Still, on the rising curve, the design of voice UI is an interesting concept in the UI/UX industry. Massive efforts are being made to implement the voice-user interface in iOS app development. User Interface experts around the world are constantly making efforts to ameliorate the VUI technology utilized around the world to simplify the cyclical and repetitive tasks of the world. 

Voice Interface- An Overview

Voice Interface or voice user interface(VUI) allows users to communicate with different devices such as smartphones, computers, digital assistants, etc., via spoken commands and instructions. It makes use of speech recognition to contemplate the user’s voice and reacts to it with simulated speech or other audio cues. 

Due to their inherent nature to constantly evolve, voice interfaces have become trendy in the world of tech. Their algorithms are programmed in such a way that every interaction with them feels human-like or close to it. This familiarity has made the world adapt to famous devices such as Google Assistant, Siri, and Alexa along with the calibration of voice interfaces in multiple mobile apps, smart devices, and cars. 

Due to the constant advancements in speech recognition and natural language processing devices, the responsiveness and precision of these voice assistants have significantly increased which has made them all the more mobile-friendly and reliable. As a result, human-computer interaction keeps on evolving with the help of voice interfaces. 

Elements that make Voice Interface

When it comes to voice user interfaces, speech recognition is considered to be the most vital aspect of it. Besides this, there are several other elements that incorporate a functioning and reliable voice user interface to offer a seamless user experience design. 

The signs of a good voice user interface would not just include functioning automatic speech recognition, but also other aspects of the feature such as being able to synthesize natural conversation, recognize spoken instructions, as well as deliver messages properly. Some of the most important elements of the voice user interface are given below:

1. Speech recognition

The most important feature of the voice interface is its capability to understand the human voice. There are specialized tools and advanced programming that help in managing voice recognition. 

The bottom line is for the interface to recognize the user’s voice as well as choose a voice command to respond. 

2. Natural Language Processing (NLP)

Natural Language Processing improves the capabilities of voice recognition. NLP makes use of machine learning as well as artificial intelligence to understand the voice commands and human language it encounters.

This speech interface ability allows the product or service to have a much clearer understanding of the natural human vernacular, the way we speak in our daily lives.

3.  Speech Synthesis

Once the machine can understand human speech, the problem arises as to utilizing what voice it will interact with. The machine needs to synthesize voice commands to do that. This is exactly where a step-by-step analysis of the spoken language should take place.

Since communication needs to be a two-way street between the human and the machine, speech must be synthesized by voice interface to provide a response based on the human’s need.

4. Feedback

Through feedback, there is a smooth dialogue flow between the machine and the human. The voice interface understands the humans’ command, synthesizes the spoken language, and then accordingly issues a response that completes the primary interactive loop between the two parties. 

Speed also becomes a certain factor when it comes to the efficiency of the voice interface to provide feedback to the users. 

Challenges to overcome while designing VUI

Designing a perfect VUI is something unheard of as there are so many complex queries it has to figure out. However close to perfection, there are multiple challenges a programmer faces while developing a VUI for an iPhone app development company. Below are a few of them that iOS developers generally overlook or are distracted by the more prevailing issues in the meantime:

Privacy and Security

With the integration of voice assistants into our daily lives, concerns surrounding privacy and security have come to the forefront. These digital assistants, while incredibly helpful, can raise valid worries about the confidentiality of personal information.

In the past, certain voice assistants, like Alexa, stored all user interactions, posing a potential risk for information leakage. Unfortunately, there have been instances where private data found its way into the wrong hands, understandably eroding trust in voice AI technology. However, newer voice assistants have taken steps to address this issue by automatically deleting previous conversations within 24 hours. It’s worth noting that enhanced security measures are not limited to voice assistants alone; mobile app security is equally crucial in safeguarding user data. The evolving landscape of voice technology and mobile app security demands ongoing vigilance to ensure user privacy remains a top priority.

Express what voice assistants cannot perform

No matter how close to reality a voice artificial intelligence (AI) may get, there are always shortcomings of such technology in general. One such limitation is if you ask the AI to set a meeting for 4 p.m. and then want to change it.

The AI would respond ‘I am not sure what you said, would you like me to set up a meeting for 4 PM?’ To avoid providing such a gnarly user experience, the response should be ‘Sorry I am still working on scheduling a meeting for you.

Issues in Prototyping and Testing

Voice UI prototyping and testing is another one of these problems. Prototyping allows you to examine things when the developers try to test the product. The developer has set a certain feature that the user can shop for groceries through the voice AI. 

The tricky part is that the user can say the same thing in various ways and different styles which can be a complicated thing to keep track of. As a result, it becomes more and more difficult to test the prototype on questions like these if they aren’t developed efficiently with the help of voice search technology.

Language Support

The most fundamental thing in a voice AI is that it must understand the language of the user. Not only that, but it must also respond in the same language. Unfortunately, there are many languages that AI cannot comprehend as of yet. Adding other languages, dialects, and accents is still a work in progress. 

Steps to Design Voice User Interface

1. Select your target audience

The first step in designing VUI includes deploying the user-first design just like in any other digital product. The focus here is to gather information and cogitate on the audience’s basic needs and requirements which sets up the foundation for the VUI. 

You need to identify the users’ desires and pain points of their experience. This will help you in recognizing the areas where the users will benefit. Collecting information on the user’s language is a given- the way they talk and how they phrase their sentences. This provides you versatility in speech and the interface would cater to a multitude of vernacular.

2. Establish the capabilities

In this stage, you need to outline the interface’s capabilities and shape the product. Some of these capabilities include:

• Simulate the scenarios of interaction with the product- These scenarios are precursors to the app ideas and must be spotted to transform them into conversational dialogue flow. These scenarios help in making the developers understand why someone would need a VUI. Hence, these scenarios must be designed keeping in mind the needs and wants of the users. At times, it can be difficult to determine the scenarios that are worth analyzing and ignore the ones that are not so important. 
• Ensure that these scenarios are voice activated- The purpose of these scenarios is to make sure that the users can deal with the problem more efficiently than they would with the alternatives. This step’s purpose is to find the specific and esoteric cases that are advantageous to the user.
• Some instances of these cases would be when users cannot access the visual interface as they are busy doing something or when they want to do something swift such as play music which takes less effort.

Three factors- Intent, Utterance, Slot

Intent- This implies the bigger picture that the product is supposed to depict and see. The intent is of two kinds- High utility (fairly accurate and straightforward command such as ‘turn the lights on in the living room’) and Low Utility (varied and more difficult to decrypt) 

Utterance- It implies the multiple ways in which a request can be phrased or put up. For instance- Instead of saying ‘Play any song’ the user commands ‘I want to hear some music’ or even ‘Can you play a song’ and so on. As a result, all the designers need to make sure that they consider different possibilities of phrases and their variations.

Slots- The slots come into play when the intent is not enough. Once the command is given and the response is in progress, they also refer to additional information and resources to provide the best results possible for the user. These resources can be optional as well as required. For music, the genre was not described hence it would be optional. However, if the query was to book a cab, the slot would come into action as a ‘destination’.

3. Build A Prototype

The flow of the dialogue determines the answer to ‘How to create a smooth voice interaction between the user and machine?’ For every requirement that you are targeting your product needs to have a smooth and human-like dialogue which is where developers need to start.

The developer needs to pre-meditate a lot where the dialogue can go as the interaction progresses forward. Some of the things to account for are the main keywords for communication, different branches where the conversation can head off, and template dialogues for both users and assistants. 

There are multiple prototyping tools available at your expense for VUI design. Some of these tools are Sayspring, Google’s SDK, and Amazon Alexa Skill Builder. Below are some of the aspects you need to consider while building a prototype VUI.

a) Writing Dialogues

A compilation of dialogues is the cobblestone through which the voice user interface flows seamlessly. Below are a few ways in which you can develop interactive dialogue:

• Try to simplify the arduous process by keeping the number of steps to a minimum.
• Don’t teach commands to users as that should happen in a natural way. As opposed to that, make your VUI more interactive.
• Make sure to keep the questions and responses short and simple. Some of the instances are given below

b) Spot Errors

It is better to find any errors or bugs in the system during the interactive process than post-release when the errors become undeniably complex to fix. Below are a few things you should consider steering clear of and keep in perspective to eliminate any interactive errors:

• Lexical Ambiguity- Words can mean several things at a time. This means that if a person uses the word ‘Good’ it can mean ‘okay’ which is a much more moderate version. Hence, ensure that your voice assistant can differentiate between the nuances of words.

• Pronunciation errors- This one is a classic as different dialects, accents, and qualities of voice can pronounce words differently. The user can get frustrated easily if the intent of his/her query is confusing to the VUI due to accent or dialect barriers. The assistant must be able to bridge the gap between speech obscurity and real words to respond accurately and efficiently.

• Not any accurate options- Make sure that users get the worth of their money and acquire relevant and credible information from the interaction. You don’t want the user to paraphrase the query incessantly until the assistant provides him/her with a credible answer. 

Regardless of the accuracy of the result, the assistant must always respond to a query put forward. For instance, if the user asks to find any hotels around him on a shoestring budget, even if the assistant is unable to do so, it should reply with “I couldn’t find any hotels around you. Would you like to raise your budget or let me find a place a little further?’

c) Display your brand’s vision

The tone of the voice matters a lot even in a human conversation. Your dialogues become the nature of your product which should always have a positive impact on the user. Dialogues must be said not for the sake of interaction but to satisfy the user’s needs.

d) Utilize existing content

Utilizing the available data can immensely customize things for the user. This data is strictly based on all the conversations your product has with the user. For instance, let’s take an OTT scenario ‘Stream a show from Netflix’ The system should reply ‘Would you like to continue the last show you were watching?’

4. Test Your Product

Once everything is in order, it is time to analyze and examine the product and witness the fruit of your labor. There needs to be quality testing of your VUI to see if it fulfills all the necessary criteria to be benchmarked as a high-quality VUI. You can test your prototype in two ways:

• With test simulators

There are multiple tests utilized in mobile app development that can aid you in reviewing your final product. These tools are also provided by famous companies such as Google and Amazon. Alexa Skill and Google Action of the product are utilized for application testing considering the hardware devices and their settings.

• With target users

You can organize groups of your targeted audience and eventually incorporate testing sessions to notice how users interact with your product. This also allows you to track task completion rate as well as customer satisfaction score (CSAT).

5. Improve

You can review your product more deeply once it hits the market. You will be able to view different UX insights. This step of the process allows you to analyze how users interact with your product. The metrics and criteria you are supposed to evaluate are given below:

• Languages Utilized
• User engagement metrics
• Messages per session along with the users per session
• Intents and utterances
• Behavior flows

Conclusion

Since VUI is one of the latest trends in technology, it is here to stay and will be optimized and incorporated into multiple products in the future. Although the VUI design interface for iOS apps can be quite a complicated and sophisticated process to complete and is a work in progress, it is still being implemented in many parts of the world. 

Implementing VUI in iOS apps is a great way to simplify daily tasks around the user. It is still a raw aspect of technology, however, there will be many advancements in VUI research in 2023. Combining voice and visuals is something that could positively impact VUIs. They cancel out each other’s drawbacks. This allows VUIs to accomplish complex tasks with realistic voice commands that interfaces are lacking at this moment.

The post Bringing Voice to Life: Implementing Voice-Driven Interfaces in iOS Applications appeared first on 360 Degree Technosoft.

Best Reasons to Integrate AI and ML into Mobile Apps

1) Personalization

Any algorithm attached to any simple mobile app can analyze the different sources of information from credit ratings to social media activities. Based on this, artificial intelligence can also provide recommendations to every user’s device. Artificial intelligence app development helps to figure out below mentioned things: 

• Your potential customers
• Likes and dislikes of customers
• Their approximate budget
• Terms they use to describe products 

Based on all this information, you can track your user’s behavior and use it for target marketing. This will help you deliver personalized content to your users which improves the probability of conversion. Here are some of the examples of popular brands that use artificial intelligence and machine learning and it’s working out really well for them.

• Uber, a popular taxi booking app makes use of machine learning to suggest an estimated time and cost to its users.
• Taco Bell has a TacoBot which takes the orders of the users answer their questions and gives recommendations from the items in the menu depending on users’ preferences.
• Migraine Buddy, a healthcare application uses ML to predict possible headaches and also shares methods to prevent them.

2) Relevant Ads

All the industry experts suggest that with too much content on the internet, the only way to win customers is to present them with exactly what they are looking for. ML app development can help make this happen. A massive amount of companies are already using machine learning in their apps for a data management platform for advertising. It helps you put all your efforts and energy into generating ads that user is actually useful to the user.

Machine learning app development companies are smartly using this technology to save money and time going to useless things and in the wrong ads. This will not just increase the return on investment but also improve the brand image.

3) Increases Security

Apart from the well-known fact that AI and ML are great tools for marketing, it is also essential for improving security. It works really well for streamlining mobile apps and securing app authentication. Functionalities like audio recognition and image recognition make it easy for users to set up biometric data for security authentication in their smartphones.

4) Increases User Engagement

When users are presented with their liked and needed products, they are more likely to engage with the content. Any user explores a website and sees the bulk of things and gets confused. By integrating artificial intelligence and machine learning into the mobile app, users stay on the application for more time scrolling to the products they are actually wanting to buy.

It offers proper customer support as well as and few incentives as well so that the users stay loyal to the app. AI assistants are also of great use to users. They are available at any hour of the day and help users solve their queries.

5) Advanced Search

With the integration of AI and ML in your app, the user can optimize their search. It makes the search result more optimized in the application. The algorithm used learns from different queries inserted by the users and put first the results based on those queries. It is not just limited to the algorithm but the mobile applications enable gathering user data involving search history as well as general actions.

This data accompanied by behavioral data and all the search queries can be used to showcase your products and services to your targeted audience. Data from the voice search should also be searched for optimum output.

6) Object Detection and Facial Recognition

It is one of the latest features in the mobile app. It helps to improve the security of your device. It also makes logging into the app faster. It also keeps your app safer from unknown users. With the security taken higher a notch, and with facial recognition, a medical professional can use it to check a patient’s health by examining the face of the patient.

7) Fraud Detection

Every industry is affected by the increasing fraud cases, especially finance, and banking. To overcome this challenge, machine learning uses data analysis to limit loan defaults, credit card frauds, and many more. It also helps in deciding the capability of an individual to complete the loan they are asking for. This eliminates the danger of loan fraud.

8) Data Mining

Data mining is also popular as data discovery. It allows analyzing a pool of data to collect information in different areas involving data warehouses and others. Machine learning provides data algorithms that will improve user experience based on the information. It helps learn new algorithms which make it easier to search for associations in the data sets and collect data easily.

Conclusion

Are you convinced that ML and AI are essential to integrate into the apps? Now, it is time to hire an artificial intelligence development company and get started with the integration of this technology into your mobile apps. 

The post Why Should You Integrate AI and Ml Into Your Mobile App? appeared first on 360 Degree Technosoft.

Emerging technologies have eased the work of several industries. Restaurant owners have started going on digital platforms with restaurant app development and see their businesses take beneficial growth. Let’s see how robotics and Artificial Intelligence change the way the food industry is functioning.  

Ways to Use Robotics in Shaping Food Industry

1) Robotics in Agriculture

Agriculture is an underestimated industry but probably the base of human existence. The base of grains, poultry, and milk products is agriculture. Since every industry has adapted to technology and automation and has benefited immensely, agriculture has also started integrating automation, artificial intelligence, and robotics for the upgrade. Robotics have eased handling every phase of manufacturing and delivering food supplies.

Robots having autonomy helps in exploring farming fields for mowing grass and watching out for harvest status. Robotic systems can also be useful in feeding the animals on the farm and collecting eggs or dairy. Machines can prove to be a helping hand in routine tasks and doing all those tasks with ease and perfection. This will also allow farmers to have some spare time to do other things. 

Unmanned aerial vehicle, popularly known as drones provides a facility for aerial evaluation of the fields. It gives a solid report of the field showing data like which crops are dying, which crops need hydration, are they in need of fertilizers. Drones have high-definition lenses and infrared camera lenses giving a wider and better view of the field. Land management works which will occupy several months for the labour will be done quickly and in no time by drones. Technology in the food industry will increase productivity, provide better and bigger output, and reduces the work of farmers.

2) Robotics in Manufacturing

The demand for food is increasing with the rapid increase in population. Autonomous food manufacturing is the solution to this demand. There is a specific process to be followed including different stages of packing and safety training to be conducted in the production facility. Robots are smart and they can assist in cleaning and categorizing different types of food. 

Say, for example, fruits and vegetables can be kept in separate containers with these systems. Certain food items can also be processed before packaging by integrating robotics into manufacturing. Different types of cutting tools are required to chop fruits and vegetables. Robotics can help match the blade with the chopping that is needed. With automation in the food industry, cutting devices can be managed with less effort and time.

Robots can be placed in the supermarket for the chopping and cooking process. Automating has proved to be great in making the process smooth while maintaining speed and hygiene. Food is differentiated properly, picked up properly, and also sorted better with automation. 

3) Robotics in Cooking

Not only chefs and wives are meant for cooking. With the advent of technology, robots can also be placed to cook. We have seen robots serving food in plenty of restaurants these days. But now, robots are also in place to chop, cook, clean, and serve. There is just one person needed to manage it all.

With smart technologies like artificial intelligence app development and IoT app development, the food industry has altered completely. Robot cooks can also garnish the dish which has made their demand impressively high. Small kitchens or large restaurants have been looking to put robot cooks in place for speedy work and catering to the heavy rush, especially during the weekend. 

Mobile application development has already rescued buying groceries and standing in the billing line with online grocery delivery apps. That time is not far when we will be seeing robots in every small kitchen. It is still in the very initiative stage at the moment. It will take some research, some awareness, and a considerable amount of time to integrate the robots into every kitchen. 

4) Robotics in Packaging

Industry owners need to freeze food in bulk to meet the demand when the supply of these food items is low. Storing the food in bulk at the right time also saves bucks. Bulk purchase provides a discounted price and saves from buying the same items when the price goes up. The food products that need to be stored have to be packed properly and organized properly in the storage facility. This is when AI and robotics come into the picture.  

Also Read: How is Artificial Intelligence Playing a Significant Role in Changing Sports Industry?

Things to Consider While Hiring an AI App Development Company

While hiring a software development company to integrate AI and robotics in the food app, you need to look for important things before you make the final decision.

1) Company or a Freelancer?

The very first thing you need to decide is whether you want to hire a company or a freelance developer to make your food app. If you decide to hire a company, the cost will be higher but it will be reliable. When you hire an AI application development agency, they will be taking full responsibility for the project from designing the app to launching it in the app stores.

In the case of a freelancer, they are just bound to do their part in the development of the project. Of course, they prove to be cost-effective. Hiring a dedicated AI developer proves to be effective if you already have the design ready and your project is for the short term. If you want to create your project from the scratch, hiring a company will prove to be beneficial.  

2) Portfolio of the Company

One of the important things to consider while hiring an AI development company is its portfolio. Check out their past projects and see how well they have done. This will also give a fair idea of the features and functionalities a company can integrate. The portfolio of the company also shows the overall experience and expertise of the company. This will in turn help the client get a better idea of whether to hand over the project or not. 

3) Interview the Developers

Before you make the big decision, interview the developers yourself. This will help them to see whether the developers have the required knowledge and skill to handle the project or not. This will also give an idea of whether you are paying the proper price for the resource you are hiring or not. For quality output, you are willing to put the best man on the job and pay high for the same. But, it is important to check whether the price is proper for the skills and experience.    

4) Client Reviews

When you hire a company with years of experience, there are bound to have reviews good and bad. It is important to speak with past clients and confirm good weighs the bad. If the bad client reviews are more than the good ones, you should not be assigning your project.

Also, it might be possible that the company’s work is good but most clients say that they are late on the timeline. If you have a project that has to be completed within the deadline, the company might not be a good fit even though with amazing output. Your goal here is to have good development with timely delivery. Hence, check client testimonials on the company website, check company reviews from different platforms, and if you are still not satisfied, you can ask the company for the client’s reference and speak with the client yourself for assurance. Having a background check helps in making the correct decision and saves from future regrets. 

5) Cost of the Project

Robotics, Artificial intelligence, and Automation are not going to come cheap. These technologies are trending currently in the market and the AI developers are also very high in demand. All these factors indicate one thing the high cost of development. However, it is advisable to conduct a market survey and know the ballpark budget of the project. You already know you need to have a high budget but it does not necessarily mean you have to pay more than the market rate. Decide the budget beforehand and make sure you are paying the right price for the artificial Intelligence app development services. 

Wrapping Up

The food industry is one of the leading industries in the market. With robotics and automation, the progress of this industry is much higher. If you are in this industry, it is high time to integrate robotics and artificial intelligence into your business and reap the maximum benefit from the same. 

The post How are AI and Robotics Shaping the Future of the Food Industry? appeared first on 360 Degree Technosoft.

The reason behind this is the applicability of machine learning algorithms mixed with computer processing. This has made sports people curious about new strategies and applications. Artificial Intelligence app development companies have started integrating it into their latest projects to get the best output.

The basic agenda of AI app development services in sports is to make the competition even more interesting on and off the field. In plenty of industries, AI and ML have worked wonders in the world of sports. Let’s dive into some of the best uses it offers:

Best Uses of AI in the Sports Industry

1)  Talent Identification

Identifying the right talent suitable for the sports industry is one of the most important job roles. Artificial intelligence can be of huge assistance in doing so. It can help in measuring players’ performance, player recruitment, and biomechanics as well.

2) Game Analytics

This will help in umpire assistance, sports betting, umpire assistance, match outcome, match event, player tracking, or ball tracking.

3) Training and Coaching

Artificial intelligence assists in training and coaching through tactical planning, team formation assessment, injury modeling, and many more.

4) Fan and Business Management

This assists in modeling demand for event attendance, ticket pricing optimization, VR/AR sports watch, measuring players’ economic value, and many more.

Artificial intelligence and machine learning comes has been of great aid in solving plenty of issues in the sports sector. Plenty of mobile app development companies have started integrating AI & ML in sports applications. To cover this in detail, here are some of the use cases of AI in sports.

Also Read: A Comprehensive Guide to Integrate AI & ML in Your Mobile App

1) Advertisement

This is one of the underrated benefits of AI. It helps in identifying opportunities and showcasing relevant advertisements depending on the demographics. Brands will get improved ads depending upon the best highlights of the game recognized by AI. The best part is that the automated learning algorithms of AI and ML also monitor players’ actions and the audience’s emotions during the ongoing match.

2) Sports Predictions

Well, sports betting is not a hidden concept. People have earned and lost a pool of money trying to predict and bet money. It’s just a probability concept for the first half of the cricket match or predicting a number of aces or scores. A probability game without experience is a lost cause. Well, Using artificial intelligence in sports will also not give exact predictions. However, it can give very close results to the outcome with the help of algorithms. Algorithms are more reliable compared to human prediction.

Plenty of sports applications can predict the outcome of the match using AI based on the below-mentioned factors:

• Formation of the team
• Number of goals scored
• Number of passes among teammates
• Main passes for goal-scoring chances

By using the above data, AI analytic tool can predict the closest score prediction of the match. This prediction is much more reliable than a wild guess or going with the gut feeling.

3) Ticketing

We have noticed time and again that people are struggling to get inside the stadium on time before the match during big sports events. The issue of managing a huge crowd was tried to resolve in multiple ways but it was almost next to impossible. But, with the advent of AI, the situation is now under control. By using an AI-based face recognition system, fans can enter the stadium without having their tickets checked. The rush was managed properly and people entered the stadium quickly and more efficiently.

AI also has predictive and cognitive analytics which are used to predict how much public will be there in the stadium attending the match along with the time schedule. This helps event managers to stay in tune with the demand without having to put in much time and extra effort. Also, merchandise and food arrangements are also properly managed.

4) Performance of the Player

With the help of predictive analysis in the sports sector, it is easy to check the performance of the player. With the wearable technology in play, the athletes can collect information strain and tear of the players and avoid any serious injuries that might happen to the players. Artificial intelligence app development can improve the health and performance of the players. AI also allows the team to frame strong strategies and tactics to improve their strength.

Analyzing players’ performance is even more easier and sophisticated with AI. The coach can also get helpful information with the data and visuals to do the SWOT analysis of the players. With the correct evaluation, they can make workable changes in the game strategies. From football, and tennis, to cricket, this applies to every sport. Computer vision is used to sense human motions and track them using video sequences which brings us to three possible outcomes:

• Color tracking
• Motion detection and tracking
• Combination of color and template tracking

AI can also be of great use in swimming to evaluate simmers performance below water filters by using human pose estimation. Advancements in technology will rule over the traditional method of manually evaluating the swimmer’s body and performance.

5) Diet Plans and Personalized Training

Artificial intelligence app development companies have taken personal training to an entirely new level. A Diet plan created with AI uses machine learning for customizing different meal plans for different players based on their physic, target, and their decided goal. People are addicted to using AI-based fitness app development. These applications are accurate and help to get results faster. Advanced tools and techniques can train algorithms to find out real-time human poses. One relevant example is a fitness app where skeleton models are used to show human joints for Pilates and online yoga.

6) Scout and Recruit

Competition is extremely high in the sports sector. This will hit another high when you use Artificial Intelligence in the scouting and recruiting process. Everything that happens on the field right from the player’s movement to their body’s orientation is tracked to make the correct decision. Also, ML algorithms are used to pull data and judge players’ skills and their capacity in different game categories. Advanced technology has not just enhanced the recruitment choices but nations have also built a strong team having the abilities to achieve newer goals and do the impossible.

7) Automated Sports Journalism

Sports journalism is a huge business and has the potential to make big bucks. This is a sector where every small thing gets covered and any small news can turn big. All the updates and details are heard. Data and statistics are given extra attention in sports journalism. AI has made this simple and turned sports journalism accurate and easier.

AI-driven platforms can have score data into the narratives with natural language. All these platforms depend on automated insights which smartly sync with computer vision and do the journal score hearing. This is an amazing take on the sports where AI can cover local matches without anyone present on the field to cover it. This is an interesting take on sports.

If you are interested in developing an artificial intelligence mobile app in the sports industry, the next step would be choosing an artificial intelligence app development company. There are various factors that affect the choice of the company. You want to pick a company that fits best your business requirements and delivers you fantastic output. Let’s start by jotting down the factors affecting the choice of the mobile app development company.

Factors Affecting Choice of Mobile App Development Company

1. Experience of the Company

One of the major things to check out while hiring a company is checking out the experience. Hiring a company with experience will assure quality development services. A startup company can also deliver good output but the probability is low. Know how many years, the company has been working in the industry before making the final hiring decision.

2. Portfolio

By checking the portfolio of the company, you will get to know what kind of projects the company has worked on to date. This will also help you know whether the company has developed apps in the category you are looking for. It is important that the company has the versatile experience and has worked on apps in multiple industries giving them wide experience to handle projects with simple and complex levels. This will help to check their competency.

3. Experience of the Developers

When you have chosen a company with great experience, you need to make sure that even the developers working on the project have a similar level of experience. After all, it is the mobile app developers who are working on the project. Check the resumes of the developers’ team and interview them yourself to check their knowledge and skills. You need to make sure the developers are knowledgeable and good enough to work on your project.

4. Multi-domain Experience

The company must have worked in different industries like real estate, food delivery, sports, eCommerce, lifestyle, banking, finance, health & fitness, travel, hospitality, and many more. Having multi-domain experience will make a mobile application development agency competent to manage different projects.   

5. Past Client References  

Start by creating a list of top AI app development companies you want to hire. Once you approach the companies and shortlist the ones you like, go ahead and talk to their past clients. This way, you will know the experience of the clients with the company. You will also know about the drawbacks of working for the company if any. A positive word is a green signal for you to go and make the final hiring decision.

6. Project Timeline

Before handing over the project to the company, inquire whether they have appropriate developers who are willing to complete the project on time. In big companies, developers are assigned to multiple projects and work simultaneously on them. It is very important to take assurance from the company that the project will be completed within the decided deadline.

7. Technologies Used

It is important to know about the technologies the company is working on. It is very important that the company has proper experience in using the trending technologies. The company must also use an agile methodology for app development. This helps in the faster development of the project.

8. Code Ownership

One of the important things to ask the companies you are looking to hire is who will be the owner of the source code. After all, it is your app and you should be the one having ownership of the source code once the project is finished. Clarify this thing before handing over the project to the company.

9. Post-launch Maintenance

Ask the company you are approaching whether they will be providing maintenance and support services after the app is live on the Play Store. Even after the app is live in the store, it will need minor bug-fixing and changes based on the user’s feedback. The company you hire should be willing to do the post-launch maintenance.

Conclusion

You know the importance of artificial intelligence in the sports field. You know how to hire an AI development company. All you need to do now is get started with the search and start developing your sports application.   

The post How is Artificial Intelligence Playing a Significant Role in Changing Sports Industry? appeared first on 360 Degree Technosoft.

Are they still not clear? Then here are some real-life examples that will help you know how the AI in apps helps you achieve your needs faster. For instance, when you access an online food delivery app, you will witness that the app shows you all the restaurants with food items you like or frequently order. Now, artificial intelligence development services play a vital role in this time-saving achievement. AI and machine learning analyze your activity and collect data to help you with the best results. 

Further, there is one more example that proves how important AI and ML are in app development. When you book a cab with any taxi app, you can notice that the app shows you the real-time location of the taxi. Thus, ML and AI enable you to finish your work faster. Therefore, if you are developing AI applications and want to boost your online portfolio, you need to integrate AI and ML into your application. 

It is because; AI and ML are spreading out in the industry at a rapid pace. Further, the people are also accepting AI and ML with warmth. They just love integrating AI and ML into the apps as they offer ease in work. 

From the ML developer’s point of view, Machine Learning and Artificial intelligence were once the most complicated technologies to learn and integrate. But these days, the developers find it easy. It has become a part of daily life. Thus, the developers are now learning it without any hassle. 

The contributions of AI and ML to the industries 

AI and ML as a technology offer an extensive range of accessibility and activities in the world of business. AI has a huge demand in the field of disaster management as well. The professional employs the AI to anticipate the disaster and takes measures to achieve the minimum impact. 

The banking industry also employs AI to offer financial bits of help with ease. The banking industry uses AI to complete transactions online. Similarly, AI in the healthcare industry also enables patients to get advanced healthcare. 

These were a few examples that enable you to know the role of AI and ML. Any business’s ultimate goal is to achieve profits in the end. In such cases, AI and ML offer you an excellent opportunity to gain consumers and retain the old users. However, the integration of AI and ML can be tedious sometimes. Here is the consummate guide that will help you integrate AI and ML into your business application.

The three vital approaches to integrate the power of AI and ML into the application

If you want mobile app development that is smarter and efficient, three approaches can help you. These three approaches even help in answering your most frequent questions. So, here are those three ways that enable you to find your goals quicker.

Reasoning

This is one way where AI deals with solving problems. The AI in mobile application development makes it efficient to solve issues. Some of the examples of reasoning in AI are the employment of AI to beat humans in games like chess. Although humans are the wisest, the computer playing against you can also defeat you. The following example is when Google Maps uses reasoning to find out the route with less traffic for you. This encourages you to get to your destination faster. 

This part of the AI enables the computer to work more efficiently. Thus, this one helps you make your application powerful.

Recommendation 

This is no doubt the most common characteristic of integrating AI and ML. Many top-notch eCommerce platforms analyze your search pattern and behavior to know what you need. Then they recommend the products that you need. This is the power of AI that enables your business to find more consumers.

Behavioral

Learning the behavior of the users is the best thing that you get from AI and ML. This has set a new trend in the world. The behavioral analysis by the AI enables your app to analyze unusual behavior. That implies; each time when the system witnesses any unusual behavior, it ceases the complete transaction. The most common example is when Gmail sends you a notification when you log in from a new device. 

These are the three vital approaches to why you should integrate AI and ML into your business application. Now that you know the three approaches to integrate, here are the few reasons that enable you to know the importance of AI and ML.

Also Read: How AI is Beneficial in the Healthcare Industry?

Why should you integrate AI and ML into your business?

Personalization 

The most vital reason why you should integrate AI and ML into your business is personalization. The integration of AI can help you crawl through several pieces of information from social media platforms. With the help of these pieces of information, you can credit ratings and render your suggestions. The AI and ML in your app can help you learn the following things:

• Who are your consumers?
• What are the likes and preferences of your users?
• What can your consumers afford?
• How many types are the words that they use for products?

The AI and ML, with the help of all these pieces of information, will make your business stronger. This is because; you can know the behavior of your consumers. Moreover, with the help of AI and ML in the application, you can get the target marketing and develop content that is more appealing for your consumers. You can render your consumers and come out with a more impressive application for your users. 

Advanced search options

This is the second most vital reason why you should integrate AI and ML into your business application. Any user who uses an app would want the auto-fill application that they want to search. In such cases, you can also optimize your mobile app to show results to your users based on their search behaviors. The algorithms here focus on the outcomes of the queries.

The AI and ML in the app will aid you in accumulating the data like search history and your user’s actions. You can use them to offer advanced search options to your users.

Predicting and forecasting the behavior

Another great reason you should integrate AI and ML into your business is forecasting the market’s future. The integration of AI in the application can help you accumulate helpful data. You can understand the user preferences and the behavioral patterns of the users. This will help you forecast the market size and growth during a period. 

With the help of the forecast, you can also make equal marketing efforts to impress your consumers. You can also use the user likings to create their profile as they want.

Offering them relevant ads

The only way to retain your consumers is by offering them what they want. The best way to market is by promoting your business when a user needs you the most. The AI and ML in the application collect the data and search history to know what the users need the most. Then with the help of these data, you can show relevant ads that your consumers are looking for. 

With the help of machine learning, businesses are now investing in a tailored way of advertising. 

Better security stage

Besides offering a great way to promote business, AI and ML can also help you make your app more secure. As a result, you can streamline your app and offer a secure platform to your users. In most cases, the payment apps and the fintech applications use ML and AI. 

Face recognition and authentication with voice and images is a form of AI. The integration of AI reads the user’s face, voice, and image. Thus, it makes these applications more secure to prevent fraud. If your business deals with the transaction, you must integrate the AI into your business; this will help you authenticate every user and secure your platform. 

Many apps have already invested in AI to integrate fingerprint scanning and Face ID into their apps. 

Top platforms that allow you to develop apps with AI and ML

Now that you comprehend why you need to integrate AI and ML into your application, here are some top-notch platforms for developing AI and ML infused apps.

Azure 

Azure is a platform developed by Microsoft. This is Microsoft’s cloud solution with an extensive support community. You get a higher volume of multilingual documents as well; The developers like this platform the most because of its accessible tutorials. The more you know about the platform, the better efficient app you will develop. The programming language that gets employed in this platform is Python and R. Due. With the help of this platform, you can develop mobile apps that have adequate predicting capabilities.

IBM Watson

The main reason why most businesses use IBM Watson is it permits the developers to work on the user request at a rapid pace. That implies; it can be any type of data, but this platform helps you access all the data with multiple approaches, and that too faster. 

Tensorflow

Tensorflow is a free platform that comes from Google. This is the best platform to develop an application with AI. The most important reason why it is recommendable is its ability to solve nonlinear issues. The app works by the interaction knowledge with app users in their surroundings. This helps your app to find the answers as per the user’s request. This is why most developers use this platform to develop AI-based apps.

Api.ai

It is another excellent application development platform that the Google Development team has developed. This platform is famous for the employment of contextual dependencies. Further, it is also an efficient platform for the development of AI-based apps. Roles and entities are the two main concepts on which this app works. 

Wit.ai

Wit.ai is another excellent platform for the development of AI-based applications. It is pretty similar to the platform Api.ai. The most significant thing about this platform is it converts the speech file into the texts. This platform is also famous for offering apps that effectively process user requests. The most common examples coming from these platforms are chatbots. Moreover, this is an excellent platform for developing iOS, windows, and Android apps with Machine learning.

Amazon.ai

This is one of the best and popular platforms developed by Amazon. This platform is famous for creating apps that can recognize human speech. In addition, the platform employs visual objects and the best machine learning process to help you create the best apps.

Clarifai

This is the last platform on the list. This is a platform that comes based on AI. It accesses the data with capacitive algorithms and complex processes. The app that gets developed using these platforms is known to enhance the user experience. 

Steps to begin with while integrating AI and ML

• Identify the problem you want to solve; this will help you create a streamlined AI-based app.
• Be clear about the data you want; this will help you accumulate the precise data about your consumer
• You should know that APIs are not precise
• Set the metrics that will help you know about the APIs.
• Hire ML developers to achieve the best result

Conclusion

Keeping it brief, this is whereby you can integrate AI and ML into your application. Now that you are clear with a comprehensive guide, you should develop the best AI-based apps by hiring the best app development company.

The post A Comprehensive Guide to Integrate AI & ML in Your Mobile App appeared first on 360 Degree Technosoft.

Machine learning and deep learning are a part of artificial intelligence, which plays a major role in data science. It is a comprehensive process that includes pre-processing, data analysis, visualization, and prediction of the system data. Let’s have a deeper knowledge of machine learning and its impact on the future of software testing.

What is machine learning?

Machine learning is basically an application of AI, which enables software applications to become accurate to predict appropriate outcomes. It focuses on the development of computer programs and, additionally, access data to learn for themselves. The learning process starts with observing the data, such as examples, direct experience, and instruction to look for any pattern change in the data. This helps in making better decisions in the future based on what data science provides.  

The machine learning process used the traditional algorithms and text in the form of sequences in keywords. There are more machine learning methods available, although machine learning algorithms are often categorized as supervised or unsupervised.

Different machine learning methods

Supervised machine learning algorithms

The supervised machine learning algorithm is generally used to make predictions. Similarly, this algorithm searches for patterns within the value labels assigned to data points. The learning algorithm builds functions, so the output can compare to the correct and intended output. This can find all the errors and modify them to develop the model appropriately.

Unsupervised machine learning algorithms

In these learning algorithms, there are no labels associated with data points. Such machine learning algorithms can organize the data into a group of clusters to analyze how the system works on the function as well as explain the unappealing structures from unlabeled data. Instead of finding the appropriate output, this detects the data to draw out inferences from the database that describes the hidden patterns from the available data.

Semi-supervised machine learning algorithms

Such machine learning algorithms utilize both labeled and unlabeled data for training. Every system that uses such a method can develop its learning accuracy. 

Reinforcement machine learning algorithms

The reinforcement ML algorithm method is mostly used for choosing an action. This machine learning method depends on each data point to analyze trial and error searches and delayed rewards, etc. Such a method allows the machines and software agents to automatically evaluate the ideal behavior within a specific context to improve the performance of the system.  

Advantages of machine learning

Instant identification of trends and patterns

Machine learning is employed in systems to review a large volume of data and determine specific trends and patterns. These processes can’t be possible for human comprehension. For example, if an e-commerce website wants to understand its website’s browsing behaviors and purchase histories, by incorporating machine learning, it would be able to cater to its customers the right products, deals, and reminders that are relevant to them. They mostly use the results to discover relevant advisements to the customers.

Automotive activity

Since machine learning methods are automotive, companies might necessitate fewer human resources to supervise every step of a project. This results in giving machines the ability to learn and permitting them to make predictions, and also improve the algorithms on their own. For example, antivirus software learns to filter new threats and spam after recognizing them.

Ongoing development

By gaining experience, the machine learning algorithms can keep improving in accuracy and efficiency to make better predictions and decisions. For example, if you need to make a weather forecast system by using a machine learning algorithm, your system would learn to make more accurate predictions faster as the data keeps growing.

Multi-dimensional application

All the machine learning algorithm methods are better at analyzing multi-dimensional and multi-variety data. AI and machine learning methods can also work well in dynamic environments or uncertain conditions.

Extensive application

Machine learning has a wide range of applications in various sectors that have the capability to help deliver much better customer services as well as targeting the right customers.

What are the applications of Machine Learning?

Machine learning in Education

The teachers can use the machine learning methods to check how far the students are able to consume the lessons or are coping with the lessons taught. This allows the teachers to help their students get a good grasp over the lessons to prevent them from failing the exams.

Machine learning in Search Engine

Getting help from AI & machine learning development company, the search engines’ do their rankings and deliver results through various searching methods such as voice recognition, image search, etc.

Machine learning in Digital Marketing

Companies utilize machine learning methods to interact and engage with customers. Through machine learning applications, they get information regarding their customers’ behavior to serve them well.

Machine learning in Healthcare

Healthcare sectors make use of AI and machine learning methods to improvise their diagnosis and treatment techniques. They are generally implemented in nerve analysis, Ayasdi, sentient, digital reasoning systems, among others.

Utilization of machine learning and AI in software quality assurance

Automation in quality assurance for business enhancement

Automation in software quality assurance focus on building a framework, which could accelerate automation with commercial tools. The automation includes keyword-driven, data-driven, and business process-driven frameworks to bring significant saving for the clients and businesses. 

In the future, the automation would include a functional side of the business in the form of an application programming interface, middleware automation, test data automation, etc., that could bring true value to testing activities. This focus has moved from UI-based automation to multi-tier or multi-stack automation to make an in the market.

AI in quality assurance for best automation approaches

The use of AI in software quality assurance is basically performed combined with the best automation approaches. AI & machine learning Development Company help their clients by focusing on three-dimensional procedures. This helps to move from defect detection to defect prevention, remove test coverage overlaps, and optimize efforts with more predictable testing.

What does machine learning offers to software testing?

Changing the software testing strategies

The simple and straightforward method of software testing has evolved because of machine learning methods. The machine learning development services enable the conventional methodology of software testing. It helps the software tester to experience the entire agenda of the system. 

It guarantees that the potential client’s means and moves were made would stay secure to determine any issues. When the traditional testing methods cannot give appropriate results, then machine learning, artificial intelligence ad predictive analytics make the approaches more relevant.

The machine will be in-charge

Since humans are prone to make errors or make poor assumptions, the traditional testing approaches are making use of machine learning methods to analyze source data. Machine learning applications help teach the systems how to learn and apply similar knowledge in the future to make the software testers produce more accurate results.

Predict the future customer requirements

The business market always requires development. Hence, the organizations develop a sense of an approach to be ahead of their rivals and foreseeing their buyers’ needs. The machine learning development services help the organizations to investigate the client’s information to comprehend and anticipate new trends and items. As predictive analysis and machine learning go parallel in software testing and software quality assurance, they are both compulsory for ceaseless and shorter testing processes.

Implementation of machine learning helps in reducing the development cycles, making mobile and hybrid application testing the number one priority. Hence, test automation will dominate shortly in the future as it has a huge impact on software testing.

The post How is Machine Learning Changing the Future of Software Testing? appeared first on 360 Degree Technosoft.

“If you think that the internet has changed your life, think again. The Internet of Things is about to change it all over again!”

-Brendan O’Brien

What is IoT?

The system where computing devices, machines, and objects can communicate with each other and transfer data without any form of human interference is termed as ‘Internet Of Things’. The Internet of Things, commonly known as IoT allows different objects to get connected via the internet and exchange information. IoT platform consists of smart sensors and IoT devices that communicate to the cloud through internet connectivity.

The data sent to the cloud is later processed to perform certain actions of either notifying the user, updating the data, etc. without human interference. Machine learning plays a vital role in the data processing. These connected objects are usually linked to IoT applications. Users can access smart devices through these apps.

Let us boil it down for you. Imagine you handover your car to your young kid and get a message from your car that your kid has exceeded the speed limit. Yes, you heard it right! Cars messaging humans!

IoT allows users to mute their TV sets with mobiles in case they lose their remote control devices.

There are IoT systems where users can feed contacts of friends and family in their cars. If crashed, the cars automatically send emergency messages to the selected contacts with the live location. Such advanced IoT Mobile application development is proving to be revolutionary as it can save the lives of thousands every day.

IoT has gone to levels no one imagined! You can use IoT devices such as sensors that measure and monitor soil levels to gain insight into the irrigation system’s efficiency of land.

Right from your refrigerator messaging you that it is out of ice, to your IoT-enabled security devices notifying you about unusual activities when you are away; IoT has been optimized immensely in the past few years.

Since IoT brings more convenience and ease in complex tasks, more and more professionals are working on it. As per a report, the number of IoT devices will reach 125 billion by 2030.

Hence, many industry giants are investing in IoT. As per McKinsey Global Institute, the total economic impact of IoT could range up to 11 trillion US dollars by the year 2015.

Advantages of IoT

Let us see some mind-blowing advantages of IoT technology.

Speed

Time is money! In today’s world, everybody is rushing so fast to reach somewhere. While we are cooped up in our daily chores, anything that saves time is precious. The Internet of things serves its primary objective of offering convenience and ease in every task. IoT devices and IoT applications are designed to work faster with great efficiency.

Easy Monitoring

For any organization especially manufacturing enterprises, an optimized monitoring system is bliss. IoT can be used as a source of monitoring resources. IoT technology allows machines to send notifications regarding lack of resources, technical issues, the requirement of maintenance, and much more. These messages are sent in real-time saving costs associated with machine failures. Another way organization IoT for monitoring is by tracking the number of materials available to place new orders without visiting warehouses. The administration department, purchase department, maintenance department, and even human resource department can accomplish easy monitoring with IoT devices.

Supports Automation

Automation is a new era. Right from all startups to large industries, every organization is adopting automation. Since the internet of things is all about connecting various objects via the internet, it plays a vital role in converting every possible object into a smart object. Considering commercial benefits, IoT helps in achieving reduced troubleshooting, modular integration, maximized uptime, and increased output.

Promotions

Since IoT is deeply connected to automation, it can be immensely beneficial for marketing and promotional activities. IoT has seemingly endless potential as it can be used to send mobile promotions to regular shoppers, analyzing customer buying tendencies, tracking shopper’s shopping history, and much more. IoT in marketing is all about the end to end experience optimization.

Research

Have you ever heard of big data?

Big data is the collection of information about consumers’ activities. As explained initially, IoT devices requests, process and sends data in real-time. Every IoT device has smart sensors that help in identifying important data and notify the user. Therefore every IoT device stores a great amount of data that depicts user behavior and experience. This data can be used to deliver personalized experiences and perform promotional campaigns.

IoT Trends that are All Set to Make Revolutionary Changes in Technology

The following are the IoT trends of the year 2020 depicting the future of technology.

Smart City

It is estimated that 60% of the world’s population will live in major cities by 2030. Tokyo, New York City, Jakarta, Mumbai are some big urban cities with huge populations. Considering the growth in the population of these cities, it is possible that there will be water scarcity, lack of garbage dumping areas, increased traffic, increased air and noise pollution, and scarcity of resources.

IoT is deeply connected to the smart city mission.

What makes a city smarter?

Any city with disciplined traffic, more greenery, water management, reduced crimes, and a strong economic race. Did you know that British, American, and Israeli highways are already using smart road IoT platforms which use solar-powered sensors to collect data, machine learning algorithms to achieve real-time data analysis, and tracks driving patterns to deliver risk alerts with accurate navigation recommendations? IoT technology comes in many forms.

Vision Van of Mercedes-Benz is an IoT platform where drones can deliver stuff within a radius of 10 km. Delivery with drones can save fuel, less traffic, and faster services. Google’s Smart Eye delivers options and accessibility features right in front of the user’s eye without any distraction. This technology encompasses sensors, Wi-Fi, and Bluetooth. Various IoT devices consist of sensors, thermal imaging, cameras, and alert systems that are interconnected to other smart objects to help detect crime, and prevent fire and accidents.

Industrial Internet of Things IIoT

Another trend that is all set to rock upcoming years is IIoT. The industrial internet of things is a system that uses smart sensors and actuators to optimize manufacturing and industrial processes. IIoT performs predictive maintenance, better field service, energy management, and resource tracking. Any defect in machinery is conveyed to the concerned employee in real-time.

Before IIoT, machines with issues were repaired after part fails or machine breakdowns. Smart sensors and actuators integrated into machines allow them to detect unusual performance and glitches if any. IIoT is a great advantage in quality control. IIoT can also be used to enhance supply chain management. Executives can track the location, status, and condition of products throughout the supply chain.

It helps organizations to put orders in time and reduce the cost associated with a lack of raw materials. IIoT also enables businesses to enhance customer satisfaction by capturing and analyzing data about how customers use their products. Industry leaders in IIoT who are investing in IIoT are focusing on asset management; transportation and utilities.

Security

Imagine your phone getting a notification about strange activities happening around your house, or an alert message about someone breaking into your house/office. These scenarios are possible with IoT. Security is one of the most talked-about IoT trends. IoT-enabled smart surveillance is a new trending thing.

With the latest image recognition technologies, IoT-enabled cameras can identify suspicious activities and detect unknown users. Such devices not only notifies users about a person on your doorstep but can also tell you whether the person is a stranger or has visited you before. Though IoT is used to provide security, it itself has a huge risk of cyberattacks as the medium to exchange data is the ‘internet’.

The trend also brings up the context of improving security measures in IoT. Since the usage of IoT is increasing, huge data is transferred over the cloud. This data can be even related to sensitive information such as financial transactions, security, etc. Though IoT service providers deliver layered protection against possible cyber attacks, the trend depicts enhanced security measures.

IoT Enabled Manufacturing

The current pandemic scenario is forcing businesses to think of new ways of manufacturing. Digital manufacturing is getting a lot of spotlight these days. In digital manufacturing, the computer platform controls various functions at a high level of integration. It uses data fetched from sensors, artificial intelligence-driven analysis to achieve real-time results.

Digital manufacturing involves the majority of work performed by computer systems, the level of human error is drastically decreased. The Internet of things is used to connect various machines that are used in digital manufacturing with computer systems to achieve faster and high-quality production. This IoT-enabled manufacturing system allows hyper-customization, 3D scanning, and modeling.

Climate Change

“IoT is the digital skin of our planet”. -Alex Gluhak. The whole world is focusing on reducing the carbon footprint of various processes. This skin with sensors, apps, and software can be a great help in achieving these objectives. Climate change is real. The world is facing various environmental challenges that require more attention. IoT will be used to tackle these climate changes and preserve the planet.

IoT platforms designed for economic and productivity objectives involve waste reduction and enhanced resource management. IoT will also be used as disaster solutions such as avalanches and floods. IoT developers are focusing more on creating IoT devices used to monitor air or water quality, atmospheric or soil conditions, wildlife, and their habitats, etc. Such sensors can also be used as early warning systems for tsunami or earthquakes.

IoT devices can also be used to measure air pollution. Waste management, BioCarbon Engineering, plastic management, garbage management, and what not can be accomplished by IoT. Integrating IoT in the app development process can prove to be a great boon for the users.

Artificial Intelligence

A combination of AI and IoT is all set to redefine the way industries, businesses, and economies operate. Internet of things is popularly used for conveying messages and remote accessing objects. Integrating IoT with artificial intelligence enables connected objects not only to notify users but also to perform actions such as decision-making with zero human interference.

Many organizations have already adopted Artificial intelligence and IoT as an integral part of product processing. AI-driven IoT manages, analyzes, and obtains valuable insights. It is used to ensure fast and accurate analysis along with obtaining personalization with confidentiality and data privacy. AI-enabled IoT boosts operational efficiency. Google used AI-enabled IoT to reduce its data center cooling costs. This excellent combination can also be used to predict risks to tackle economic loss, employee safety, and cyber threats.

One good example of AI-enabled IoT is driverless cars. Tesla, a globally known automobile giant uses AI-enabled IoT where the sensors determine road conditions, optimal speed, weather, the behavior of pedestrians, and much more. Organizations can also deliver personalized services since IoT devices produce huge customer behavior data that may include buying history and purchase patterns. On the other hand, Artificial intelligence is widely used in marketing campaigns to offer personalized solutions.

Voice User Interface

Voice interactions and voice user interfaces can be seen almost everywhere in the form of electronic assistants. People from urban areas are familiar with Amazon Alexa, OK Google, Apple’s Siri, etc. The integration of voice assistants to connected devices is the latest trend in the industry.

Would you like to live in a house where you can speak to almost every home appliance? You can command your fridge to increase cooling, you can talk to your AC to turn on, and much more.

Sounds interesting?

This all can be achieved by integrating voice and IoT.

Conclusion

End-users, as well as enterprises, will notice great advances from IoT. It is recommended that businesses should consider IoT in their business processes to achieve faster outputs. Many IoT mobile app development agencies deliver IoT services to the manufacturing, healthcare, security industries. These IoT companies should consider the above-mentioned trends for a promising future.

The post Top IoT Trends to Follow in 2021: Get Ready for the Future appeared first on 360 Degree Technosoft.

Developed in 1950, by Alan Turing, who proposed a test to provide an operational definition of AI. It said a computer passes the test if it is not possible to know whether the answers to a set of questions given are delivered by a person or a computer. Such a computer would have capabilities like;

• Natural language processing
• Knowledge representation
• Automated reasoning
• Machine learning
• Computer vision
• Robotics

The period of the 1950s–1970s saw early work in Neural networks making it possible to imagine that machines could think.

From the 1980s to 2010 Machine learning gained its importance, wherein AI systems learned from data, identify patterns, and performed tasks or made decisions without human assistance.

Today, AI concentrates more on Deep learning, in which computers are trained to recognize speech or identify images. Deep learning concentrates more on making the computer learn its own way of recognizing patterns using many layers of data. Examples being, your virtual assistants, Siri or Cortana.

How Does Artificial Intelligence Work?

“AI is a family of technologies that perform tasks that are thought to require intelligence if performed by humans,” – Vasant Honavar

AI has two chief aspects, one the engineering, building tools that apply intelligence. And the science of intelligence, that is enabling a machine to give the same result as the human brain. AI combines a vast amount of data with intelligent algorithms. Learning from the patterns or features and presenting the desired output.

The subsets or branches of AI technology that work collectively to deliver the required automated solutions include;

Machine learning involves analyzing and identifying the patterns from past data. And decision-making based on it. All without any human intervention. Machine learning is the process of automation by evaluating data. To save human time and make better decisions.

Deep learning is a part of Machine learning that helps a machine process the input data. Through layers to classify, infer, or predict the output.

The Neural network is creating an algorithm that allows the machine to learn like a human brain. By inputting new data. For instance, while recognizing an object, the neural network gets presented with a large number of objects of a particular kind. Like a street sign and the machine by analyzing the patterns recurring in the presented images learns to classify. Even sort the new images.

Natural language processing is the capability of the machine to understand and interpret languages. Upon knowing what the user intends to communicate with the machine, it can respond likewise.

Computer vision is an algorithm to check any image by breaking it down and studying the different parts of it. This helps the machine to learn in-depth. And make a better outcome decision based on such observations.

Cognitive computing includes algorithms that imitate the human brain. For text, speech, an image in the way a human does, thus giving the desired and proficient output.

Artificial intelligence incorporated in machines allows them to perform any tasks smart. Or even better than humans. To make AI more responsive and smarter, the usage of plenty of data is ideal. Like the human mind which has the most data around.

Importance of AI

When you look at modern applications like Google Maps or Lyft that use AI to make the commute easier, you wonder about the potential of AI. AI not only brings automation that can ease out any human task but also offers;

Intelligence

AI makes it possible for systems to improve. Or come up with better solutions. For example, Smart machines, bots, or even conversational platforms that take up large amounts of data to enrich the processes and systems.

Computerized Tasks

Instead of automating manual repetitive tasks, AI can operate frequent, high volume tasks free of error.

Deeper Data Analyses

AI works on neural networks that penetrate through big data and become more accurate.

Skilled Output

AI employs a large amount of data in a way that the algorithms get the skill of teaching themselves. It can adapt to the new data, check all, and learn new things on its own even, adjust through training.

For example, Alexa or Google Search, and Photos are all AI-based and they keep getting more revised, more accurate, and more futuristic the more they are used.

Application of AI

Artificial intelligence is marking its presence and automating enormous tasks for humans. Let’s look at a few uses of artificial intelligence across industries;

Education

AI has helped in automating the grading system. Presenting with smart content. Providing teachers and students with personalized learning and instant feedback. And customized online tools for adaptive learning. AI has become an integral part of the education sector while the traditional methods have faded. For example, a chatbot that can provide answers to a variety of questions, or formatting assignments.

Autonomous Vehicles

Self-driving cars are one of the best examples of AI in action. AI has made car driving effortless and very safe. With alerts to drivers and for instance help at the time of negligence or rash driving. For instance, CarVi is a delivery service where users can shop and pick items through their app. Even have them delivered. It is also used as a personal vehicle, for ride-sharing, or under auto insurance companies.

eCommerce

AI-based systems help storage companies to manage huge amounts of stock or inventory. With proper management, automated warehousing, and supply chain management system. Like, Amazon uses AI to help customers shop and get purchasing recommendations. Its other services include estimating home prices, visual search, and much more.

Banking & Finance

AI systems in the banking segment mainly provide customer support, check for any errors or card frauds. While it has become the main researcher for the trading and stock market performance. AI solutions are also used for better security across the banking and finance sector. For example, Socure, an identity verification platform. It analyses an applicant’s data to help clients meet KYC conditions. The system investigates information such as email addresses, phone numbers, IP addresses, and proxies. To see whether an applicant’s information is being used correctly.

Travel

AI is seen in the travel industry as a time and money saver. This sector relies on delivering excellent customer service to build its reputation. AI technology can assist with this most efficiently and easily. Artificial intelligence can be used to provide personalization, customize recommendations, and assure a fast response. For instance, Utrip provides personalized travel recommendations by using AI and machine learning algorithms. For this, it partners up with airline companies, marketing organizations, convention and visitor bureaus, cruise lines, and even hotels.

Entertainment

AI has helped shift the user experience from luxury to personalization. It has become more on-demand entertainment, search optimization that influences the user substantially. Like, popular music and video streaming platforms Spotify and Netflix which offer content to people across the world. Providing them customized feeds based on their different tastes and preferences.

Also Read: What are the Challenges of Making Wearables App?

Social Media

The powerful social networks across the world like Facebook, LinkedIn, or Snapchat use AI for content, advertisement, and regulation. For instance, Sprout Social’s automated technology generates tweets that can be used to reply to fans, customers, or followers.

Agriculture

Autonomous tractors or drones have helped farmers increase crop protection and productivity. Automated machines and robots are in fields to track crop health or weather monitoring conditions. And also harvesting. For example, Prospera has developed cloud-based solution sensors and aerial images that combine it with an in-field device. The inputs from these sensors are then used to find a connection between varied data and make predictions like weather or crop health.

Gaming

AI-based games have improvised the gaming experience. It has made it a very exciting and diverse exploration for the gaming industry. For example, The Mind Game presents its players with situations to test their mental strength. The game is very procedural, generating environments and situations. It allows players to perform several actions in a virtual world that they could in the real one. Even responding to adapting the emotional and psychological state of its players. And human behavior is evolving.

Food

From sorting food to managing the supply chain and delivering the food at the doorstep. AI solutions are helping the food industry serve savory delights at low cost and most customization. Like, TOMRA Sorting Food, uses sensor-based sorting solutions with machine learning capabilities that have cameras and near-infrared sensors. To view, classify, and efficiently sort the food. It saves the hours spent on manual sorting, wastage, and ensures no error with quality.

RPA

RPA is a technology that automates business processes that are repetitive or highly structured. RPA tools can be used to capture data, manage transactions, and store information. RPA is the best solution to reducing labor costs as well as preventing any human errors. For instance, Kryon Systems is a provider of RPA solutions to the claims sector. Fastening their process, smoothening their systems, and improving their management.

Artificial Intelligence has touched several applications in today’s time. It is becoming an essential element for every industry. To become more efficient and effective. AI is making daily human life comfortable and faster.

Impact of AI on the Healthcare Industry

The industry that is most positively affected by AI and transformed to a different level in the healthcare industry. AI in healthcare has helped in the simplification of human tasks performed, in less time and cost. Making it trouble-free for the patients, doctors as well as administration.

According to a 2018 CB Insights report, about 85% of healthcare provider organizations, life science companies, and technology vendors to healthcare are using artificial intelligence. It is said by 2021, an average of $56 million will be spent on projects on artificial intelligence in healthcare. It is predicted to reach $150 billion by 2026.

Imagine a world of secure and personalized healthcare. Giving doctors instant access to patients’ real-time or past history. Let’s have a look at the processes that become easy with artificial intelligence in the medical industry;

Managing data

Digital automation of data and patient information has helped to store, format, and trade medical records. In a consistent and faster way.

Executing tedious tasks

Analyzing tests, scans, X-Rays or any routine tasks can easily be done via automated systems or even robots.

Analyzing treatment

AI systems help to research and provide clinical expertise. On the selection of customized individual treatment courses.

Consultation online

Medical consultation online has gained much popularity in the past few years. Patients can input their symptoms into the app and based on personal medical history, the database provides details of the type of illness. As well as the solution to it. Like, Babylon app.

Digital nurse

A virtual nurse helps track patient’s conditions and assist in follow-ups. For example, Molly, a digital nurse by sense.ly.

Managing the medication

The National Institutes of Health came up with the AiCure app to supervise the use of medication by a patient. A smartphone’s webcam is enabled with AI to autonomously confirm that patients are rightly taking their prescriptions.

Creating medicines

Normally, creating a drug takes a lot of trials and invests loads of time and money in the process. An automated process can make it faster and cheaper. For instance, a program powered by AI was used to scan existing medicines that could be redesigned to fight the Ebola virus. The medications were identified in a day where normally it would take a year. It not only saved time but also thousands of lives.

Scanning with precision

AI helps body scans that go very deep and help look for any mutations or links in the DNA too. This assists in spotting cancer or vascular diseases early on, which can further aid in faster treatment.

Monitoring the health

Wearables like FitBit help keep a tab on the heart rate or activity levels. It sends alerts about exercise, diet, and even habits of the users.

Surgery

Medical surgery assisted with AI helps reduce surgical differences or inefficiencies. Surgeons utilize AI systems to better understand the techniques that bring into line better results. Such insights can also aid in patient’s postoperative and long-term health monitoring.

For instance, an AI-assisted surgery robot was employed to join small blood vessels, no larger than .03 millimeters. The patient had lymphedema, which is a chronic condition where fluid builds up inside the body and causes swelling. Also, a condition of breast cancer. Microsurgery was performed on the patient with the aid of an AI robot, which came to be a possibly much better treatment for the condition.

The procedure involved linking lymphatic vessels to blood vessels to re-establish the flow of lymphatic fluid and lessen the swelling. AI helps surgeons find out what is happening during a complex surgery by providing real-time data on the movements the surgeon makes during such a procedure. Such a tool provides medical professionals with appropriate content to manage and leverage surgeries to further help identify and decrease risk, expand quality, and enable data-enabled training.

The new generation of AI tools and systems is making health care more efficient. Urging into a new era of quality and stimulating breakthroughs in patient health care. It is developing methods and treatments that reduce errors from diagnosis to treatment.

“By augmenting human performance, AI has the potential to markedly improve productivity, efficiency, workflow, accuracy, and speed, both for [physicians] and for patients … What I’m most excited about is using the future to bring back the past: to restore the care in healthcare.” — Eric Topol,

Conclusion – Future of Artificial Intelligence

“I imagine a world in which AI is going to make us work more productively, live longer, and have cleaner energy.” -Fei-Fei Li, Professor of Computer Science at Stanford University

With rapidly changing technologies, the world is getting prepared to witness an innovative wave of existence. Whether deeptech or fintech, foodtech or conversational chatbots, mobility, or healthcare AI has revolutionized most industry sectors today. Artificial intelligence future will have machines perform even more like humans, learning and adapting over time, this is the new age reality.

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