DEV Community: Evgeniy Krasnokutsky

Metaverse Technology for Business: How to Build Your Brand Application

Evgeniy Krasnokutsky — Mon, 04 Jul 2022 14:06:38 +0000

What once began as a revolution of communication in the form of text, images, and two-dimensional formatting is now evolving into new dimensions. The metaverse has been with us in one form or another for some time, but the progression of critical technologies has vastly pushed the envelope of how we define and use the web. As a business owner looking to remain competitive in the future, you need to stay informed on the technologies involved in this space if you are to develop applications and even entire digital worlds for your customers.

Let’s talk about what technologies are used in metaverse development and how businesses can create their own metaverse applications.

The Metaverse for Business: Explained

Neal Stephenson’s Snow Crash coined the phrase that became the name for the metaverse, and executives like Mark Zuckerberg are ironically fulfilling the book’s prophetic predictions about the future of business, the economy, and society. The metaverse, as it is understood in modern business, is an immersive next step of the Internet where users participate in virtual, immersive worlds.

It’s clear that the future of the Internet will arrive sooner rather than later. According to Bloomberg, metaverse technologies will be an $800 billion market this year in 2022. It’s important that if businesses want to remain relevant over the course of that evolution, they need to stay on top of the technologies driving the development of ‘web 3’ forward.

Technologies the Metaverse Relies On

There are a number of different technologies that are powering metaverse development. Understanding each of them and recognizing where they intersect is critical to innovation.

AUGMENTED REALITY (AR) AND VIRTUAL REALITY (VR) IN THE METAVERSE

Virtual reality is one of the most prolific technologies moving the metaverse forward, but it has several limitations. Since these are emerging technologies, the world isn’t quite ready for widespread, easily accessible VR. First of all, this is due to limited mobility. In order to experience virtual reality, the user needs a special VR headset, which is quite bulky and doesn’t allow the head or body movements needed to get the full experience. Moreover, the cost of VR devices is quite high, and there are no standards for virtual reality yet, so content created for one platform may not work with another.

Augmented reality is a more accessible technology that is breaking the boundaries between the real and digital worlds. It’s available on nearly all modern smartphones, making it a viable option to support a freer and more open metaverse. Even though AR doesn’t provide a fully immersive experience, overlaying images and information over the real world may be a more effective strategy for companies this early in the game.
Andrew Makarov
Head of Mobile Development

Realizing this, in 2022, Meta introduced additions to its Spark AR platform such as voice effects, improved depth mapping, and effects blending to help developers create metaverse applications.

AR Spark’s Audio Visualizer feature for audio integration in your effects

Source: Spark AR

Features like this that allow AR effects to respond to sound can help you to create an even more engaging environment.

Another way that augmented reality can be used for more immersive experiences overlaid on top of the real world is the ‘try before you buy’ retail strategy. Through the use of virtual fitting room technology or similar technologies, shoppers can try on items virtually with AR before they decide to make a purchase. IKEA Place is a great example of how this functionality extends to furniture and interior design, allowing shoppers to place virtual furniture in their house to see how it may look before they buy it.

ARTIFICIAL INTELLIGENCE IN THE METAVERSE

Extended reality technologies like AR and VR have many limitations, but developers can overcome them with another advanced technology. This technology is artificial intelligence.

Creating lifelike 3D spaces, delivering more realistic experiences, and running complex calculations necessary for AR face tracking and other tasks are best handled by AI. Artificial intelligence can complete these tasks more efficiently than humans in far less time. Self-supervised learning will greatly increase the efficiency of AI-powered systems.

NATURAL LANGUAGE PROCESSING (NLP)

A subset of artificial intelligence that is important for the development of the metaverse is natural language processing. This is an advanced way for AI to interpret and emulate human speech. Not only will this be a great way for users and AI to interact in the form of customer service chatbots and virtual assistants, but it also can make the metaverse more accessible for diverse groups of people. For example, conversational artificial intelligence enables rich real-time language translation, even though it’s a challenging task.

In fact there is a non-monotonic relationship between the source speech and the target translations. It means that the words at the end of the speech can influence words at the beginning of the translations. So, there is no real real-time speech translation because there is always the need to check the translated text consistency against the original speech (so called re-translations). There is always a small delay even if you can’t see it. Therefore, you need advanced algorithms to stabilize the translation of live speech, as Google does in its Google Translate to reduce the number of re-translations.

Internally real-time speech translations may be organized as follows: user says something, the user’s speech turns into a text, the text is then translated into the other language. After the speech is paused/ended and the final re-translation is done, the text turns into a speech using speech-to-text technologies.

NLP also can provide live captions for users with hearing impairments. For example, AI technology can instantly transcribe the conversation of a group of people, making communication within a metaverse application accessible to users with hearing disabilities.

VIRTUAL ASSISTANT TECHNOLOGY

NLP also makes digital voice assistants and AI avatars possible that can help users with hands-free operation of their devices, as well as targeted suggestions. Meta is already developing a voice assistant that will be used in metaverse applications in coming years. Virtual assistants can perform language translation, financial management, and much more.

The representation of users in the metaverse as AI avatars or digital humans also relies on NLP. Conversational AI allows avatars to process and understand human language as well as respond to voice commands. Last year NVIDIA introduced the Omniverse Avatar avatar modeling platform. It allows you to create virtual versions of people who not only recognize speech, but also capture emotions on the faces of users.

One important role that virtual assistants can help with in the metaverse is customer service. Since shopping experiences in the metaverse will be highly immersive, conversational AI will be very useful for giving shoppers the opportunity to ask virtual customer service avatars about the characteristics of the goods, payment terms, discounts and the like.

COMPUTER VISION

Computer vision can enable machines to better create digital copies of objects, recognize images and patterns, and even recognize the expressions and moods of users.

One of the limitations of VR and AR experiences is control. Hardware controllers, gloves, or other kinds of physical devices can be used to input into the device. However, computer vision can help make this experience more natural by using hand tracking. By recognizing gestures and finger positions, users can interact with their devices more naturally and freely.

AR hand-tracking demo by MobiDev

This is one of the scenarios of how it can work. AR implementation includes the coordination of both the cell phone’s video camera and LiDAR. Video camera captures the image/video of the real world and the user’s hand. LiDAR estimates the distance between the real-world objects and the user’s hand. With that information we can correctly place some virtual objects on the phone screen, so from the user’s perspective the virtual object looks like a part of the real world.

With the help of computer vision technologies we can recognize if the user tries to interact with the virtual object with the hand. Examples of such interactions can be putting the virtual object to a cart in a virtual shop or animating objects (useful for AR-interactive games).

Computer vision in the metaverse doesn’t just stop there. ReadyPlayerMe uses face recognition to create a virtual avatar from a user’s selfie. Most video games and platforms require users to create a brand new avatar to use for each service. However, these avatars created by computer vision are designed to be used across thousands of different platforms.

HUMAN POSE ESTIMATION

Given that users interact with the metaverse in the form of digital avatars with bodies, it’s important that the posture of those characters be accounted for. Human pose estimation (HPE) uses motion sensing devices like controllers, gloves, and more to accomplish this. HPE recognizes body parts and their positions in an environment, while another practice called action recognition can identify more complex interactive activities like grabbing items or pushing buttons.

Source: MANUS

INTERNET OF THINGS (IOT) AND THE METAVERSE

Artificial intelligence is just a part of the metaverse story and it’s usually not the answer to every problem that developers face when making metaverse projects. AI needs high quality data, and that data needs to come from somewhere. Internet of Things devices and sensors are critical for providing high-quality real-time data to AI systems for analysis.

One of the most useful applications of IoT in the metaverse are digital twins. This technique utilizes IoT sensors to create a digital version of an environment or system. With VR relying heavily on virtual environments, being able to create a virtual representation of an environment using sensors is in high demand.

The metaverse is not simply a new, digital world. It is the intersection and seamless crossover between the real and digital world. Using augmented reality technologies with IoT sensors to bring the real world into the digital, and the digital into the real world will revolutionize metaverse technologies.

THE BLOCKCHAIN’S ROLE IN THE METAVERSE

As a global and decentralized system, blockchain platforms are in demand for use in metaverse projects. Centralized data storage is problematic in the metaverse because of the barriers to the flow of information. A more open solution like a blockchain can allow for a more fluid flow of information and proof of ownership for digital assets. Due to this, there is high demand for development of systems that can support cryptocurrencies and non-fungible tokens.

Non-fungible tokens or NFTs, today are the most promising way to develop the metaverse economy. Since each token is unique, it can be a reliable proof of digital ownership recorded in the blockchain. For example, users can buy in-game assets and digital real estate in the form of non-fungible tokens representing the right to own these items.

3D MODELING

With the metaverse relying heavily on virtual worlds, 3D modeling is a skill that’s in high demand. From decorating homes to creating skins for avatars, modeling is something that virtual worlds can’t do without. With such a large number of objects that need to be digitized, it’s clear why IoT sensors need to be used to create digital twins of environments. Large databases need to be made of real world objects that have been ‘3D captured’ and digitized.

However, there are challenges to digitization of the real world. The higher resolution that an object is digitized with, the greater the memory it will use. Finding space for all of these objects and rendering them on lower end hardware isn’t always possible. This is especially challenging for VR support. VR experiences have to be rendered at higher framerates to maintain immersion. However, if all the objects in a scene have very high poly counts, then performance could take a hit. Managing this is critical for providing successful metaverse experiences.

How to Start a Metaverse Project

As we continue into the wild west of the development of metaverse and web 3.0, there are countless opportunities for ideas to flourish. Whether these ideas ride the wave of disruption that these technologies bring to the table or if these ideas extend the potential of your business to reach new markets, keeping your business competitive is a must.

As an example, let’s say your team wants to build an immersive retail store based on metaverse technology. To build this, the project would need either a fully 3D VR environment and objects to serve as products. This virtual store would also need digital customer service agents to help users find what they need.

The metaverse use cases don’t stop there. It could be a virtual meeting room where you and your colleagues can interact as avatars. Or imagine how cool it would be if a designer could place decor, change the color of walls, and furniture right in the VR metaverse. Modern technology makes all this possible.

So, how to build a business application for the metaverse? The best projects start with a vision for success for a metaverse space. Ask yourself, how will the user interact with your metaverse environment? What are you looking to gain? Bring these questions to tech experts and make your dream a reality with real world technologies.

Using Explainable AI in Decision-Making Applications

Evgeniy Krasnokutsky — Fri, 17 Jun 2022 09:42:49 +0000

There is no instruction to a decision making process. However, important decisions are usually made by analyzing tons of data to find the optimal way to solve a problem. That’s where we truly rely on logic and deduction. That’s why surgeons dig into anamnesis, or businesses gather key persons to see a bigger picture before making a turn.

Relying on AI decision making can significantly reduce the time spent on research and data gathering. But, as the final decision is up to the human, we still need to understand how our support system came up with these insights. So in this article we’ll discuss AI explainability and why it’s important for such areas as healthcare, jurisprudence, or finance to justify any piece of information.

What is explainability in AI?

AI decision-support systems are used in a range of industries that base their decisions on information. This is what’s called a data-driven approach – when we try to analyze available records to extract insights, and support decision makers.

The key value here is data processing, something a regular person can’t do fast. On the flipside, neural networks can grasp enormous amounts of data to find correlations and patterns, or simply search for the required item within an array of information. This can be applied to domain data like stock market reports, insurance claims, medical analysis, or radiology imagery.

The problem is that, while AI algorithms can derive logical statements from data, it remains unclear what these determinations were based on. For instance, some computer vision systems for lung cancer detection show up to 94% accuracy in their predictions. But can a pulmonologist rely just on model prediction without knowing if it is not mistaken tumor for fluid?

This concept can be represented as a black box, as the decision process inside the algorithm is hidden and often can’t be understood even by its designer.

Block Box Concept Illustrated

AI explainability refers to techniques by which a model can interpret its findings in a way humans can understand. In other words, it’s a part of AI functionality that is responsible for explaining how the AI came up with a specific output. To interpret the logic of AI decision processes, we need to compare three factors:

Data input
Patterns found by the model
Model prediction

These components basically are the essence of AI explainability implemented into decision-making systems. The explanation in this case, can be defined as how exactly the system provides insights to the user. Depending on the implementation method, this can be presented as details on how the model derived its opinion, a decision tree path or data visualization.

AI decision-support system types

Decision-support systems can be categorized by purposes and audiences. But we can split them into two large groups: consumer and production-oriented.

Consumer-oriented AI Workflow

The first group of consumer-oriented systems unites AI decision making systems that are generally used for everyday tasks. Tools like recommendation engines don’t require explainability as it is. We don’t question the logic behind music recommendations made by neural networks. Unless it suggests complete garbage, it’s unnecessary to know how the system picked that song and brought it to you. You’ll probably just listen to it.

Production-oriented AI Workflow

Production-oriented systems, in contrast, usually deal with critical decisions that require justification. This is like how a person in charge of a final decision is responsible for any possible financial loss or harm. In this case, the AI model acts as assistant for professional activity, providing explicit information for a decision maker.

So now, let’s look at the examples of explainability in production-oriented AI systems and how it can support decision makers in healthcare, finance, social monitoring, and other industries.

Explainable AI for medical image processing and cancer screening

Computer vision techniques are actively used in the processing of medical images, such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Whole-Slide images (WSI). Typically, these are 2D images used for diagnostics, and surgical planning, or research tasks. However, CT and WSI can often depict the 3D structure of the human organism reaching enormous size.

For example, WSI of human tissues can reach 21,500 × 21,500 pixel size and even more. Efficient, fast and accurate processing of such images serves as the basis for early detection of various cancer types, such as:

Renal Cell Carcinoma
Non-small cell lung cancer
Breast cancer lymph node metastasis

The doctor’s task in this case, is to strip the information down to visually analyze the image to find suspicious patterns in the cellular structure. Based on the analysis, the doctor will provide diagnosis, or make a decision for surgical intervention.

However, since WSI is a really large image, it takes heaps of time, attention and qualification to analyze. Traditional computer vision methods will also require too much computational resources for end-to-end processing. So how can explainable AI help here?

In terms of WSI analysis, explainable AI will act as a support system that scans image sectors and highlights regions of interest with suspicious cellular structures. The machine won’t make any decision, but will speed up the process and make the work of a doctor easier. This is possible because WSI is more accurate and quicker in terms of image scanning, giving less chance to omit specific regions. Here is an example of renal clear cell carcinoma on the low resolution WSI.

Kidney renal clear cell carcinoma is predicted to be high risk (left), with the AI-predicted “risk heatmap” on the right; red patches correspond to “high-risk” and blue patches to “low-risk”.

As you can see on the image on the right, the AI support system highlights the regions of high risk where cancer cells are more likely to be. This eliminates the need to physically analyze the whole image of a kidney, providing hints for medical expertise and attention. Similar heatmaps are used for even closer and more high resolution analysis of kidney tissue.

Example of WSI kidney tissue in high resolution (left), with the AI-predicted attention heatmap on the right; Blue color indicates low cancer risk zones, red — high risk.

The advantage of such systems is not only in operational speed and greater accuracy of analysis. The image can also be manipulated in different ways to provide a fuller picture for diagnostics and to make more accurate surgical decisions if needed. You can check the live demo of kidney tissue WSI analysis to see how the interface looks and how exactly it works.

Explainable AI for text processing

Text processing is a standalone task, as well as a part of bigger workflows in different industries such jurisprudence, healthcare, finance, media, and marketing. There are plenty of techniques for deep learning that are capable of analyzing text from different angles. Here we can list natural language processing (NLP), natural language understanding (NLU), and generation (NLG). These algorithms can classify text by its tone and emotional shades. It can also detect spam, produce a short summary, or generate a large contextual text based on a small initial piece.

Besides use in document processing, machine translation, and communication technologies, there are other applications based on explainable AI.

SELF HARM OR SUICIDE PREDICTION

One of the applications of text classification was found in Meta. Facebook employees have developed an algorithm that allows you to determine users’ suicidal moods based on an analysis of the text of their posts, as well as responses to these posts of relatives and friends.

If suddenly the system gives a signal that there is a high probability that a person can harm himself, then this information is submitted to the community operations team for manual processing and verification. Explainable AI supports teams with constant scanning of media fields to find risky commentaries. After that, the human agent can use data explanations presented by the model that will point to how the machine understood if that unique person is showing the risk for suicide.

FINANCIAL NEWS MINING

Another interesting case is, for example, the use of financial news texts in conjunction with fundamental analysis to forecast stock values. A prime example of this would be “trading on Elon Musk’s tweets,” whose tweets had a dramatic effect on the value of several cryptocurrencies (Dogecoin and Bitcoin), as well as the value of his own company. The point is that if someone can impact stock prices so much through social media, it is worth knowing about these posts in advance.

AI algorithms allow you to process a huge number of news headlines and texts in the shortest possible time by:

analyzing the tonality of the text
identifying the runoff or company referred to in the article
identifying the name and position of the specialist quoted by the publication

All this allows you to quickly make decisions on a purchase or sale, based on the summary of social media information. In other words, such AI applications can support individuals and businesses in their decision by supplying information about recent events that could impact stock prices and different fluctuations in the market.

The explainability part is responsible for justifying the data by recognizing company, event, persona, or resource entities with named entity recognition technology. This allows the decision-support system user to understand the logic behind the model’s prediction.

What do we need to make the work of AI algorithms with text understandable? We need to uncover the patterns AI “saw” in the text. We want to see what words and connections between the words that the algorithm relied on, proving its conclusion about the tonality of the text.

How AI explainability works in text processing

Consider explainable AI using the example of financial news headlines of articles. Below will be several headings and the reader is invited to assess the tonality of these headings (positive, neutral, negative), as well as try to track and reflect on which words in the title you, as a reader, relied on when you assigned the title to a particular group:

Brazil’s Real and Stocks Are World’s Best as Foreigners Pile In
Analysis: U.S. Treasury market pain amplifies worry about liquidity
Policymaker Warns of Prolonged Inflation due to Political Crisis
A 10-Point Plan to Reduce the European Union’s Reliance on Russian Natural Gas
Company Continues Diversification with Renewables Market Software Firm Partnership

Now let’s compare your expectations with how these headers classify AI and see what words were important for the algorithm to make a decision.

In the pictures below, the words on which the AI algorithm relies in making a decision are highlighted. The redder the shade of the highlight, the more important the word was in making the decision. The more the shade was blue, the more the word tilted the algorithm toward making the opposite decision.

1.Brazil’s Real and Stocks Are World’s Best as Foreigners Pile In
Class: Positive

2.Analysis: U.S. Treasury market pain amplifies worry about liquidity
Class: Negative

3.Policymaker Warns of Prolonged Inflation due to Political Crisis
Class: Negative

4.A 10-Point Plan to Reduce the European Union’s Reliance on Russian Natural Gas
Class: Neutral

5.Company Continues Diversification with Renewables Market Software Firm Partnership
Class: Positive

How long did it take you to read all these headlines, analyze and make a decision? Now imagine that AI algorithms can do the same, but hundreds of times faster!

How to integrate Explainable AI features into your project?

One might think that integration of explainable AI into a project is quite a sophisticated and challenging step. Let us demystify it.

In general, from the perspective of explainability, AI systems may be classified into two classes:

Those which support explainability out-of-the-box
Those which need some third-party libraries to be applied to make them explainable

The first class of AI includes instance and semantic segmentation algorithms. AI algorithms based on attention are good examples of self-explainable algorithms we’ve shown above based on examples of WSI kidney tissue cancer risk images.

The second class of AI algorithms in general doesn’t support out-of-the-box self-explainability. But this doesn’t make them completely unexplainable. As an example of such an algorithm we’ve shown text classification above. We can use 3rd-party libraries on top of AI algorithms that make them explainable. Usually it is easy to use LIME or SHAP libraries to make your AI explainable. However, the explainability requirements may differ on the project or user objectives, so we suggest you enlist the support of an AI expert’s team to receive proper consulting and development.

AI-explainability enhances trust

One of the key factors that prevents mass adoption of AI is the lack of trust. Decision-support tools in data driven organizations already perform routine tasks of reporting and analysis. But at the end of the day, these systems are still not capable of providing a justified opinion on business critical or even life critical decisions.

Explainable AI is a step further in data-driven decision making, as it breaks the black box concept and makes the AI process more transparent, more verifiable and trusted. Depending on the implementation, a support system can provide more than just a hint, because as we track down the decision process of an AI algorithm, we can uncover new ways to solve our task, or be provided with optional types of decisions.

Featured image credits: Tara Winstead.

Optical Character Recognition Technology for Business Owners

Evgeniy Krasnokutsky — Thu, 16 Jun 2022 19:03:23 +0000

With the growing interest in OCR and Machine Learning, more and more business owners are looking for ways to apply this killing combination to optimize their business processes, and if you are one of them, this article is for you.

Let’s find out more about what OCR is, how OCR powered with machine learning is different from the original technology, and how it can be used in business.

What is OCR and How Does It Work?

Optical character recognition (OCR), also known as text recognition technology, converts any kind of image containing written text into machine-readable text data. OCR allows you to quickly and automatically digitize a document without the need for manual data entry. That’s why OCR is commonly used for business flow optimization and automation. The output of OCR is further used for electronic document editing, and compact data storage and also forms the basis for cognitive computing, machine translation and text-to-speech technologies.

There are different types of OCR depending on the tasks they solve:

Intelligent Word Recognition (IWR) is used for the recognition of unconstrained handwritten words instead of recognition of individual characters.
Intelligent Character Recognition (ICR) is a more advanced form of OCR based on updating algorithms to gather more data about variations in hand-printed characters.
Optical Word Recognition (OWR) scans typewritten text word by word.
Optical Mark Recognition (OMR) is used to identify the information that people mark on surveys, tests, etc.

Let’s find out how OCR works. The functioning of the traditional optical character recognition system consists of three stages: image pre-processing, character recognition, post-processing.

STEP 1. CHECKING THE DOCUMENT TYPE & IMAGE PRE-PROCESSING

The main challenge of text recognition is that each document template has its own set of entities, values, and location of entities in the document. For OCR software to work accurately, it must be able to identify different types of documents and run the correct predefined pipeline based on that. For example, PDF documents may or may not contain a text layer. If the PDF does not contain a text layer, we must process it differently than if it did.

After choosing the right pipeline the image comes to the pre-processing step. This is a preparation step that affects the outcomes. Image pre-processing helps to remove image noise and increase the contrast between the background and text, which will help improve text recognition. At this step, the OCR program converts the document to a black and white version and then analyzes it for the presence of light and dark areas. Light areas are identified as the background, while dark areas are identified as characters to be processed.

STEP 2. CHARACTER RECOGNITION

With the use of feature detection and pattern recognition algorithms a single character is detected. Then, a set of the characters are assembled into words and sentences. Characters are identified using pattern recognition or feature detection algorithms.

Pattern recognition is a method based on finding matches between the image text and text samples embedded in the system in various fonts and formats. This method works best with typescript and it doesn’t work well when new fonts are encountered that are not included in the system.
The feature detection algorithm makes it possible to recognize new characters by applying rules regarding character’s individual features. Such features may include the number of slanted lines, intersecting lines, or curves in the comparison symbol.

Most often, OCR programs with feature detection use classifiers based on machine learning or neural networks to process characters. Classifiers are used to compare image features with stored examples in the system and select the closest match. The feature detection algorithm is good for unusual fonts or low-quality images where the font is distorted.

STEP 3. POST-PROCESSING

Once a symbol is identified, it is converted into a code that can be used by computer systems for further processing. We should mention that the output of any OCR and OCR-related technology/algorithm has a lot of noise and false positives. It makes it difficult to use OCR’s output directly, so we have to:

Filter out noisy outputs and false positives
Combine recognized entities with their extracted meaning
Check for possible mistakes and prevent output to the user if any

Based on statistical data, the system can detect some typical OCR errors, for example, those related to the similarity of characters and words. Thus, at this stage, the system corrects flaws in order to improve the quality of the OCR output.

OCR is a Machine Learning and Computer Vision Task

Optical character recognition is one of the main computer vision tasks. Сomputer vision allows systems to see and interpret real-world objects and recognize texts separating them from complex backgrounds. Early versions of OCR had to be trained with images of each character and could only work with one font at a time. Modern machine learning algorithms make the text recognition process more advanced and provide a higher level of recognition accuracy for most fonts, regardless of input data formats.

Advances in machine learning (ML) have given a new impetus to the development of OCR, significantly increasing the number of its applications. With enough training data, the OCR machine learning algorithm now can be applied to any real-world scenario that requires identification and text transformation. For example, receipts scanning, scanning of printed text with the further conversion of it into synthetic speech, traffic sign recognition, license plate recognition, etc.

The use of modern machine learning algorithms can significantly improve the technology and expand its use cases to more complicated ones. For example, OCR with deep learning allows not only image classification, but image analysis and extraction of more complex data from different objects including hundreds of handwritten fonts or languages.

OCR Business Cases

OCR application in business has numerous scenarios. Since text recognition using machine learning provides greater accuracy than the earlier versions of optical character recognition, this allows business owners to create OCR solutions to address a wider range of business challenges. Modern OCR systems are used in security, banking, insurance, medicine, communications, retail companies, and other industries.

Use cases for OCR technology include checking test answers, real-time translations, recognizing street signs (Google Street View), searching through photos (Dropbox) and more. Optical character recognition is also widely used by security teams. This technology helps to analyze and process documents such as a driver’s license or ID for verifying a person’s identity. For each case a completely different OCR solution is used.

OCR IN FINANCIAL SERVICES

Financial transactions involve a huge amount of data entry. Manual processing of this data takes a lot of time and effort while digitization of financial documents and extracting the necessary information from them using OCR makes business processes smooth and optimized. As a result, the OCR technology improves customer onboarding and enhances the overall customer experience.

Optical character recognition uses in the banking and financial sector include the following:

Client onboarding. Whatever financial transactions you want to perform, whether it be opening an account, withdrawing cash or transferring money, you first need to authenticate to prove your identity. OCR technology provides a fully automated onboarding process consisting of scanning an identity document (e.g. ID, passport or driver’s license), extracting the necessary data using OCR (e.g. name, dates of birth, gender, photo, signature, etc.) and checking it. For example, the OCR engine can inspect in real-time whether the provided signature matches the signature on the identity document.
Scan to pay feature. Manual entry of payment details does not exclude errors and takes more time than expected. The scan to pay feature uses optical character recognition to instantly capture invoice data and automatically process it. The user only needs a smartphone camera to do this (for example, you may need to take a photo of your credit card).OCR can also act as an extra security feature when making payments. Usually, users store cardholder data in the application desiring not to enter the card number and other details every time. With OCR, all you need is to enable the OCR feature which extracts data in seconds for each new payment and then removes it.
Receipt recognition. OCR allows automating data extraction from receipts for further accounting, archiving or document analytics. You can find this feature implemented in financial assistant apps with money tracking elements for automated data entry of expenses and expense categories. Expensify is an example of such an application.
The high variability and often low quality of receipts are the main challenges for accurate receipt recognition with OCR. In such a case, the rule-based approach cannot be effective and this is where optical character recognition with deep learning comes in. The deep learning approach to OCR allows the system to learn from the received data and improve. This technology lets training a model to identify regions of interest (RoI) in an image that are highly likely to contain text, ignoring redundant data such as the background.
Loan processing. OCR and machine learning text recognition tools can speed up the processing of loan and mortgage applications by up to 70 percent. Automation of data entry makes the process of reviewing applications and approving or rejecting them much faster and more cost-effective for the company. AI algorithms can parse the required data from the application to determine if it should be approved or rejected based on the financial institution’s rules.

Use cases of OCR in finance are not limited to the above. The technology can be used for processing other financial documents like invoices, contracts, bills, financial reports, etc.

OCR IN HEALTHCARE

OСR cases in the healthcare industry are closely related to data management. According to the World Economic Forum, hospitals produce an average of 50 petabytes of data per year. This data includes medical reports, prescription forms, claims, laboratory test results, and medical records. The digitalization of medical documents and the efficient extraction of data from them is a critical aspect of the functioning of a healthcare institution.

By applying optical character recognition technology hospitals can translate papers into a digital format much faster and store them as PDF documents that can be easily searched using keywords. Electronic medical records solve one of the main problems of hospitals, the loss of medical information about patients. Also, OCR allows data to be pulled from certificates or test results and sent to hospital information management systems (HIMS) for integration into patient records thus forming a complete medical history of patients.

Pharmaceutical systems can take advantage of OCR as well. Powered with an OCR module such systems allow you to scan medical prescriptions and import them into software to check the presence of the medicine in pharmacy databases or even use it to control picking robots.

OCR technology is also used to help people with visual impairments. By scanning the text on the image, the OCR system provides the base for using text-to-speech technology. All you have to do is scan the text to get synthetic speech output. For example, the Voice Speech Scanner app uses the smartphone’s camera to capture a photo with text and then reads all of the text back. This is a new level of assistance to people with visual impairments after the technology of deep learning image captioning which provides automatic generation of textual description of an image.

OCR IN RETAIL

Retailers produce many different documents such as packing lists, invoices, purchase orders, receipts, product descriptions, and others. These are huge amounts of information, which, however, are not used properly due to the complex and time-consuming processing.

Using OCR with machine learning, retailers can experience the rapid development of internal business processes and improve the customer experience by making the most of the existing data. For example, merchants can extract valuable insights from purchase order analytics to create more effective marketing campaigns, promotions, and manage pricing better. By converting invoices and receipts into digital format and incorporating them into accounting systems, retail companies get a chance to automate their accounting processes.

Implementing OCR is a great way to handle the large workloads of retail workers. With automatic data entry and data extraction, employees are left with only manual verification to achieve optimal results.

Cases of using OCR in retail are not limited to the above. The text recognition feature can address some specific challenges of retail companies. For example, the technology can be helpful for wine merchants who offer a wide range of products. With OCR-based wine label recognition, users can take a photo of a wine label and get product information such as reviews, description, etc. to help them make the right choice.

OCR IN SECURITY AND LAW ENFORCEMENT

Almost any industry can take advantage of OCR as part of its security strategy. Using OCR powered by machine learning, companies have a chance to build advanced user authentication and verification systems. Usually, manual comparison documents with provided personal info and a selfie are used to verify the authenticity of the identifier presented by the user. The OCR model eliminates these manual efforts by scanning ID cards, passports or driver’s licenses and checking their authenticity, comparing them with the info in the database.

In this case, the OCR engine must first recognize the document type. For example, if a user chooses to authenticate with a driver’s license, the document they upload to the system must conform to that document format. Then the system should analyze and process uploaded user documents to get relevant data.

Since documents of the same type may have a different format depending on the country or state, the system must be able to find and extract the necessary data from all variations. Using deep learning algorithms helps the OCR system understand the relative positional relationship among different text blocks and combine pairs of semantically connected blocks of text to find relevant data such as name, date of birth, etc.

It is also worth mentioning that secure authentication OCR software should have features to prevent spoofing attempts when parsing documents. Anti-spoofing techniques will help the system detect fake ID scans and other fraudulent attempts.

Optical character recognition technology is also widely used for automatic number plate recognition (ANPR). This technology is very helpful for cameras that enforce traffic laws. ANPR is also used for electronic toll collection on toll roads, car park management, bus lane enforcement, and traffic management. In general, systems based on OCR assistance ensure road safety in most countries of the world.

For example, in the USA, all police departments use some form of ANPR. According to the California State Auditor’s 2020 report, the Los Angeles Police Department (LAPD) alone has amassed over 320 million license plate scans. In the UK, automatic number-plate recognition is used to record the movement of vehicles from nearly 8,000 cameras that capture millions of records daily. This data helps deter and stop crime, including organized crime groups and terrorists.

Hardware for OCR

A high-quality text recognition system is a well-coordinated work of software and hardware. The hardware required for OCR is a special scanner or just a camera on your phone. The hardware is used for taking an image of a text on a paper sheet, and the software does the rest of the work by recognizing/extracting a text out of an image. Hardware plays the role of the eyes (receptors) of software. And software plays the role of the brain that processes the eye’s information and extracts meaning from the perceived data.

Modern OCR solutions can turn a smartphone or PC camera into a full-fledged document scanner. Most current OCR applications upload images to a server for recognition and then return the recognition output to the client. Many iOS and Android app creators develop their own intelligent camera interfaces that detect document borders, correct perspective, and optimize image quality. The output of mobile OCR depends primarily on the mobile device’s camera and shooting conditions.

Out-of-the-box Solutions vs Custom OCR Development

When a business owner needs software for optical character recognition, the question arises of which solution is better to use: ready-made or customized solution. There are many options for OCR systems on the market, but it is important to understand that they are mostly focused on processing standard business processes and may not meet your specific needs. That’s why it’s so important to determine the goals and requirements of your project and then explore the options.

COMMERCIAL VS OPEN-SOURCE OCR SOLUTIONS

There are commercial and open-source OCR solutions. Commercial ones are usually provided as a service. GoogleOCR is an example of such software. If you need to quickly implement OCR functionality in your application, then GoogleOCR is a great choice. But it is worth remembering that this solution is paid and requires an Internet connection.

Open-source OCR can be integrated as separate client application cloud services. Such solutions don’t require a direct payment for the service, but involve the cost of maintaining the infrastructure for the functioning of OCR (for example, a microservice). Having a microservice also requires an Internet connection for OCR to work. However, there are also standalone optical character recognition systems that can function without the Internet. In this case, the user device must provide enough computing resources to solve the OCR task. Also, open-source OCR may have slightly lower output quality compared to commercial solutions in some specific tasks.

Customization options are another key factor in choosing OCR. Commercial solutions most often cannot be customized to the specific needs of the client, even if the datasets necessary for this are available. Open-source OCR can be tailored to specific user requirements, for example, such as handwriting recognition in a rare language.

Limitations of OCR Technology and How to Overcome Them

Although optical character recognition is a widely used technology, it has limitations, especially if we talk about classical text recognition systems. Combining OCR with computer vision and deep learning improves the accuracy of OCR in many cases, but it is important to understand that it is impossible to achieve 100% results and you will need additional software solutions to improve the outcomes.

The list of key limitations of optical character recognition technology includes the following:

The lower the quality of an image the lower the quality of the OCR output

The OCR result is very dependent on the quality of the original image, which is why the image pre-processing stage is so important. Common OCR errors include misreading letters, missing unreadable letters, or mixing text from adjacent columns. The most commonly used methods for normalizing an image include aligning and rotating the document, removing blur and applying filters, and deleting elements that are not characters (like tables, separator lines, etc.).

Complex image background

Elements such as small dots or sharp edges that make up the background can often be read as characters and distort the results of the text recognition process. That’s why the pre-processing stage for OCR should include noise removal and text field isolation. To overcome the issue of the noise presence like dots, lines, stains, etc. in the background, nowadays OCR approaches use computer vision-based algorithms trained on augmented data sets. Augmented data sets are just regular data sets with artificially added noises to teach an OCR model to tackle the noise properly.

OCR works better with printed text than with handwritten text

Handwritten fonts have hundreds of variations, which complicates the text recognition process. Plus, many options include cases of connecting letters, which are difficult for the system to separate and which lead to a distorted output. For handwriting recognition, the development team needs to train the OCR model using deep learning algorithms and advanced computer vision engines.

It’s worth noting that the more quality the dataset that is used to train the model, the faster it will improve and bring better results. In this case, it’s better to use less data, but the most relevant. Using huge datasets which do not accurately represent your particular project’s real data will not yield successful results.

Other limitations of optical character recognition technology include

Small text (font size of less than 12 points).
Form processing because it requires systems where OCR is only a small part of the mechanism.
Blurry copies. Sometimes inaccuracies can be restored from context, but when it comes to names or numbers, the context may not be enough to restore them.
Document formatting may be lost during text scanning. For example, bold, italic & underline texts are not always recognized and require subsequent formatting of the document, which is a separate task. The result of OCR always requires spell checking and reformatting for the desired layout.

Key Takeaways

Optical Character Recognition (OCR) based on AI and machine learning is a widely used technology for text recognition and digitalization of documents. Even though OCR is not yet 100% accurate, its use cases are growing with the development of deep learning and computer vision. Today, one or another type of OCR is used in retail, communications, finance, healthcare, security, tourism, and other industries.

The definition of business goals greatly influences the approaches, architecture, and tools that will be used to develop OCR software. The data should correspond to the objectives of your project and be as real as possible.

AI Application Development Guide for Business Owners

Evgeniy Krasnokutsky — Fri, 25 Mar 2022 12:38:27 +0000

To start deeply investigating the AI app development process, it’s important to first understand how these projects differ from regular app development projects. When it comes to AI, every problem requires a unique solution, even if the company developed similar projects earlier. On the one hand, there are a variety of pre-trained models and verified approaches for building AI. Also, AI is always unique as it is based on different data and business cases. Because of this, AI engineers often start the journey by diving deep into the business case and available data, exploring existing approaches and models.

Due to these aspects, AI project creation is much closer to scientific research than it is to classic software development. Let’s explore why this is and how understanding this reality can help you prepare to execute these processes and budget for your project.

AI Project Classification by the Level of Complexity

AI projects can be classified into four groups:

Straightforward projects
The typical examples include production-ready models that can be implemented with the application of public datasets and well-known technology. For example, ImageNet is suitable for projects aiming to classify images.
Well-known technology projects
In these cases, we know the appropriate technology needed for the project, but we still need to put in the effort to collect and prepare data.
Thorough research requiring projects
In principle, we can figure out how the model works and how to apply existing data, or what steps should be taken to train the model for specific tasks. Experience alone won’t allow us to make any predictions because we don’t know how the model behaves. The process of launch requires additional testing and handling of cases.
Extra effort required for production projects
Cases from this group envisage difficulties both with data and models that haven’t been sufficiently tried in practice.

Why Are AI Projects So Unpredictable?

AI project development environments can be visualized as a three-layer pyramid consisting of technologies and ready-to-use solutions.

The upper level contains ready-made products suitable for AI usage – like third-party libraries or proven company solutions. For instance, Google’s solutions for detecting cheque fraud, facial recognition and object detection serve as good examples.

The second level consists of new niches describing business challenges. We may have the appropriate model to solve the challenge, yet the technology requires a slight modification or adaptation to prove its effectiveness during the implementation. The model is supposed to be specialized for its particular use case, and this leads to the emergence of a new niche in AI usage.

Scientific research constitutes the low-level layer. Here we may find papers and new models – let us mention GPT-3 as an example. Scientific research isn’t production-ready since we don’t know which results such models will demonstrate. It’s a deep level in the AI system, though we can work in this direction.

How Does AI App Development Differ from Usual Apps?

Application development with AI isn’t fundamentally different from the non-AI application, but incorporates PoC (Proof of Concept) and a demo. Turns out the stage of BA and the UI/UX stage starts when the demo and the AI component are ready.

The first thing an app development company does when it has been tasked by a product owner to create an AI-powered application is ask about clients’ needs and data. Is AI the core of the product or an additional component? The answer to this question impacts how sophisticated the solution will be.

The clients may not need the most accurate and contemporary solution. Therefore, it’s important to find out whether the lack of the AI component is blocking the full-fledged product development and if there is any point to create the product without the AI component. After this is worked out, we can move on.

At the beginning, we can classify AI projects into two subcategories – whether an app is built from scratch or an AI component is integrated into an existing app.

Building an AI App from Scratch

So, you’ve decided to develop a new AI-feature application from scratch. Because of this, you don’t have any infrastructure to integrate the AI-app with. Here we come to the most important question: can AI feature development be handled the same way usual app features are handled, such as login/logout or send/receive messages and photos?

At first glance, AI is just a feature users can interact with. For example, AI can be used to detect if a message should be considered as spam, to recognize a smile on a face in the photo, to implement AI-based login with the help of face and voice recognition.

However, development of AI solutions is quite young and research-based. It leads to the realization that AI features of an application are the riskiest part of the whole project. Especially if the business goal requires coming up with an innovative and complex AI solution.

Let’s consider a small example. You want to build a chat app with a login/logout screen, message system, and video calls. Video calls should support Snapchat-like filters. Here is a table of risks and an overview of the complexity of different features of the app:

Looking at the table it is clear that from the point of risk minimization strategy it’s not reasonable to start the development process with the tasks that have the lowest complexity and risks.

You may ask, how come Snapchat-like filters have the most risk? Here is a simple answer: to create a snapchat-like filter you have to involve a lot of cutting edge technologies like AR and deep learning, mix them properly together and put them on mobile phones that operate with low computational resources. To do so, you have to solve a lot of extraordinary engineering tasks.

That’s why AI application development from scratch has a very specific PoC structure which will be discussed further in this article in the “PoC Development Stage” section.

Integrating AI Component into an Existing App

Integration of an AI feature into an existing project has some differences from building AI apps from scratch.

First of all, it is a common case from our experience that existing projects we have to enhance with AI were developed without any architectural consideration of AI features. Taking into account that an AI feature is a part of some of the data pipeline, we conclude that development of an AI-feature will definitely require at least some changes in the application architecture.

From the perspective of AI, existing applications may be classified as follows:

DB-based projects:

Text processing
Recommendation systems
Chatbots
Time series forecasting

Non-DB based projects:

Image / video processing
Voice / sound processing

Below, we will overview the PoC development for a new AI feature development project and integration for both DB and non-DB based projects.

Main Stages of AI App Development Process

Let’s overview how a typical AI app development process grows in five stages.

1. BUSINESS ANALYSIS STAGE

At the first stage, we obtain the client’s input or vision that could be the document with the general idea overview. Here we start the business analysis process.

To prepare input, we need to consider the business problem. Businesses address app development companies with the business problem, and it’s the job of the latter to find the intersection point of the business and the capability of AI.

For example, in the case of a restaurant or grocery chain, business owners are interested in reducing food waste and achieving a balance through the analysis of purchases and sales. For AI engineers, this task turns into time series prediction or a relational analysis task whose solution enables us to predict specific numbers. We have worked on similar projects for our clients, and this case study can be looked at as an example of machine learning-based demand forecasting module development.

2. MACHINE LEARNING PROBLEM DETERMINATION STAGE

The next stage is determination of the ML (Machine Learning) problem that should be discussed and solved. This must take into account the technological capabilities of Artificial Intelligence subfields, such as Computer Vision, Natural Language Processing, Speech recognition, Forecasting, Generative AI, and others.

3. DATA COLLECTION STAGE

Data is the fuel of machine learning. There are two main data types — specific and general. General data can be obtained from open-source data websites, so all we must do comes down to narrowing the scope of the target audience, putting emphasis on the particular region, gender, age, or other crucial factors. Plenty of general data can simplify the process.

Therefore, if the client has an app based on the fitness tracker activity, we can apply data and transfer learning to start implementation as fast as possible. The same applies for image classification where plenty of collections are available to start with.

Another option would be the lack of available data, its irreconcilability with the target audience, or the need for data generated by the particular business. For example, the sales statistics of a particular business or defect detection list of an assembly line. What can be done if the client has no data, yet intends to implement the AI component?

There are six sources to draw data from.

The first is a treasure trove of open-source data that enables lots of projects to start with public datasets.
The second source is scraping which helps to implement AI projects with text data. Web pages like Wikipedia contain textual information or specific data. For instance, a database of vacancies, without regard for AI, may be useful for recruiting departments. Such kind of data is half open-source.
The third source implies data annotation carried out manually or automatically. In the case of manual data annotation, a third-party company can be involved to collect and label pictures or other data.
The fourth source is data acquisition by means of a collection with a simpler product. A typical case point is chatbot development. If we do not have data for implementation, we ask the client a set of questions. Answers to these questions serve as the input for further development. Further examples of character recognition may also be cited: users correct errors in the text obtained as the output of the raw model, and this information allows us to improve the model.
However, specific data is the fifth source of information, and plenty of work should be done to collect it.
The last source, synthetic data, appears when we generate something like graphs, charts, or diagrams on our own. AI engineers generate graphs or diagrams with Python and use them as a dataset for starters. Subsequently, more realistic production data is being added here. Another example of a synthetic approach for data collection is the use of 3D game engines like Unity or Unreal Engine for different types of computer vision tasks and even for tasks of training self-driven cars.

The role of data in AI projects shouldn’t be diminished. How effectively an algorithm is working depends on data, so the vastness of input makes it more accurate.

There are special techniques capable of improving data quality.

4. POC DEVELOPMENT STAGE

The next step is to outline business and technical metrics that can differ significantly. A business owner may wonder whether the accuracy of the project would be sufficient. An AI engineer isn’t always ready to answer this question since it could be a new niche. PoC comes to the rescue, showing the minimal accuracy that can be obtained. Most remarkably, binary decisions are 50% accurate, just like a coin toss.

Let’s take a look at the first example in which the client needs to reduce food waste. By transforming a business problem into a technical solution, we have reached the conclusion that we need to predict purchases, so metric MAPE (Mean Absolute Percentage Error) would be optimal.

The second example is secure login for which the equal error rate (EER) is a business metric. EER corresponds to the equality of false acceptance rate and false rejection rate.

The third example is a classification problem for which the accuracy of correctly recognized entities serves as a business metric. By classifying entities we can consider text with spam, images with cats, whether the employee wears a mask, etc. Engineers are free to use the same metric or make the task a bit more complicated and apply an F1 score for illustration purposes.

The next point to be defined before starting with PoC is limitations. This is a non-functional requirement that could become clear later, during implementation. PoC must be based on one hypothesis and solve the particular task. The illustration of a security limitation is the necessity to blur everything behind the person in the background.

Once the input has been prepared, the AI team works on PoC, metrics, measurement of the results, and the demo. A report can be made in parallel with a demo, describing investigations, pitfalls, and confirming or denying the hypothesis.

Every research included in PoC is accompanied by a report: what has been found out, what could be done in the future, and what information has become clear and should be taken into account in the next iterations.

Although similar to a product, PoC isn’t actually ready for usage. It can be converted into a product if the demo suits the client. It is important to realize that for full-fledged product development in addition to AI engineers, we need other specialists — front-end, back-end, and mobile developers, to name but a few.

DEVELOPING AI POC FOR NEW PROJECTS

The PoC stage of a fresh new AI project should be AI-centric. What does this mean? To meet the risk minimization strategy, we should start with the riskiest part of the project, the AI feature, and not touch any other features of the project, if possible.

According to CRISP-DM, the PoC stage may be repeated several times to achieve suitable results.

After satisfactory results are achieved we are free to proceed to the MVP/industrialization stage with the development of all remaining features of the application.

DEVELOPING AI POC FOR EXISTING PROJECTS

To make an AI feature available for end-users we first have to develop the feature and then integrate it with the existing application. Namely, with the application codebase, architecture, and infrastructure.

The most fascinating thing about AI-features is that they can be researched, developed, and tested without touching the main application. This leads us to the idea that we can start AI-isolated PoC without risks for the main application. This is in fact the essence of risk minimization strategy.

Here are three steps to follow:

1. Collect data from the existing application by:

Making DB dump
Collecting image/video/audio samples
Labeling collected data or getting relevant data sets from open source libraries

2. Build an isolated AI environment with the use of the data collected earlier for:

Training
Testing
Profiling

3. Deploy the successfully trained AI component:

Changes in preparation for the current application architecture
Codebase adaptation for the new AI feature

Depending on the project type, adaptation of the codebase may lead to:

Changes to the database architecture for simplification and speeding up the AI module’s access to it
Changes to the microservice topology for video/audio processing
Changes to mobile application minimum system requirements

ROUGH ESTIMATION OF THE POC STAGE

Business owners often ask software vendors about the budget, timeline, and effort the PoC stage might take.

As we’ve shown above, AI-projects are characterized by a high level of unpredictability compared to the regular development process. This is due to high variability of tasks types, data sets, approaches, and technologies.

All these conditions explain why giving estimates for a hypothetical project is quite a difficult task. Still, we have shown one of the possible classifications of the AI projects above based on the project’s level of complexity.

The next table shows rough estimates for projects of different complexity levels. Please keep in mind that the estimates in the table may vary significantly depending on the project type, and data set properties. The numbers are given for a single CRISP-DM iteration.

5. NEW ITERATION AND/OR PRODUCTION STAGE

The next step after the first PoC can be a new iteration of PoC with further improvements or deployment. Creating a new PoC implies data addition, processing of cases, error analysis, etc. The number of iterations is conditional and depends on the project.

Starting Up

Any AI project is directly linked to risks. We can face risks derived from data suitability, as well as algorithmic or implementation risks. To mitigate the risks, it’s wise to start product development only when the AI-component’s accuracy meets the business’s goals and expectations.

Machine Learning Application in the Manufacturing Industry

Evgeniy Krasnokutsky — Wed, 23 Mar 2022 16:44:39 +0000

Manufacturers, to keep up with the latest changes in technology, need to explore one of the most critical elements driving factories forward into the future: machine learning. Let’s talk about the most important applications and innovations that the ML technology is providing in 2022.

Machine Learning vs AI: What’s the Difference?

Machine learning is a subfield of artificial intelligence, but not all AI technologies count as machine learning. There are various other types of AI that play a role in many industries, such as robotics, natural language processing, and computer vision. If you’re curious about how these technologies affect the manufacturing industry, check out our review below.

Basically, machine learning algorithms utilize training data to power an algorithm that allows the software to solve a problem. This data may come from real time IoT sensors on a factory floor, or it may come from other methods. Machine learning has a variety of methods such as neural networks and deep learning. Neural networks imitate biological neurons to discover patterns in a dataset to solve problems. Deep learning utilizes various layers of neural networks, where the first layer utilizes raw data input and passes processed information from one layer to the next.

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Factory in a Box

Let’s start by imagining a box with assembly robots, IoT sensors, and other automated machinery. At one end you supply the materials necessary to complete the product. At the other end, the product rolls off the assembly line. The only intervention needed for this device is routine maintenance of the equipment inside. This is the ideal future of manufacturing, and machine learning can help us understand the full picture of how this can be achieved.

Aside from the advanced robotics necessary for automated assembly to work, machine learning can help ensure the following tasks: quality assurance, NDT analysis, localizing the causes of defects, and more.

The factory in a box example can be thought of as a way of simplifying a larger factory, but in some cases it’s quite literal. Nokia is utilizing portable manufacturing sites in the form of retrofitted shipping containers with advanced automated assembly equipment. These portable containers can be used in any location necessary, allowing manufacturers to assemble products on site instead of needing to transport the products longer distances instead.

Quality Assurance

Using neural networks, high optical resolution cameras, and powerful GPUs, real time video processing combined with machine learning and computer vision can complete visual inspection tasks better than humans can. This technology ensures that the factory in a box is working correctly, and that unusable products are eliminated from the system.

In the past, machine learning’s use in video analysis has been criticized for the quality of video used. This is because from frame to frame, images can be blurry, and the inspection algorithm may be subject to more errors. However, with high quality cameras and greater graphical processing power, neural networks can more efficiently search for defects in real time without human intervention.

Machine learning can help test the created products without damaging them using various IoT sensors. An algorithm can search for patterns in the real time data that correlate with a defective version of the unit, enabling the system to flag potentially unwanted products.

Non destructive testing

Another way that we can detect defects in materials is through non-destructive testing. This involves measuring a material’s stability and integrity without causing damage. For example, an ultrasound machine can be used to detect anomalies like cracks in a material. The machine can measure data that humans can analyze to look for these outliers by hand.

However, outlier detection algorithms, object detection algorithms, and segmentation algorithms can automate this process by analyzing the data for recognizable patterns that humans may not be able to see with much greater efficiency. Machine learning is also not subject to the same degree of error that humans are prone to.

Predictive Maintenance

One of the core tenants of machine learning’s role in manufacturing is predictive maintenance. PwC reported that predictive maintenance will be one of the largest growing machine learning technologies in manufacturing, having an increase of 38% in market value from 2020 to 2025.

With unscheduled maintenance having the potential to deeply cut into a business’s bottom line, predictive maintenance can enable factories to make appropriate adjustments and corrections before machinery can experience more costly failures. We want to make sure that our factory in a box will have as much uptime with the fewest delays possible, and predictive maintenance can make that happen.

Predictive maintenance is made possible through extensive IoT sensors that record vital information about the operating conditions and status of a machine. This may include humidity, temperature, and more.

ML MODELS USED FOR PREDICTIVE MAINTENANCE

A machine learning algorithm can analyze patterns in data collected over time and reasonably predict when the machine may need maintenance. There are several approaches to achieve this goal.

Regression Models: these predict the Remaining Useful Life (RUL) of the equipment. This uses historical and static data and allows manufacturers to see how many days are left until the machine experiences a failure.
Classification Models: these models predict failures within a predefined time span.
Anomaly Detection Models: These flag devices upon detecting abnormal system behavior.

PROBLEM LOCALIZATION

Thanks to the IoT sensors powering predictive maintenance, machine learning can analyze the patterns in the data to see what parts of the machine need to be maintained to prevent a failure. If certain patterns lead to a trend of defects, it’s possible that hardware or software behaviors can be identified as causes of those defects. From here, engineers can come up with solutions to correct the system to avoid those defects in the future. This enables us to reduce the margin of error of our factory in a box scenario.

DIGITAL TWINS

Digital twins are a virtual recreation of the production process based on data from IoT sensors and real time data. They can be created as an original hypothetical representation of a system that doesn’t yet exist, or they could be a recreation of an existing system.

The digital twin is a sandbox for experimentation in which machine learning can be used to analyze patterns in a simulation to optimize the environment. This helps support quality assurance and predictive maintenance efforts as well. We can also use machine learning alongside digital twins for layout optimization. This works when planning the layout of a factory or for optimizing the existing layout.

ML MODELS FOR ENERGY CONSUMPTION FORECASTING

If we want to optimize every part of the factory, we also need to pay attention to the energy that it requires. The most common way to do this is to use sequential data measurements, which can be analyzed by data scientists with machine learning algorithms powered by autoregressive models and deep neural networks.

Autoregressive models: Great for defining trends, cyclicity, irregularity, and seasonality of power consumption. To improve accuracy, data scientists can transform raw data into features that can help specify the task for prediction algorithms.
Deep neural networks: Data scientists use these to process large datasets to find patterns of data consumption quickly. These can be trained to automatically extract features from input data without feature engineering like autoregressive models.
Neural networks for sequential data: RNN (Recurrent neural networks), LSTM (Long short-term memory)/GRU (Gated recurrent unit), Attention-based neural networks to store information of previously inputted energy usage data using internal memory.

Generative Design

We’ve used machine learning to optimize the factory’s production processes, but what about the product itself? BMW introduced the BMW iX Flow at CES 2022 with a special e-ink wrap that can allow it to change the color (or more accurately, the shade) of the car between black and white. BMW explained that “Generative design processes are implemented to ensure the segments reflect the characteristic contours of the vehicle and the resulting variations in light and shadow.”

Generative design is where machine learning is used to optimize the design of a product, whether it be an automobile, electronic device, toy, or other item. With data and a desired goal, machine learning can cycle through all possible arrangements to find the best design.

ML algorithms can be trained to optimize a design for weight, shape, durability, cost, strength and even aesthetic parameters.

Generative design process can be based on these algorithms:

Reinforcement learning
Deep learning
Genetic algorithms

Improved Supply Chain Management: Cognitive Supply Chains

Let’s step away from the factory in a box example for a bit and look at a broader picture of needs in manufacturing. Production is only one element. The supply chain roles from a manufacturing center are also being improved with machine learning technologies, such as logistics route optimization and warehouse inventory control. These make up a cognitive supply chain that continues to evolve in the manufacturing industry.

WAREHOUSE INVENTORY CONTROL

AI-powered logistics solutions use object detection models instead of barcode detection, thus replacing manual scanning. Computer vision systems can detect shortages and overstock. By identifying these patterns, managers can be made aware of actionable situations. Computers can even be left to take action automatically to optimize inventory storage.

DEMAND FORECASTING

How much should a factory produce and ship out? This is a question that can be difficult to answer. However, with access to appropriate data, machine learning algorithms can help factories understand how much they should be making without overproducing.

Artificial Intelligence in Manufacturing: Industrial AI Use Cases

Evgeniy Krasnokutsky — Wed, 23 Mar 2022 15:10:26 +0000

Among large industrial companies, 83% believe AI produces better results—but only 20% have adopted it, according to The AspenTech 2020 Industrial AI Research. Domain expertise is essential for successful adoption of artificial intelligence in the manufacturing industry. Together, they form Industrial AI, which uses machine learning algorithms in domain-specific industrial applications.

Let’s explore some of the important trends in artificial intelligence technologies in the manufacturing industry to get a clearer picture of what you can do to keep your business up to date.

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AI IS A BROAD DOMAIN

For all of the technologies that we’ll discuss that have applications in manufacturing industries, artificial intelligence is not the most accurate way to describe them. AI is a very broad subject that has many different methods and techniques that fall under its scope. Robotics, natural language processing, machine learning, computer vision, and more are all different techniques that deserve a great deal of attention all on their own.

With that said and done, let’s move on to talk about the many applications of artificial intelligence in the manufacturing industry.

THE GOAL OF AI IN MANUFACTURING

Artificial intelligence studies ways that machines can process information and make decisions without human intervention. A popular way to think about this is that the goal of AI is to mimic the way that humans think, but this isn’t necessarily the case. Although humans are much more efficient at performing certain tasks, they aren’t perfect. The best kind of AI is the kind that can think and make decisions rationally and accurately.

Probably the best example of this is that humans are not well equipped to process data and the complex patterns that appear within large datasets. However, an AI can easily sort through sensor data of a manufacturing machine and pick out outliers in the data that clearly indicate that the machine will require maintenance in the next several weeks. AI can do this in a fraction of the time that a human would spend analyzing the data.

Robotics: The Keystone of Modern Manufacturing

Many, if not most, applications of artificial intelligence involve software instead of hardware. However, robotics is primarily focused on highly specialized hardware. The manufacturing industry utilizes this technology a great deal for many different types of applications. According to Global Market Insights, Inc, the industrial robotics market is forecasted to be worth more than $80 billion by 2024. In many factories, such as Japan’s Fanuc plant, the robot to human ratio is about 14:1. This shows that it’s possible to automate a great deal of a factory to reduce product cost, protect human workers, and achieve higher efficiency.

Industrial robotics requires very precise hardware and most importantly, artificial intelligence software that can help the robot perform its tasks correctly. These machines are extremely specialized and are not in the business of making decisions. They can operate supervised by human technicians or they can be unsupervised. Since they make fewer mistakes than humans, the overall efficiency of a factory improves greatly when augmented by robotics.

When artificial intelligence is paired with industrial robotics, machines can automate tasks such as material handling, assembly, and even inspection.

ROBOTIC PROCESSING AUTOMATION

A term that often gets thrown around related to artificial intelligence and robotics is robotic processing automation. However, it’s important to note that this is not related to hardware machinery and is instead related to software.

Robotic processing automation is all about automating tasks for software, not hardware. It applies the principles of assembly line robots to software applications such as data extraction, form completion, file migration and processing, and more. Although these tasks play less overt roles in manufacturing, they still play a significant role in inventory management and other business tasks. This is even more important if the products you are producing require software installations on each unit.

Computer Vision: AI Powering Visual Inspection

Within the manufacturing industry, quality control is the most important use case for artificial intelligence. Even industrial robots can make mistakes. Although these are much more infrequent than humans, it can be costly to allow defective products to roll off the assembly line and ship to consumers. Humans can manually watch assembly lines and catch defective products, but no matter how attentive they are, some defective products will always slip through the cracks. Instead, artificial intelligence can benefit the manufacturing process by inspecting products for us.

Using hardware like cameras and IoT sensors, products can be analyzed by AI software to detect defects automatically. The computer can then make decisions on what to do with defective products automatically.

In the video below, you can learn more about MobiDev’s approach to AI-based visual inspection system development.

Natural Language Processing: Improving Issue Report Efficiency

Chatbots powered by natural language processing are an important AI trend in manufacturing that can help make factory issue reporting and help requests more efficient. This is a domain of AI that specializes in emulating natural human conversation. If workers are able to use devices to communicate and report the issues and questions they have to chatbots, artificial intelligence can help them file proficient reports more quickly in an easy to interpret format. This makes workers more accountable and reduces the load for both workers and supervisors.

WEB SCRAPING

Manufacturers can leverage NLP for better understanding of data gained with the help of a task called web scraping. AI can scan online sources for relevant industry benchmark information, as well as costs for transportation, fuel, and labor. This can help optimize the entire business’s operations.

EMOTIONAL MAPPING

Machines are far behind humans when it comes to emotional communication. It’s very difficult for a computer to understand the context of a user’s emotional inflection. However, natural language processing is improving this area through emotional mapping. This opens up a wide variety of possibilities for computers to understand the sentiments of customers and feelings of operators.

Machine Learning, Neural Networks, and Deep Learning

These three technologies are artificial intelligence techniques utilized in the manufacturing industry for many different solutions.

Machine Learning: an artificial intelligence technique where an algorithm learns from training data to make decisions and recognize patterns in collected real-world data.
Neural Networks: using ‘artificial neurons’, neural networks receive input in an input layer. That input is passed to a hidden layer that assigns weight to the input and directs this to the output layer.
Deep Learning: a method of applying machine learning where the software emulates the human brain just like a neural network, but information passes from one layer to the next for higher processing.

Machine learning is a huge trend in manufacturing, and we have an entire blog post about machine learning’s applications in the manufacturing industry that you should read if you are interested in how ML is fundamentally changing the way that manufacturing operates.

Future of AI in Manufacturing

What comes next for artificial intelligence’s role in manufacturing? There are many thoughts about this, some coming from the realm of science fiction and others as extensions of technologies that are already being utilized. The most immediate noticeable evolution will be an increased focus on data collection. Artificial intelligence technologies and techniques that are being employed in the manufacturing sector can only do so much on their own. As Industrial Internet of Things devices increase in popularity, use, and effectiveness, more data can be collected that can be used by AI platforms to improve various tasks in manufacturing.

However, as advances in AI take place over time, we may see the rise of completely automated factories, product designs made automatically with little to no human supervision, and more. However, we will never reach this point unless we continue the trend of innovation. All it takes is an idea. It could be a unification of technologies or using a technology in a new use case. Those innovations are what transform the manufacturing market landscape and help businesses stand out from the rest.

AI Virtual Assistant Technology Guide 2022

Evgeniy Krasnokutsky — Mon, 21 Mar 2022 16:01:42 +0000

They can help you get an appointment or order a pizza, find the best ticket deals and bring your attention to the fact you are spending a lot on entertainment instead of investments. We are talking about AI virtual assistants, which have already become a familiar part of our daily lives. But what technologies are under the hood of AI assistants and how can you leverage them in your business? Find all the answers in this article.

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Intelligent Virtual Assistants Market Insights

Intelligent Virtual Assistants (IVA) also known as Intelligent Personal Assistants (IPA) are AI-powered agents capable of generating personalized responses, pulling from contexts such as customer metadata, prior conversations, knowledge bases, geolocation, and other modular databases and plug-ins. The Intelligent Virtual Assistant market, experiencing rapid growth in the 2020s, is forecasted to reach USD 6.27 billion by 2026, according to Mordor Intelligence.

AI assistant technology is in many ways similar to a traditional chatbot but integrates next-generation analytics, machine learning, AR/VR and data science. While conventional chatbots can generate responses to inquiries based on Markov chains and other similar processes, their static responses pale in comparison to the dynamic insights generated by intelligent virtual assistants.

One of the best-known virtual assistants is Apple’s Siri, a consumer-facing product packaged as a personal assistant. Examples of other IVAs include Amazon’s Alexa, Microsoft’s Cortana, and Google’s Google Assistant. Siri and competitors help customers easily execute commands with voice prompts, automating tasks such as setting alarms on a smartphone, verbally reading out e-mails with text-to-speech technology, playing and searching for music, and sending text messages. The ubiquity and popularity of IVAs in consumer smartphones led to the inclusion of Intelligent personal assistant technology by car manufacturers.

The Asia Pacific region is a critical market to watch when it comes to intelligent virtual assistants, with major growth across the healthcare, technology, and financial sectors. The industry’s heavy hitters include Apple Inc., Inbenta Technologies, IBM Corporation, Avaamo Inc., and Sonos Inc.

The end-users utilizing AI assistant technology can be found in the healthcare, telecommunications, travel and hospitality, retail, and BFSI sectors. Consumer products utilizing IVAs or IPAs include smart speakers, smartphones, cars, commercial vehicles, home computers, home automation appliances, and many more.

Underlying technologies upon which IVAs and IPAs depend include Machine Learning, Cognitive Computing, Text-to-speech, Speech Recognition, Computer Vision, and AR. We will talk about them in more detail later.

Why Do Companies Create AI Assistants?

If you’re an Apple device owner, you probably can’t imagine your life without Siri. Amazon Alexa, Google Assistant, Samsung Bixby – the majority of big brands are investing in the development of AI assistants. So why do companies do this?

The main advantage of using artificial intelligence to create such solutions is that AI can efficiently and quickly process huge amounts of data, find insights and provide smart recommendations. Powered by voice and speech recognition, AI assistants make it much easier to perform many daily tasks such as adding events to your calendar, setting a reminder, or tracking monthly expenses. According to Statista, there will be over 8 billion digital voice assistants in use worldwide by 2024, roughly equal to the world’s population.

The key benefits of building virtual assistants for business include the following:

Improved customer support while cutting down on the number of calls and service requests to human agents. With AI assistants you can automate the business flow of interacting with customers. This will allow your employees to focus on more complex tasks and not waste time on requests that can be processed in an automated way.
The ease of key data collection. Customer experience data collected by traditional support calls or chats requires analysts to scrub through countless hours of phone calls and information collected and recorded by a live customer support agent. With IVAs, a customer’s queries and the associated metadata can be instantly filed away and categorized for analysis without the need for a customer support agent to take perfect notes.
Personalized user experience. AI assistants adapt to the needs of each user, providing the client with a high level of personalization. For example, IPAs can remember not only the user’s name but also their preferences. This helps to increase user engagement, as well as improve customer satisfaction and loyalty.

The ability for companies to piece together customer support and complex parts of their corporate toolchain like Lego bricks is one of the biggest appeals of intelligent virtual assistants. With some modification, a virtual assistant can plug into any database, or any resource to provide critical information and optimize workflow at every level.

Types of AI Virtual Assistants

There are several different types of AI virtual assistants: сhatbots, voice assistants, AI avatars, and domain-specific virtual assistants.

Chatbots have been a mainstay of the E-commerce sector since their inception, but modern implementations of chatbots are powered by artificial intelligence, which gives them the ability to think through customer queries rather than push the customer through a chain of static events.
Voice assistants use automatic speech recognition and natural language processing to give vocal responses to queries, such as the well-known Siri and Google Assistant products.
AI avatars are 3D models designed to look like humans, used for entertainment applications, or to give a human touch to virtual customer support interactions. Cutting-edge technology from companies like Nvidia is capable of producing nearly true-to-life human avatars in real-time.
Domain-specific virtual assistants are highly specialized implementations of AI virtual assistants designed for very specific industries, optimized for high performance in travel, finance, engineering, cybersecurity, and other demanding sectors.

Also, we can find virtual assistant technologies created for specific tasks. For example, “Avatar to Person” (ATP) technology based on artificial intelligence and 3D modeling technology allows people with disabilities to perform tasks such as “virtual face reconstruction” and “voice generation simulation” to communicate online freely.

The Technology Behind AI Assistants

Let’s say you want to create your own virtual assistant like Siri. How would you go about making it? Your first and possibly least difficult option would be to integrate Siri into your application directly. Siri, Cortana, and Google Assistant are three well-known examples of AI assistants that many developers integrate into their applications. In 2016, Apple Inc. announced SiriSDK, a development kit that allowed programmers to integrate functions of their own apps as “Tasks” that Siri could perform. SiriSDK uses “Intents” as labels for user intentions and associates Intents with custom classes and properties.

If your company doesn’t want to rely on existing AI assistant options, you’d need an expert team of AI engineers to build your own solution. Let’s dive into the key AI technologies behind intelligent virtual assistants.

SPEECH-TO-TEXT (STT) AND TEXT-TO-SPEECH (TTS)

If we’re talking about intelligent virtual assistants, they at the very least require Speech-to-text (STT) and Text-to-speech (TTS) capabilities.

Speech-to-text allows apps to convert human speech into digital signals. This is how it works. When you speak, you create a series of vibrations. Using an analog-to-digital converter (ACD) the software converts them into digital signals and extracts sounds, then segments them and matches them to existing phonemes. Phonemes are the smallest unit of a language capable of distinguishing the sound shells of different words. Based on complex mathematical models, the system compares these phonemes with individual words and phrases and creates a text version of what you said.

Text-to-speech does the opposite. This technology translates text into voice output. TTS is a computer simulation of human speech from text using machine learning. The system must go through three steps to convert text to voice. First, the system needs to convert text to words, then perform phonetic transcription and then convert transcription to speech.

Speech-to-text (STT) and Text-to-speech (TTS) are used in virtual assistant technology to ensure smooth and efficient communication between users and applications. To turn a basic voice assistant with static commands into a proper AI assistant, you also need to give the program the ability to interpret user requests with intelligent tagging and heuristics.

COMPUTER VISION (CV)

Computer vision is an AI technology that extracts meaningful information from visual inputs like digital images or videos. CV is an integral part of creating visual virtual assistants. These assistants can respond with creator-generated videos, not just sounds, which greatly enhances the user experience.

Computer vision allows the system to recognize body language which is a significant part of communication. Visual virtual assistants powered by this technology use a camera that stores data and utilizes real-time face detection to catch when someone is looking at the screen, this sends a signal to the rest of the system, which converts the user’s speech into text.

CV can also greatly increase the accuracy of speech recognition by comparing what the user has said verbally to the movement of the user’s face and mouth.

NOISE CONTROL

Noise control is another critical feature for voice assistant accuracy. While many smartphones include software-based noise control and suppression features, you can’t count on this being the case for all of your customers. To compensate for a lack of onboard noise suppression software, top-shelf Bluetooth headsets also include hardware noise suppression, but once again there are no guarantees that your AI assistant is going to be able to detect what your customers are saying in a busy train car. By integrating in-house noise control packages, you minimize the risk of misunderstanding voice queries.

SPEECH COMPRESSION

Your AI assistant will also need to at least temporarily store voice information for processing unless you’re going to fill up the customer’s hard drive locally with voice data. Speech compression is critical, but developers toe a fine line with compression. It’s possible to compress an audio file so much that substantial amounts of fidelity are lost, making it difficult or impossible to recover what was said during the processing. Compression technology is rapidly improving, but when developing your voice assistant, audio codecs and compression solutions merit a thorough investigation.

NATURAL LANGUAGE PROCESSING (NLP)

Once you have the voice data, the AI assistant needs to process and interpret the data with Natural Language Processing (NLP) and then execute the requested command. NLP simplifies the speech recognition process. While many AI kits are pre-trained on countless hours of voice samples, you’d still need enough data from customers to adjust for precision for your use cases. If your AI assistant is going to respond verbally, you’ll need speech synthesis such as Google Cloud’s top-of-the-line solution, which produces realistic and clear voices.

However, speech processing is not enough to derive a person’s actual intent and maintain a normal conversation. The request still needs to be interpreted right, and that’s when Natural Language Understanding comes into play.

NATURAL LANGUAGE UNDERSTANDING (NLU)

Natural Language Understanding (NLU) is a different approach to Natural Language Processing and is considered by most computer and data scientists to be a subtopic of NLP. While NLP methods parse, tokenize, and standardize natural language into a standardized structure for command processing, NLU interprets the natural language without standardizing it and derives meaning from queries by identifying the context. In short terms, NLP processes grammar, structure, and compensates for the user’s spelling errors while NLU examines the actual intent behind the query.

NATURAL LANGUAGE GENERATION (NLG)

Natural language generation produces natural language output. Thanks to this technology, users receive a human-like response from virtual assistants and chatbots. Models and techniques used for NLG can be different and depend on the goals of the project and development approaches. One of the simplest approaches is a template system that can be used for texts that have a predefined structure and require only a small amount of data to be filled in. This approach allows such gaps to be automatically filled in with data retrieved from a row in a spreadsheet, a record in a database table, and so on.

Another approach is dynamic NLG which does not require the developer to write code for each edge case and enables the system to react on its own. This is a more advanced type of natural language generation that relies on machine learning algorithms.

DEEP LEARNING

Chatbots that utilize text-based responses only are substantially less complicated than voice assistants. Because you don’t have to then convert speech into text for interpretation, you remove a lot of tooling from the equation when constructing a chatbot. Next-gen text generation such as GPT-3 is capable of producing not only responses to basic queries, but entire news stories from a “seed”. Deep learning makes it happen.

Virtual assistants and chatbots powered by deep learning algorithms learn from their data and human-to-human dialogue. Chatbots that utilize deep learning examine existing interactions between customers and support staff and create paired messages and responses and compensate for the user’s typos and grammatical errors.

AUGMENTED REALITY (AR)

Augmented reality allows you to overlay 3D objects in the real world for an immersive experience. AR-based mobile chatbots and AR avatars are great examples of using this technology. For example, Arcade created a mobile AR Avatar Chatbot called Miss Perkins for the Ragged School Museum of East London. This assistant serves as a guide for museum visitors and quizzes them ensuring an interactive user experience.

Another example of an intelligent AR chatbot was developed for the Vienna Museum of Technology. The creators also used mobile AR. The functionality of the chatbot includes conducting tours and answering user questions about specific display items in the text, images, videos, and audio formats.

The rise of the Metaverse and VR technology leads to the logical conclusion of virtual assistants: 3D AI avatars. Combined with artificial intelligence, AR virtual assistants become more functional, bypassing the limitations of existing AR tools. For example, deep learning allows IVAs to capture user behavior in real-time to drive neural networks that automatically train and improve virtual assistant performance.

GENERATIVE ADVERSARIAL NETWORKS (GANS)

Being algorithmic architectures that use neural networks, Generative Adversarial Networks create new instances of synthetic data. GANs consist of real image samples and generators fed into discriminators to generate a realistic 3D face for AI avatars and 3D assistants.

The technology has been utilized in many video games and other products to create true-to-life human figures. GANs can also be utilized to turn still images into full-depth 3D images. Perhaps the most advanced integration of AI avatars so far is Nvidia’s Omniverse Avatar Project Maxine, which creates a photorealistic real-time animation of a human face speaking a text-to-speech sample.

EMOTIONAL INTELLIGENCE (EI)

When it comes to AI avatars or 3D virtual assistants, it’s not so much the voice that matters, but the body language and human emotions. Emotional Intelligence powered with AI helps IPAs track the user’s non-verbal behavior in real-time when communicating and react accordingly. This will make virtual assistants more responsive thanks to Emotion AI that monitors human emotions by tracking facial expressions, body language, or speech.

At the heart of Emotion AI are computer vision and machine learning algorithms. Facial recognition technology analyzes facial expressions using a standard webcam or smartphone camera. Computer vision algorithms identify the main points of a person’s face and track their movement to interpret emotions. Next, the system determines the person’s feelings based on a combination of facial expressions by comparing the collected data with a library of template images. Solutions such as Affectiva or Kairos can measure the following emotional metrics: joy, sadness, anger, contempt, disgust, fear, and surprise.

We should also mention recognizing emotion from speech. Such software analyzes not only what humans say, but also how it was said. To do this, the system extracts paralinguistic features that help to identify changes in tone, volume, tempo in order to interpret them as human emotions.

Challenges and the Future of Virtual AI Assistant Technology

We cannot get around the issue that the adoption of virtual assistant technology is associated with certain challenges. One major obstacle to the future of AI assistant technology is laws concerning data storage and usage. Unchecked use of customer data as training data for AI implementations could easily be challenged by changing data security laws in countries across the world. Controversial data handling policies by companies like Meta (Formerly Facebook) have stoked fears of corporate overreach and privacy concerns after the events of high-profile whistleblower scandals.

Therefore, when developing an AI assistant app, take into account the requirements of privacy and data protection, such as GDPR in the EU legislation. Make sure your app is fully compliant.

In parallel with the first challenge, there is a question of security and protection from security branches. Security mechanisms such as end-to-end encryption, two-factor authentication and biometrics are some of the best features to protect AI assistant apps. In addition, an experienced team of AI engineers will help you implement custom security systems powered by machine learning algorithms.

Despite all the challenges, the future of AI assistant technology looks bright. Advances in technology are also driving the development of smarter virtual assistants. As the NLP process continues to evolve, virtual assistants will be able to perform more complex tasks. In particular, IVAa will be able to make proactive suggestions based on self-learning algorithms and be even more helpful for users.

The development of the metaverses is also closely linked in AI with virtual assistants. Intelligent avatars are the best way to provide a user’s identity in a 3D universe. Artificial intelligence is what will allow us to achieve greater realism of avatars. Based on the study of physical movements, the model learns and can, for example, accurately predict the position of the shoulders and elbows depending on where your headset and controllers are.