Automatic transcription tools can transcribe hours of videos in minutes or seconds and cost far less than manual transcription. The best part? A few of them also offer a limited free trial for you to explore. Here is the list of the top 5 free transcription tools you can try.

1. Descript

Descript is a transcription tool that allows users to transcribe audio or video files into written text. It uses advanced machine learning algorithms to transcribe the files, which means that it can produce highly accurate transcripts.

Key features:

• Simple interface

• Option to record your screen

• Allows to add speaker labels

• Can transcribe the audio in 22 languages: Spanish, German, French, Italian, Portuguese, Romanian, Malay, Turkish, Polish, Dutch, Hungarian, Czech, Swedish, Croatian, Finnish, Danish, Norwegian, Slovak, Catalan, Lithuanian, Slovenian, Latvian, (and English).

• Offers cloud sync for collaboration

• Allows a single-click removal of all filler words

• Easily generate captions or subtitles

Cons:

• Transcription is generally accurate, but it may not be perfect, and users may need to review and edit the transcript to ensure that it is completely accurate.

2. Sonix

Sonix automatically transcribes audio and video files in over 40 languages. To use Sonix for transcription, users first need to upload their audio or video file to the platform. Once the file is uploaded, Sonix's machine-learning algorithms will automatically transcribe the file into written text. This process typically takes a few minutes, depending on the length of the audio or video file.

Sonix also offers a range of integrations with other platforms and tools, such as Google Drive, Dropbox, and Slack, which can make it easier to work with transcripts and share them with others.

Key features

• Editing within audio/video files. Sonix allows users to edit transcripts directly within the audio or video file, which can help to improve the accuracy of the transcript.

• Easily remove unnecessary words.

• Ability to export highlighted phases separately

• Automatically identifies speakers and separates exchanges into different paragraphs.

• Open to adding notes or comments for collaboration

• Can build your custom dictionary with specific words and phrases

• Integration with web conferencing systems like Zoom and video editing platforms like Adobe Premiere

Cons

• Only 30 minutes of free transcription

• Consumer reviews point out that their major con is their confusing price model.

3. Trint

Trint’s AI based transcription quickly converts your audio & video files to text, making them as editable, searchable and collaborative as a doc. The platform is designed to be user-friendly, with a simple drag-and-drop interface for uploading audio or video files and features for customization, collaboration, and integration with other tools.

Key features

• Can transcribe in 30+ languages.

• Simple editing within audio/video files. Trint allows users to edit transcripts directly within the audio or video file, which can help to improve the accuracy of the transcript.

• Easy collaboration. Trint offers a range of collaboration features, including the ability to share transcripts with other users and work on them together in real time.

• Multiple audio and video file formats are supported like MP3, MP4, M4A, WMA, AAC, AVI, WAV, and MOV

• Transcriptions can be made from audio in multiple English accents, like North American, British, and Australian.

• Users can highlight the quotes and export them exclusively

• Strong focus on security. Trint places a strong emphasis on security, and it uses a range of measures to protect the privacy of its users and their data.

• Easy customization. Trint allows users to customize their transcriptions with different fonts, colors, and timestamps to make it easier to read and review.

Cons:

• Subscription is expensive

• Poor accuracy observed in complex files

4. Maestra

Maestra transcribes audio automatically and exports the file in PDF, DOCX, or TXT format, as well as various subtitle formats, including but not limited to VTT or SRT files.

Key features:

• Translate in multiple languages like English, Spanish, French, German, and 80+ languages.

• Option to create a custom dictionary. Ability to increase transcription accuracy using custom dictionaries with technical terminology.

• Integration with Grammarly. So you can auto-correct content inside the Maestra transcript editor.

• Files are completely encrypted.

• Multi-channel uploading is available. You can upload your audio files by pasting in a link to your browser or uploading from your device, Drive, Dropbox, YouTube, Instagram, or a public link of the file.

• Teams can collaborate and edit the subtitle file. Maestra's subtitle maker allows for editing and sharing the auto-generated subtitles in a collaborative environment.

Cons

• Maestra offers only up to 15 minutes of free transcription.

5. ScribeRabbit from Sentient.io

ScribeRabbit is a transcription service that converts audio and video files into written transcripts. The service is designed to be helpful for a wide range of applications, including business meetings, podcasts, webinars, and more. Users can upload their audio or video files to the ScribeRabbit website, and the service will process the files and provide a written transcript.

Key features:

• Simple drag and drop of video into the interface to finish transcribing

• Option to add subtitles to the video. So it can be easily used as a video or movie subtitle generator.

• Choose the AI model of your preference for accurate subtitling

• Easily assign editing and reviewing tasks to different team members to increase productivity

• Multiple export options are available, like SRT, .TXT, and .VTT format

• Free translation in 7 languages: English, Malay, Chinese, Tamil, Bahasa Indonesia, Japanese, Thai, Vietnamese

Cons:

• Turnaround time is relatively high for bigger files

Kickstart your transcription journey with Scriberabbit

Looking for a quick way to start your transcription journey? Start transcribing with Scriberabbit and get up to 1.5 hours of free transcription. Create your Scriberabbit account for free now.

Application programming interfaces (API) are an essential part of modern web development, allowing applications to communicate with one another and share data. In this article, we will explore the different types of Web API and API protocols, including REST, SOAP, and RPC. We also discuss the factors to consider when choosing the right type of API for your project.

Introduction to API

Application Programming Interface, generally called API, is a set of programming instructions specifying how two software programs should interact.

1.1 What makes APIs so important

API calls are requests made to an API to access specific functionality or data. They are important because they allow different software programs to communicate with each other and share data and functionality. For example, a website might use an API to access a database of user information or to send a message through a messaging service.

API calls are often made over the internet, using a protocol such as HTTP or HTTPS. These calls are a crucial component of modern software development and are utilized in various settings, such as web development, mobile applications, and data integration. They allow developers to build more complex and powerful software by leveraging the capabilities of other software systems.

1.2 Overview of different types of API calls

There are several types of APIs that can be used in different contexts.

API protocols, such as REST, SOAP, and GraphQL, provide rules and guidelines for how APIs should be designed and implemented. Each of these types of APIs can be implemented using a variety of technologies and can be exposed through different protocols depending on the needs and requirements of the application or service being developed.

Web APIs allow developers to access and manipulate data or functionality provided by a web application or service.

Industry-specific APIs are designed to meet the specific needs and requirements of a particular industry or sector, such as the healthcare industry or the finance industry.

API Protocols/Architecture

2.1 The REST API Protocol

REST, or Representational State Transfer, is a popular style of API that is based on the HTTP protocol. REST APIs use a set of standardized HTTP methods, such as GET, POST, PUT, and DELETE, to perform actions on resources. These resources can be anything from a simple data object to a complex collection of interconnected resources.

The 4 most common types of REST API calls are:

GET: A GET request is used to retrieve information from a server.

POST: A POST request is used to send data to a server for processing and is often used to create a new resource or update an existing one.

PUT: A PUT request is used to update an existing resource on a server. It is similar to a POST request, but the PUT request specifies the exact resource that it is updating.

DELETE: A DELETE request is used to delete a resource from a server.

One of the key features of REST APIs is that they are stateless, meaning that each request is independent of any previous requests. This makes them easy to scale and maintain, as the server does not need to store any information about past requests.

REST APIs are often used to create web-based APIs that can be accessed over the internet. They are commonly used in web-based applications and are a popular choice for building APIs because they are simple to use and widely supported.

2.2 SOAP

SOAP, or Simple Object Access Protocol, is another popular type of API that is used to exchange information between systems. Unlike REST APIs, SOAP APIs use a defined XML-based protocol to send messages between systems.

SOAP APIs are often used in enterprise environments where security and reliability are important. They support features such as error handling, encryption, and authentication, making them well-suited for secure data exchange.

2.3 RPC Protocol

RPC, or Remote Procedure Call, is a type of API that allows a client to invoke a function on a remote server. It is similar to a function call in a traditional programming language, except that the function is executed on a different machine.

RPC APIs are often used in distributed systems where it is necessary to invoke a function on a remote server.

Industry-specific APIs

Industry-specific APIs, also known as vertical APIs, are designed to meet the specific needs of a particular industry. These APIs are built with the specific requirements and use cases of that industry in mind, and often provide access to specialized data or functionality. For example, a healthcare industry-specific API may provide access to patient records, electronic health records, or insurance information. A retail industry-specific API may provide access to product data, inventory management, or point-of-sale systems.

Industry-specific API vs General API

Using an industry-specific API can be beneficial for a number of reasons. For one, they often provide access to data and functionality that may not be available through more general APIs. They can also be more efficient and easier to use, as they are tailored to the specific needs of the industry. However, it's important to note that industry-specific APIs may be less flexible than more general APIs, as they are designed for a specific set of use cases. They may also be less widely available, as they are focused on a specific industry rather than being more broadly applicable.

Overall, industry-specific APIs can be a valuable tool for companies looking to access specialized data or functionality within a particular industry. They can help streamline development and provide access to resources that may not be available through more general APIs.

Web APIs

Web APIs are APIs that are accessed over the internet using the HTTP protocol. These APIs can be used to retrieve data from a server or send data to a server for processing. Web APIs are often used to create web-based applications or to allow third-party developers to access and use the functionality of a company's software.

Web APIs can be either REST or SOAP APIs, depending on the style of communication used. They are a popular choice for building APIs because they are easily accessible over the internet and can be used by a wide range of clients, including web browsers, mobile devices, and desktop applications.

There are several ways to divide Web APIs into groups based on their intended level of access and scope of use. Here are a few common ways:

• Public APIs: These APIs are openly available for anyone to use. Developers often use these APIs to expose certain data or functionality to them.

• Private APIs: These APIs are only accessible to a specific group of users, such as employees of a company or members of an organization. They are used to facilitate internal communication and data exchange within an organization.

• Partner APIs: These APIs are only accessible to a specific group of partners, such as business partners or external developers who the API provider has granted access. These systems aim to facilitate the exchange of data or the integration of two or more organizations.

• Internal APIs: These are only accessible within an organization and are not exposed to external users. Organizations use them to facilitate communication and data exchange between teams and departments.

• External APIs: These APIs are exposed to external users and allow them to access certain functionality or data provided by the API provider. External developers can build applications or services on top of the API provider's platform using them.

• Open APIs: These are openly available for anyone to use, similar to public APIs. However, open APIs are typically more open and flexible in terms of how they can be used and may not have as strict terms of use or restrictions as public APIs.

Choosing the Right Type of API for Your Project

When it comes to choosing the right type of API for your project, there are several factors to consider. Here are a few key points to keep in mind:

Purpose: What do you want the API to do? Different types of APIs are designed for different purposes, such as retrieving data, modifying data, or interacting with external services. Consider the specific needs of your project and choose an API suited to those needs. For example, if you are building an e-commerce platform and need to retrieve product data from a supplier, you may want to use a REST API. Another example is Finance APIs, which help facilitate transactions, information access, and payments.

Data: What kind of data will the API be handling? Some APIs are designed to handle large volumes of data, e.g. text processing APIs, while others are better suited for smaller amounts. Consider the scale of your project and choose an API capable of handling your project's data needs. For example, if you are working with real-time data such as stock prices and need stock API, you may want to use a WebSocket API to ensure low latency.

Security: How secure does the API need to be? If you are working with sensitive data, you will need an API with strong security measures to protect that data. On the other hand, if you are working with more general data, you may not need as much security. For example, if you are working with financial data, you may want to use an API that supports secure protocols such as TLS or SSL.

Integration: How easy is it to integrate the API into your project? Some APIs are easier to integrate than others, so consider the level of complexity involved in integrating the API and choose one that is a good fit for your project. For example, if you are working with a legacy system, you may want to use an API that uses simple HTTP requests, such as a REST API.

Documentation: Is the API well-documented? Good documentation is essential for understanding how to use an API and troubleshooting any issues that may arise. Choose an API with thorough documentation to make your development process as smooth as possible.

Ultimately, the correct type of API for your project will depend on your specific needs and requirements. By considering these factors, you can choose an API that is well-suited to your project and helps you achieve your goals.

Conclusion

Overall, APIs are a powerful tool for enabling communication and integration between different software systems, and software development APIs are an essential part of the modern world.

There are several different types of APIs, including REST, SOAP, and web APIs, each with its own unique characteristics and uses. REST APIs are popular due to their simplicity and flexibility, while SOAP APIs are often used for more complex integrations due to their robustness and security features. Web APIs, such as those offered by major tech companies, allow developers to access the functionality of a platform or service through a standardized interface. Ultimately, the choice of which type of API to use will depend on the specific needs of the application and the resources it needs to access. To add AI functions to the application, you can explore AI APIs from Sentient.io. Sentient.io is a free API marketplace where you can find APIs for your needs and place APIs of your own.

Have you ever wondered how your recent marketing campaign affected the footfall in your retail store?

Do you go to busy airports and wonder why there isn't staff to prevent this near stampede?

Do you want to analyze the customer behavior in your store?

Then gone are the days when a staff has to sit at the door, count people, and assess if things are going well. Technology has advanced enough to bring an automated people counting system for you.

What is a People Counting System?

A people counting system is an electronic device used to measure the number of people traversing a particular passage or entrance.

Three common types of people counting systems are:

1. Infrared-based counting

Infrared-based counting uses infrared sensors to detect the presence of people in a particular space. The sensors transmit a beam of infrared light, which is reflected back by the body heat of any people in the area.

1. Thermal counting

Thermal counting is a type of people counting where systems detect heat from people’s bodies and count the number of people. These systems can provide real-time information on the number of people in a given area, allowing for better crowd management and safety.

1. Computer vision-based counting

Computer vision-based counting uses advanced algorithms and machine learning techniques to analyze images/video streams to identify and count people. This method is highly accurate and can provide detailed information about the movements and behavior of people in a space.

Three Main advantages of People Counting AI

1. Enhance safety

AI-powered people counting systems can enhance safety in potentially crowded premises. These systems can provide highly accurate counts of the number of people in a given area, allowing managers and security personnel to keep track of the size of a crowd and take appropriate action if necessary. It can be crucial in emergencies, where a sudden influx of people could cause a potential safety hazard. An important point to note is these systems work in real-time. So the alerts are always timely.

2. Improve customer experience

People counting using AI solutions can improve customer experience in several ways:

• Firstly, by providing accurate and real-time information on the number of people in a given area or at any point of time. These will help management to cater to customers effectively.

• Secondly, AI-powered people counting systems can be used to monitor customer flow and traffic patterns, allowing managers to identify bottlenecks and optimize the layout of their facilities to improve the customer experience. This can help reduce congestion and make it easier for customers to navigate the space and find what they are looking for.

• Lastly, AI-powered systems can provide data and analytics on customer behavior and preferences, allowing managers to make informed decisions about product placement, promotions, and other factors that can impact the customer experience.

3. Optimize staff scheduling

By having the data history of people expected in any given area, managers can anticipate customer demand and adjust staffing levels accordingly, ensuring enough staff members are on hand to provide timely and efficient service. Also, real-time monitoring of people counting can help to adjust staffing levels in peak hours or a sudden increase in footfall.

Challenges of people counting

There are several ways to implement a people-counting system. We discussed three of these above. So when it comes to the challenges of implementing a people-counting system, it comes down to choosing the right method. That would mean understanding the suitable environment, cost, and your requirements. Let us break it down for you.

1. Environment

You have to first understand your environment to choose an appropriate method. Computer-vision based method is the most accurate, but it does work in areas that have appropriate light. On the other hand, infrared and thermal detectors can comparatively cover areas that have less light, but they would cover smaller areas at a time. So, it's really important to understand the environment you are stepping in.

1. Cost

Computer-vision systems are more expensive than infrared and thermal people counters. So you need to also check into your budget before deciding anything.

1. Requirement

Computer vision can give you a sense of demographics as well. Was a boy detected or an old woman? You can further classify data to understand your customer base. On the other hand, infrared and thermal technology only confirms if it detected a human as those two technologies work on body heat. So, it's crucial to understand your requirements as well. Do you need only count or what kind of details or demographics do you need?

Four Popular business use cases of people counting

1. Improve visitor experience in the retail sector

Retail businesses can optimize staff schedules and marketing strategies to improve the experience and, eventually, profits with actionable data. Product placement also plays a key role in conversion. Analyzing customer behavior and accordingly tweaking the product placement will improve conversions.

2. Crowd management at airports

Crowd management at airports can be improved using people counting systems. These systems can provide real-time information on the number of people in a given area, allowing managers to anticipate and respond to potential safety hazards, such as overcrowding or unauthorized individuals. This can help prevent accidents and ensure the safety of all individuals present.

3. Conduct visitor analytics

People counting systems can provide data and analytics on the demographics of visitors, such as their age, gender, and other characteristics. This can help managers understand the visitor base and tailor their operations

4. Optimize marketing strategy

People counting systems can be used to track the effectiveness of marketing and promotional efforts by providing data on the number of visitors who have responded to a given promotion or advertisement. This can help managers understand the impact of their marketing efforts and make informed decisions about future promotions and campaigns.

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You don't have to reinvent the wheel or build from scratch. Instead, use our microservices to implement people counting. With our computer vision technique, the most accurate one, you can accelerate your app development, focus on critical features and leave people counting on us. Check out the gallery of demo AI apps built using our microservices now, no credit card required.

Introduction

The technology of Automatic Speech Recognition (ASR) allows computers to understand and interpret spoken language. It is a type of artificial intelligence (AI) in which computers can transcribe, analyze, and comprehend spoken language in a variety of contexts.

In the last decade, the field of automatic speech recognition has seen a number of significant advances fueled by advances in signal processing, algorithms, computational architectures, and hardware.

Speech recognition becomes easier as the number of distinct words to recognize decreases. So tasks with a two-word vocabulary, such as yes/no detection, or an eleven-word vocabulary, such as recognizing sequences of digits in what I know as the digits task, are relatively simple. On the other hand, tasks with large vocabularies, such as transcribing human-human telephone conversations or transcribing broadcast news, are much more difficult.

The primary goal of the speech recognition field is to develop Speech-to-machine input systems. The act of speaking is the primary means of human-to-human communication. Since the mid-2000s, most ASR has been based on traditional machine learning techniques like Hidden Markov Models. While these traditional models were once the industry standard, their accuracy has plateaued in recent years, paving the way for more advanced strategies backed by Deep Learning technology, leading to advancements in self-driving cars and other industries.

Modern Automatic Speech Recognition (ASR) Architecture

A modern ASR system has four processes: (1) Feature Extraction, (2) Acoustic Models, (3)Language Models, and (4) Decoder. The acoustic model, decoder, and language model all work together to recognize an unknown audio word or sentence.

Feature Extraction

In order to extract the features, a series of preprocessing phases must be completed first. This step is called pre-emphasis and is achieved by passing the signal through an FIR filter.

The feature extraction process is where feature vectors are first extracted. Here, features are created using unstructured, raw data to define features that could be applied to statistical analysis.

Feature extraction methods typically produce a multidimensional feature vector for each speech signal. To parametrically represent the speech signal for the recognition process, a variety of options are available, including perceptual linear prediction (PLP), linear prediction coding (LPC), and Mel-frequency cepstrum coefficients (MFCC).

The most popular MFCC computation technique emulates the human hearing system by artificially implementing its working principle. It is based on signal disintegration using a filter bank and provides a discrete cosine transform (DCT) of a real logarithm of the short-term energy shown on the Mel frequency scale.

Acoustic Models

In modern SOTA automatic speech recognition systems (ASR), acoustic modeling plays an important role. Acoustic modeling's goal is to create robust statistical models for the acoustic properties of speech signals.

One fundamental issue in developing an acoustic model is selecting basic modeling units. When the target language of the speech is specified, several types of sub-word units can be used for acoustic modeling. Different acoustic modeling units can significantly enhance the performance of speech recognition systems.

Acoustic models contain statistical representations of each of the unique sounds that make up a word. By using an extensive database of speech known as a speech corpus and specialized training algorithms, statistical representations for each phoneme in a language are created. Each of these statistical representations is given a label called a phoneme acoustic model.

Several types of acoustic models are used in automatic speech recognition (ASR) systems. Some of the most common ones include:

• Gaussian mixture model (GMM): This type of acoustic model uses a mixture of Gaussian distributions to model the distribution of speech sounds within a particular context. In speech recognition, GMMs are often used to model different speakers' voices and the various sounds that make up a language. It can then be used to classify speech sounds and recognize words and phrases.

• Hidden Markov model (HMM): This type of acoustic model uses a sequence of hidden states to model the temporal dynamics of speech sounds.

• Deep neural network (DNN): This type of acoustic model uses a deep neural network architecture to learn the complex relationships between speech sounds and the corresponding text transcriptions.

One of the key developments in the field of automatic speech recognition (ASR) in the 2000s is the use of hybrid HMM/ANN (artificial neural network) systems. In a paper published in 2006, researchers describe a hybrid HMM/ANN system that achieved significant improvements in ASR accuracy over traditional HMM-based systems. The key advantage of using a hybrid HMM/ANN system is that it can incorporate the strengths of both HMM and ANN models, resulting in more robust and accurate speech recognition.

Language Models

The language model is trained on a large corpus of text and learns the probabilities of different sequences of words or sub-word units occurring in natural language.

The language model is an important component because it assists the ASR system in distinguishing between different words or sub-word units with similar acoustic properties. For example, the words "to" and "too" may be pronounced similarly, but the language model can use context to determine which is more likely to be correct in a given sentence.

N-gram models, recurrent neural networks (RNNs), and transformers are all types of language models that can be used in ASR systems. The choice of which language model to use can depend on the quality and quantity of the training data, the particular application, and the available computational resources.

Decoding

Now that we've looked at Acoustic Modeling and Language Modeling, the next logical step is to figure out how to combine them. The Decoding Process entails running each of the models and combining the results into a list of hypotheses.

During the decoding process, individual frames of audio input are divided into individual segments and then analyzed to determine their spectral content. The decoder then used this information to determine the most likely sequence of words or phonemes that comprise the spoken input. To find the closest matching words or phonemes, the input is usually compared to a database of pre-recorded speech samples. Additionally, the decoder may use context information to distinguish between possible input interpretations.

Applications of ASR technology

Some of the key applications of automatic speech recognition (ASR) technology include:

Voice Assistants - ASR technology is used in virtual assistants like Apple's Siri and Amazon's Alexa, which enable users to command their devices and access information using voice commands.

Language translation - Speak-to-text ASR technology converts spoken language into text, which can then be translated into a different language using machine translation technology.

Other applications - ASR technology has a wide range of other applications, including voice-controlled smart home systems, real-time captioning for individuals with hearing impairments, and voice-controlled navigation systems for vehicles.

Conclusion: The Future of ASR

Recent advances in deep learning have led to significant improvements in the performance of ASR systems. However, training these models requires a large amount of data and computational resources, and fine-tuning them for specific applications can be difficult.

Aside from the technical challenges, there are several important ethical and social issues to consider when developing ASR systems. ASR systems, for example, can be biased if the training data is not representative of the population, resulting in unfair and inaccurate results. There are also privacy and security concerns, as ASR systems have the potential to monitor and record people's private conversations.

Overall, ASR technology has the potential to transform our interactions with computers and is continuing to improve in accuracy and performance. However, it is critical to maintain and improve these systems in a responsible and ethical manner.

With Sentient.io you can run demo app which transcribes conversational English in real time, or you can contact us for a customized solution.

One of the most challenging aspects of understanding a string of text is understanding the context in which it is used. Consider homonymous words. They have the same spelling and pronunciation but have different meanings depending on the context in which it is used. Similarly, there are many challenges in identifying text and classifying them. Named entity recognition (NER) helps to solve such challenges.

Named entity recognition

Natural language processing (NLP) is a category of artificial intelligence that deals with understanding speech and text. Named entity recognition (NER) is a subcategory of NLP. NER deals with identifying the context of words and classifying words appropriately. An example would make the role of NER clearer.

Consider there is the word ‘Tesla’ in a document. What does the word stand for? It could be the name of the electric car maker Tesla. It could be the name of a prominent inventor, Nikola Tesla. It could also represent things like the Tesla coil or Tesla valve. The word ‘Tesla’ in the document may refer to any of these. To know which one of the different entities the word is referring to, you need the context in which it is used. NER helps to identify and classify the word appropriately.

The use of NER is not just limited to the use of homonymous words. It can be used to classify words in a document according to predetermined classification buckets. Consider the following sentence.

“Apple share price surged 5% in 2022.”

Running the text through NER will identify and classify the following things:

• Apple - Company

• 5% - Statistic

• 2022 - Time

The categories are predetermined. The NER program will have ‘Company’, ‘Statistic’, and ‘Time’ as predetermined categories. The NER algorithm identifies the different entities and classifies them into these predetermined categories. Some examples of the types of entities we can extract with NER are people (John Smith, Joe Bloggs), locations (New York, London, Paris), organizations (Ford, IBM, Facebook), dates (January 1st, 2019), and so on. It's important to note that the general concept of NER is not limited to text and can be applied to other types of data, such as audio or images. For instance, in images, we can have entities like people's faces or car plates which can be extracted by computer vision techniques.

NER is usually considered the first step in information extraction since it allows the system to start building an internal representation of the relevant pieces of information for a specific application.

Use cases of NER

Named entity recognition has a wide range of applications across different fields. Some of the popular use cases of NER are discussed in the following sections.

Named entity recognition in the context of customer service

If you have customer support chat logs and would like to know what the text is about, for example, if someone said, “I want to cancel my order,” or “I need help with my payment,” or even “This product doesn't work!”, NER can be used as a first-pass tool to identify the entities in these sentences so that you can

Another example of a microservice application could be a mobile phone company using NER to identify the customer's name, address, phone number, and other information. This can help resolve customer issues faster by allowing agents who receive customer text messages to quickly access their account details without manually searching through multiple database pages.

Named entity recognition in the context of product categorization

Product categorization is the process of assigning a product to a category. In e-commerce and retail, this task is essential for providing users with relevant information about products they're interested in buying. For example, if you're shopping for new shoes on Zappos and click on a pair of sneakers, the website will likely show you other products that are similar to what you've chosen (e.g., other basketball shoes). The more accurate the classification algorithm is at grouping items together into categories, the easier it will be for users to find what they want.

There are two ways to accomplish product categorization: named entity recognition (NER) and machine learning. While NER can be used as part of an ML pipeline due to its accuracy and speed, there are still situations where it's appropriate—for example, when trying to collect training data or when working with large datasets that would require too much memory if using ML techniques instead

Named entity recognition in the context of sentiment analysis

Named entity recognition is a process that automatically identifies and extracts subjective information in the text. This can be used to detect the sentiment of a piece of text or to categorize the tone of a piece of text as positive, negative, or neutral.

Named entity recognition has become an increasingly important tool for businesses that want to use social media data for marketing purposes but also need it to be as accurate as possible. A good example would be an airline company receiving customer reviews about its products on Twitter. They would need this data in order to improve their product offerings over time; however, if there were any false statements among all those tweets (such as “the seats were uncomfortable”), then they would not be able to take action based on those false statements since they don't know what exactly needs improving until they see what people are saying positively or negatively about them (i.e., when reading through these tweets).

Best practices for NER

Use a standard vocabulary

The first step to effective NER is to use standard and consistent terminology. If you're working in English, there are several vocabularies that you can use:

• Penn Treebank (PTB)

• MARC standards

Use a standard format for your documents or input text
You should also ensure that your documents or input text are formatted in the same way as each other so that they can be compared easily by your NER toolkit when determining which words match the entity tags for known concepts from the standard vocabulary used above. For example, using “header1” instead of “HeaDer 1” might cause problems because it would not match correctly with "Heading 2" when searching through your document metadata sent over from another system like Wordpress CMS software platform.

Wrapping up…

Named Entity Recognition might become a lifesaver for several business needs across different industries, therefore its adoption and implementation might be worth considering if you are looking for ways to optimize and automate your procedures related to data processing.

With Sentient.io, implementation is just that easy: try the free demo version (no credit card required!) or contact us for a more tailor-made solution.

Object detection is a part of this AI-enabled computer vision. In this article, we will look at what object detection with deep learning is and explore business use cases related to it. Also, if you hold on till the end, you will find an easy way to implement it.

What is Object Detection

Object detection is a computer vision technique for identifying and locating objects within an image or video. Unlike image recognition, where a machine can only identify objects, object detection with deep learning takes it one step ahead by the ability to identify and locate the object. It’s a more precise way to classify objects in an image, even partially hidden or complex.

It all became possible due to deep learning. Deep learning is a subset of machine learning inspired by brain function. In this practice, models are trained using data to develop an understanding similar to the human brain and perform certain human-like tasks, such as speech recognition, object detection, predictions, etc.

9 Business Use Cases of Object Detection

Let’s see how businesses are taking advantage of this technique.

1. Contactless checkout

Imagine the huge queues at a store in rush hours, the inconvenience to customers, and the chances of manual error. You can cut back all of it by contactless checkout, where cameras in a store can automatically scan the products you picked up and send you an electronic bill.

For example: Nourish & Bloom, a Georgia grocery startup, uses contactless checkout to stand out. Customers can check into the store, pick up what they want, and receive an e-receipt when they leave.

1. Visual Listing for Brands

Listing any product is a lot of effort both in an online and offline store. First, a person has to identify the product, make an entry and then put it into the right categories. For instance, when you look for a blanket on Amazon, it's actually under Home & kitchen -> Bedding -> Blanket & Throws. However, this effort can be automated when a seller can scan a product, and it automatically gets listed under the right categories.

Example: Airbnb uses an object detection technique to find the amenities available on the property when a host uploads a photo. That's a great way to cross-verify that there are no false promises. No wonder Airbnb is so popular among tourists.

1. Managing Inventory

Inventory/warehousing is another management task brands often struggle with. How about a machine tracking inventory and sending you alerts when a product is low in quantity? If that sounds exciting, object detection is your technique.

Example: Auchan Retail, one of Europe’s largest grocery retailers, uses computer vision to monitor inventory levels. A robot moves around the stores up to three times daily to capture photos of every shelf and aisle. These images are further analyzed using object detection and developed into reports. Store staff gets these reports on their mobile, which helps them to resolve shelf issues and prioritize orders.

1. Fraud detection

Businesses dealing with sensitive data/products can use object detection for the biometric identification of employees. Object detection is also excellent for detecting suspicious activity. Using an AI-enabled CCTV camera, you can understand what's happening in your property/store, and it can send an alert when it finds something fishy.

Example: Omnilert, a Virginia-based company, used AI-powered video monitoring to send alerts when it detects handguns and rifles. An alert that can save not only your property but also lives.

1. Anomaly and Defect Detection

Maintaining product quality and accuracy is one hell of a task for manufacturing industries. Object detection can reduce manual monitoring by constantly monitoring production and flagging products that don't meet quality benchmarks.

Example: Our client, Daido Steel, a Japanese Steel Manufacturer, used our object Detection services to monitor the quality of steel rods. This has automated their quality control process and cut back on human examination of rods.

1. Customer Experience

Eventually, every business is trying to win customers, and object detection can give you innovative ways to create a lasting impression. For instance, you can analyze customer activity in your store and improve your process. Or do something out of the box using object detection.

Example: Sephora, the leader in global beauty retail, launched a virtual try-on app that can scan customers' skin and recommend relevant beauty products and compatible shades.

1. Self-driving cars

Now, coming to more specific use cases. One of the common uses of object detection is in self-driving cars. These cars can detect the objects around them and decide whether to apply brakes or turn. While self-driving cars are not fully automatic yet, it's definitely where the future is heading.

Example: Tesla, an American automotive company, uses object detection to train cars. "Obviously, humans drive around with vision, so our neural net is able to process visual input to understand the depth and velocity of objects around us. But the big question is can synthetic neural networks do the same. And I think the answer to us internally, in the last few months that we've worked on this, is an unequivocal yes." Andrej Karpathy, Former Director of AI at Tesla, said.

1. Medical Image Analysis

The healthcare industry is constantly looking for expansion as per the growing needs, and one of the areas where manual intervention can be reduced is analyzing reports. Reports can be verified using object detection for possible health issues.

Example: Healtech software solutions used AI-driven image analysis to reduce the workload of their radiologists. The technology can interpret medical images like ultrasound scans, CT scans, MRIs, X-rays, etc.

1. Robotics

While we listed the eight major use cases, this point highlights that the opportunities are truly endless. You can create a robot for any loophole you find in systems, and using object detection will make the robot more capable.

Example: Alibaba uses robots to manage their warehouse and also deliver products. As of March 2022, Alibaba's driverless robots made 10 million deliveries.

How to implement Object detection

If you want to scale your business using object detection, then you are in the right place. You can simply use our pertained model for object detection by integrating our API in your code. And it doesn't stop there. Having a pay-per-API-call model, you can easily scale up or down anytime. Sign up to take a free demo now.

As per a McKinsey survey, 56% of businesses have already adopted AI. And the most common use case was product and service development.

It's not surprising, though, if you think about the current consumer base.

Customers are changing fast. A 2022 Accenture survey of 25,000 global participants shows 60% of consumers are changing their priorities. For these changing customers, AI is helping to:

• Keep up with fast-changing than ever requirements

• Automate repetitive tasks and deliver results faster

• Provide top-notch customer service

These are the top changes AI software development brings at a high level. In this post, we will further see the use of AI in software development at the ground level across different SDLC(Software Development Life Cycle) phases.

1. Improve requirement gathering

A project with incorrect or not enough initial data is doomed right from the start. AI helps businesses to take a more data-driven approach by listening to customers. One way of doing it is to analyze the brand's call center data, reviews, and social media conversations using AI, then find customers' key pain points and address them head-on.

Steve Jobs, Co-Founder of Apple, once said, "You’ve got to start with the customer experience and work backwards to the technology…I’ve made this mistake probably more than anybody else in this room…As we have tried to come up with a strategy and a vision for Apple, it started with ‘What incredible benefits can we give to the customer? Where can we take the customer?’...I think that’s the right path to take." Apple continuously uses customer feedback to bring enhancements.

2. Increase in plug-and-play development

Developers now don't have to build everything from scratch. AI code assistants enable developers to write code faster by suggesting code. Also, many AI microservices have been launched. Microservices are small modules or functions which you can quickly plug into your code. This is one of the biggest benefits of AI in software development. You don't have to reinvent the wheel by spending hours on what's already available.

Sentient, the AI development company, provides microservices for:

• Text-to-speech

• Automated Speech Recognition

• Object Detection

• Named Entity Recognition

• ...and many more

3. Automate testing

According to the Tech Beacon World Quality Report, 88% of enterprises are considering using AI for testing, and 80% are already in the trial or proof of concept phase. Manual testing is a laborious task whereas AI in testing provides benefits like:

• Automation of repetitive test cases

• Better test coverage

• Saving time

• Getting advanced features like visual testing, where AI can recognize patterns on the web page and run tests accordingly

And guess the best part? You don't have to write these AI scripts from scratch. Testing tools powered by AI are already available like:

• Applitools

• Functionize

• Testim

• TestCraft

• Mabl

4. Optimize DevOps

Code deployment and regular maintenance are also other big tasks post testing. DevOps team can use AI to do post-prod support efficiently by:

• Timely alerts

• Forecast failures

• Better resource management

• Detecting bugs

• Auto-suggesting code for bugs

• Fastening deployments

• Auto-running test cases

For instance, Facebook built a tool called Getafix which can automatically find fixes for bugs and engineers only need to approve it. It allows engineers to work more effectively and promotes better overall code quality.

5. Be future-ready

AI helps the development team to be future-ready in three ways:

1. AI automates repetitive tasks so developers can focus on solving bigger and more interesting problems for the future.

2. Advanced AI services/tools are easily available. So, it's easier to do a proof of concept and promote your tech stack with time rather than spending years building from scratch.

3. AI can help you understand user behavior and come up with future product changes.

For example, the automaker Tesla has a loyal customer base. And they worked hard smart to earn that loyalty. Its algorithms process data from its fleet of over millions of cars in real time and pass on the findings to multiple product development teams. Those data-driven insights enable the teams to develop new versions at speed. AI helps Tesla in continuous improvement not based on guesses but on data.

While there are many other advantages of AI development, these are the top five ways we see AI can turn around software development.

Shorten your development life cycle with Sentient

Jeremy Morgan, Senior Developer Advocate at Pluralsight, says, "We are not paid to write code. We are paid to solve problems." So make use of all the tools/microservices available, reduce development time, and solve problems faster.

Sentient can help you with:

• Object Detection

• People Counting

• People Recognition

• Natural Language Processing

• Word-sense Disambiguation

• Entity Recognition

• Voice AI

• Automatic Speech Recognition

• Text to Speech

• ... and more

Take a demo now and save development time.

Large businesses have been using artificial intelligence for many years with positive business outcomes. Businesses that have been forerunners in AI adoption have large amounts of capital at their disposal and were able to reap the benefits of the same. Small businesses with limited resources were not able to utilize AI technology to implement such technologies due to the technological barriers to entry and the high capital required.

Small businesses should consider AI

Implementing artificial intelligence for businesses used to require dedicated infrastructure and development expertise. This was the reason AI adoption was limited to large companies. In the past few years, remarkable changes have happened in AI technology that make it viable for even smaller businesses.

The cost of the technology has come down rapidly, both for the infrastructure and software. With cloud technology, you need not invest in infrastructure and pay only for the services you need and only for the time you use those services. This means the cost to implement AI applications has significantly come down.

Many solution providers have come up with AI solutions that do not require great developer talent to implement. There are also plug-and-play AI platforms that have an intuitive user interface, while the AI heavy lifting happens in the background. The benefits of using AI far outweigh the cost of implementation. Businesses, large or small, that do not use AI platforms are bound to be crushed by competition in the long run.

AI applications for small business

Small businesses can implement various AI platforms to solve their business needs at a very low cost. This helps them to save considerable money that you might have to spend otherwise. The reduction in operational expenses comes in the form of less reliance on employees and reduced error rates. The improvement in productivity also adds to the bottom line of small businesses.

AI platforms are capable of taking over or assisting business functions. This enables small businesses to deploy less employees and resources for the same. AI platforms enable you to get more work done at a lower cost. The dramatic reduction in cost with the introduction of AI solutions is the main reason you should consider those for your small business. Some of the common AI applications that small businesses can adopt are discussed in the following sections.

Customer service

Customer service is critical to improving customer satisfaction and retaining users. But customer service generally requires multiple employees and other associated overheads to operate. AI platforms significantly help small businesses replace customer service professionals to deliver a better user experience.

AI chatbots are one of the common solutions implemented for helping customers. These chatbots understand what customers type in the chatbox, identify the problem, and recommend various solutions. This automated process, once set up, does not need any employees to operate. There might be rare use cases when customer tickets need to be escalated to human employees. AI makes such instances rare. Beyond chatbots, small businesses can also use phone-based support with automatic speech recognition to improve customer experience.

Recruiting

Hiring employees is a challenging task and small businesses tend to spend an inordinate amount of time sorting applications and choosing prospects. AI can help to automate a majority of the recruiting process and significantly reduce the effort small businesses have to put in that respect.

AI platforms for automating recruitment have the ability to read resumes and applications from candidates, e.g. using Named Entity Recognition. This can be compared to the requirements you have set for shortlisting candidates. The responses and communication to candidates can also be automated to reduce the workload for small businesses.

Finance

Maintaining the books for a business is critical to managing the operations and for regulatory purposes. You generally need skilled accountants to manage books and make financial decisions for your business. Such skilled professionals cost a lot of money that small businesses find burdensome. AI applications can be used to manage the finances of your business to reduce the reliance on finance professionals.

AI bookkeeping tools can automatically connect to your billing software and bank account statements to maintain records automatically. Financial reporting and other regulatory requirements can also be automated. AI applications can also delve deep into the finances of your company to identify inefficient allocations of capital you might not have noticed. This helps to streamline the finance department and improve the bottom line of small businesses.

Logistics

Logistics is one of the complex aspects of business with many stakeholders and many moving parts. If the supply chain function of your business is in disarray, the whole business can fall into chaos. AI platforms can automate many parts of the supply chain operations for a small business. AI can automatically track supplies and deliveries without any human interference. Automatic replenishment of inventory is another supply chain task that AI applications can handle. Product delivery to customers can also be tracked, automated, and improved with the help of AI solutions.

Case study

The implementation of artificial intelligence by Sentient.io for a Singapore-based media company is an example of how small businesses can implement AI applications to improve their business and profitability. The media company uses microservices from Sentient.io AI and data platform to automate the process of creating video subtitles. Manual transcription to create subtitles takes at least 30 minutes to complete. The AI transcription can complete the same process in less than 5 minutes. This has drastically reduced the reliance on employees to create subtitles and the associated costs.

The AI solution implemented by Sentient.io relies on the following technologies for transcription:

• Speech activity detection

• Text fusion

• Automatic speech recognition

• Facial recognition

• Time encoding

With these technologies, accurate subtitles can be generated in a few minutes. This lets the people at the media company devote more time to content creation and less time to mundane tasks like subtitling. The man hours and money saved can be allocated to better endeavors with a higher return on investment. The productivity boost from this helps the company have sufficient resources to expand and improve its operations.

The entry barriers to using AI platforms have come down in terms of cost and access to technology. Small businesses should not shy away from exploring the use of AI for their business. You can have a meaningful impact on your business by using the right technology, and now that comes at an attractive cost structure.

Modern businesses and business applications rely on a large volume of data generated from different avenues. It is not humanly possible to synthesize this large volume of data to generate actionable insights. You need the power of artificial intelligence to process ‘big data’. It requires hardware and AI expertise to build solutions to tackle this problem.

Businesses that do not operate in the software and computing sector find it difficult to build the infrastructure, resources, and talent required to use AI for their applications. Such businesses will have to invest a lot of effort and resources to build such capabilities. There is a feasible alternative for businesses that do not have computing expertise. They can use existing data platforms to build and implement AI applications for their use cases.

What are data platforms?

Service providers with expertise in data management and artificial intelligence have opened up their computing hardware resources and software capabilities for anyone to use. You can build AI capability to suit your requirements on top of the infrastructure of these service providers. This eliminates the need to build your own dedicated data infrastructure. Such platforms that allow other businesses to build AI applications with their hardware infrastructure and IP are called data platforms.

Using such data platforms, you will be able to set up AI solutions for your business without significant upfront costs and effort. The best part is that you only have to pay for the resources you use. For example, if you only have to perform an analysis once a month, you only have to pay for the resources you consume. This makes the use of data platforms a cheap proposition in addition to ease of use. This article will introduce you to the top seven data platforms that you can utilize to start building AI solutions for your business right now.

Google Cloud

Google is known for its search, advertising, mail, and streaming business (YouTube). They also have a large cloud computing business called Google Cloud. The company provides a data platform as part of the Google Cloud suite of solutions. You can power data solutions for your company with hardware and software capabilities on Google using the platform. The service provides modern data warehousing solutions and data lakes for any business on a pay-as-you-go structure. Their services can be used for applications such as,

• Streaming analytics

• Business intelligence

• Marketing analytics

• Geospatial analytics, etc

Microsoft Azure

Microsoft, the pioneer in desktop operating systems, also has a fledgling cloud computing business named Azure. The service also has extensive data solutions anyone can use. It helps to store and process data in cloud environments. It can be integrated with Azure Active Directory for secure and role-based access to data. This means data security is not a worry when using the data platform from Azure. The service has options for SQL databases, Synapse Analytics, blob storage, Cosmos DB, and streaming analytics. The data solutions from Azure can be easily integrated with Microsoft Power BI, which is an interactive data visualization software developed by Microsoft.

IBM Watson

International Business Machines was one of the leading companies in the computing industry. The artificial intelligence solutions from IBM became well-known with Deep Blue, a supercomputer that beat the reigning chess world champion in 1997. Today you can use the Ai and data capabilities of IBM from the service dubbed Watson. It helps businesses to make smart decisions with data analytics. 70% of global banking institutions use Watson for their analytics needs. You can use the service for multiple uses such as IT operations, customer service, healthcare, video streaming, and business automation.

C3 AI

C3 AI provides data platforms for enterprise AI applications. The major advantage of the platform is that it uses common tools used in AI programming, making it easier to work with the platform. You, as a developer, can use Python, Jupyter notebook, R, and Scala to build solutions with C3 AI. It has a service called C3 AI studio that helps you to get applications up and running with low code. It also has a service named C3 AI Ex Machine that can be used to build applications without any code. C3 AI is used to build solutions for various industries such as manufacturing, financial services, government, utilities, oil and gas, chemicals, agribusiness, and defense.

H2O.ai

H2O.ai is a cloud AI platform that helps businesses make data models and applications fast. The key benefits of the data platform from H2O.ai are accuracy, speed, and transparency as stated by the company. The platform supports a flexible architecture where you can integrate AI applications and machine learning ops. The platform also has an AI app store where businesses can purchase third-party applications that suit your requirement. The platform helps you to generate better insights from data, personalize experiences for users, streamline data-related operations, and reduce platform risks.

Rapid API

Rapid API is a platform where developers can build, use and share applications through APIs. Third-party application developers can build solutions and you, as a business, can use the existing solution to fit your needs. The platform has several AI and data applications that can be used with API calls. As of this writing, Rapid API has more than 50 artificial intelligence applications that you can use with just API calls. The platform has support for different types of APIs such as REST, GraphQL, Kafka, SOAP, and RapidQL. This makes it easier for your developers to integrate AI applications with your digital infrastructure.

DataRobot

DataRobot is an AI platform that fosters collaboration with different stakeholders of your business. The platform can be used by data science experts, IT & DevOps teams of your organizations, and even executives of your firm who do not have a computer science background. With DataRobots, you can build, deploy, operate, monitor, and optimize your data applications on a single platform. The platform can be deployed in the public cloud, private cloud, and on-edge networks. The ability to scale the platform helps growing companies scale their AI capabilities according to your growth.

Sentient.io TeamSpaces

Sentient.io is an artificial intelligence company based out of Singapore. The company has a data platform called TeamSpaces. It allows your organization to collaborate on data solutions for improved efficiency. It helps the people in your organization to collaborate through the use of APIs regardless of source. You can have all your algorithms and datasets in one place which allows seamless sharing and teamwork. TeamSpaces allows you to publish and upload models and datasets using Rest API. The API is secured for granular access control and authorized sharing. Sentient.io TeamSpaces is a must-have for businesses that use data platforms for collaboration.

Wrapping up…

Modern businesses have to rely on data and analytics to have efficient operations and improved profitability. Building data infrastructure from the ground up is extremely difficult and costly. Instead of that, using an existing data platform to build custom solutions for your businesses is a better approach. This article introduced seven of the best data platforms you can use. The service that customizes and adapts solutions for your specific use cases should be preferred. Try Sentient's AI API demos to find out what suits your needs best.

Artificial intelligence (AI) is changing the landscape of software development. By automating repetitive tasks and providing more accurate insights, AI is helping developers work smarter and faster.

Algorithm-based machine learning is accelerating the development lifecycle and AI is supporting developers to optimize software workflows at each stage of the development process.

AI has the potential to change everything about the way software is developed.

How AI is Revolutionizing Businesses

In the past decade, artificial intelligence (AI) has revolutionized businesses across the world. Because of AI and machine learning algorithms, computers now have the ability to harness massive amounts of information and data. Using that data, they make optimal decisions in mere seconds – something most humans simply aren’t capable of.

AI software is able to automate tedious tasks in a fraction of the time it would typically take a human.

Does this mean humans are becoming obsolete? Not quite. In most industries, humans are still very much needed, but their roles are changing. No longer are they needed to perform the same menial tasks over and over. Rather, they’re needed for strategy and big-picture thinking. They’re needed to implement and monitor AI solutions in order to get the most high-quality work done in the least amount of time.

AI is revolutionizing businesses in all kinds of industries, including software development.

5 Ways AI is Changing Software Development

1. Faster Software Development with AI

One of the most significant ways that AI is changing software development is by making the process faster. In the past, developers would have to write lengthy lines of code in order to create even the most basic of applications. However, with AI, developers can now create applications in a fraction of the time.

AI can automate many of the time-consuming tasks that were previously done manually by developers. Software developers can hand over any and all repetitive tasks to computers. When they have proper direction and data, they will almost always get the job done faster and more accurately.

The time it takes to roll out changes, deploy, and address bugs are all significantly cut down with the help of AI and machine learning solutions.

2. Shifting Software Developer Roles and Skills

We’re still years away from the possibility of software developers being replaced entirely by AI and machine learning. But with advancements in AI, developers’ roles are shifting. They can now assign and automate tasks using AI, while they work on different sets of tasks and skills alongside the computer.

With AI solutions taking on repetitive tasks, developers and programmers now have the capacity to shift their attention to more complex and big-picture problems.

The most sought-after skills in the future of software development might not be coding speed or accuracy, but the ability to collaborate effectively alongside AI and machine learning solutions.

3. Efficient Decision Making

Most product teams spend hours, days, and weeks debating different features to roll out and which products to further invest in. AI can streamline the decision-making process through its ability to analyze massive amounts of data in seconds.

Machine learning applications can gather, analyze, and leverage data in moments. Though human intuition and gut instincts will always be valuable, computers can make intelligent and informed decisions based on data and analytics humans simply couldn’t process on their own.

AI will help product teams make better, more data-driven decisions in order to produce better software and grow their businesses more effectively.

4. More Efficient Bug and Error Management

Software development is all about creating high-quality, accurate code. In the past, this was a time-consuming and difficult task that often resulted in errors. Bug and error management can be a hassle for software teams, but AI is changing the status quo. AI is being used to create more accurate code with fewer bugs out of the gate.

Computers may not know the best approach to building an application, but when given the proper direction and data, they will make fewer mistakes than a human.

Even in post-deployment, machine learning can be used to proactively flag errors and surface potential issues by analyzing logs.

5. Real-Time Feedback

Real-time feedback is essential for making sure your software development process is on track. In the past, developers would have to wait for testing results to come back before they could make adjustments. With AI, developers can get real-time feedback so they can make changes as they work.

This is possible because AI can automate the testing process. So as developers are working, AI can be running tests in the background and surfacing results in real-time. The applications here are endless.

Machine learning algorithms can be trained to notice how a user interacts with a certain platform and give insight to the developers. AI can serve dynamic content and then provide data on how different aspects performed.

This continuous feedback loop means product teams can iterate more quickly and roll out new features in record time.

Working with AI Development Companies in Singapore

Working with an AI development company can be a huge catalyst in creating or improving your software. They can dramatically improve your workflow by helping you build AI capabilities into existing software quickly and efficiently.

Many AI development companies offer pre-trained microservices like speech recognition, text-to-speech, entity recognition, and many more that can improve and streamline the development of your software.

Here are a few tips for finding and engaging a helpful AI development company:

• When looking for an AI development company, it’s important to partner with one that has a strong understanding of your business’s specific needs.

• The best AI development companies will work collaboratively with your team to help you find the right solution for your business.

• Be sure to ask about the company’s experience with AI development and request case studies or examples of previous work.

• It’s also important to set clear expectations and objectives for the project from the outset. This way, you and the company can be on the same page about what needs to be accomplished.

AI is changing the landscape of software development. By working with an AI development company, you can stay ahead of the curve and reap all the benefits that AI has to offer.

In this globalized world, businesses have to work across national boundaries and continents. This brings forward many challenges due to working across geographies, ethnicities, cultures, languages, and time zones. Companies find different ways to cope with the challenges of operating a multinational company. With modern-day technology, we can use artificial intelligence to overcome some of the challenges. This article will outline some of the ways in which multinational companies can make use of AI to make it easier to run an organization across national boundaries.

Supply chain

The supply chain is a complex yet critical function of a company. It is responsible for the transportation of raw materials and goods from one location to another at the right time. The supply chain function will have to move goods to different countries, rely on multiple logistics partners, numerous vendors, and customers from different countries. The supply champion operation becomes an intricate web that is difficult to untangle.

Digital tools made available with digital transformation help to bring all the available data to a central location. This data can be analyzed to make business decisions to make supply chain processes efficient and effective. This could range from demand planning to procurement planning. Supply chain data can be integrated with sales data, production data, and information from suppliers to efficiently plan the supply chain. AI can help to extract useful information from a large amount of data to streamline supply chain processes.

Communication

One of the most common struggles of multinational companies is the language barrier. One or the other party will have to resort to a language they are not comfortable with. In some areas of the world that is not even feasible. AI applications can bridge this gap in different ways.

Speech recognition

Artificial intelligence tools can be used to recognize speech and convert it into text. In MNCs when different people know different languages, a translation service is essential. For translation, the first step is often to convert speech to text. AI solutions from Sentient.io provide the capability for automated speech recognition. It can be easily integrated into the digital infrastructure of MNCs, often requiring less than 30 minutes to complete the integration.

Translation

Understanding language is essentially pattern recognition in language use. Artificial intelligence has successfully proven its ability to recognize different languages. Such a tool with an understanding of many languages has the ability to translate text from one language to the other. Even in the case of spoken language, speech recognition tools can convert speech to text, and this text can be translated by translation AI. There are many translation AIs that are available in the market which can be integrated into your system.

Text to speech

The final piece of the speech translation puzzle is translating text to speech. The speech is converted to text and translated. But it is still in text format. Text-to-speech can convert this translated text to relay it to the other person. The biggest challenge with text-to-speech is to have a natural-sounding speech output. Most text-to-speech tools sound robotic and can be off-putting to use. Text to speech from Sentient.io sounds extremely natural and is pleasant to listen to. The service can be integrated into your systems by embedding the API in a few simple steps.

Chatbots

Chatbots can be built using artificial intelligence to facilitate communication between employees in different languages. It can also work for communication with customers as part of customer service. Such chatbots can also be integrated with communication platforms such as Slack, email, Microsoft Teams, etc. Smart chatbots are able to do much more than just translation. It can resolve customer complaints without even the need for a human service representative.

Cybersecurity

Today, cybersecurity is a huge concern for businesses of all sizes. This year, the damages caused by cyberattacks have crossed $6 trillion. The volume of cyber attacks is also increasing at a rapid pace. A cyberattack is executed every 39 seconds. This means businesses that use digital tools and want to protect their data should gain a strong security posture with stringent security measures.

The cybersecurity challenge becomes even more daunting when it comes to MNCs. Thousands of employees and customers have to use its digital infrastructure from different parts of the world. The digital infrastructure itself would be hosted in multiple countries and even multiple regions within the same country. Such digital infrastructure cannot implement effective firewalls and ensure smooth information transfer. The solution is to monitor the traffic in the infrastructure and identify threats to the system before it becomes a problem.

Surveillance of complex systems of MNCs is not humanly possible. Artificial intelligence can help in monitoring such a system and automatically flag suspicious activity. AI cybersecurity tools also can quarantine infected devices and users automatically before the threat laterally spreads over the network. AI tools enable the automation of cybersecurity protocols and digital safety mechanisms for MNCs.

Fraud detection

Another problem adjacent to cyberattacks is fraudulent transactions from employees, customers, or other stakeholders. Such instances can lead to significant erosion of trust and capital in the company. When MNCs have millions of transactions happening every day, it becomes difficult to track fraudulent activities. AI tools can help in monitoring all transactions and detect fraud by analyzing behavioral patterns and historical transaction records. It is recommended that MNCs implement some form of fraud detection in place to prevent such activities from occurring.

Final words…

Running multinational corporations with complex structures spread over the world is difficult. There are multiple challenges MNCs need to overcome from cultural differences to frauds originating from the other side of the globe. AI tools can help in overcoming these challenges with different intelligent applications for different use cases. Bridging the language barrier is one of the easy tasks AI can help MNCs with. There are many other applications for AI in MNCs. You can identify the requirements within your firm and deploy the necessary AI tools to overcome such challenges.

What is AI?

Artificial Intelligence or AI tries to mimic human intelligence with computing power. Complex statistical operations are applied to huge amounts of data to train computers to learn some aspects of human intelligence. The rapid rise in computing power and software capabilities has helped the field of artificial intelligence grow by leaps and bounds.

Over the course of time, the cost of computing power came down with innovations in the field. The accessibility of AI has increased with the democratization of information. With such changes, AI has come out of laboratories to take on real-world challenges. In addition to that, advances in AI have helped AI models to take on complex problems and provide efficient solutions for them. The benefits of artificial intelligence are transformational for many fields of human life, especially business.

Modern business challenges

In a dynamic market environment, businesses have to face a series of challenges to remain competitive and profitable. Some of the major challenges businesses face are:
Expensive labor - Labor shortages and high wages for skilled workers put businesses in a difficult position. Labor challenges are eroding the profitability of firms.
High error rates - Even skilled employees make mistakes while performing their tasks. This costs your organization dearly as wastage and effort to fix errors.
Frauds - Malicious actors try to conduct fraudulent activities to hurt your business and your customers. This has a negative impact on your business operations and profitability.
Cybersecurity threats - As your business adopts more digital tools to run your business, it gives cybercriminals more opportunities to gain unauthorized access to your systems and wreck your business.
Performance - Efficiency of operations impacts the revenue and profitability of your firm. Inefficient practices in your business hurt your top line and bottom line.
Customer service - Your customers demand more and better service levels for the same revenue. This demand increases your operational costs and hurts your bottom line.
Data explosion - With the evolution of Internet of Things (IoT), organizations will have a treasure trove of data that can be used to mine business insights. It is a great challenge to handle the large volume of data and generate useful insights from the same.
Regulation - Governments all over the world are increasing regulatory requirements for businesses. Adhering to all the legal requirements takes significant effort and manpower.
Competitive business environment - The entry barriers to enter businesses are coming down due to the large-scale digitization of businesses. This is another challenge businesses have to navigate to remain relevant in the marketplace.

Navigating these challenges with manual efforts combined with raw computational power is insufficient. Manpower has limitations to scale and raw computing power does not translate to much. Smart solutions that can scale are required to overcome modern business challenges.

AI for business

AI provides scalable solutions for many business challenges. The major benefits of artificial intelligence that make such solutions possible are the scale and low cost of implementation. AI can help businesses in many different ways, some of which are discussed in the following sections.

Automation

Many repetitive tasks are performed in organizations that make up for mundane work for employees. Such tasks can be automated with AI to reduce the reliance on employees to carry out the task. When AI takes over cumbersome work, human capital can be delegated to more creative and impactful endeavors.

Insights

Data is at the core of the new digital economy. Large swathes of data are collected and stored from different sources. This data contains meaningful insights that can improve businesses from marketing to manufacturing. Humans cannot comb through petabytes of data generated every day to extract these insights. AI is up to the task, effectively analyzing vast volumes of data to extract meaningful insights and deliver business improvements.

Productivity

Efficient utilization of capital and resources is major challenge businesses face. Automation with AI significantly improves the productivity of organizations. AI can also generate meaningful insights that help to improve efficiency in various facets of business operations. This could be AI helping businesses to deliver higher returns on investment for marketing campaigns. AI can also be used to reduce the use of resources to generate the same output. According to projections from Accenture, AI in Singapore is touted to help manufacturers increase output by 40% in 2035.

Decision-making

Simpler decision-making is one of the key benefits of artificial intelligence. The availability of relevant information at the right time is key in decision-making. AI helps to synthesize data into simpler, meaningful, and actionable insights for decision-makers. This means they do not have to spend a lot of time pondering through vast amounts of information to make decisions and move forward.

Speed and agility

AI assisting in automation, decision-making, and productivity improvement, helps businesses to move faster in dynamic market conditions. The ability of artificial intelligence to synthesize vast amounts of information and scale operations according to changing requirements helps businesses be agile.

Wrapping up…

The various benefits of artificial intelligence find use cases in many different industry sectors. AI in Singapore is used from public transportation to finance. AI is a tool that can suit any kind of business. The technology's efficacy depends on how it is used and implemented. For that you need trusted partners who understand both artificial intelligence and business to deliver the true benefits of artificial intelligence for your business.

Sentient.io offers turnkey AI solutions for enterprises with custom-trained model or custom-built model specifically for the client. At the same time, Sentient.io delivers AI capabilities as microservices that developers can use to integrate AI capabilities with existing software. There are different AI microservices for applications like object identification, people counting, automated speech recognition, and text to speech and more. For enterprises and developers who are looking for a collaboration workspace or sandbox, Sentient.io offers TeamSpaces. This workspace is both private and secured and enables them to tap on Sentient.io Platform suite of microservices as well as client’s own models and datasets which they onboard onto TeamSpaces. Find out more about TeamSpaces.