longer that this goes on, the more that the very nature of work and society is likely to change. Already, companies that had begun to require employees to come back to the office full time are reassessing those policies in the light of rising case numbers, though at least at this time the potential of going back into a full lockdown mode seems unlikely – hospitalization and death rates are not rising as quickly as has been the case before.

One of the more intriguing aspects of the pandemic has been that it has forced companies into thinking hard about what exactly they do. The demand for more sophisticated AI systems is rising, but there are also multiple indications that the field of AI itself needs to evolve dramatically first, taking into account not just faster AIOps platforms but increasingly needing to integrate with contextual stores and provide more intuitive interfaces that can adapt dynamically (and automatically) to given needs.

These won’t come from better algorithms. Rather, AI itself needs to adapt more readily as a component within a broader matrix of services that organizations use. This will become especially important as work becomes increasingly virtual and distributed, and is a key reason why data scientists and modelers need to start thinking beyond the analysis and toward the applications that rely upon that analysis.

In media res,

Kurt Cagle
Community Editor,
Data Science Central

To subscribe to the DSC Newsletter, go to Data Science Central and become a member today. It’s free! 

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E-R Diagram Cardinality and Participation

Cardinality and participation explained for E-R diagrams.

• How to show cardinality/participation for several methodologies.
• Examples of notation and diagrams.

In my previous posts, I discussed some E-R diagram basics and some common mistakes to avoid. In this post, I’ll cover cardinality and participation. Cardinality is a count of the number of times one entity can (or must) be associated with each occurrence of another entity. Participation refers to whether an entity must participate in a relationship with another entity to exist.

Cardinality and Participation Constraints in E-R Diagrams

In general, cardinality tells you “How many”. Cardinality can be:

• One to one (1:1): every time one entity occurs, there is exactly one occurrence of another entity.
• One to many (1:m):  every time one entity occurs, there are multiple occurrences of another entity.
• Many to many (m:m): for each occurrence of an entity, there can be one or many occurrences of another—an vice versa.

In some notations, a cardinality constraint corresponds to maximum cardinality. In other notations, cardinality may be combined with participation (a “minimum”).

Participation can be total or partial (optional):

• Total participation is where an entity must participate in a relationship to exist. For example, an employee must work for at least one department to exist as an “employee”.
• Partial (optional) participation is where the entity can exist without participating in a relationship with another entity [1]. For example, the entity “course” may exist within an organization, even though it has no current students.

How to Show Cardinality and Participationon an E-R Diagram

While there are many ways to create E-R diagrams, a straightforward way is to create a rough draft of your ERD first, then tackle cardinality. While most methodologies (e.g., Chen, I.E, min/max) use the same shapes for entities (rectangles) and relationships (diamonds), when it comes to cardinality each has its own specific notation. This is where you must make a choice about what methodology to use, as you must stay consistent when building cardinality constraints.

One of the most important choices is between Look-Here and Look-Across methods. Look-Here and Look-Across refers to where the cardinality and participation constraints are specified in ER diagrams.

With the “Look-Here” cardinality constraint, you’re literally looking “here” (i.e. next to) the entity to determine cardinality.

In the above example using the min/max method, the upper bound cardinality constraint 1 states that each employee may have the relationship “work for” at most 1 time, meaning they can only work for one department. The lower bound cardinality constraint 1 states that each employee must have at least 1 appearance in the “works for” relationship. On the other hand, there are no restrictions on how many employees a department may have; the upper bound N indicated no limit [2].

With the “Look-Across” constraint, you must look to the other side of the relationship to garner meaning:

 

Different Methodologies and Ways to Show Cardinality

The following examples all show how different methodologies show cardinality and participation:  

 

Crowsfeet is one of the most popular methods for creating E-R diagrams. With crowsfeet notation, cardinality is represented by decorations on the ends of lines.

A cardinality of one is represented by a straight line perpendicular to the relationship line.

────┼   

A cardinality of many is usually represented by the three-pronged ‘crow-foot’ symbol, but may also be represented by a two-pronged symbol [2]:

────<    many

Other basic symbols:

───┼<    one or many

──o─<    zero or many

───┼┼    exactly one.

The IE method is very similar to crowsfeet but does not show attributes related to a relationship: the relationship is depicted as a named line between two entities. Cardinality and participation constraints are combined into min/max (bar and crowfoot) notation. Cardinality is shown as follows [1]:

• One and only one: Two bars at end of line or single bar
• Zero or one: Hollow dot and one bar
• One or more: One bar and crowfoot
• Zero, one or more: Hollow dot and crowfoot
• More than one: Crowfoot.

Your choice of which methodology to use is probably going to be determined by your company’s preference (or perhaps your instructor’s). Unfortunately, differences between notations make the diagrams challenging to transfer from one format to another [1].  

References

Images: By Author

[1] A Comparative Analysis of Entity-Relationship Diagrams1

[2] Entity Relationship Modelling

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Doing Python Math Operations With Numpy (Part III)

In my previous post, I explored how to use Pandas to work with data frames and similar structures. In this post, I want to go to the next level and discuss the magical operations available with the NumPy (Numerical Python) library, including fast array manipulation.

Numerical Python = NumPy

Why should go with NumPy

• Provides Data Structure, Algorithm for the Scientific application which requires numerical data.
• Which supports multi-dimensional array manipulation. NumPy’s array object is called ndarray. 
• Easy to reshape, slice, and dice the array. And fast array process capability.
• Makes complex mathematical implementations very simple. 
• To perform different numerical and trigonometry functions (i.e., sin, cos, tan, mean, median, etc.)
• Excellent support for Linear Algebra, Fourier Transformer, etc.,
• NumPy arrays are very efficient than list arrays, The way it processes manipulate id fast.
• It is often used along with other packages in Python environments like SciPy and Matplotlib. 

What library supports and how should we import NumPy?

import numpy as np

What NumPy Cando? 

The below picture represented the capabilities of NumPy. Let’s discuss it one by one.

I. Exploring the dimensions in the array

a. ONE Dimensions and Multi-Dimensions

(a.1) 1-D

import numpy as np
a = np.array([100,200,300,400,500])
print (a)
OUTPUT
[100 200 300 400 500]

(a.2) n-D
a = np.array([[100, 200], [300, 400]])
print (a)
OUTPUT
[[100 200]
[300 400]]

b. Number of Dimensions
x = np.array(1)
y = np.array([1, 2, 3, 4, 5])
z = np.array([[1, 2, 3], [4, 5, 6]])
print(x.ndim)
print(y.ndim)
print(z.ndim)
OUTPUT
0
1
2

c. Finding Type of the array
arr = np.array([1, 2, 3, 4, 5])
print(arr)
print(type(arr))
OUTPUT
[1 2 3 4 5]
<class ‘numpy.ndarray’>

d. Accessing array elements
arr = np.array([1, 2, 3, 4])
print(arr[0])
OUTPUT
1

e.Slicing array element
import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7])
print(arr[1:5])
OUTPUT
[2 3 4 5]

II. Data Type Objects Specfic to NumPy

NumPy has additional data types, Let’s see those and simple code to find the type of the variable.

III. Finding shape and re-shape of the ndarray.

a. Shape of the array

(a.1) The Array has the attribute that returns array dimensions.

import numpy as np
a = np.array([[1,2,3],[4,5,6],[4,5,6]])
print (“Shape of the array:”,a.shape)
a = np.array([[1,2,3,4],[3,4,5,6]])
print (“Shape of the array:”,a.shape)
OUTPUT
Shape of the array: (3, 3)
Shape of the array: (2, 4)

b. Re-shape of the array

(b.1) Certainly, you can resize the array, with simple steps

import numpy as np
a = np.array([[1,2,3],[4,5,6]])
b = a.reshape(3,2)
print (“Array a(Actual shape):\n”,a)
print (“Shape of the array:\n”,a.shape)
print (“Array b(After reshape):\n”,b)
print (“After Re-shape of the array:\n”,b.shape)

OUTPUT
Array a(Actual shape):
[[1 2 3]
[4 5 6]]
Shape of the array:
(2, 3)
Array b(After reshape):
[[1 2]
[3 4]
[5 6]]
After Re-shape of the array:
(3, 2)

IV. Converting List and Tuple into ndarray

(a) List into Array

import numpy as np
x = [1,2,3]
print(“List:”,x)
a = np.asarray(x)
print(“As Array:”,a)

OUTPUT

List: [1, 2, 3]
As Array: [1 2 3]

(b) Tuple into Array

import numpy as np
x = (1,2,3)
print(“Tuple:”,x)
a = np.asarray(x)
print(“As Array:”,a)

OUTPUT

Tuple: (1, 2, 3)
As Array: [1 2 3]

V. Array Join and Split

(a) Join Array

This is similar to SQL joining two or more arrays into a single array using concatenate function

import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
print(arr1)
print(arr2)
arr = np.concatenate((arr1, arr2))
print(“After concatenate :”,arr)

OUTPUT

[1 2 3]
[4 5 6]
After concatenate : [1 2 3 4 5 6]

(b) Splitting Array

Splitting is the opposite operation of Joining, It breaks one array into multiple. using array_split()

import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6,7,8])
print(“Actual Array:”,arr)
newarr = np.array_split(arr, 4)
print(“Split:”,newarr)

OUTPUT 

Actual Array: [1 2 3 4 5 6 7 8]
Split: [array([1, 2]), array([3, 4]), array([5, 6]), array([7, 8])]

VI. Create an Array with Ranges

– Start-value, Stop-value, Step-value

import numpy as np 
x = np.arange(10,50,5)
print (x)

OUTPUT

[10 15 20 25 30 35 40 45]

VII. Array Multiplication

This simple multiplication provided Row and Column counts are equal. This concept is so-called broadcasting. it has limitations 

• Arrays should have exactly the same shape.
• Arrays have the same number of dimensions and the length of each dimension.
  
  

a = np.array([1,2,3,4])
b = np.array([5,5,5,5])
print (a)
print (b)
c = a * b
print (c)

OUTPUT

[1 2 3 4]
[5 5 5 5]
[ 5 10 15 20]

As mentioned earlier, NumPy supports a number of mathematical operations to handling numbers.

• Arithmetic Operations
• Statistical Functions
• Trigonometric Functions
• Linear Algebra

(a) Arithmetic Operations

This is very straightforward operations, as we are very familiar with add(), subtract(), multiply(), and divide(). the array should have the same shape or should conform to array broadcasting rules.

a = np.array([2,2,2])
b = np.array([10,10,10])
print (‘First array:’,a)
print (‘Second array:’ ,b)
print (‘Add the two arrays:’,np.add(a,b))
print (‘Subtract the two arrays:’,np.subtract(a,b))
print (‘Multiply the two arrays:’ ,np.multiply(a,b))
print (‘Divide the two arrays:’ ,np.divide(a,b))

OUTPUT

First array: [2 2 2]
Second array: [10 10 10]
Add the two arrays: [12 12 12]
Subtract the two arrays: [-8 -8 -8]
Multiply the two arrays: [20 20 20]
Divide the two arrays: [0.2 0.2 0.2]

(b) Statistical Functions

NumPy has very useful statistical functions.

• Minimum
• Maximum
• Percentile
• Standard Deviation
• Variance

import numpy as np
a = np.array([[3,7,5],[8,4,3],[2,4,9]])
print (‘Given array is:’)
print (a)
print (“Minimum :”,np.amax(a))
print (“Maximum :”,np.amin(a))
print (“Standard Deviation :”,np.std(a))
print (“Variance :”,np.var(a))
print(“Percentile :”,np.percentile(a, 4))

OUTPUT

Given array is:
[[3 7 5]
[8 4 3]
[2 4 9]]
Minimum : 9
Maximum : 2
Standard Deviation : 2.309401076758503
Variance : 5.333333333333333
Percentile : 2.32

(c) Linear Algebra

NumPy package contains numpy.linalg library, it provides linear algebra functions

import numpy.matlib
import numpy as np

a = np.array([[1,2],[3,4]])
b = np.array([[11,12],[13,14]])
print (“Dot Operation:”, np.dot(a,b))

a = np.array([[1,2],[3,4]])
b = np.array([[11,12],[13,14]])
print (“vdot :”,np.vdot(a,b))

OUTPUT

Dot Operation: [[37 40]
[85 92]]
vdot : 130

IX. NumPy with Matplotlib

As mentioned earlier, Numpy will work along with the Matplotlib library and helping us to create various charts. Quickly will see those. a really interesting piece of work.

import numpy as np
from matplotlib import pyplot as plt
x = np.arange(1,11)
#simple linear equation
y = 2 * x + 5
plt.title(“Matplotlib demo”)
plt.xlabel(“height”)
plt.ylabel(“weight”)
plt.plot(x,y)
plt.show()

OUTPUT

import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0, 4 * np.pi, 0.2)
y = np.sin(x)
plt.title(“Sine Wave Form”)
plt.plot(x, y)
plt.show()

OUTPUT

from matplotlib import pyplot as plt
x = [5,15,10]
y = [12,16,18]
x2 = [6,9,14]
y2 = [6,15,7]
plt.bar(x, y, align = ‘center’)
plt.bar(x2, y2, align = ‘center’)
plt.ylabel(‘Performance’)
plt.xlabel(‘Slots’)
plt.show()

X. Working with a String array.

NumPy provides various options to play around with string.

import numpy as np
print (“Capitalize (hello world):”,np.char.capitalize(“hello world”))
print (“Title (hello how are you?):”,np.char.title(‘hello how are you?’))
print (“Lower (HELLO WORLD):”,np.char.lower([‘HELLO WORLD’]))
print (“Upper (hellow):”,np.char.upper(‘hellow’))
print (“Split (hello how are you?):”,np.char.split (‘hello how are you?’))
print (“Split (Python,Pandas,NumPy):”,np.char.split (‘Python,Pandas,NumPy’, sep = ‘,’))
print (“Strip (welcome watts):”,np.char.strip(‘welcome watts’,’w’))
print (“Join (dmy):”,np.char.join(‘:’,’dmy’))
print (“Join (dmy):”,np.char.join([‘:’,’-‘],[‘dmy’,’ymd’]))
print (“Replace (Python is a programming language):”,np.char.replace (‘Python is a programming language’, ‘programming’, ‘powerful programming’))
print (‘Concatenate two strings Hello,Mr.Babu:’,np.char.add([‘Hello’],[‘Mr.Babu’]))
print (‘Concatenation example [Hello, Hi],[ Shantha , Babu]:’,np.char.add([‘Hello’, ‘Hi’],[‘ Shantha ‘, ‘ Babu’]))

OUTPUT

Capitalize (hello world): Hello world
Title (hello how are you?): Hello How Are You?
Lower (HELLO WORLD): [‘hello world’]
Upper (hellow): HELLOW
Split (hello how are you?): [‘hello’, ‘how’, ‘are’, ‘you?’]
Split (Python,Pandas,NumPy): [‘Python’, ‘Pandas’, ‘NumPy’]
Strip (welcome watts): elcome watts
Join (dmy): d:m:y
Join (dmy): [‘d:m:y’ ‘y-m-d’]
Replace (Python is a programming language): Python is a powerful programming language
Concatenate two strings Hello, Mr.Babu: [‘Hello Mr.Babu’]
Concatenation example [Hello, Hi],[ Shantha , Babu]: [‘Hello Shantha ‘ ‘Hi Babu’]

Hope you all are enjoyed NumPy and its capabilities. Still many more… I have covered whichever is more important and mainly used during Data Science and Machine Learning implementation while dealing with datasets. 

Thanks for your time in reading this article. Leave your comments. Shortly will get back to you with interesting topics. 

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How Artificial Intelligence is Revolutionizing Mental Healthcare

Science fiction author William Gibson has been attributed as saying, 

“The future is already here, the fact is it’s not just very evenly distributed.”. 

Revolutionary artificial intelligence algorithms are creeping into mental healthcare and are reshaping its dimensions. You might already be discussing with an AI bot right now that “how does it make you feel to hear that?” Your AI therapist might be successful enough to take you out from the feeling of worry about what direction the future will take with the advent of artificial intelligence. In case, looking beyond the horrifying headlines of Skynet coming true, the progressive utilization of artificial intelligence in mental healthcare is absolutely splendid news for many of us. 

Depression is a global problem with serious consequences. Almost 1 out of 5 Americans deal with mental health conditions at some point in their lives. Yet, in numerous cases, we are relying on individuals to seek treatment, despite the continuing stigma against asking for health for mental health struggles. Almost 40% of the world’s population lives in such areas where there are not enough mental healthcare professionals to meet the requirements of the community. Currently, we are facing a severe mental health crisis aggravated by the pandemic outbreak.

Thanks to the rapid technological innovations and advancements due to which AI algorithms are capable of spearheading a positive transformation in the space that has been a long journey for change. Here is the list of five optimistic ways artificial intelligence and its innovative algorithms are revolutionizing mental healthcare in the best way possible. 

#1  Helping to Characterize Mental Health Issues 

Making mental health treatment and diagnosis less subjective and more quantifiable helps to destigmatize such conditions and enhance outcomes. No blood test is required for mental health conditions. Artificial intelligence and promising machine learning algorithms prove to be equivalent research-based objective tests that make the requirement to seek treatment more about evidence-based data along with best medical practices, and less about the patient’s subjective experience of distress. 

Democratizing access to mental health treatment will certainly mainstream this kind of healthcare. The more people who seek treatment for mental health issues or who know someone who is suffering from mental health struggles, the less mental health treatment will feel like a secret or a shame. 

#2   Making Support Available Anytime Anywhere

Mobile devices are in the hands of literally every individual. Applications and chatbots are accessible to everyone no matter where they are. Undoubtedly, that’s the most affordable treatment option.   They are always awake and on-call. According to researchers and analysts, most people feel comfortable sharing their feelings with an anonymous chatbot as compared to a human being.  

Obviously, such tools are still new and experimental. There would be no wrong in saying that chatbots and other existing app-based tools for tracking and enhancing mood prove to be tremendously beneficial mainly for those patients who usually struggle to access care. There already exists a wide range of mobile-based tools that make patients walk through exercises that are based on cognitive behavioral therapy, as well as other research-based techniques. Hence chatbots are lifelines for those who are suffering from depression and anxiety in the middle of the night. 

#3   Flagging Early Warning Signs of Dangerous Troubles 

Just think what would it be like if your smartphones were smart enough to notify your doctor that you are at risk of depression on the basis of how often you leave your house or how fast you are typing. According to a certain study, language analysis algorithms were 100% accurate at identifying teens who were likely to develop psychosis. Such tools already exist and without a doubt, they are really very powerful. 

Language analysis is utilized to monitor patients who are going through certain treatments and warn doctors when they take a turn for the worse. Most patients do not visit their doctor or therapist regularly, but answering a few questions online on a day-to-day basis helps the app to detect early signs of trouble. AI tools provide invaluable support to human providers and patients, establishing daily checkpoints that can catch a downturn before it turns into a serious spiral.

#4   Reducing Bias and Human Error 

Artificial intelligence algorithms have proven to be successful at detecting signs of certain conditions such as anxiety, depression, and post-traumatic stress disorder with the analysis of facial expressions and voice patterns. Physical and mental health providers are utilizing such AI-powered tools to serve as a backup during the meeting with patients. These meetings are usually very brief. 

Such tools are beneficial when healthcare providers, who are usually rushing from appointment to appointment, may not notice what signs of trouble the patient exhibits. Moreover, AI tools help in reminding a busy healthcare physician to push past that surface-level appearance and dive into those problems that are not acute yet. 

#5   Integrating Mental Health Care with Physical Healthcare

Can you think of a future where machine learning algorithms can warn surgeons and doctors that a patient is at risk based on its existing medical record?  According to one study, it is successfully predicted that which of the patients who were brought into the hospital because of self-injuries will likely give a suicidal attempt anytime soon. 

For instance, when it boils down to the opioid crisis, according to data, 10% of the patients who use opioids for the next 90 days after their surgery will continue to depend on those medications. That patient could be referred to the therapist who is a medication tapering specialist so that patients can widen the array of techniques they utilize to manage their pain and other symptoms. 

Health is the Greatest Wealth 

Mental health plays a pivotal role in our lives. Anxiety, depression, and stress are caused when people prefer to please others instead of pleasing themselves. The biggest stigma with mental health is most people still don’t talk or fear to talk about it, although it’s a great wealth. 

People fear that AI bots will soon replace human therapists, but that’s not true. Instead, artificial intelligence will support human therapists. AI can serve as an early warning system providing support all day every day. It can offer lifelines to those who live in rural areas where mental health support is difficult to find. It can also prove to be beneficial for those who cannot afford to visit therapists regularly. Without a doubt, mental healthcare is an enormous challenge in our society, and the COVID-19 outbreak is the only increasing support to this challenge.  

At the heart of it all, artificial intelligence has the potential to revolutionize mental healthcare, making care more affordable, responsive, and accessible. In the approaching years, AI algorithms will be our first line of defense against mental health struggles that can be deliberating for a tremendous amount of people.  

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Information Communication Technology Ensures a Safe Social Media Ecosphere

Overview – Information Communication Technology (ICT)

Information and Communication Technology (ICT) Essentially, technology may be described as an electrical medium for generating, preserving, altering obtaining, and transmitting information from one location to add in its most basic form. It facilitates the transmission of messages by making them more accessible, accessible, understandable, and interpretable. This is accomplished via the use of technological devices such as mobile telephones, the Internet and wireless networks, computers, radios, televisions, spacecraft, and cell towers, among others. These elements are used in the creation, storage, communication, transmission, and management of data. 

Technology is a critical motivator in empowering people, communities, and nations, facilitating growth and enhancing skills, all while maximizing the benefits of the democracy dividends for everyone.

Information technologies have made it feasible for companies to operate around the clock, from anywhere in the world. This implies that a company may be open at any time and from any location, making international transactions simpler and more convenient. You may also have your items delivered to your door without having to move a single muscle, which is a huge convenience.

In so many different economic areas, there is a fresh chance for additional study to enhance certification. A degree may be done entirely online from the comfort of one’s own home. It is feasible to hold down a job while pursuing a degree.

Importance of Information and Communication Technology (ICT)

The widespread popularity of technology has created uncertainty in the management of many companies as a result of their expanding use. The management and support of these multifaceted environments – consisting of a diverse range of PCs, desktops, and laptops, as well as mobile and wireless equipment, printers, connectivity, and implementations – has proved difficult and costlier for information technology government agencies to manage and support over time.

1. It develops in students an analytical mindset that allows them to analyze and provide answers to issues arising in all connected areas that make use of it as a learning tool, among other things.
2. Because it is a new academic area of study, it encourages students to be creative and to come up with new scientific approaches to problem resolution.
3. It simplifies the procedure of storing and retrieving data.
4. It contributes to the development of computer interacting, which is now called the internet and intranet.
5. It comes up with the potential to stimulate economic growth at a national level since it is a reliable source of national revenue for all countries which has properly recognized its value.
6. It generates meaningful work, thus providing a sustainable source of income.
7. It makes it easier to comprehend other topics as a result. Almost all principles of development, such as the use of a projection in the classroom, are accessible to the use of information and communications technology.
8. In this way, it serves as a platform for the exchange of ideas and innovations amongst information technology academics both locally and globally.

9. It serves as the foundation for e-learning and the creation of an online library. As a result, disseminating information is now simpler than ever.
10. It plays an important role in globalization in every appearance and the achievement of the Millennium Development Goals.
11. it is utilized in a variety of offices to ensure which official actions and organizations are properly documented.

The technology of Info and Communication towards the social and commercial spheres,

There is no disputing that, from the beginning of social media and social systems have developed to be a part of our everyday existence, the whole thing has changed. Starting with the means we mingle, engage, arrange events, go outing, we can make a difference. We will not engage in a discussion regarding the principled implications of the technique of social media is affecting everyone’s lives at this time. It is proposed that we instead concentrate on many methods social media is altering the technique our educational system operates in this post. So, watch this space to find out what impact social networking has on the way our kids are taught, both inside and external of the classroom.

Technology advancements have always been utilized by for-profit organizations to enhance their income. Government agencies and non-governmental organizations (NGOs) have, on the other hand, failed to effectively use them for social benefit. The social company, a new kind of enterprise that is developing, is working to close the generation gap.

The Internet of Things (IoT) is playing an increasingly important role in the creation and growth of social enterprises. It is impossible to overstate the importance of information and communications technology infrastructure for social enterprises. It has made social impact cheaper and more scalable, and it has opened the door to new methods of connecting with and engaging with local communities (a key characteristic of the social business) Social media websites and apps enable users to produce and share user-generated content on the internet. Social media websites and applications are becoming more popular.

Businesses may now react to consumer inquiries more quickly than ever before thanks to new channels such as social media and instant chat. Doctors and nurses may assist healthcare organizations through video-conferencing systems. The possibilities for connection are almost limitless.

Furthermore, the use of information and communications technology (ICT) in corporate communications is enhancing the way that businesses cooperate. Everything from displays and files to videos may now be shared online, including documents and presentations. This guarantees that organizations may collaborate more efficiently, regardless of what they’re located.

 

Final words

Embracing communication technology in businesses and other areas

Eventually, ICT has an enormous influence on the business scenery nowadays. It does not matter if your business is small or big and what type of industry it is So there is no question that you’re using a type of information and networking technology platform. A good company growth plan, such as other types is critical to attaining outstanding outcomes. Getting a proper approach and plan in place is essential to obtaining outstanding achievements

Business leader’s requirement to contemplate prudently regarding the delinquent which they wish to resolve with its information communication technology solutions and work rearward through there. Identifying the problems which you wish to resolve can aid them in the selection of the most appropriate contractor to engage with. Once you’ve established your strategy, you may start developing a method for improved information sharing which is tailored to your organization’s needs.

Source Prolead brokers usa