Artificial intelligence (AI) has been transforming the globe with its many salient features, touching upon our lives personally and professionally. Almost all segments of industry  are influenced by the spread of AI, leveraging its potential to the utmost degree. AI has turned out to be instrumental in bringing other modern-day technologies (like IoT, machine learning, big data, and robotics) closer to us.

Moreover, the recent pandemic has served as a catalyst in encouraging the business world to adapt to AI-based technologies for enhancement of their organizations. Indeed, the size of the global artificial intelligence market is estimated to reach 309.6 billion dollars by 2026, at a compound annual growth rate of 39.7% during the forecast period.

AI has been expanding the technology landscape by serving multiple layers like continuous delivery, serverless microservices, orchestration, multi-cloud computing, and data analytics. It has been generating a lot of economic value for organizations, and we are witnessing an increasing involvement of AI in our daily routines.

Nonetheless, however rich the technology may be, it can’t always outperform humans. There are still some limitations; certain activities are not yet able to be executed through artificial intelligence development alone.

But, before we have a look at what AI can’t do, let’s take a quick glance at its numerous capabilities.

Artificial Intelligence – What Can It Do?

AI is everywhere, be it in your business, your home, or your smartphone. You can certainly say AI is ubiquitous and very useful to humankind, yet it does have its problems—privacy concerns and difficulty in troubleshooting errors, to name a few.

Overall, there is a lot coming together with AI—both good and bad—and we need to find a way to properly use it and live with it.

Here are some of the significant things that AI can do:

• Self-driving cars
• Predict your preferences and offer relevant recommendations
• Track down illegal activities such as human trafficking or smuggling
• Help diagnose rare diseases and treat them
• Utilization of AI chatbots for human-like assistance
• Online shopping and advertising
• Machine translations
• Digital personal assistance
• Help people with disabilities to read, write, see, speak, smell, touch, and move with limitations
• Play games
• Recognition of emotions in speech or written text
• Security in homes and organizations

And many more…

Artificial Intelligence – What Can’t It Do?

At the same time, there are many things that AI still has not been able to accomplish. Though there is a speedy advancement in AI-related mechanisms, there are a few things that still lag.

Briefly, we could say that AI can’t multitask, and it can’t feel sympathy or empathy for anyone. As a result, AI can’t completely replace human labor and certainly not professionals such as lawyers, writers, designers, developers, or psychiatrists.

With this in mind, let’s take a closer look at some of the things AI still can’t do very well, if at all.

AI Can’t Write Software

AI, however developed it may be, lacks the human understanding needed to write software code. Developing code requires a great deal of human thinking, as it involves error detection, possible hurdles, customer perception, real world scenarios, etc.

All these thought processes may not be feasible through AI since it can’t think deeper. AI can surely help with finding patterns, software testing, test case generation, and test arrangements, but it can’t write the code itself.

Furthermore, AI can’t find malware or do creative writing. It can create content directly based on guidelines, but it must be overseen by humans since AI has limited creative capability with a less genuine focus on emotions.

AI Can’t Independently Accomplish Tasks With Compassion, Ingenuity, and Innovation

AI is of great help to humankind, in the sense that we feel it is replacing certain laborious human activities. However, it lacks a few attributes that only humans possess. AI is not able to conceptualize, plan, and create something, given a set of associated goals. It can’t choose objectives and implement activities based on those chosen tasks.

AI cannot feel any emotion. It may try to replicate human feelings but can’t bring the necessary authenticity to the table. There may be robots that can replace humans but only in mundane tasks, not in offering emotional services.

AI may fall back when it comes to performing complicated tasks. It can work on predefined simple tasks, but it may not be able to give perfect results when there are uncertain or complex areas of handling things.

AI Can’t Perceive the Reason and Impact Equation

AI techniques can find out detailed patterns and data correlation, provided they have the underlying raw data with them. But AI is unable to understand real-life scenarios, including the reasons behind their occurrence and any further impact they may have.

Overall, AI can function in situations but cannot understand the basic cause behind it. It will not be able to see the world the way we do, and that will always be a gap that humans must fill.

AI Can’t Make Moral Decisions or Invent on Its Own

AI cannot make moral decisions on its own. It does not have any emotion, and hence, when a moral judgment must be made, AI needs to depend upon human interaction. Self-driving cars are in vogue, but there must be human involvement, since on the road, the car can’t make all the decisions on its own.

Undeniably, AI can help accelerate and automate, but when it comes to complete innovation, it can’t help without human resources. AI is excellent at playing by the rules, but it can’t create the rules from the start. It can only base its decisions on past experiences and can’t think out of the box.

Moreover, AI cannot make instant decisions based on the current scenario or surroundings like humans can. It sticks to its predefined rules and involves no morality in its decision-making.

AI Can’t Utilize Common Sense

A very significant quality that humans have—that AI does not—is common sense. This is very important in all routine activities, and that is where humans have the upper hand. AI cannot grasp any concept on its own using reason, since it depends only on predefined facts and figures.

There are many facts that exist in this world because of common sense used by humans. AI may not be able to perceive these facts if they don’t coincide with an established set of actions. AI can’t prepare models of things mentally using the environment and experience, as humans do; it can merely link the relationship in the raw data to the model that it is looking at.

AI Development Continues to Rule

Artificial intelligence and AI solutions are instrumental in our lives, on a personal and a professional level. Almost all industry segments—healthcare, transportation, finance, entertainment, education, retail, etc.—have been benefiting from the use of AI.

Yes, as we saw in this post, there are things that AI has not been able to accomplish as of now. But the way it is growing, and AI development companies are expanding, the next few years might see AI outperforming humans in the activities above! Only time will tell how widespread AI will be in the coming years.

The post 5 Important Things AI Still Can’t Do appeared first on Simple Programmer.

Moreover, the core properties of 5G make it a key investment to support the future needs of the internet—as we’ll see in more detail below, 5G isn’t simply a better version of 4G. According to the mobile economy report, 5G is expected to reach two billion users by 2025.

Inevitably, a powerful upgrade like 5G is likely to have implications for software development as well. As a result, 5G is an immense opportunity for programmers to showcase their skills using the latest technology and deliver an improved user experience through next-generation mobile applications. Here is how developers can prepare their mobile applications to make the best use of 5G.

How 5G Differs From Everything That Came Before It

As I mentioned above, 5G is not just an improved version of 4G. Based on its properties, 5G promises to deliver the following features:

• High speed wireless connectivity. 5G is expected to have a data rate of up to 10 Gbps, a significant improvement over the existing 4G.
• Low latency. Equipped with Ultra Reliable Low Latency Communication, 5G will be able to deliver in milliseconds data that usually takes a lot longer on 4G networks.
• Greater bandwidth. The 5G network is expected to have better connectivity across a wide range of mobile applications.

As a result of major enhancements, 5G will provide improved security levels for data transfers over the network. Keeping this in mind, developers will have to work on safer ways of authentication, like biometric measures. Apart from this, as 5G will have minimal issues regarding connectivity and latency, developers can introduce new features for mobile apps without worrying about performance. Let’s see how, in more detail.

Seamless Adoption of Artificial Intelligence

5G is going to be a game changer for the Internet of Things (IoT). High connectivity speed will enable broader adoption of IoT for wearable devices, smart gadgets, and sensor-based equipment. Along with this, developers can also leverage Artificial Intelligence (AI) and Machine Learning (ML) into their mobile apps to provide smarter services. With the help of AI-integrated apps, developers can provide powerful authentication to ensure safe and secure data distribution across networks.

Furthermore—and in order to ensure personalized experience—developers can take advantage of AI on the 5G network and work on features like voice recognition and automated chats. This is extremely helpful in cases of applications designed for highly mobile environments where users cannot constantly type their requirements.

Moreover, developers can aim toward self-learning apps that collect user data to improve engagement. Providing relatable content is extremely important in today’s competitive market, and it will become even simpler with 5G.

The rise of 5G in the future is going to make adoption of AI in mobile applications inevitable. Take this as your cue to start learning what you need to know about how AI development works. Focus on programming languages for AI such as Java, C++, and Python.

Next, try to get familiar with advanced Big Data technologies as AI developers have to tackle large volumes of data. While theoretical learning is a bonus, don’t forget the soft skills. Participate in projects that help improve your business intelligence skills to develop commercial ventures.

Fast Streaming for Immersive Gaming

5G promises broad coverage with faster download speeds and stable connectivity—all features expected to take Augmented Reality (AR) gaming to the next level. AR along with 360 video could help drive user engagement due to ultra fast streaming. Using 5G, developers will have the chance to build high-quality mobile applications to get users to experience immersive gaming.

Low latency for 5G will play a huge role in delivering smooth gameplay, even with a large number of concurrent users on the network. Such an improvement is key for competitive gaming where users require minimal delays. According to these updates, developers can focus on multiplayer online games as they will become easier to support with 5G.

In order to build a mobile application that involves AR and VR, developers must be comfortable working with game engines like Unity or Unreal. Such engines enable you to construct 3D settings. Along with game engines, you need to be familiar with programming languages such as C++, Javascript, and Swift. As an aspiring AR/VR developer, your go-to resource must be a coding bootcamp, which gets you up to speed in a short amount of time.

Explore Remote Machine Connectivity

Machine remote control has immense potential all over the world. With the advent of 5G technology, machine remote control is now possible in environments too dangerous for people to reach. In cases of disasters, emergency responders need real-time imagery access to perform time-sensitive actions. Enabled by 5G, responders can get 4K video quality to control drones and perform searches effectively.

So, what does all this mean for you? As a developer aspiring to make the most out of 5G, remote machine connectivity can be a new area for you to explore. A good example to look at are drone control apps. To start off you need a suitable Software Development Kit (SDK) along with some Application Programming Interfaces (API). It is important that you pay attention to non-functional requirements (NFRs) such as scalability, performance, maintenance, and security.

Testing is the key to developing a good drone control application. In order to carry out successful testing, ensure that your app works on a variety of mobile phones—your team can help you access hundreds of devices in the cloud for testing. Lastly, stay up to date with the current developments in the remote machine access or drone industry. Reading up will help you cater to the relevant market and needs of the customers.

Target the Right Industries

The 5G technology is slowly rolling out to multiple different industries with notable applications. Stakeholders are making arrangements to install 5G on a massive scale in order to bring out substantial change. As a developer you must be aware of where 5G is going to have the most impact and how you can use it to your advantage. Making just any mobile application is not going to have much value unless it’s targeted toward the right industry.

Manufacturing is a major industry where 5G will be used to monitor the status of millions of items in real time. Here technologies like IoT, robotics, and autonomous vehicles running on 5G networks have enabled 50% more time saved and 30% more productivity. 

The next big industry to benefit from 5G is healthcare. In hospitals, 5G can do much more than track equipment. Cue Telehealth: 5G will enable remote monitoring via wearable devices and let surgeons perform a procedure on a patient in a different location.

Another viable industry that would be interesting to look at is retail. 5G will optimize supply chain operations through ultra accurate tracking abilities. Apart from this, the integration of AR/VR will change the way consumers and brands interact, giving customers access to physical stores just through their smartphones.

Keeping these examples in mind, it is possible to say that there are endless opportunities for developers once 5G hits the markets. However, as a developer, you should specialize in areas you have a strong interest in. Mass implementation of 5G is still underway and this gives you time to explore your strengths and learn more about what works for you.

Learn About Ambient Computing

The adoption of 5G is a pathway to integration of new technologies in our daily lives. One such technology on the hype these days is known as ambient computing. The goal of ambient computing is to reduce the interaction between machines and humans. With ambient computing enabled, you won’t need to interact actively with a device; instead, devices will respond to your actions.

The mass adoption of 5G will facilitate the prevalence of ambient computing in your everyday life. Cue mobile app development: Ambient computing is a field that has immense potential. While ambient computing sounds a lot like IoT, there are a few differences. IoT is still considered to be screen centric while the goal of “ambient” technology is to remove the role of the devices altogether.

Some examples of ambient applications include self-driving cars and automatic sensors to detect wear and tear in devices to schedule maintenance.

As a developer you need tools that help you create app-centric technology, such as Flutter. By using Flutter, you can focus on creating the app rather than thinking about catering to a certain device. To develop with Flutter you need to learn a programming language called Dart—mainly focused on front end development for mobile and web applications.

Embrace the Upcoming 5G Revolution

The 5G technology is almost here and you should be ready for it. Due to its high speed connectivity and bandwidth, 5G is going to offer benefits never seen before. With unique features such as IoT, AR/VR, and remote machine control, 5G is your signal to explore your interests and dive out of your comfort zone. As a developer, you should focus on areas that drive your passion and acquire the skills to pursue it.

5G adoption is going to eliminate a lot of development limitations and this is going to be a huge opportunity for programmers to build apps with new functionalities and solutions. Consequently, the development industry will slowly change its pace, adjusting to the new stability offered by 5G.

Having fewer limitations means more freedom to create high-performance applications. So now is the time to prepare—learn the skills, practice the tools, and get ready to face the internet revolution head-on!

The post 5G App Development: How To Prepare for the Future of the Internet appeared first on Simple Programmer.

But it was more than simply the logistics of working locations; general processes also ran into trouble. This meant disruptions to supply chains, cybersecurity, and much more. Systems were exposed, and many businesses struggled to keep up. Suddenly, organizations needed to deploy automated systems to stay afloat.

The truth is, this change was inevitable. Pre-Covid, we had already seen a push toward artificial intelligence-powered robotic process automation. The pandemic only hyper-accelerated this change. The businesses able to adapt the quickest were those that had already taken steps toward automation.

If you’re new to the world of automation, you might be wondering what robotic process automation is, or maybe you’re looking to grow your understanding. Well, let’s start here: What exactly is the process of automation?

What Is Robotic Process Automation?

Robotic process automation (widely known as RPA) is a form of automation unlike any other. Whereas automation relies entirely on coding, a process that has preset outcomes, RPA uses artificial intelligence (AI) to complete tasks more autonomously. AI has had a bad rap in the mind of the public—largely thanks to pop culture—but it isn’t a terrifying machine coming to take over the world (at least, not yet!).

Artificial intelligence, in short, is when a machine tries to mimic human thought patterns to complete certain tasks. In today’s world, this technology is found everywhere, from self-driving cars to race recognition technology.

RPA uses AI to process tasks, removing the element of human error. Think about all the different processes in your business. Each will probably need the oversight of an experienced worker. If, for whatever reason, a worker is unable to activate a process, your business could grind to a halt.

Not only does AI keep your business running efficiently, but it can also provide data-based insights, helping to improve processes. Organizations that have adopted RPA have seen improvements across the board. This includes improved compliance (92%), improved quality/accuracy (90%), improved productivity (86%), and cost reduction (59%).

How Automation Is Transforming the Workplace

Some form of automation is present in practically every modern business and there’s a good chance your organization might have invested in some of the following forms:

Sales: When you make a sale, it’s unlikely that you’ll process the order yourself. The majority of organizations have a system in place to process and track orders. It’s always a good idea to run crowd testing to make sure your store functions as it should.

Customer Service: An increasing number of businesses are turning to automated chatbots to deal with certain queries. This helps keep your phone lines less busy and means that agents can focus on more difficult queries. Also, VoIP software, such as Vonage or one of these Vonage competitors, is another excellent use of AI in the workplace.

Website: Your website likely contains several automated elements. This could include a contact form, mailing list sign-up, or downloadable files. For all these different elements to operate smoothly, you need the right support (from AI!).

If interested in learning more on how AI powered automation is transforming the workplace, check out Intelligent Automation Simplified by Debanjana Dasgupta, which provides some fantastic insights.

What Does RPA Mean for My Business?

Put into action, AI-powered automation can affect almost every aspect of your organization. Let’s look at some of the benefits of the technology, and why it might be time to revamp the automation within your business.

Speed Up Processes and Slash Costs

With RPA, data can be copied into a spreadsheet in a matter of seconds, and with a much-reduced risk of (human) error occurring during data entry. The decrease in time and error are reasons enough to see the value of AI.

Think about the difficulties of handling manual processes. You’re probably collecting a large amount of data. Even after carrying out process mapping, data can be difficult to keep up with since information needs to be stored properly so it can be accessed later.

There are two significant issues with properly storing data. First, there’s a time factor to consider. Your employee is entering each piece of data manually, and the scale of data means that this task could take an unknown amount of time. Second, it only takes one slight error for your data to be rendered useless unless more time is spent trying to find a correction.

Remember, the faster data is added to the database, the quicker it can be used to improve your business. For example, this might mean new and inventive forms of marketing. Or it could be used to help show how to improve product quality.

Additionally, RPA can be a great way to reduce costs. With an automated system, you’ll need to employ fewer people, saving you some cash. The workers you do employ can move away from data entry and put their skills toward other valuable tasks that can help you grow your business.

Stay On the Right Side of the Law

Another aspect of properly handling data is the legal side. Today, people are much more aware of their data and how it’s being used, and this has brought about new legislation to regulate the ways businesses can collect and handle data.

Many of the new laws come with painful fines attached. For example, the EU’s General Data Protection Regulation (more commonly known as GDPR) sets fines of up to €20 million. It’s fair to say that most penalties won’t reach anywhere near this number, but it does show how seriously the issue is being treated.

These new laws pose challenges for many businesses—big or small. To reduce the risk of breaching legislation, it’s better to limit data access to as small a number of employees as possible. Again, this is where RPA can come in handy. Because processes are automated, there’s a limited need for human oversight.

More Secure Data

The idea of data leaks is becoming an increasing concern for many businesses. According to a recent report, small to medium-sized businesses are hit by a cyberattack every six months. Similarly, contracting a computer bug can be extremely damaging to your data.

The less secure your data is, the more damage will be done to your reputation.

Some people have expressed concern about RPA and how easy the system may be to exploit. But this concern is largely ill-founded, and it’s your responsibility to show customers their information is safe in your hands. Most modern RPA tools come with end-to-end encryption, meaning your data is extremely secure.

Important note: Do proficient research to make sure the RPA solution you choose comes with the right security features for your company. 

Provide Better Customer Support

Customer support is a lifeline for businesses. If you get something wrong, your support line is your best chance to stay on good terms with a customer. But handling customer support isn’t easy. Many businesses suffer for their lack of attention when it comes to valuing customer support.  

You need to make customer support a priority. There’s a good chance that you’ve experienced frustration when an organization misses the mark on your support. This usually means long hours stuck waiting in queues, only to find that your query isn’t resolved. The average customer feels the same frustration.

One of the main reasons that customer support is slow is that staff are bogged down in admin tasks. Although obviously important, these jobs are repetitive and often take up a lot of time (especially if careful attention is needed, as with data entry).

With RPA, you can automate these tasks so they don’t consume your employee’s valuable time with otherwise delegated responsibilities and allot more time to customer support. In other words, shorter queues and more query help!

It’s important you learn how to calculate CSAT to see if customers are responding positively to changes that you make.

For additional help overcoming common issues with customer support, check out The Customer Support Handbook by Sarah Hatter.

Engage with Data

Data is king in the modern world. With the right data, we can improve every aspect of our organization. This could involve improving workflows, creating better marketing, or making a more engaging website. But first you need to gather the right data. You’ll also need the right analytics software to unpack data into usable information for your business.

RPA operates on a constant feed of data, and you can learn from this information in a multitude of ways. For example, you can put RPA data into a machine learning algorithm, which can be used to spot issues with your processes and suggest steps toward optimization. This way, you can boost the overall efficiency of your organization.

Better Email Automation

Email can be a great way of staying in touch with customers—most businesses use email for marketing. With an email referral program, you can even grow your customer base.

But sending all emails manually isn’t practical. For this reason, almost every business uses some form of email automation. This ensures customers receive regular communications and reduces the workload for your staff. But could this automation be improved upon?

There are several ways RPA can help your email automation. While other automation tools require you to write lines of code, RPA does not. It has been specifically designed to be integrated quickly and easily without any previous programming experience.

This means your RPA can get to work scheduling your emails, validating email addresses, and cleaning up your email list.

It can also be adapted and modified to suit your changing email needs without having to employ an experienced programmer. Not only do you save money, but you also have the flexibility to make modifications to your email campaigns as needed.

Improve Admin

No one enjoys doing administrative work, but it’s a necessary evil. RPA can make this irritation a thing of the past, dealing with menial tasks and letting staff focus on more important matters.

It can, for example, complete data entry for invoice processing, help onboard new employees, and support the payroll process from start to finish by checking time records and generating paychecks. It can also help with accounts reconciliation, placing sales orders and keeping customer information updated.

You can use RPA alongside other tools to further help with admin, too. For example, a free online contract generator can help reduce the amount of time spent building contracts. There really is no end to the ways RPA can support your team.

Will This Affect the Number of Workers I Need?

You might be thinking that with the rise of AI, you won’t need staff at all. But this isn’t quite the case. Bringing automation into your organization will reduce the number of manual tasks that you’ll need to complete—say goodbye to time-consuming data entry, basic customer service, and emails! This will inevitably mean a smaller number of staff is needed. But effective automation needs a knowledgeable workforce to go alongside it.

AI has limitations and is far from reaching its full potential—there are tasks that go beyond the skill set of AI. Meaning, you’ll need employees with the right skills if these tasks are to be completed effectively. Similarly, while AI may be able to learn, you won’t be able to assign it an innovation challenge since it can’t think creatively.

Used in tandem, a strong workforce alongside the right RPA technology can make a strong team.

Will My Workforce Need to Change?

Automation is the future. Whichever way you look at it, the world is changing. But as technologies change, so must employees. This isn’t unusual. Look at the world 30 years ago compared to now: technology has changed dramatically, and workers now have different skills to complete tasks.

Looking to the future, this kind of change will need to happen again. Your existing staff will need time and training to adjust to new technology. Staff will need to be capable of working with AI-powered systems from the back end.

For example, this could involve learning to work with drones when handling the supply chain. They also need to know how to test automation metrics to ensure systems are working correctly.

You should also consider the skills you look for in new employees. These, too, will need to change if you’re to find the right people. It’s always good to introduce people who already have experience working with AI so they can share their expertise with their teammates.

Start Preparing Now

The change to automation is well underway. It’s understandable if this change seems daunting; new systems and procedures can be difficult to get your head around. There’s no doubt, however, that automation can give your business a boost.

As we’ve explored here, there isn’t really an aspect of your business that wouldn’t benefit from automation. From HR to finance, and customer service to marketing, RPA can revolutionize the way you do business and make life easier for your employees. But, remember to take your time when introducing new systems and really evaluate which areas of your business would benefit most. Review each department carefully and make sure that you carry out automation testing to ensure that it’s working as it should be.

Of course, change needs to start from the bottom up. Your staff will also need support to adapt. Make sure you give time for training, and that your employees know it’s okay to ask questions.

So, start preparing now and embrace the future today!

The post Understanding Post-Covid Automation and Artificial Intelligence appeared first on Simple Programmer.

Indeed, the statistics are telling. In May 2021, Tinder saw 6.5 million downloads. It was followed by Badoo with 3.9 million and Bumble with more than 1.7 million.

Dating apps—particularly for casual dating and hookups—are a growing trend. The forecast for 2024 indicates a total number of 280 million users of dating apps, with 113 million users seeking a match and 70 million users looking for casual dating.

Nonetheless, the dating app development process is not as easy as it may sound. A lot of homework must be done before creating a dating app like Tinder.

Though it would be unlikely for your app to dethrone Tinder, people are still likely to use it if it’s been properly developed by considering factors like graphics, UX, and features. If you are thinking about how to create a dating app, I’ve got you covered. Keep reading and I’ll give a walk-through of everything you need to know to set up a dating app like Tinder.

Identify Your Competition

There are already so many dating apps active in the market—why would we need another one? Simple: Because there’s always room for something new if it fills a gap in the dating app world.

If a new dating app has a unique feature that can delight users, it can promise better results in terms of more downloads and increased conversions.

Hence, competitor analysis is mandatory, since it gives deeper insights into creating the right dating app. Competitor analysis identifies the weak and strong points of your rivals. Such details will help in determining an effective strategy for building a dating app.

Taking a closer look at the features of a competitor’s app never did any harm. It is vital to figure out what a competitor does that makes it stand out from the crowd. Similarly, noting their blunders can help prevent you from making potential mistakes in your own development process.

Matching Algorithms

Popular dating apps like Bumble, OkCupid, Tinder, Hinge, Grindr, etc., have a similar tech stack and a different set of algorithms. It is the algorithm magic that binds all dating app users together. Users tend to stick with an app as long as the app’s algorithms are displaying desired results.

Users are divided based on the search results shown in these apps. For instance,  some users prefer potential matches who have common tastes, whereas some prefer those who appear charming.

Consequently, there are not many mathematical calculations involved; it’s mainly preferences that matter. And here is where experimenting with artificial intelligence comes into the picture. Dating software development companies (and this section) focus on certain algorithms that cannot be ignored.

Location-Based Algorithms

The location-based algorithm specifies results for the user within a particular area. Individuals can find their potential partner as per their preferred location within a city or state.

Many dating app developers monetize the app by offering in-app purchases for expanding the search radius. Users need to render a certain fee for expanding their specified location.

Behavior-Based Algorithms

Nothing feels better when you and your potential partner have the same tastes and choices! A behavioral approach is another way of accumulating users’ data and bringing up profiles with similar interests.

Since people do not disclose everything related to their interests, the behavioral-based algorithm is the best solution. It gathers the data of the user based on their social media handles, preferred playlists, etc., which helps in determining the right match based on common interests.

Non-Overwhelming Structure and Design

The golden, unspoken rule for making a successful dating app like Tinder or OkCupid, is zero complexity for the user. An app that doesn’t overwhelm its users comes out ahead in the long run. Let’s dive in and understand more about the specifics of design for dating apps.

A Catchy Name

The name should complement the core functionality of the app, helping to make it relatable to users. OkCupid, undoubtedly, is the best example of a catchy name for a dating app.

Target Audience

An app cannot sustain itself in the competition unless it is being utilized by the target audience. This applies to every dating app on the Google Play Store and App Store. Young adults are the prime demographic for dating apps, and it is vital to integrate the right set of features for the target audience.

For example, Bumble has a set of advanced features in its premium version. It allows users to filter the preferences based on physical features like height, weight, smoking/drinking habits, education, etc.

Pleasing UI Design

Modern yet intuitive UI design keeps the user hooked on the dating app. Be it Tinder, Bumble, or OkCupid, the UI design is trendy yet simple. Look at the login designs of Tinder and Bumble compared below.

They neither overwhelm nor confuse the user. In fact, such designs are easily understood by new users because they’re directing the user to log in through different options. Simple as that!

Technology Stack Needed

It’s now time to… pop the question: How do you make a dating app?

Software developers must choose the correct software for each development. Dating app development is no different and has its own specific requirements.

• Programming: Java, Kotlin, Swift. These languages do not require hardcore coding skills. The code can be easily amended without starting from scratch.
• Database: MongoDB, SQL, Redis. All these are open-source platforms with features like ad-hoc queries, replication, file storage, load balancing, etc. These databases are considered perfect for storing user data in the cloud.
• Framework: React Router, Node.js, Express.js. These open-source frameworks help in building scalable network apps.
• Cloud Storage: AWS. For many dating applications, AWS is the king of storage, because it gives access to data anywhere, at any time. It eliminates the demand for buying your own storage space.
• Web Server: Nginx. A web server is a vital element for distributing content on the web/app. Nginx is one of the top choices for handling over 10K connections simultaneously.
• Payment Gateways: Stripe, PayPal. Users need to render a fee for utilizing paid features of the app. PayPal and Stripe are regarded as useful because of the international usage of these payment apps.
• General Utilities: Google Maps, Google Analytics, Optimizely. Tracking the number of users from every location helps developers to bring in custom features/services that are desired.

The MVP Requirements

Everyone wants to create a dating app that stands out from the competition and has unique features. However, a particular set of features must be present in every dating app development arsenal to ensure smooth operation. The following sections share which features must be included and what each entails.

• Easy Sign-in. The traditional sign-in process is history. Users appreciate the instant sign-in process that keeps the hassle of forgetting their password at bay. Hence, you should allow users to sign in using their Google or Facebook accounts.
• User Profile. The user profile is the identity of the dating app user. Here, all details and preferences of the user are shared. Since the information is extracted from social media handles, no user needs to put extra effort into filling in the information manually.
• Geolocation. This feature makes it easier for the user to identify the location of the potential match. They may change their location preferences for dating purposes.
• Chatting. The feature of chatting allows the user to initiate a conversation with potential matches. Such a feature makes it easier for both users to break the ice and strike up a friendly conversation.
• Matching. The matching feature is the heart and soul of dating apps. When there’s a match, users can then initiate a conversation and may choose to meet in person.
• Push Notifications. Dating app users will receive all notifications related to chats, profile visits, heart ratings, etc., under the push notification section.
• Swiping Feature. Swiping is common in dating apps. Such a feature simplifies selecting/rejecting profiles of potential matches. Swiping right indicates selection, whereas, swiping left means not interested.

How Do Dating Apps Generate Revenue?

Dating apps can generate revenue through widely accepted monetization strategies. Here’s a quick look at some of the ways to monetize a dating app.

• Ad-free Content. Users can pay a certain amount to subscribe to the ad-free content of the app. This is a biannual or annual subscription where users do not receive any ads.

• Gifts. Premium subscription app users can gain access to additional features, such as sending gifts to potential partners while chatting. This feature can be integrated with a fixed fee.

• Unlimited Right Swipes. Dating apps like Tinder and Bumble follow this evergreen strategy. They offer unlimited swipes through the premium upgrade where users get the benefit of more right swipes.

How To Build a Dating App Like Tinder

The concept of dating apps has proved to be a superb innovation. Building a dating app isn’t rocket science, but a streamlined development approach with the right set of strategies related to its marketing is required.

By integrating the right features, algorithms, and tech stack, building a successful dating app will be smooth sailing. It doesn’t demand out-of-the-box thinking, just addressing specific requirements and desires of users.

The ideas and points shared above will help you on your journey to creating a stellar dating app. And if you already have a unique idea for a dating app that will fill a gap in the market, you’re well on your way!

The post How To Create a Dating App Like Tinder appeared first on Simple Programmer.

The future of cloud computing is changing rapidly. It is a domain that has been at the forefront of technological advancements and will continue to be for the foreseeable future.

The post 6 Cloud Computing Trends to Watch in 2022 appeared first on Simple Programmer.

IoT was merely a technological concept some years ago, and today, it has become an influential part of our life!  IoT is a blend of sensors, software, devices, and networks that suppress human intervention to the minimum for accumulating data.

Technological upgrades and the introduction of new devices have given a boost to IoT technology. Today, people are purchasing devices equipped with sensor-based technology. Be it home automation devices or medical IoT devices, mankind is making the best use of technology.

As per the reports compiled by Statista, it is estimated that there’ll be over 75 billion IoT devices in use by 2025, considering the increasing use of smart devices in daily lives.

IoT Devices

A gamut of appliances, gadgets, and sensors that gather & exchange data/information over the internet are called IoT devices. Such devices are specially programmed to embed into other devices, and via IoT devices, other applications and gadgets can be controlled from any corner of the world.

Before proceeding to the main part of the article, here’s a look at some key statistics that deserve your attention.

• The worldwide revenue of IoT is expected to surpass 1 trillion USD by 2030
• Up to 14.2 trillion USD could be added to the global economy through the industrial internet of things (IIoT)
• According to Cisco reports, M2M businesses are expected to rise to 3 billion in 2022
• MTA has predicted that there will be the connectivity of 15 devices per person by 2030

All these statistics point toward the adoption of the internet of things at a speedy pace. Not just the adoption of IoT, but the economy will also upsurge due to vast investments expected ahead.

IoT has been playing a significant role in the development of technologies like 5G networking, artificial intelligence, etc., and it is evident that we’ll be witnessing the rise of IoT in the future.

Now that we’ve discussed the importance of IoT and how quickly it’s growing, let’s dive a little deeper into the specifics of what IoT is, global markets, and expected trends for 2022.

The Innovative Existing Reality

Gone are the days when the concept of the application of IoT in business was a topic of discussion. Today, numerous business realms have integrated IoT while making continuous improvements.

Customized software for purposes like marking attendance, tracking workflow of employees, cams for residential/corporate security, smoke sensors, etc.  Laptops, smartwatches, smart bulbs, smartphones, etc., can be connected to the internet effortlessly! They are simply examples of IoT devices that people use daily.

If observed closely, the concept of the internet of things revolves around the robotization of the work. The higher absorption rate of IoT in businesses magnetizes higher efficiency in performance.

IoT devices keep the aforesaid in the loop to strengthen the performance of different industries and businesses in the market.

How Do IoT Devices Work?

An IoT device like a smartphone or smartwatch contains an integrated CPU, a network adapter, and firmware. These are connected to a dynamic host and require an IP address to operate. Also, the apps designed for smartwatches and smartphones are connected to software for performing different functions that make them smart devices.

For example, wristwatches previously were just used for tracking time. But, IoT technology allows individuals to track the number of steps walked, heartbeat rate, calories burned, etc. Thus, turning a regular wristwatch into a smartwatch.

Prominent IoT Devices

Amazon ECHO voice controller, Google Home voice controller, Amazon ECHO spot, Nest Audio, etc., are examples of some major IoT devices in the market. Equipped with the voice controller feature, individuals can operate and manage different tasks by voice.

A Voice controller is a prominent feature in most smart devices. Once activated and configured to certain voices, it responds by executing the command given by the user.

Examples include Playing music, operating alarms, TV, switching on/off lights, typing messages, controlling other compatible devices, etc., which can be done smoothly using such voice controller IoT devices. The embedded components like sensors and speakers make it possible to operate all prominent IoT devices.

Key IoT Devices in The Market

Several IoT devices have entered the market and they’re here to stay. Equipped with the right technology, these devices have proved to be beneficial in the long run. Manufacturers are upgrading the existing versions for better performance. Take a look at some popular IoT devices.

Air Quality Monitors

Using air quality monitors, individuals can track the level of impurity indoors. These devices are easy to install, maintain temperatures, and purify the air.

Lighting System

Lighting systems are widely used personal IoT devices. By installing these indoors, individuals can control lights through the app installed on their mobile phones.

Smart Doorbells

Installing a smart doorbell at the door will eliminate the need for opening the door whenever someone comes. A smart doorbell allows the user to answer the door from anywhere in the house using the mobile.

Smart Locks

Using a smart lock, people can secure their homes futuristically! A smart lock is an IoT device that can be unlocked using biometrics or Bluetooth when the user has connected it through the app. In case the door is not locked properly, a beep will alert you for the same.

Mesh Wi-Fi

Whole-home mesh Wi-Fi is an IoT that circulates Wi-Fi signals throughout your home on the existing internet service provider. The mesh Wi-Fi can be configured through the Orbi app. It eliminates the buffer zone and strengthens the Wi-Fi signal for a seamless experience.

With the ongoing advancement in the field of IoT, it is expected to surpass one trillion USD by 2030 as upcoming IoT devices enter the market. Not just the revenue, but the number of IoT devices is forecasted to increase thrice the current figures.

93% of enterprises have adopted IoT in their regular operations. Energy meters and smart appliances are prominent devices that have been adopted widely. With that in mind, let’s take a look at the global markets of IoT.

Global IoT Markets

As per expert reports of the 2021-2026 forecast period, the global IoT market is expected to grow colossally. With a high CAGR of 10.53%, the global IoT can rise to USD 1,386 billion by 2026 from USD 761 billion in 2020.

The growth in global IoT has been possible owing to technologies like conversational AI, machine learning, cloud-computing platforms, and low-cost sensor technology.

Industrial IoT

Industrial IoT, or simply IIoT, refers to the implementation of IoT in the industrial space, with respect to the technologies that control sensors & devices.  Industries across the world have absorbed the technology of machine to machine learning (M2M) for acquiring automated control.

But, with the emergence of machine learning, establishing new revenue models would become easier. Some common uses of IIoT are listed below:

Connected Vehicles

Vehicles like delivery trucks and buses are equipped with additional IoT technologies for monitoring mandatory safety practices.

Smart Cities

IoT deployments in smart cities help in monitoring practices that affect their municipality. Using IoT, municipal authorities can generate better insights for building safety for the community.

Smart Traffic Management

Upgraded traffic management systems have simplified traffic-related mitigation. Sensors in the traffic light adjust the signal brightness as per the daylight. Bridge/flyover monitors detect the structural health and generate periodic maintenance reports.

Smart Supply Chain Management

Tracking the location of deliverables after dispatching has been made easier by integrating IoT technologies. GPS trackers and software help delivery managers & stakeholders about the location of the items that have been shipped.

IoT and Big Data in Rail

Considering the industrial aspect, the IoT has an impressive influence on the railways. Without a second thought, the railways have been a fragmented department. Many countries in the world rely on railways as a mode of transporting goods.

As per the industry IoT analytics, the railways have the most complex environment of digitization. It has been considered a challenge by experts because of 5Vs namely:

• Value– Geographically dispersed and high value

• Veracity– Reliable for processing data

• Velocity– The pace of processing data

• Volume– Reaching up to the set limit that cannot be processed through traditional methods

• Variety– Updating the obsolete technology

These technical obstacles can be cleared using edge computing. When IoT is blended with edge computing, it becomes the solution for the above-mentioned challenges and issues related to the rolling stock.

Using this advanced technology, individuals can not only access the data generated but can figure out real-time solutions that are cyber secure. When cyber security levels up, new trends emerge. Find out what’s there in the box for IoT trends for 2022.

IoT Trends To Watchout For 2022

Here’s a quick look at some hot IoT trends to be on the lookout for in 2022.

The Rise of IoT in Big Data

IoT is highly fertile in terms of dispersing information-driven data because it has billions of IoT devices interacting 24×7. While IoT devices gather information and data using their sensors, the big data technique analyzes huge chunks of data which can be utilized multifariously.

The entire process of integrating IoT technology can be simplified by feeding the information to machine learning algorithms. By doing so, significant improvements in the functionalities (seen below) happen within the machines.

• Improved decision making capability– Immediate decision making based on results
• Better data performance– Processing data at a higher rate
• Increased analytical performance– Able to perform complex analyses easily
• Enhanced pattern recognition– Identifying and adapting repeated patterns easily

Development of 5G Network

Unarguably, 5G is the future. By 2025, China is anticipated to reign with 866 million 5G connections. On the other side, the USA is expected to have 247 million 5G connections followed by Japan with 138 million 5G connections. The 5G technology promises better connectivity speed, improved DTR, and better network credibility

With 5G being the core of cellular networking, the IoT devices would play a key role in transferring data speedily between connected devices.

IoT in The Healthcare Industry

The healthcare sector has been the largest adopter and consumer of IoT technology. Even during the pandemic outbreak when everything else seemed to slow, this industry contributed to the rise of IoT technology.

Various IoT-based surgical instruments, blood monitors, cardiology/neurology, and smart wearables positively impacted the lifestyle of people. healthcare could become even more important now with post covid life and not having been active/invested in ‘normal’ health for the past 2 years.

Having said that, the trend of using IoT devices and technology in the healthcare space will continue in 2022.

Improving Cybersecurity

Undoubtedly, the number of people using the internet is scarcer as compared to the number of connected devices. This imbalance is scary because it opens the entryway for invasion by hackers

With the advancement of technology, comes the dark side of cyber security. Although many people know how to keep their devices (and all personal information stored) safe, even the most minute loophole can be enough for an impactful cyber attack.

Considering cyber security concerns like these and many others, companies have started to fix the problem of security in IoT devices. Cyber security is upgraded through monitoring IoT devices, implementing network segmentation for defense, adopting secure password practices, and fixing security patches via periodic updates.

Internet of Things – The Future

Without second thoughts, IoT is the future with limitless potential! The ever-evolving IoT is set to deliver the agility for deploying and automating different use cases.

The potential of IoT is not just about enabling communication between multiple devices but leveraging bigger volumes of data through AI.

Considering the existing global market and trends of IoT adoption in multiple industries, it is expected to grow speedily quite soon.

The new wave of advancement will have human-to-machine and machine-to-machine interactions with similar sensory experiences. This will unveil several new opportunities for innovation and apparently, it can permeate to become a key interface in the world.

The post The Ultimate Rise and Growing Trends of IoT appeared first on Simple Programmer.

Multi-Cloud Strategy

\n\nThe multi-cloud strategy is a must to read about. With a multi-cloud strategy, companies can select different cloud services from other cloud companies to transfer more significant data portions. It is a speedy and reliable way to move large amounts of data, especially suitable for large data transfers and with integrated machine-learning capabilities.\n\nMost companies follow the multi-cloud strategy due to the following reasons:\n\n

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• Flexibility: If a company has its choice of multiple cloud environments, that offers flexibility and allows the company to stay away from vendor lock-in. The transition to any other product, even of a competitor, is easy.

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• Data protection: If you link with the multi-cloud strategy, you can avoid issues resulting from technical or mechanical reasons, computer, and human error. If you have multiple cloud environments, you are always protected from sudden, unavoidable circumstances, as you have resources and data storage available to avoid downtime.

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• Efficiency: Multi-cloud environments can help companies achieve their goals as planned due to their efficiency in managing and storing data.

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\n\nMulti-cloud management is involved in multi-cloud computing, as information is transferred from one cloud platform to another. It requires expertise to handle multiple cloud providers and complex cloud management systems.\n\nA multi-cloud platform can put together all the best services that each platform offers. It helps the companies customize an infrastructure unique to their business goals. Further, this multi-cloud concept provides lower risk: If one web service does not function, a business can operate with other platforms in a multi-cloud system as it stores all data in one place.\n\nSo far, so good. But how about downsides or problem points?\n\nGenuinely multi-cloud security has a big challenge of protecting data in a consistent and secured way across various cloud platforms. When a company uses a multi-cloud concept, the deposit is handled by third-party partners. Therefore, the cloud deployment has to identify the matter and distribute the security responsibilities among the other parties to ensure safety.\n\n

Rapid App Development and Low Code/No Code

\n\nIn a world where everything is interconnected and relies on the speed of delivery, many inventions want to make the processes speed up. Rapid app development (RAD) comes into play here as a critical subject that many of us are likely already exposed to.\n\nFor example, Agile software focuses more on current software projects and user feedback. Can you believe that it does not follow a strict plan? The software helps rapid prototyping over costly planning.\n\nThe process of rapid app development is straightforward, as shown below:\n\n

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• Requirements: Rapid app development does not have any strict requirements and has the permission to change any of the conditions at any point of the cycle. The client provides the company’s vision for the product, agrees with developers, and finalizes the requirements that fulfill the goal.

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• Prototype: This rapid application development develops a prototype to make the client understand the result. The RAD programming has a finalizing stage where all the errors are corrected and cut, and the final product comes out.

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• Feedback and final product: Along with the feedback, the RAD makes the final product, yet they are ready to perform possible changes, as step two of the process describes. However, if the client is satisfied with the outcome and the positive feedback, what is made will be the final product.

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• Last stage: The last stage is to submit the final product through demonstration.

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\n\nAs for Low Code/No Code development platforms, they are a kind of visual software development. Such a procedure allows industrial developers and citizen developers to drag and drop application components, combine them quickly, and create mobile or web apps useful for different functions.\n\nLow-code development platforms reduce the amount of time spent, enabling faster delivery of business applications.\n\n

Containerization

\n\nContainerization is a way of packaging software code with just the operating system, and its libraries and dependencies required to run the code. It comes in a single, lightweight executable, called a container, that runs without disturbance on any infrastructure.\n\nInstead of complying with the entire operating system or with your software, your code is in a container that can run anywhere. As these containers are pretty small, you can pack a lot of such small containers onto a single computer.\n\nThe concept of containerization allows developers to create and deploy applications with great speed and more safely. Earlier, code was developed in a specific computing environment. But when you transfer it to a new location, it often results in bugs and errors.\n\n

Deep Learning Libraries

\n\nDeep learning is simply a machine learning technique. It instructs a computer to filter inputs through layers to learn how to predict and classify information. It is operated through images, text, or sound, and is inspired by the way the human brain filters information.\n\nFor example, deep learning is a critical technology used in driverless cars, enabling them to recognize a stop sign or identify a pedestrian from a lamppost.\n\nIt is a continuously growing method of a broader family of machine learning based on data representatives. As a relatively new concept, the vast number of resources can be fascinating for those trying to get into the field of IT or those already in it.\n\nThere are Libraries that are famous for this deep learning technique. Below is a list of deep learning libraries that are commonly in use.\n\n

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• TensorFlow Keras: TensorFlow is a library for multiple machine learning tasks and also an open-sourced, end-to-end platform. Keras is a network library that runs on top of TensorFlow.

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• Caffe: Caffe is a deep learning program made with expression, speed, and modularity in mind.

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• Microsoft Cognitive Toolkit: (Previously CNTK). The Microsoft Cognitive Toolkit is an open-source library that is used to create machine learning prediction models. Generally, it creates deep neural networks, which are at the top of artificial intelligence attempts such as Cortana and self-driving automobiles.

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• PyTorch: The PyTorch framework helps about 200 different mathematical operations. It is very popular because it simplifies the creation of artificial neural network (ANN) models.

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• Apache Mxnet: MXNet is an open-source deep learning application that allows you to identify, train, and deploy deep neural networks on a wide range of devices, from cloud infrastructure to mobile devices.

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• DeepLearning4J: DeepLearning4J is a support framework for deep learning algorithms.

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• Theano: Theano is a Python library built for fast numerical computation that runs on the CPU or GPU. It is used as the key foundational library for Deep Learning.

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• TFLearn: Tflearn is designed to support a higher-level API to TensorFlow. It facilitates and speeds up the experimentations while being transparent and compatible with it.

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Multi-Model and Multi-Purpose Databases

\n\nIn database design, the multi-model database helps to manage multiple data models against a single and integrated backend. The single data model is mainly organized and stored in database management systems.\n\nA multi-model database is a database that stores all the data in more than one model. In the beginning, databases have primarily supported only one model: relational, document-oriented database, triple store, or graph database. A database that combines all of these is called a multi-model.\n\n

Artificial Intelligence

\n\nArtificial Intelligence is among the top of the latest techno trends. It is a unique technology that makes a machine simulate and interpret human behavior.\n\nMachine learning is a subcategory of Artificial Intelligence that allows a device to automatically learn from past data stored in the memory without programming further. The goal of Artificial Intelligence is to make an intelligent computer system similar to the human brain that helps solve complex problems.\n\nSo now the world is moving toward this concept and, soon, it will take over all the complicated processors.\n\n

AI-Powered Cybersecurity

\n\nAI-powered cyber security is one of the most vital concepts in terms of security aspects. Artificial intelligence can prioritize risk instantly and spot viruses or malware on the networks. It can also detect threats even before they activate.\n\nAI is at the top as a significant aspect of cyber security. It will increase the efficiency of all aspects of life like online shopping, information technology, telecommunications, etc., in protecting it against cyber threats.\n\n

Clean Technology

\n\nClean Technology is one of the most concerning terms in tech developments. How do you define clean technology?\n\nClean Technology or cleantech is any process, product, or service that reduces negative environmental impacts by energy efficiency improvements, better use of resources, or any other environmental protection activities.\n\nThe benefits of clean technology in any industry or field can include less waste, recovery of by-products, improved environmental performance, improved productivity, better efficiency, and reduced energy consumption which results in overall cost reduction. It is the same with information technology and apps.\n\n

Collaborative Technologies

\n\nCollaborative technology is also known as groupware. Collaborative technology, a commonly used term today, refers to tools and systems designed to improve group efficiency. It is related to both in-office and remote work.\n\nThese pieces of technology can cut down the costs and time related to facilitating group work. Such technologies can designate roles and responsibilities to route in-situ documents to checking and approving project parts. This concept offers more inherent and coordinated group problem solving across the entire workflow.\n\nA good tool for collaborative technologies is Creately, though I may be a little biased since I work there! Creately is a web-based work management tool that runs on a smart visual canvas, offering visual solutions for brainstorming, planning, project management, and capturing knowledge. Creately is used in project management, education, and many other sectors today.\n\n

Blockchain Technology

\n\nBlockchain is a system of recording information. Because of its design, it is difficult or impossible to change, hack, or cheat the system. No outsider can get into the system and steal or damage data.\n\nA blockchain is a digital ledger that duplicates transactions distributed across the whole network of computer systems on the blockchain. It also increases trust, security, transparency, and the traceability of data shared across a business network.\n\n

The Future Is Now

\n\nIn this post, I shared with you the most popular programming tech trends that have emerged in information technology.\n\nFrom multi-cloud strategies and rapid app development to AI and, perhaps most crucially, clean technology, the future is now!\n\nThese can be really helpful for your professional careers as well as daily tasks performed through information technology. I hope these facts are informative and helpful for those who seek knowledge on this subject matter.

The post 11 More Programming and Tech Trends in 2022 appeared first on Simple Programmer.

Machine Learning and Deep Learning are the two subsets of Artificial Intelligence. Whereas machine learning refers to computers able to think and act with less human intervention, deep learning involves computers able to use structures modeled on the human brain.

AI is everywhere—using digital personal assistants like Siri or Alexa, opening your phone with Face ID—from getting driving directions to getting recommendations on movies or music and everything in between.

For programmers in particular, AI offers new tools that help them write their code or determine errors. In this article, I will share with you twelve amazing facts about AI that every programmer must know. This way, you can stay informed and up-to-date about the fascinating upcoming opportunities offered by AI.

The 12 AI Facts a Programmer Should Know

Artificial Intelligence is getting into an omnipresent phase. AI is no longer an exotic endeavor to replicate or simulate human intelligence but a tool that we have been using in our daily life for convenience and efficiency. Indeed, advancements in AI will continue to bring a revolution to the tech industry.

With this in mind, let’s see 12 facts about AI you should know to make sure you don’t miss out on these amazing developments.

AI Is Evolving

AI has been ruling since its inception and has been slowly spreading to various spheres of development. Today, a Google-owned AI known as DeepMind can already beat Starcraft Two players.

In another example, the Chinese Alpha Dog is a robot dog that not only acts as a pet but also functions as a delivery agent. This goes to show the multiple opportunities AI presents to developers.

AI Can Pose a Threat too

Artificial intelligence, while acting as a simulator, can cause a threat too. Companies like Microsoft and Google have already issued a warning that bad AI decisions can harm potential businesses, as the AI can be programmed to do something devastating.

Moreover, autonomous weapons are artificial intelligence systems programmed to kill that can cause huge casualties.

Overall, such examples show how programming an AI—at least on such a scale—requires careful consideration on behalf of the programmers.

Increased Education In AI

Decision-making by AI can lead to a 40% increase in productivity, allowing programmers to spend their time more effectively, with a greater work-life balance. This can also lead to more opportunities for studying and furthering one’s knowledge. This means AI can possibly allow you more available time to learn something new that can help your career as a developer.

Moreover, students are opting for AI as an educational and career shift. Today the world’s top universities have increased their AI-related education to educate developers on AI matters.

AI Bots Are Programmed to Sound Female

An independent survey has shown that the majority of people prefer to talk to AI bots that have a female voice. Various voice assistants like Alexa or Siri have a female voice, as people find it more pleasant than a male voice.

As a programmer, you should keep this in mind whenever you design such systems—though of course there’s always room for exceptions.

AI Recognizes Emotions

Artificial Intelligence is turning into an integral system of our lives. Our actions, situations, and relations will all correspond to AI in the coming future. We, as a society, are so incredibly influenced by technology that we prefer to talk and spend our major time with machines, i.e., phones, computers, etc.

AI and neuroscience researchers agree that programmers develop AI so that it exhibits empathy. Moreover, machines have started recognizing human emotions. In the 1990s, a robot named Kismet recognized the emotions of the human body by merely listening to the tone of voice, then interacting with humans accordingly.

AI Will Become Smarter Than Humans

AI was developed by humans as a technology revolution. Things have changed to a major extent, with machines using their artificial intelligence and providing solutions. As we are progressing in the automated world, AI will be smarter than humans in the sense that it will be able to decide without human intervention.

AI Will Affect Human Employment

AI offers uninterrupted workflow without taking days off, needing rest, or expecting bonuses. Investment in AI can earn you good returns and reduce business costs to a significant amount.

On the other hand, millions of people have lost their jobs due to AI. As AI is proving to be more efficient than humans, it can turn out to be a threat to programmers’ employment—perhaps ironically, considering that programmers themselves are constantly finding ways of getting work done more easily with AI.

AI Is Being Used by All

Today almost all organizations, whether big or small, are making use of AI. Businesses are looking for AI programmers in the fields of customer relationship management, underwriting, fraud detections, and social media monitoring.

The Covid 19 pandemic further made Python development companies plan to invest more in AI. As soon as the pandemic was declared, WHO signaled that artificial intelligence (AI) can be an important technology to manage the crisis caused by the virus.

AI Developers Pave the Way for Investment Opportunities

Leave behind FDs, cryptocurrencies, and bitcoins. Invest in AI and get a return better than anything else.

Market research suggests that AI investment increased by almost 10% as a response to Covid 19. As many as 80% of enterprises believe that investing in AI will yield them a better competitive advantage than any other investment opportunity. As per the AI Index, the annual investment from venture capital firms into US startups with AI systems has increased as much as eight times.

AI Competition Among Tech Giants

Tech giants are sourcing AI and using it as a base foundation to compete. Some of the most innovative AI companies in the world, like Alphabet’s Google and Nvidia.Microsoft, along with other collaborators, will host a workshop on video analytics and intelligent edges in March 2022.

According to a study by Bespoken, Google’s AI has far excelled that of Alexa and Siri. Programmers can make the best use of such workshops to know the latest trends about AI and get a competitive edge.

Countries Leading the Way in AI

Singapore has become the global frontrunner of AI, as it has been strategically leveraging it in various operations that have already proved to be fruitful.

The top positions leading the way are Canada, New Zealand and Australia, and China. India stands third in the Asia Pacific region and 20th in the Nature Index overall. So investing your career in AI can certainly earn you big anywhere in the globe.

Undefined Ethics of AI

Ethics in the field of Artificial Intelligence are still undefined. There are no agreements, benchmarks, or boundaries clearly set. This may turn out to be dangerous regarding security and privacy levels.

As a concept, AI ethics refers to the moral principles and techniques intended to make an informed development and responsible use of artificial intelligence technology by programmers. As AI has become integral to products and services, organizations are starting to think more about AI codes of ethics.

AI Is the Future

AI has offered us some life-changing applications. From education opportunities to changes in employment and from investment opportunities to the still-undefined ethics of AI, one thing is for certain: AI is revolutionary.

This also affects you, as a programmer. Artificial intelligence, as a technology that keeps evolving, means that a career in AI will be in demand for decades, and is therefore an excellent career choice.

The post 12 Amazing Facts About AI appeared first on Simple Programmer.

The IT industry

At the same time, Artificial Intelligence (AI) is gaining momentum as well. This innovative technology was expected to make $22.6 billion in 2020, according to Statista.

Therefore, it seems that both of these industries are very attractive for tech masterminds. But the question is, how to get there?

In this post, I am breaking down the path to an AI career into separate phases to learn what it takes to become a successful Artificial Intelligence engineer.

What Is Artificial Intelligence?

The technology of artificial intelligence is innovative and controversial. Because it is very powerful—while at the same time still not well explored—people are afraid of it, and start telling stories of AI taking over the world and replacing humans as workers.

Now, what I am certain about is that AI sprang from machine learning technology. Instead of training machines all the time, engineers created a combination of algorithms so the machines could train themselves.

And bingo! We’ve got the technology for the 21st century.

In years, the algorithms became so proficient that they now engage in what we call “deep learning.” They need less and less human input, and are becoming much more autonomous. Besides, AI has at its disposal much more information now than it ever did, which creates even more opportunities to learn.

AI technology finds application in almost any industry: healthcare, transportation, finance, VR gaming, advertising, manufacturing, and many more.

What Does Cloud Storage Have to Do With AI?

For a couple of years, we have been witnessing massive cloud migration. In other words because of affordability, environmental issues, and cybersecurity, businesses are relocating online services from physical servers to cloud storage.

Why is AI relevant here? Because AI powers cloud servers and is able to learn from the data it stores. In such a way it can solve the problems before anyone even notices, or even predict and prevent problems from ever happening.

Amazon’s Alexa and the Google Assistant are great examples of how wondrous merging between cloud and AI can be. Also, these features only announce the upcoming megatrend of similar devices we can expect in the future.

In turn, this trend creates a huge demand for AI engineers that will maintain AI-powered cloud systems.

Where Can I Work in AI?

All that being said, the next question is “Where can you work as an AI expert?” Here are some industries, companies, and even government agencies that are currently thriving from AI.

Technology and Computer Science

Quality assurance (QA) testing is a crucial service for software development. The QA market is expected to reach $49.9 Billion by 2026, which is more than twice the worth it has today. So, knowing how to employ AI and machine learning (ML) for software testing and debugging could be a profitable skill.

Another example would be Facebook or, as they prefer to call it nowadays, Meta. It is a well-known social media platform that developed Oculus virtual reality using AI.

• Open vacancies: At the moment, there are 100+ AI-related vacancies at Meta, and these include Research Scientist, Optical Scientist, SWE specialist, and many more.
• All experience levels are welcome, from interns to specialists.
• Usually, a Bachelor’s degree in AI engineering is the minimum requirement.
• In most cases, at least 1+ years of professional experience is required
• Over 80% of all AI engineers in the world work at either Facebook or Google.

Moreover, don’t forget that Facebook/Meta also owns one of the most popular messaging apps, Whatsapp. Whatsapp for business created a chatbot so that people can communicate with businesses as if talking to a real person. For example, the people behind an eCommerce site can train the chatbot to provide information about the current delivery status of something that has been ordered.

As previously mentioned, Amazon is the creator of Alexa, an interactive AI system. In September 2021, the company announced that it planned to hire 55,000 people for corporate and technology roles.

• Open vacancies: At the moment, there are 800+ AI-related vacancies at Amazon, including Data Scientist, Applied Scientist, Software Development Engineer, Deep Learning Architect, and many more.
• Both remote and in-office jobs are available.
• Usually, at least 2+ years of professional experience are required
• In general, at least a Bachelor’s degree in computer science is required.

Healthcare

Healthcare is where AI technology is most promising. In 2019 alone, investors poured more than $4B into healthcare AI startups. After the pandemic, the interest in healthcare improvements will only continue to grow.

A basic Google search reveals hundreds of jobs related to AI and healthcare, such as Learning Specialist, Principal Architect, Senior Healthcare IT Consultant, and more.

Agriculture

AI systems help food production by improving the overall quality and profitability of harvest. In 2020, the percent of AI job posts in the agricultural sector doubled.

The most common agritech job posts are:

• Software Engineer
• UI/UX Specialist
• Data Analyst
• AI Specialist
• Business Development and Sales
• Digital Content Creator
• Marketing Communications
• Finance (Operations and Corporate Finance).

Public Sector

AI algorithms help scientists at NASA understand huge amounts of data about the universe. Currently, there are more than 150+ AI-related vacancies at NASA Jet Propulsion Laboratory, including Data Scientist, Senior Software Engineer, Software Systems Engineer, and more.

Marketing

There are a lot of startups that develop apps to help marketers automatize their strategies. And, as you might guess, most of them are looking for AI engineers.

SMS marketing strategies can largely benefit from AI. For example, an AI-powered SMS API can execute SMS marketing campaigns quickly and efficiently. Beyond that, AI can help with content creation, generic or frequent questions, and many elements of personalized customer experience.

Still, don’t forget that although companies on this list are the world’s most successful companies, they are not the only ones applying AI. On the contrary, AI is becoming a major trend and more businesses are implementing it as we speak.

Not all of them are large-scale, so, while there currently are a lot of job opportunities in AI, you may need to know where to look and how to present yourself.

Technical Skills Required in AI

In order to become an AI expert, there is a complex set of skills an individual needs to have. To begin with technical skills, here is what an average AI and ML engineer is expected to know:

• R, Python, Java, C++
• Quantitative analysis
• Business acumen
• Excel
• SQL
• Tableau
• Hadoop, Spark
• Probability and statistics calculations
• Reporting and presentation skills
• Database administration
• Data analysis
• Data visualization
• Extraction and signal processing techniques
• Unix tools (awk, grep, cat, sort, find, cut, tr, etc.)

Of course, depending on the seniority level and the particular job post, you might not need to know all of the things on this list. For example, to apply for the entry-level Research Intern post at Facebook, you would need:

• Ph.D. or Masters in computer science or related field
• Published papers in the domain of computer science or related field
• To know how to work in C, C++, Python, Lua, or other
• Quantitative analytical skills
• Experience in deep learning
• Experience in data analytics

On the other hand, to work as a Senior Software Development Engineer at Amazon you would need:

• 2+ years of experience contributing to the architecture and design of new and current systems.
• 3+ years of programming experience with Java, C++, or C#.
• 4+ years of professional software development experience.
• 2+ years of experience as a mentor, tech lead OR leading an engineering team.
• Experience with deep learning systems.

As you can see, each job post is special, but you can expect that entry-level positions will put emphasis on what you are familiar with, while senior-level positions will value what you can actually do.

Personal Skills

Other than having knowledge of the aforementioned software and tools, an AI expert is expected to be:

• curious
• creative
• patient
• persistent
• up-to-date with the latest trends
• a quick learner

As an aspiring AI engineer, you should pay attention to your soft skills for several reasons. First of all, for most of the job positions listed above, you will most probably not work in a vacuum. You will be a part of a team, and you will need to navigate your way through a complex social and corporate system.

Secondly, while AI is about programming and computers, its purpose is to understand, adapt to, and imitate real human behavior. Therefore, in order to make AI systems that are useful to customers, you have to understand human needs and psychology.

Formal Education

In general, companies consider a master’s degree in computer science a minimum for hiring for an AI post. Of course, higher-level education comes as a plus.

Besides, the particular type of computer science degree is also relevant. A general course in computer science can only briefly touch upon AI. On the other hand, a specialized degree in AI is much more valuable for the employer.

Another important element for employers is your portfolio. If you have strong previous experience in programming, then they might disregard a lack of a postgraduate degree. Simply, employers value experience much more than theoretical knowledge.

Additionally, there are a variety of alternative ways to learn about AI. There are development courses and bootcamps, webinars, online courses, and so on. You can either start like that or complement your general knowledge in computer science.

Required Experience

Once you get your degree, you’re up against at least fifty other people who also have a degree. How do you distinguish yourself from them? The answer is, of course, experience.

Remember that it is crucial to think about your portfolio even before you get your degree.

One of the ways to gain distinctive experience in AI is to become a member of AI-focused communities at your university. You can participate in exciting personal or school projects, or do an internship.

Of course, you will see that some opportunities are better than others and more valued by future employers. Try to look out for courses and internships at acclaimed companies.

For example, let’s say you are choosing between free AI courses offered by your university and the $200 professional machine-learning engineer certification by Google. Unless the course provided by your university is something really special, you probably want to invest in Google’s training. It will sound much better in the ears of your future employer.

For most employers, a working experience of at least one year is extremely important. It shows them that you have at least a basic understanding of how things work in practice. Also, it helps them determine how relevant you are for the particular job post.

Locating a Job in AI

Finally, once you have your diploma, an exciting portfolio, and a burning desire to have an excellent career, you will start looking for your first job.

The most important places to look for a job are LinkedIn, ZipRecruiter, and Google Job Search. These platforms are the most popular and connect you with the greatest number of employers. As you’re looking for the ideal job, consider the career path you hope to take, as technology jobs have many different directions.

The other way to do it is to approach a company directly. The choice of a company might depend on your personal interests and previous knowledge. For example, if you have some interest in language and linguistics but still want to work as an AI expert, then you can approach companies such as Grammarly or Jarvis.

Alternatively, you could join the explosion of AI that’s taking over web development. You would work at any of the companies that will be established to meet rising need over the next couple of years, in addition to existing giants such as Amazon and Google.

Finally, a number of professional organizations and public institutions in your country might require AI specialists.

A Career in Artificial Intelligence and Salary

One thing is certain: AI engineers are among the best-paid experts in the world. The average annual income for AI-related positions in the US was $126,830/year in 2020. And, in 2022, it grew to $137,750. Most AI engineers start with $112,355/year while senior-level positions make up to $195,000 annually.

On the other hand, machine learning is paid a bit less, around $112,930 annually. However, the forecasts say that the demand for ML is growing and that engineers will soon earn as much as AI experts.

Now You’re Ready for a Rewarding AI Career

Working as an AI specialist is a very promising career. However, as usually is the case, starting can be tough at first.

In order to stand out in the AI labor market, you need to provide a specific mix of formal education, working experience, and personal skills.

Artificial Intelligence is here to stay. It finds application in a wide set of activities, from web development to healthcare and gaming. That is good news because it means that the demand for AI experts will only continue to grow in the future.

The post A Programmer’s Guide to Creating Successful Career in the AI industry appeared first on Simple Programmer.

Almost all industry segments have started depending upon AI and ML algorithms for their processing cycles. The technology landscape is advancing at a rapid pace, and it is interesting to witness the advantages acquired. AI and ML have already been revolutionizing the life of developers big time.

Both these technologies are revolutionizing the workplace, and the way businesses operate. These two game changers are enhancing productivity and profitability, driving digitization, and making machines think just like us humans. The real effect is being witnessed in the changing technology landscape.

Before we delve deeper into what transformations are being leveraged due to AI and ML, let us briefly have a glance at what AI and ML mean.

What Is Artificial Intelligence?

AI is a trending area of computer science that deals with the creation of intelligent machines that can perform human tasks with ease. Moreover, it has automated activities or robots that are controlled by a computer to perform tasks as done by humans. Truly, AI is instrumental in developing systems that are embedded with human intelligence, generalization, and past learning experience.

AI aims towards the intelligence that machines show and its simulation in normal human circumstances. Certain examples of AI touching our daily lives are Deep Learning, Siri, Alexa, self-driving cars, conversational bots/chatbots, quantum computing, email spam filters, voice and facial recognition, cyber security, and many more.

Key Benefits That AI Offers

• Automation of simple and repetitive activities
• Data ingestion
• Creation of neural networks
• Facial recognition and chatbots
• Simulation of human intelligence

What Is Machine Learning?

Machine Learning is an integral arm of AI that offers computers the ability to learn. It programs analytical model building and helps in identifying patterns, taking decisions with least human interaction, and predicting outcomes without any explicit programming. It makes use of historical data as an input.

Machine Learning is significant since it offers organizations futuristic trends in client behavior, workflow patterns in business, etc. It is most used in cases like fraud detection, malware detection, spam filtering, business process automation, and predictive maintenance. It is now a centralized ingredient for most business segments.

Key Benefits That Machine Learning Offers

• Automatic data visualization
• Accurate analysis of data
• Client engagement and satisfaction
• Enhanced work efficacy
• Pre-processing of data

If you’re interested in an in-depth analysis of AI, you could also consider reading Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence.

5 Top Business Segments Enjoying the Benefits of AI and ML

Having seen what AI and ML are and how they bring amazing technological advantages, it’s time to see the five top business segments that use AI and ML to enhance their processes.

Healthcare and Medicine

Healthcare is one industry that is relying more on systems that use artificial intelligence and machine learning. There is much prediction-based analysis being done here, with deep learning training computers to identify and diagnose medical conditions, along with robotic surgical procedures that use AI for precision.

Implementing AI in different areas in the healthcare sector can lead to saving time and reducing costs. Taking care of patients efficiently is of prime importance and  can best be done with AI-based implementations. Examples are the discovery of new medicines, efficient robotic processes, virtual nursing, flexible treatment options, radiological imaging, computational outputs, disease detection methods, and many more.

AI tools are apt in identifying probable illnesses well in advance, leading to timely solutions. They also help train patients in self-care skills and offer appropriate assistance and resources. It is interesting to see how AI could predict medical conditions and revive the healthcare system.

Different AI technologies—like content analytics, predictive analytics, deep learning, and natural language processing (NLP)—have been instrumental in enhancing healthcare and analyzing patient conditions much earlier than before.

AI and ML algorithms are leveraged in the diagnosis sector to extract the best results effortlessly, without much human intervention. A dearth of human resources has driven the adoption of AI technologies at a faster rate.

Transportation

According to Markets and Markets, the use of AI in the transportation market is projected to grow at a CAGR of 17.87% between 2017 and 2030. The penetration of AI and related technologies in the transport sector has revolutionized the way operations are being managed in this industry. Transport is now faster, flexible, conventional, cheaper, and more effective.

The competitive AI and ML algorithms have offered a great service level to users at very affordable prices. Self-driving/autonomous vehicles, portals like Uber pool/Ola, lane changing portals, automatic vehicle guidance and braking systems, traffic management, drone taxis, and delay predictions are all live proof of AI and ML influencing the transport sector in a positive manner.

Advanced AI and ML technologies can facilitate driving from monitoring other vehicles for parameters such as driving behavior, weather, road conditions, etc. The relevant tools help drivers get a smooth ride. There are also light detection programs that measure the parameters in a certain area and alert the driver in case of any upcoming trouble.

The different tools used in the transport sector help analyze the factors affecting traffic conditions so they can alert users well in advance in case of obstacles. They also help drivers avoid a collision with other vehicles and, overall, take necessary action in case of unforeseen circumstances such as an accident or extreme weather conditions.

Over-reliance on tools does have repercussions if not handled properly—for instance, blindly following navigation systems has proved to be dangerous—but with AI going strong, the future has a lot of positive to offer in the transport sector.

Speech, Voice, and Language Recognition

The voice biometrics market is expected to reach $5,889.9 Million by 2028, an increase of 22.3%. It’s clear that the penetration of AI and ML into the voice and speech sector is causing more people to use these increasingly high-quality systems.

Indeed, voice recognition and translation algorithms are now nearing perfection in terms of accuracy levels, helping people with speech and hearing issues. The utilization of voice recognition is increasing day by day, both in personal and professional life. People are now getting used to offering their voice controls in extracting information from portals.

Even translation into multiple languages is increasing its spread for many portals. The Google Translator is helping in different areas, like reading street signals, instruction manuals, signboards, etc. Real-time conversation can be translated into desired languages by users, leading to a flexible and effective output.

Software like Google Home can take voice commands in national languages too. Popularly known as Voice AI, this technology is a conversational AI tool that leverages voice commands for receiving and interpreting directives.

Voice and speech-based technologies are easy to integrate with multiple devices and software solutions. With their multilingual support and voice-embedded algorithms, they offer enhanced security of information that is being passed on from one place to another.

These AI and ML-driven speech and voice software have attractive GUI embedded with algorithms that can connect easily to external systems. Live examples—like Apple’s Siri and Amazon’s Alexa—are using AI-driven speech recognition techniques for offering voice or text support. Google Dictate is one such voice-to-text portal that transcribes the dictated sentences into text.

Education

Education and eLearning have been one domain that has seen increasing adoption of AI and ML techniques in recent years, and there is much more expected in this year. In fact, the use of AI in the global education market is projected to reach $3.68 billion by 2023, a growth rate of 47%.

Newer activities like virtual tutoring, online colleges and universities, competitive exam preparation, assisted learning, modern-day education tools, and online learning material access have been the new way of studying and administering colleges/schools.

There has been digitization of textbooks through AI methods so that the material can be easily distributed at cost-effective rates. Organizations are offering customized software solutions with embedded AI and ML algorithms to support. These solutions are thinning the gap between the physical and digital worlds.

The role of teachers is also changing, with help from AI and ML. Teachers are using new techniques for things like assessing each student’s individual performance and needs. These techniques can help analyze the student’s strong and weak areas and recommend actions that can strengthen the student’s academic future.

Relevant course material, tests, online worksheets, etc., can be offered to students through AI-driven algorithms. Personalized training through test sheets can be offered through AI methods. Even usage of chatbots has been increasing for instant help and guidance.

The entire education and eLearning sector—which had a back seat until recently—has now progressed as a full-fledged, competitive business domain, thanks to the smooth integration of AI and ML techniques.

Even natural language processing technology has played a pivotal role in spreading the wings of education far and wide. Smart content creation, task automation, personalized and flexible learning, and universal access are some of the key benefits the education sector is enjoying owing to AI and ML.

IoT and Smart Cities

The Internet of Things has been bringing the physical and digital worlds closer. Thanks to the IoT, there is a lot of valuable data that is getting generated and can be leveraged for future benefits. It is AI and ML-based algorithms that have played an instrumental role in extracting the best of information and modernizing the entire IoT infrastructure.

To understand how important AI is to the global IoT market, it’s expected to grow at a CAGR of 27.3% by 2026.

The novel “Smart Cities” concept is also picking up pace, with AI techniques playing a key part in its implementation. The utilization of data science techniques and sensors is making urban places smarter and interwoven with technology. It is helping cities take smarter moves and get increasingly modernized.

There are many visible benefits that AI and ML are bringing to smart cities through IoT devices:

• controlling pollution and offering effective water and energy management
• predicting and lessening traffic and noise congestions
• predicting the future needs and business trends in different industrial sectors
• offering efficient car parking systems
• tracking recycled material and overall managing waste properly

These are only some of the possible ways in which AI has been helping smart cities become smarter.

If you’re interested in finding out more about the future possibilities of AI, you could also read A Thousand Brains: A New Theory of Intelligence.

The World Leverages the Power of AI and ML

As you have likely realized reading through the above five segments that are enjoying the benefits of AI and ML, the world is changing. We find ourselves getting used to these modern-day technologies in our daily lives—both personal and professional.

From healthcare and transportation to language, education, and smart cities, AI and ML offer a plethora of solutions to all kinds of problems, making our lives easier and our procedures smoother. And this is just the beginning!

The post How are AI and ML Expanding the Technology Landscape? appeared first on Simple Programmer.