AI and ML jointly have crafted some amazing, intelligent, and smart solutions. These tools can even understand the pattern of humans. This tool can even do behavioral analysis and develop strong algorithms. It can deploy mobile apps to interact, entertain, and deliver the best-personalized experience to the users. A mobile app development company can implement the best solutions. 

For instance, Facebook has employed this tool to understand the behavioral pattern of the users. With this, they are offering customized solutions and experiences for their users. 

Combination of Legacy: Mobile App Development and Machine Learning

Examples of ML-Based Mobile Apps

• e-Commerce mobile apps
• Mobile apps for fitness and health
• Apps for Fintech
• Mobile apps based on Data Mining
• Healthcare mobile apps
• Telemedicine mobile apps
• Travel and tourism mobile apps

So, why should you build a Machine Learning mobile app?

1. After the integration of Machine Learning, Almost 76% of enterprises saw higher sales.
2. This technology can predict the user behavior, process all optimization, cross-sell, and lead up the sell.
3. 50% of business owners have applied this technology to refine the marketing issues.
4. Machine Learning has assisted numerous European banks in expanding the sales of the product by 10%.

These stats and facts clearly clarify that this is the right time to invest in Machine Learning mobile application development.

Types of Algorithms of Machine Learning for Android and iOS apps

This innovation can automate the algorithms for decision-making and data processing. These kinds of algorithms improvise their operation following the results of their work. Thus these tools can provide great support for Machine Learning app development. 

To develop a model that reveals all the networks, ML utilizes the below-mentioned algorithms.

1. Reinforcement Learning

The engineers will train the algorithms of ML to make quick and specific decisions from the system. ML captures all the possible outcomes to make a quick decision.

2. Unsupervised Learning

Here ML trains itself from data examples. The algorithm calculates the patterns of the data all by itself.

3. Supervised Learning

Like unsupervised learning, ML trains itself from data examples and associates to target the responses. The data will be enclosed with string labels or numeric values. 

This includes tags and classes. In the next level, it can be posed with other examples. Thus, it can give the correct prediction responses. 

Advantages of Machine Learning-Based Mobile Apps

# Enhance the options of searching and results

The option of searches keeps evolving with the blink of an eye. This includes search engines and results. What we cannot modify is the design of mobile apps to deal with the searches.

This technology assists in automating and evolving the same. By employing ML and its subsets, the search options can display the best matching results. Even if you misspelled the keywords, Google would display the results.

When you integrate ML with mobile apps, it will understand all the patterns. The human being can barely resolve this when required.

In order to make efficient mobile apps, this innovation can assist in enhancing the results and searches. This process is not all time-consuming and hassle-free. Ml even employs behavioral and graphical data to improvise the personalized experience for the user.

# Identification of frauds

ML in mobile app development has helped many businesses to identify potential threats and frauds. Security of data is a must-have feature in your mobile apps. Why? Well, your users would be sharing their personal and banking details. And you, as their partner, must secure such data.

Hre, employing ML would benefit you in a better way. This tool will train with the existing trends and patterns. And when anything “out of the pattern” arises, it will raise the alarm. 

You will be able to take all the preliminarily and preventive measures to control any such activities. It will keep the data of the user sound and secure. 

For example, an unknown transaction took place; ML will quickly alert you about this activity. You can report and get sorted with the transactions. 

The financial sector is yet another domain where this technology has showcased its potential. It will be tough to figure out the frauds in money apps, wallets, and credit cards. 

Suppose you have your business in this domain and its surfaces in your institution. If not focused on this area, it may lead to poor growth of your business and client dissatisfaction. This may lead to poor productivity.

# Improvises route of Logical Development

In Machine Learning app development, there are high chances that the mobile app developers get baffled to enhance the comprehensive logic development. And this development consumes lots of energy and resources. Therefore, it will take time to amplify and deploy the app to the market. 

It will help to simplify the path of logical development. This, in turn, will help to understand all the coding aspects. ML will assist the developers in understanding different trends and patterns enclosed in the development of mobile apps. It will enhance the experience in coding and overall logic. 

Suppose you need to add new categories in your mobile app. This, otherwise, you cannot tackle without the assistance of the mobile app developer. But now, thanks to Machine Learning, you can handle that with ease.

Note:- Assure that the mobile app development company buffers you with commands and trains Machine Learning.

Essential of Machine Learning for Mobile Apps

Since by now, we already know the advantages of implementing ML in mobile apps. Let us further check some of the reasons why ML is essential for your mobile apps:-

a. Filter the Spams

While handling the Machine Learning app development process, the developers can even train the users who would be using the apps. They can give training to the modules of Machine Learning to strain the spam out. 

Your development team can program these intelligent machines to clear up unreliable websites and emails as these unreliable sources can overload the user’s inboxes. Hence, there might be a high chance of skipping the fraudulent activities. You can overcome this by including ML with your mobile apps. 

b. Predictive Analysis

ML can handle huge data and acquire significant calculations. It can assist in predictive analysis. This will give a personalized app experience to the users. 

c. Boost The Engagement of The User

You must understand that ML does not have the power to convey the real objective of your mobile app. But it has the power to expand the capacitance to enhance the engagement of the user. 

In a conclusion

Machine Learning-based mobile apps are evolving in the market at an exponential rate. This innovation is highly credited because of the reduction in time and effort, cost, and security.

Hence it is highly recommended to hire a mobile app development company. They will provide reliable and effective solutions for AI-based mobile apps. 

MobileCoderz Technologies is an efficient mobile app development company. We work on comprehensive technologies such as AI, ML, IoT, iBeacon, etc. Our team is also proficient in developing customized apps for a personalized experience.

The post Why Consider Machine Learning in Your Mobile App Development Process? appeared first on MobileCoderz.

According to the most recent stats, there are almost 2.8 million apps on the Google play store and 2.2 million on the Apple store, it has been noted that the trend of ordinary and traditional apps has faded with the evolution of new technologies. 

Source: World Economic Forum

Machine Learning (ML) and Artificial Intelligence (AI) have opened new opportunities and doors for the business world. These technologies will benefit you and your business in the best way (if adopted correctly). It will effectively handle all your operations and help you ultimately in making the money. 

According to an article published in Financial Times, Iberdrola,  a global energy company based in Spain with its operations on Electric utility (energy) have accomplished efficiency gains. This benefited both the environment and their business. They employed Artificial Intelligence for the maintenance of their assets and improvise operations using data analytics. This Basque electric utility multinational company developed systems with machine learning apps to coordinate the planning as well as delivery of the optimized distribution, monitor the usage of electricity, and delivery of maintenance. 

MobileCoderz is among India’s one of the top companies for providing the best Android App Development Services in India & USA. Since the establishment of MobileCoderz, it has helped a lot of startups as well as big enterprises by giving the high-quality IT talent to achieve their business goals.

Some stats and facts based on machine learning apps

1. According to a post published on LinkedIn, 75% of Netflix users opt for films or shows recommended to them by the algorithm of Machine Learning. 
2. As per reports shared by Markets and Markets, the market of Machine Learning globally was valued at $1.58B in the year 2017 and by the year 2024, it is estimated to rise by $20.83B.
3. Pursuant to reports shared by Market Research Future, the market is projected to expand at a CAGR of 42.8% from the year 2018 to 2024. 
4. ML improvised the quality of the product up to 35%  in the industries related to manufacturing, as per Deloitte.
5. Machine Learning has the possibility to minimize the shortage of chronic labor in manufacturing while at the same time opting for alternatives to retain the employees, as shared by Forbes. 
6. Everest Group shares that machine learning apps and Artificial Intelligence apps will steer the digital revolution. 

Source: Google

source: Everest group

The expanding accessibility stand and gigantic capacity are of no use if you do not come up with some exciting plans for your mobile app. It is believed that the software of Artificial Intelligence will grow by 154% year-after-year at a conjecture of $22.6 Billion. 

Source: Markets and Markets

Focusing on this, we have listed some phenomenal Artificial Intelligence/ machine learning apps of 2021 that will do an excellent job in 2021

Machine Learning App Ideas for  2021

ML-Powered AI-Based Apps for Healthcare Equipment & Tools

It is believed by the end of this year, ML-powered Artificial Intelligence will play a major role in the industry of healthcare. AI/ ML apps can help you to analyze the data of the patients and also improve the results. As a result, you can use this app as a decision-maker enhancement tool to improve the stability of work, security, predictability, quality, and reliability. 

Source: aisoma.de

According to Global Insights Market, the size of the Healthcare market will surpass USD 21 billion by the year 2026. 

source: GM Insights

It is a great time for business owners to invest in this technology. Let’s check some of the examples set by leading brands:-

2. BiliScreen is a prominent mobile app that was developed using smart cameras, algorithms of computer vision, and the tools of ML and AI tools that can identify the bilirubin’s increased level in the white area of the human eye. This app was also used to find varied crucial health conditions like pancreatic cancer.
3. Redivus Health is a versatile app that is widely utilized by numerous healthcare practitioners to prevent errors in medical data. It additionally gives the support of clinical decisions (that are uninterrupted) during the events.

ML-Powered AI-Based Apps on IT Services

With the digital world exponentially rising, data hacks & cyber-attack are the threatening reality that is a major concern for many businesses. Thanks to the evolution of machine learning apps and Artificial Intelligence, a great illuminance was imprinted in IT services, solutions, and security to handle different workloads in the system of computing. 

source: Grand View Research

Want to know more, let’s check this example:-

AI2, an AI-based platform was introduced by Artificial Intelligence Laboratory (CSIL), MIT’s Computer Science, and Pattern X has claimed to forecast the cyber attack. They have used contextual modeling ( i.e totally proactive) which is a persistent feedback loop between a human analyst and the system of Artificial Intelligence.

ML-Powered AI-Based Apps on eCommerce

This innovation can offer customer-driven sites a competitive edge. Nowadays, it is adopted by numerous companies (based on eCommerce) of any size or budget. It has empowered e-customers to associate the products/ items with the correct shape, size, brand, and color. 

Nowadays numerous client-centric web apps are developed more sophisticatedly using the cutting-edge capabilities of Artificial Intelligence.

Source: Smart Insights

Here are some examples,

2. Amazon’s most well-known app “Alexa”, a famous AI tool that learns the marketing base (targeted) of Amazon. This calculates and learns the algorithm of the most-demand services and goods as per the user’s searches. It also sends personalized recommendations, this (in turn) has helped Amazon to earn major profits.
3. Likewise in order to keep a competitive edge, eBay utilized AI. The shopbot platform of eBay is valuable for finding various items utilizing Natural Language Processing (NLP). As per the article published in Built in, Machine Learning is also assisting eBay to solve its unstructured problem of data. Now, it is very much evident that ML is the essential part and the right strategy for the eBay business. 

Similarly, you can also develop apps that are not only effective but also can help you to earn maximum profits. For this, you must hire a Mobile App Development Company with extensive years of experience. The main reason for this is, they will be enriched with deep insight and have their hands on the challenges, latest technologies, and tools.

Machine Learning Apps for Finance & Accounting

The features of machine learning apps and Artificial Intelligence have added feathers to many industries and accounting and fintech industrial segments are no exception. Nowadays, accounting and finance specialists are implementing Machine Learning powered Artificial Intelligence to make the work process easier by using practices such as reporting and data entry. 

According to Business Insider, the potential of aggregate savings on costing for banks using AI apps is estimated to reach $447 by the year 2023.

Source: Business Insider

Let’s delve into some of the examples,

5. Xero accounting mobile apps have made the execution (information of the finances) quicker and notify the live updates on cash flow, turnover, bank feeds, etc. 
6. VOD is a prominent mobile app with easy-to-use systems of POS. These apps can send updates based on finances directly to the clients. It has been stated that it has the greater capabilities of the reporting. 

Machine Learning Apps for Exploitation & Vulnerability

machine learning apps or AI applications are an extraordinary thought to foresee a specific vulnerability in an area of the software applications which can be utilized by attackers. This additionally permits us to have sufficient time before new attacks. 

This is actually a vulnerable issue, yet by focusing on an elementary stratification of “will be assaulted” or “won’t be assaulted”, we can (without much of a stretch) train the exact model with high sets of updates and reminders.

Machine Learning Apps for Fast Learning 

The concept of Machine Learning or AI learning tools has changed the way one writes, reads, and searches text. There are advanced applications of Machine Learning and AI that have opened new paths for researchers to learn about the recent innovation of the latest tools, technology, and information. 

Let’s go through some of the examples:-

1. Udemy is probably the best illustration of a fast learning mobile application that gives a wide range of courses for the development of the web, self-improvement, and so on. You can likewise figure out how to play the guitar, Udemy offers numerous courses to help an individual. 
2. Likewise, Goodreads is one of the most ideal approaches to adapt nearly anything about innovation, fiction, self- improvement, and so forth. This application has in excess of 40 million enrolled individuals and is growing in the market at a faster pace. The application assists the users to track down the perfect book at the perfect time.

Wrapping it up!!

Here in this blog, we have highlighted the list of Artificial Intelligence apps which are powered by Machine Learning suggestions that can help you to expand your business in the digital world. It is said that this year will be fruitful for the businesses that will implement the use of this powerful tool. Even though leading enterprises are already using this technology to enhance their reach and business. 

If you also have plans to have your own Artificial Intelligence/ machine learning apps, then you must choose a company that offers a featured-packed app solution.

MobileCoderz is an established mobile app development company (employed with aspiring and adjunct mobile app developers) and has its hand-on contemporary tools like AI, ML, etc. 

Have a competitive deal with us and explore the new magnitude of the digital world!!

The post Best Ever Machine Learning App Ideas of 2021 for Business appeared first on MobileCoderz.

“By allowing them to make mistakes & consolidating the lessons learned along the way!”

What is “intelligence” anyway?

As per the Oxford dictionary, “Intelligence is the ability to acquire and apply knowledge and skills.”

When we talk about ‘human intelligence’ or ‘IQ’, some might be of the view that it is quite relative & depends upon our ‘heredity’ or how intelligent our ancestors have been.

The academia negates this utopian view & as per the scientific studies “Intelligence” remains a matter of experience i.e. it depends on ‘continuous training’, ‘exposure’, ‘accepting challenges’ & ‘eradicating mistakes’ which in turn allows our brains to ‘re-wire’ its complex biological neural chain & gradually increase our IQ level.

Also Read: Dawn of a New Era with an Alloy of Artificial Intelligence and Mobile Application

A similar phenomenon applies to the field of artificial intelligent training machines where machines are turning more & more intelligent with each passing day. Their problem solving, pattern recognition, differentiation, logical reasoning, and calculated-decision-making skills are improving alongside aiming to touch the sky high human-level conscience.

But it is a way easier to be said than done.

In reality, we only have a partial understanding of the ‘process’ that goes underneath the complex chain of neural networks, either biological or artificial that results in a certain level of ‘IQ’ in humans or a certain level of ‘smartness’ in machines.

It’s more of a ‘natural outcome’ that shows up after an elongated period of training under countless diverse & adverse environmental conditions; consolidating the scenarios that are giving optimum results & eliminating the ones that are not.

AI/ML Bots ruling over the cyberspace

While browsing the internet, we knowingly or unknowingly come across AI/ML-based algorithmic bots that try to collect our data or manipulate our user-interface decisions as per the objectives defined for them. They frequently track our usage stats, browsing flow, & even local storage to comprehend our interests, our mood, preferences, needs, personal issues, or even paying potential.

The close enough examples could be Google Ads, Facebook suggestions, LinkedIn Ad campaigns, YouTube video recommendations or even the Amazon/ Flipkart product advertisements that we must have viewed on web-pages that are nowhere related to these websites.

For example, while booking airline tickets, we must have seen ‘dynamic price updations’ that may offer different prices to different sets of users at the same moment.

Apart from our usage statistics, all these scenarios depend upon the ‘digital-profiling’ done by these bots on us & how much ‘exploitable-potential’ a profile has.

The stock market trading on International stock exchanges, big financial transactions within multinational banks or even intelligence sharing among governments, all of them require hefty investments in the futuristic technologies like AI & ML for filtering out ‘fraudulent’ & ‘suspicious’ transactions among a dynamic list of gazillion financial transactions that could update within a fraction of a second.

Process of training intelligent machines (AI/ML) bots

In all the above examples, massive chunks of ‘user data sets’ & ‘financial data sets’ are exposed to these self-learning bots, which in turn are helping these bots to enhance their quality of future predictions based on the patterns in existing data.

Lately, humans were creating abstract algorithms with limitations on what could be achieved.

The initial bots were also trained using these abstract man-made instructions:

E.g. “IF <this> à THEN <that>”

To address the complexities that arose while solving large & complicated scenarios, this manual training process fell short in potential.

But with the advent of AI & ML, the complete cyberspace has taken a giant leap forward and the process of training these Algorithmic bots has been automated.

They don’t rely on humans to supervise or train them. Once their artificial neural structure (ANN) achieves a certain level of maturity, they start learning on their own when exposed to different training sets/ data sets. Meanwhile, the effort that goes into training a bot in its nascent stage can’t just be denied.

Unfortunately, these pieces of training are ‘highly guarded trade secrets’. But here we will try to make sense of these pieces of training, using a case study of our own.

Structure of Artificial Neural Network (ANN)

Let’s first get a birds-eye view of the input, processing, & output layers within an Artificial Neural Network that could also be considered a prototype to the biological human brain.

While academia is well aware of the working of an artificial/ biological neuron and how a cluster of neurons communicate with each other is vaguely grasped, detailing out the working of ‘complete package of neural wirings’ & ‘how a certain level of intelligence’ is achieved is still beyond scientific comprehension.

“Image-recognition”- A case study in AI/ML bots training

It is easier for humans (even children) to differentiate b/w two different objects by looking at their respective instances, e.g. say, the deformed images a dog & a bear.

This happens because wirings in our brain help us ‘relate’ the current visuals with memories of similar visuals & do the ‘memory-based pattern-recognition’.

We as humans have different shades of thought processes. Apart from memory-based learning we could relate, infer, assume, imagine, introspect, & innovate solutions that have never even existed before. Such cases are never perceived with machines/bots.

Their conditional programming makes them think linearly.

Therefore, in this scenario, it’s almost impossible for a bot to differentiate a dog & a bear by looking at their deformed images without incorporating AI/ML features into that bot.

This could only be achieved by incorporating ANN to bot’s programmable sets, creating infinite copies of that bot, & grilling all those bots through vast, rigorous, diverse, & indefinite ‘user-data sets/ training-sets’(e.g. Internet) that would allow them to make mistakes & re-wire their ANN.

In nascent stages, the tweaks made in their ANN would be manual & hence, more or less random. One can’t be sure if the individual modifications would result in an improved ‘image recognition’ skill or not.

Hence, an iterative ‘test-err-build-test’ cycle would be repeated indefinitely till a desired level of performance is attained by any of the bots. Let’s look into this training scenario.

“Test-Err-Build-Test-Repeat”: A intelligent machines training scenario for nascent bots

As we know that humans are unable to train a complex scenario to these bots. We would create two bots (say, a teacher bot & a builder bot) that would train a bunch of other bots (say, student bots). The ANN of these bots would be simpler and could easily be programmed by a human programmer.

At first, the wirings in student bot’s brains would be done at random & they are exposed to ‘training sets’ by Teacher-bot. The ‘training sets/ tests’ & ‘answer keys’ to each set is provided by the human overseer. The teacher bot ‘evaluates’ the tests under a controlled setup & maps the respective performances to each of the student bots.

These ML bots are then transferred to Builder-bot that sort out the best performers, creates multiple copies of them & does some non-calculated tweaks in their neural wiring intending to improve performance.

The bots are again transferred to Teacher-bot’s end that tests them with even harder training-sets. This ‘test-err-build-test’ cycle repeats indefinitely until a desired level of performance is achieved.

The success of this process also banks-on ‘discarding the under-performing bots’ and ‘re-wiring the top performers’ in each subsequent cycle. Furthermore, the human overseer is expected to keep the ‘bot’s-count’ & ‘training-sets/ user-data-sets’ as magnanimous as possible.

The initial performers would be lucky but with passing iterations & gradually increasing the complexity of ANN-wirings, the bots would incorporate ‘data-pattern-recognition’, ‘object-differentiation’, ‘logical reasoning’ & ‘calculated-decision-making’ skills depending on the nature of training sets they are exposed to.

E.g. In our case study, a bot is supposed to emerge who could successfully differentiate a dog & a bear even by looking at their deformed instances.

A real-life example similar to our case study is Google’s ‘image search algorithm’.

Intelligent Machines Training scenario for Mature enough ML bots

When these bots cross their nascent stage, they acquire an ability to learn on their own (similar to humans). Hence, from thereon, it becomes easier to deploy them over the internet where they would get continuous exposure to a large chunk of ‘user-data’/ ‘training sets’ and without the help of a human overseer or evaluator, they would self-assess & improve.

E.g. you must have come across ‘image-based captchas asking to identify a particular object (say, statues) among a bunch of other images before allowing you to access specific web content.

These captchas are covertly acting as training sets & helping these bots develop their object differentiation & image recognition skills.

Other such examples include “Are you human?” or “Confirm you are not a robot” kinds of tests that are helping such bots learn how to read & count.

Tests on the social media sites like “pick the photos of your friends that are most likable, or choose the articles that have been shared the most”, all have this covert objective that goes behind the scenes.

The more such user-data these bots would be exposed to, the better their humanistic-skills would shape up.

This is the reason why big corporates are obsessed with collecting user-data these days.

The dark side of intelligent training machines: Data Privacy issue

There is also a contrasting side of all we have gone through.

Countries nowadays are becoming more & more overprotective about their citizens’/user’s data privacy, the reason being the malpractices that go into training these bots to manipulate user’s interaction on the internet in such a way that they can’t be termed legitimate.

For instance, Cambridge Analytica’s case in which Facebook & Twitter were tracking their user’s tweets, posts, & activities without even their consent, which was later found to be used in manipulating the overall mood of people before the USA’s presidential elections of 2015.

Conclusion

AI/ML is the most fascinating field for the entire academia. Not just because a slight glimpse of it is showcasing immense possibilities for the future, but also because it’s a quest to create ‘ourselves’. To create our own-selves, it is pre-requisite to ‘understand’ our own-selves & this is where the trick lies.

Concluding this blog with a simple question,

“If AI is a quest to create consciousness, how come an unconscious being is going to create a conscious machine?”

The post The Process & Philosophy Behind ‘Training Intelligent Machines’: AI (ML) Bots appeared first on MobileCoderz.