LiteratureSurvey_MIM_07 Essay

?Title: AI and ML: Developing the next level of smart mobile apps

Name of the Student: Siddhesh Virendra Bhide

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LiteratureSurvey_MIM_07 Essay
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MIM – SEM V, Roll No.: 07

Name of Guide: Prof. Chandrakant Nimkar

S.P. Mandali’s

Prin. L. N. Welingkar Institute of Management Development & Research Matunga,

Mumbai – 400019.


Recently Artificial Intelligence and Machine Learning have created a massive impact on human interaction with machines and devices. Whether it is any industry ranging from travel, utility, machinery, telecom, or advertisement industry, AI and ML have enhanced the smartphone experience hugely.

Both iOS and Android mobile platforms are integrating AI and ML in various kinds of apps. Also, with AI and ML, decision making has become a much quicker and precise by accumulating a massive volume of information.

When we think about the smart apps or artificial intelligence today, Siri, Cortana, or Google Assistant probably come to our mind. The hundreds of linguists and software engineers dedicate countless hours to building these services into responsive personal assistants that can answer questions, track down information, send messages, launch services, and more.

Artificial Intelligence vs. Machine Learning

AI is defined as an imitation of human behavior by machines or by computers. AI deals with that branch of science where the objective is to make machines or computers exhibit the same level of intelligence, smartness, articulation, and artistic traits as humans. The scale and applications of AI vary and now, for example, if there is hypothetical humanoid that can wash the dishes, clean car, and cook meals, we will call it the product of AI but so will be a software that can understand a speech and recognize whether the tone in that speech is angry, envious or cheerful. AI is vast, and there are many of the aspects involved in that. There is data science which helps in collecting, categorizing, and processing of the data, there are algorithms which helps in understanding and applying the intelligence traits, and then there is Machine Learning. Machine Learning is a part of AI which fulfills the objective of learning and training the programs that will be capable of making decisions. These programs are fed with an enormous amount of data, uses algorithms to process the data and train on that data so that when it is given a new input, it is capable of making a decision.

AI is becoming hot in the last few years, but it is not a new concept and has been around since the ’70s. But only now has it become feasible to use practically to solve problems because of the availability of high processing power, hardware, and the vast data required to do it. ML requires not a few thousands but millions of data items to work upon to train models and give reliable output.

AI and ML in smartphones

Artificial intelligence and Machine learning (ML) are much more than smart technology, as it brings all the emerging technologies, especially in technology, telecom, and media industry. It is looking at the development of new technologies like chatbots, self-driving cars, virtual assistants, etc. Moreover, in the upcoming years, there will be an incredible flow of AI and ML, which can only be seen in sci-fi movies.

In an early days of Android and iOS, the apps were highly fragmented. Since then, the ecosystem has become much more integrated. Device and OS manufacturers don’t want users scrolling through tens of apps to get to the one they need. To improve navigability and convenience, developers are integrating deep linking into their apps, such as Android intents, iOS URLs, and Facebook App links, allowing users to work across apps without needing to launch them separately.


These days by looking at the impressive growth of AI and ML, it can be said that businesses are focused on influencers and advertisements, which are making the use of micro-targeting for expanding their customer reach.

Retail giants like Amazon and eBay have already proved the success potential of AI mobile apps. With new advancements in technology and shifting consumer demands, AI mobile app development is the new digital frontier for enterprises.

The major tech companies are integrating these AI algorithms into various products to strategically secure users further into their brand ecosystems. This helps businesses deeply engage users, providing more incentive to use their services, such as Amazon’s Prime delivery service that pairs well when using the Echo.

As we see more AI and ML-driven apps, businesses can leverage the data apps are collecting via point-of-sale machines, online traffic, mobile devices, and more to strategically improving the user experience. The algorithms shall be able to sift through this data, finding trends and adjusting the apps themselves to create more meaningful and context-rich opportunities to engage the users. Forward-looking enterprises are capitalizing on the advantages AI and ML provide as it continues to connect users to brands.

How Artificial Intelligence and Machine Learning transforming Mobile App Technology?

Empowering Search Engines:-

Artificial intelligence and machine learning have introduced users a new method to search using images and voice, unlike text mode and because of the integrated AI and ML into mobile apps, it has now become compulsory for the developers to develop an image recognition system and voice recognition system. Moreover, AI will also provide app localisation for improving the conversion rate.

Artificial Intelligence Combined with the Internet of Things (IoT):-

AI lets device communicate with each other and AI is going to make drastic changes by collecting all the real-time data and processing that data so that machines learn to function on their own. Therefore, AI is helping the mobile development companies to learn and execute with every information which is exchanged between various devices and then take all the necessary action.

Smartphone Camera is getting smarter in Subject Detection:-

This is one of the essential areas where Android phone manufacturers and Android app developers are working on AI and ML. By using AI, the interface of a smartphone camera can easily detect the subject in its frame like food, landscape, fireworks, etc. It works accordingly by tweaking the settings for getting the best possible image.

Additionaly, artificial intelligence and machine learning can also quickly identify facial features and enhance them to get a super portrait image automatically.

Translating Languages in Real-Time:-

There are so many translation apps available that allow users to take an image of a text in one language to convert it into another language.

However, most of these apps use the internet to upload images for analyzing and then translating the text.

After integrating the AI and ML, your smartphone will become capable of translating different languages in real-time without any need for an internet connection.

Face Unlock to Power More Smartphones:-

With the launch of the iPhone X in Sep 2017, face unlock has become one of the most popular features in Android smartphones. Apple uses AI and ML-based algorithms for its Face ID unlocking system, and with the combination of premium hardware, the artificial intelligence system works to identify the face of the user to secure unlock.

Moreover, in the upcoming years, we will see many mobile app developers and smartphone makers implementing the face unlock feature using AI processing, where the smartphone can quickly identify the user’s face with facial changes like beard or spectacles over time.

High App Authentication:

AI has an enormous responsibility when we talk about cybersecurity. With ever-changing technology & the increasing usage of smartphones, we all need the advanced level of data security. While developing new mobile apps, security is one of the biggest concerns for developers. With AI and ML, a security concerns and issues have been decreased by sending alerts to the users about possible threats and vulnerabilities by analyzing user behavior.

Creating App Marketing:-

For marketing an app, marketers are required to collect and maintain vast sets of data, both offline and online. This needs a lot of time and effort because retaining the data for each customer is a tough job when you are asked to deal with millions of worldwide customers.

AI-based smartphones and apps help in analyzing and researching the market and purchase history of the user. As per, by the year 2020, almost 30% of businesses and companies are going to use artificial intelligence and machine learning to increase their sales. Also, AI and ML are going to help companies to make better marketing decisions for increasing their user engagements and sales.

The Potential Threats Surrounding AI and ML

Artificial intelligence and machine learning are the most viral topics discussed in this age. It has been a big controversy among scientists today, and their benefits to humankind cannot be overemphasized. 

With the advent of intelligent systems that learn from massive amounts of data to achieve high ef?ciency and minimum computational cost, the threat of vulnerable security attacks has also increased. While researching on various ML models such as SVM, decision tree, Naive Bayes, logistic regression, clustering, deep neural networks, etc. researchers have found out several security threats against these learning algorithms. Most of the researchers agree that a superintelligent AI is unlikely to exhibit human emotions like love or hate and that there is no reason to expect AI to become intentionally benevolent or malevolent.

So we need to understand the potential threats surrounding AI and ML.

AI Security Vulnerabilities and Threats

The attacks can be divided into the most common triad of confidentiality availability and integrity.


The objective is to gain insights about the system and utilize the received information for his or her profit or plot more superior attacks.

In other words, a malicious person deals with the machine learning system, say, an image recognition engine to drill down and learn more about the internals like dataset. Hackers can for instance, guess the sexual orientation of a particular person on Facebook by making two targeted ads and check which one will work for that particular person. A realistic privacy incident happened when the Netflix published their dataset. While the data was anonymized and the hackers were able to identify the authors of a particular review.

Personal assistants collects a great deal of personal information to provide a better service. It can be helpful for the attackers. If a voice can be copied, a perpetrator will make your assistant tell any secret. In the world of a systems and proprietary algorithms one of the goals would be to capitalize on a system’s algorithm, the information about the structure of the system, the neural network, the type of this network, several layers, etc. This information can be used for further attacks. If we come to know the type of the network and about its details, the network can be reconstructed it at home, and other methods of attacks can then be discovered.


The objective of this is to disable the functionality of an AI system.

Some ways of sabotaging are:

Flooding AI with requests, which requires more computation time than an average example.

Flooding with incorrectly classified objects to increase manual work on false positives. In case this misclassification takes place, or there is a need to erode trust in this system. Example, an attacker can make the system of video recommendation recommend horror movies to comedy lovers.

It is modifying a model by retraining it with wrong examples so that the model outcome will let down. It only works if the model is trained online.

Using the computing power of an AI model for solving your tasks. This attack is called adversarial reprogramming.


Fraud in AI stands for misclassifying tasks. A simple example is a need to make AI (such as autonomous cars) believe that there is a cat on the road, whereas it’s a car. Attackers generally have two different ways to do it one by interacting with a system at the learning or at the production stage. The former approach is called a Poisoning, where attackers poison some data in the training dataset, and the latter is Evasion, where attackers exploit vulnerabilities of an algorithm to instruct the AI system to misoperation like misclassification.

Security Threat On ML Models

The taxonomy of the security threats for ML models is mainly based on three different perspectives, and those are the influence on classifiers, the security violation, and the attack specificity. From the standpoint of these three influences, security threats towards machine learning models can be further divided into seven categories. They are mentioned below.

Causative Attack:- This attack includes the modification in the distribution of training data such as change of parameters of the learning models. This modification in the already trained classifiers, which results in decreasing the performance of classifiers in the classification tasks.

Exploratory Attack:- This attack causes misclassification for adversarial samples, which results in exposing sensitive information from training data as well as learning models.

Integrity Attack:- The integrity attack achieves an increase of the false negatives (FN) in the performance measure of existing classi?ers when classifying harmful samples.

Availability Attack:- This attack is opposite to the integrity attack. Unlike integrity attack, the availability attack achieves an increase of the false positives in the performance measure of classifiers for the positive samples.

Privacy Violation Attack:- With the privacy violation attack, adversaries can obtain sensitive as well as confidential information from training data and learning models.

Targeted Attack:- This attack is mainly used to reduce the performance of the classifiers on a specific sample or a particular group of samples.

Indiscriminate Attack:- This type of attack causes the classifiers to fail in an undiscriminating way on a broader range of samples.


Currently, most of the smartphone companies are started integrating AI in smartphones, which means that users can get all the benefits of AI and ML, which has the potential to collect, store, and process all the real-time data. Moreover, with AI, you will get a superior personalized user experience. Therefore, the businesses are going to take advantage of this for increasing their business and Return on Investment. The upcoming future is going to be smart for smartphones with AI and ML, and with that mobile economy will grow hugely with the time.


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