Wednesday, 27 May 2020

Things Need To Be Considered For Developing Internet of Things (IoT) Mobile Apps

A Forbes survey shows that two-thirds of consumers say they may purchase IoT devices by 2019. Typical IoT products include smart homes.

Consumer IoT devices usually use mobile applications to operate, that is, mobile phones will now install applications that drive IoT devices, which is an upgrade to the current development of mobile applications.

Both enterprises and developers need to have a basic understanding of the Internet of Things, including Internet of Things devices, hardware, software, development platforms, challenges, etc.

The Four Main Components of The Internet of Things

Any IoT system contains four main components:

The hardware includes various sensors and mobile devices.

The software is a cloud-based application that manages and controls all devices connected to it, and collects information from sensors and displays it to users.

Cloud is used to process and arrange data. The biggest advantage is that it does not occupy space for storing data. Cloud is one of the most important technologies of the Internet of Things.

The network links all devices in the IoT system and sends data: from the mobile phone to the sensors and devices and then sends back the data collected on the sensors and devices.

Internet of Things Challenges

Before developing IoT applications, you should understand the unresolved challenges in this industry.

There are two main challenges

1. Security is the biggest challenge of the Internet of Things
The cause of IoT security problems is that each manufacturer and software development company has developed its own standards, such as API, connection, and security standards, which has led to a low degree of standardization and ambiguous security rules for the entire IoT industry. Due to the large number of connected devices in the Internet of Things, the entry point is vulnerable to hacker attacks, and access to some sensitive information on the device and control the entire system. Therefore, IoT application developers should have some expertise in network security.

2. Inconsistent technology is another challenge for the IoT industry
The applications you develop may not necessarily connect all smart devices.
Mobile applications have many standards that allow them to work on different devices and connect to each other via API. Some IoT devices do not yet have these standards.

                                                       7 Layers of the Internet of Things

Develop An IoT Application

After having a basic understanding of the Internet of Things, you should pay attention to the following when developing Internet mobile applications

1. Choose the platform
To develop Internet applications, we must first choose a suitable platform. When choosing a platform, we must consider which technologies they support and whether the cost is reasonable, and whether it has been verified.

Here are some verified IoT platforms

Android Things
Azure IoT Suite
IBM Watson
Oracle IoT

These platforms are trying to solve the inconsistency problem, so that IoT application developers can connect devices from different vendors using different protocols into a unified system. For example, Google's Internet of Things platform (called Android Things) now supports devices from many manufacturers and can be connected to sensors.

2. Choose hardware
The hardware must be reliable, the quality must be guaranteed, and it must meet the needs of your product.

Ensure fast speed
The speed of IoT applications must be very fast, and more and more IoT mobile applications will be used on 5G networks.

Ensure safety
Since a large number of devices are connected in a network, the data is easily compromised and it is sensitive data, so security must be guaranteed.

Tips for securing IoT applications include:
Choose the hardware supplier carefully
Use a proven IoT platform
Encrypt data and ensure that it is not easily deleted
Use a protected network

In Conclusion

From now on, mobile applications will be upgraded to IoT mobile applications. When developing IoT mobile applications, enterprises should pay special attention to strengthening security, protecting data privacy, and providing consumers with safe and reliable mobile applications.

#IoT #mobileapp #cybersecurity #IoTplatform

Tuesday, 26 May 2020

6 Roles of Artificial Intelligence Team and Career Opportunities of Artificial Intelligence

This article has a certain constructive point of view, which has some inspiration for young people who are looking for career development and job search. Not only that, it also has certain ideas and value on how to form an artificial intelligence team for medium-sized companies or new tech startup companies.

For small companies how to solve the application of artificial intelligence, also put forward some suggestions.

The main idea of the article is that the whole world is in a period of pandemic and economy depression stage, but for companies, this is the best time to develop technology. The article even quotes "don't let the crisis waste" To emphasize.

The entire article suggests that the company's artificial intelligence team should be a combination of six roles.

6 Roles of Artificial Intelligence Team and Career Opportunities of Artificial Intelligence

Business and artificial intelligence strategist
This role is a combination of business strategist and data scientist. Usually, this role should not only understand business strategy, operation, management, marketing, etc., but also have a technical background. That is to understand the technical difficulties in business operations, where are the problems, and how to use technology to solve these problems. As a strategist, he should also deal with how to use technology in business in the next 5 to 10 years, and the impact of technology development on business has a certain accurate estimate.

Data engineer
The role of data engineer is responsible for data collection, collation, conversion, and storage. This role requires strong coding skills and requires proficiency in programming languages such as SQL, Python, C ++, and Java. At the same time, for the processing of some data, you need to use some newly developed tools, such as the tools and solutions provided by startups such as Trifacta and Forge.AI.

Data modeler
Data modelers usually require strong mathematical and statistical skills. They use data models to predict results. They are also responsible for training models for prediction.
Large technology companies usually hire people with PhD degrees in mathematics and statistics. After entering these companies, they are usually engaged in the development of related platforms. Technology startups usually develop similar platform products and also hire talents with such backgrounds.

Data Conversion Engineer
This role requires cloud expertise and software engineering skills. The main job is to convert the prototype code to a production version and establish a cloud environment to deploy the model. It also includes others, such as managing version control, shortening response time, and building APIs.

Infrastructure Engineer
This role also requires proficiency in cloud technology and software engineering skills. The main job is to establish a database to store data and facilitate access, as well as maintain security and privacy.

Data analyst / visualizer
This role is to transform obscure and difficult-to-understand data into a clear and readable pattern, which is expressed in graphics and other forms for management and other teams to use.

Required data analysis tools include

Artificial Intelligence Solutions For Small Business

For general small business, it is unlikely that such an artificial intelligence team will be a component. The best solution for how to use technology strategy is outsourcing, using outsourcing teams and freelancers.

Overall technology strategic planning
Usually outsourced technology strategists and professional teams can help planning and implementation. The time is longer and the cost is higher, but it is more comprehensive and there is generally no problem of project duplication.

Individual technology projects
For individual technology projects, you can find freelancers. Freelancers often use ready-made tools to complete these projects. Usually the project time is short and the investment is small. The disadvantage is the lack of overall planning.

In Conclusion

The technology strategy of small companies can be planned and implemented by affordable outsourced strategists and professional teams. Individual technology projects can also be completed by freelancers.

For those who seek development in the field of artificial intelligence, both large and medium-sized technology companies and startups provide opportunities. For freelancers, they can also work on technology projects of small companies through learning and using artificial intelligence tools.

#ArtificialIntelligence #BusinessandArtificialIntelligenceStrategist #DataEngineer #DataModeler #DataConversionEngineer #InfrastructureEngineer #DataAnalyst

Friday, 22 May 2020

Use Google Assistant To Develop A Chatbot

Google Assistant is a voice-based help service developed by Google and a virtual assistant using artificial intelligence technology. At the beginning of the market, it was only a part of Android and Google devices, mainly on mobile and smart home devices. Since 2016, Google has allowed other manufacturers and developers to integrate Google Assistant with their products and services, which has led to rapid development of Google Assistant. Google Assistant can conduct two-way conversations, plus other excellent features, making it one of the most advanced voice assistants.

Main Functions of Google Assistant

Some key features of Google Assistant include

Recognize voice
Google Assistant can recognize different people according to their voices and allow them to adjust their voice response accordingly. This feature is Google ’s leading.

Reduce coordination
Google Assistant can process multiple input commands at once, so as to complete various tasks easily and quickly. This function is also Google's leading.

Understand the context
Google Assistant understands the meaning of input instructions, understands the intent of the question, and provides answers accordingly.

Easy integration
Google Assistant can be easily integrated into smart devices of other manufacturers, like smart home products, making them easier to manage and control.

With the rapid development of artificial intelligence, chatbots are widely used. Voice recognition has become a part of chatbots. It enables your company ’s bots to listen to customers ’voices, understand their needs, and immediately provide relevant information, enhancing the interaction between customers and also enhancing the customer ’s experience and satisfaction. 

Advantages of Google Assistant Chatbot

The main functions of the Google Assistant mentioned above make the development of chatbots using the voice-driven technology of Google Assistant become an important option for enterprises, and has the following advantages

Integration with multiple devices
Distinguish between different sounds
Process multiple instructions simultaneously
Provide real-time answers
Understand natural language
Support multiple programming languages

In Conclusion 

Google assistant, with its excellent voice recognition technology, will provide more options for small and mid size businesses to develop chatbots, and build more efficient and multi-function intelligent chatbots.

#ArtificialIntelligence #Chatbot #Business #SoundRecognition

Thursday, 21 May 2020

Deepgram - To Realize Speech Recognition And Text Transcription Using Artificial Intelligence For Business

Speech recognition is a technology that can recognize spoken words and then convert them into text. Voice recognition, which is a technology for recognizing people based on voice, is a branch of voice recognition.

Facebook, Amazon, Microsoft, Google, and Apple have developed speech recognition products.

Amazon Echo and Alexa, Google Home and Assistant, Microsoft Cortana, Apple Siri, these products have begun to be mainly used in home, entertainment, and some also come with some business functions.

Business Speech Recognition Systems Have Emerged

Deepgram's artificial intelligence-based search platform has the function of voice-to-text. It can search for keywords in text and convert and record at the same time, it helps businesses to process phone, audio, video and demo record files. This record is relatively accurate and will not reach the level of accuracy of human records, and it is gradually improving. But for the company, it can indeed save a lot of precious time and greatly reduce costs, and collect quite precious data.

This is typical of companies using voice to collect and analyze data.

At Deepgram, an end-to-end deep learning speech recognition system is used to create a completely different solution, which makes collecting speech data faster, more accurate and reliable, and truly meets the needs of enterprise companies.

Deepgram's innovation is to use artificial intelligence to process text and graphics, so that they form mixed custom models, and then fully train these models to enable them to use files from telephone and podcasts to recorded meetings and videos.

The innovative method of Deepgram voice storage can help customers search for words according to their pronunciation, even if they are misspelled, Deepgram can find them.

Deepgram CEO Stephenson said that Deepgram's model automatically picks up the noise profile of the microphone, as well as background noise, audio coding, transmission protocol, accent, price point (ie energy), emotion, conversation theme, speech rate, product name and language. In addition, he claims that they can improve speech recognition accuracy by 30% compared to industry benchmarks, increase transcription speed by 200 times, and process thousands of simultaneous audio streams.

Deepgram Speech Recognition And Conversion System Workflow

1. Prepare the data
With sufficient preparation and training to ensure higher accuracy

2. Train the model
This process implements an end-to-end custom speech model

3. Implement transcription
Use models to achieve large-scale, automated transcription

In Conclusion

Deepgram is one of the services that provide voice-to-text services for companies. In the future, more technology startups will get involved in this field. This technology will do a lot of records for companies' daily meetings, speeches, product launches, exhibitions and other events, also collect and analyze data, and ultimately create value for them.

#artificialintelligence #VoiceRecognition #TextConversion #Business

Source :  Deepgram

Tuesday, 19 May 2020

Uipath’s Artificial Intelligence Invoice Processing System

Handling invoices manually by accountants began 1,000 years ago. This kind of work was repeated for 1,000 years. With the development of artificial intelligence, the era of manual invoices is coming to an end.

The difficulty of invoice processing currently lies in the following two points

There is no uniform format for invoices
Recognition of invoices is very difficult

Suppose an accountant processes 100 invoices a day, he may deal with 100 different formats, which means that the past invoice processing is a rather tedious and repetitive labor, making the invoice processing personnel like robots.

Uipath Artificial Intelligence Invoice Processing System Features

Have you heard of Uipath? It is a system that uses artificial intelligence to process invoices. Uipath's slogan and goal is "We make robots, so people don't have to be robots." Now it seems that they are achieving this goal.

The system developed by Uipath overcomes the disadvantages of the previous technology and trains artificial intelligence to understand the files in the real world. It can automatically select from the specific needs and requirements of your company ’s accounts payable, accounts receivable, and other expense processes. Identify and extract various business information from receipts and invoices. Even if your document contains unclear information, the robot will find relevant information, such as supplier name or invoice number.

Even if the invoice format is changed, their artificial intelligence can still automatically determine the location of key information that needs to be extracted from the document, which will easily process the invoice.

UiPath's Studio tool allows users to drag and drop artificial intelligence directly into their workflows and seamlessly integrate with other key document processing functions (For example, Taxonomy Manager and Validation Station).

The most innovative is that users can easily send robots to enter the extracted data into the correct account payable or expense management back-end system to complete the end-to-end automation of these processes.

These models have self-learning capabilities. As more users interact with them and use them in the workflow, they will continue to improve, become smarter, and be able to complete tasks more accurately.

Uipath System Workflow

Uipath process is as follows

Find opportunities for artificial intelligence and employee automation collaboration

From simple to advanced, quickly build automation

Enterprise-wide management, deployment and optimization automation

Run automation with robots used in conjunction with your applications and data

Integrate people and robots into a team for seamless process collaboration

Measure operational performance

In Conclusion

With the development and use of artificial intelligence more and more widely, many manual and highly repetitive tasks will be replaced by artificial intelligence, which will help commercial companies achieve higher efficiency and lower costs.

#ArtificialIntelligence #Business #Invoice #Receipt #Accounting

Source :  Uipath

Friday, 15 May 2020

Challenges and Opportunities of Enterprise Big Data Strategy

Big data has 5v characteristics.

Enterprises face four challenges and six opportunities  (success factors) when using big data.

4 Challenges of Big Data Strategy

According to research on the big data industry, companies still face many difficulties in using big data today. It mainly includes four types of challenges: strategy, talent, data assets and tools.

Strategy: Only about 23% of companies have a clear big data related strategy, decide and know how to effectively apply big data analysis to enterprise operations, and establish corresponding organizational capabilities, processes and incentive mechanisms to empower data analysis to support decision making.

Talent: Only about 36% of companies have a dedicated data analysis team.

Data assets: Only about 19% of companies have high-quality, consistent, and easily accessible and applicable big data.

Tools: Only 38% of companies are using advanced big data tools

6 Key Factors for Companies to Establish Big Data Strategies

1. Discover unique "data assets"

The steps to follow are
Discovery: Discover the source and type of current data assets.
Assessment: Assess the quality and importance of data, whether data assets are relevant to business development, and whether there are differences between the company ’s business strategy objectives.
Management: Clean up and store the acquired data to maintain the availability and consistency of the data.

2. Understand how data assets "create value"
After evaluating the enterprise's data assets, it is necessary to determine how to use it to support and lead the enterprise strategy. Specifically, big data can bring five strategic values ​​for enterprises:

Optimize the internal operation process of the enterprise: change the existing marketing strategy.
Optimize existing products and services: enhance customer experience.
Develop new products and services: Insurance companies can launch insurance products with different discounts based on customers' driving behavior.
Establish a new business model: Financial management service companies give away personal financial software to users for free, analyze their consumption data when users use them, and then push relevant advertisements to them accurately.
Gain control of the ecosystem: For example, an e-commerce company ’s data product team develops various big data products for the sellers on the platform based on the large amount of transaction data collected by its platform, helps it realize data operation and increase revenue, and improve the attractiveness of sellers.

3. Identify priority application scenarios
Because the company's resources are limited, the application scenarios of big data should be specifically evaluated. Whether it is a business department or a functional department, they should combine their actual needs and evaluate the application of big data through two dimensions: value creation and business maturity, to decide prioritization.

4. Normalization of data analysis
Enterprises need to constantly transform data analysis capabilities into internal application products and normalize data analysis. At the same time, it is necessary to continuously maintain data analysis products and monitor the actual use effect to provide data analysis support for business and functional departments.

5. Provide strong guarantee and support for big data
The implementation of big data strategy requires the support of organization, talent, and IT technology. Need a diverse and talented team, development, analysis, management etc, in addition to organization and talent, the implementation of big data also requires a strong IT system architecture as a support.

6. Strengthen big data privacy and security management
Big data involves personal privacy, business secrets, etc. Once leaked, it will have a negative impact on individuals and companies, and countermeasures should be taken when collecting and analyzing. Distinguish privacy sensitivity and decide whether to collect or how to collect, manage data privacy in accordance with international and domestic relevant regulations, and adopt a variety of technical methods to reduce privacy risks. If necessary, you can seek the support of professional companies.

6 Typical Applications of Big Data

Personalized marketing
User interest data is increasing day by day, and consumers are showing a long-tail trend, which leads to personalization becoming the application direction of big data. The large-scale production with the best cost of the enterprise shifts to the direction of customization. At the same time, personalized recommendation has become a typical application.

Identification and mining of customer value
The evaluation and analysis of customer lifetime value supported by data will help the company establish a market segmentation strategy, confirm which types of customers are worth the cost to establish customer relationships, and finally find their true target customer base.

Customer churn warning
Customer churn warning is of great significance to the company's strategy formulation. Different algorithms can find the cause of the end customer churn, and ultimately help the company decide whether to retain these users.

Data-driven precision advertising
Data helps companies to identify target consumers before placing ads, to achieve precise positioning during placement, and to monitor the effectiveness of ads using a series of data tools after placement.

Help companies make decisions
Various departments of the enterprise, various activities, what results are received, and what problems exist, are based on data.

Inventory management and logistics
Online retailing can accurately calculate inventory based on data analysis to optimize the use of funds, and at the same time can improve the time efficiency of logistics distribution and enhance the customer experience.

In Conclusion

The application of big data is becoming more and more extensive. The enterprise's big data strategy has both challenges and huge opportunities for success. A reasonable planning and layout will help enterprises successfully implement the big data strategy and benefit from it.

#BigData#Business #Strategy

Thursday, 14 May 2020

Five Issues That Companies Should Pay Attention to When Deploying Artificial Intelligence

At present, many companies have some misunderstandings in the deployment and use of artificial intelligence, which has caused some problems. These problems are summarized as follows.

Five Issues That Companies Should Pay Attention to When Deploying Artificial Intelligence

1. Artificial intelligence is used as a purpose (or decoration) rather than a tool to solve practical problems

Many companies stay at this point and are proud to announce that we have artificial intelligence, but in fact artificial intelligence has not played a great role in solving problems for them. On this point, we can first return to the era when the Internet first appeared. At that time, website construction was in the ascendant, so many companies made the goal of owning a website, but many websites did not solve the actual problem after the completion of the website. The website became a furnishings, not doing enough work in other areas, such as the ranking of the website, how to rank relatively high, how to make the website attract visitors, and improve the conversion rate. The current deployment of artificial intelligence has encountered a similar situation.  Companies should start deploying artificial intelligence by solving existing specific problems, such as fraud prevention and market forecasting.

2.Adopt a portfolio-based artificial intelligence deployment strategy

The deployment, development, and use of artificial intelligence should not focus on a particular project. Instead, it should be scattered on multiple projects. For some projects that are already in the work and need to be resolved urgently, and can be resolved in a short period of time and can be effective, this is a short-term artificial intelligence investment project, and investment should be made early. , To solve these problems, and use the gains from solving these problems, to develop other projects that require long-term investment to solve.
For example, a car insurance company ’s long-term project may involve creating a fully automated claims process where customers can photograph their car ’s damage and use the app to resolve the claim. Building such a system to increase efficiency and create a seamless new customer experience requires AI technology and consensus.

3. Focus on internal company personnel training

To develop and apply artificial intelligence, not only rely on external experts and teams, but also need to carry out relevant training for the personnel within the company. Artificial intelligence is a brand-new project for many companies, and it is unlikely that there will be a ready-made team, so it is important to provide related training. This is especially applicable to the application of artificial intelligence in small and medium size companies.

4. Focus on the long-term benefits created by artificial intelligence

Because artificial intelligence is a brand new project, some companies encountered many difficulties in the early stages of development and application, which may cause them to give up. This will create difficulties for the subsequent development of these companies. These developments are not just the application of artificial intelligence, but the strategic level of the entire company. These companies should keep working on it rather than giving up because of a temporary setback.

5. The risks and prejudices in the application of artificial intelligence should be actively addressed

Because artificial intelligence is a product developed by humans based on various data, its algorithm will inevitably reach a conclusion consistent with humans, resulting in prejudice and discrimination in terms of race, ethnic group, culture, gender, etc. For the company itself, and society will have a lot of negative effects. So companies should consider these issues when applying artificial intelligence and correct their algorithms in a timely manner.

In Conclusion

The company's development and application of artificial intelligence is imperative, but there are many problems in the early stage of application, which will lead to prejudice, errors and even loss of profits.

We should make plans for long-term investment, and correct errors in use in a timely manner, so that artificial intelligence can play a positive sense.

#artificialintelligence #business

Tuesday, 12 May 2020

Perfect Corp Uses artificial intelligence to provide beauty solutions for WeChat users

As we all know, Perfect Corp is a beauty technology service provider that utilizes artificial intelligence. Its AI + AR, artificial intelligence (“AI”) and augmented reality (“AR”) technologies realize more attractive beauty. The YouCam Makeup program is to use the most accurate face mapping technology for realistic virtual makeover.

Perfect Corp and YouCam Makeup provide three digital beauty solutions for WeChat users.

AI Smart Shade Finder Mini Program
The plan is to recommend personalized products to WeChat users. Through the use of advanced artificial intelligence and deep learning, users can try a variety of foundation shade virtually. It is characterized by many types. This small program can detect nearly 90,000 skin tones. By analyzing the results, consumers can easily try several foundation shade in a few seconds in order to be able to find the most suitable.

AI Skin Diagnostic Mini Program
This program is used to perform skin analysis on users. Users can get personalized suggestions based on their skin conditions, including wrinkles, spots, texture, greasiness, dark circles, etc. Customers receive immediate skin diagnostics directly from their mobile devices, which enables them to find skincare products specific to them in their purchase decision.

AR Virtual Makeup Try-On Mini Program
Perfect Corp's AR technology is based on innovative, real-time, high-definition facial 3D Live Mesh technology, which can provide WeChat beauty shoppers with an ultra-realistic virtual makeup try-on experience that is comparable to physical stores. Consumers can choose between 7 different texture lipsticks and try to use eye shadow, eyeliner, blush, concealer, etc. to find their favorite products.

WeChat beauty product suppliers can also attract customers through the Artificial Intelligence-based Beauty Advisor service, which provides personalized appearance suggestions based on facial features and preferences, so customers can save time.

Judging from this case, the new technology of artificial intelligence is applied in beauty. The most important thing is to embody personalized services and virtual services, so that consumers can buy products that are more suitable for them, and save a lot of time for consumers and merchants. ,

WeChat is China's largest and most dominant social platform. There are various brands of beauty WeChat public accounts. Consumers often visit these WeChat public accounts to seek information on beauty products and services. Perfect Corp and YouCam Makeup provide three digital beauty solutions for WeChat applets, which will provide consumers with accurate and fast services so that they can make more suitable decisions for themselves.

In Conclusion

This case is to combine artificial intelligence, virtual technology, and social media to improve personalization, precision, and a wide range of beauty services. It saves time and omits repetitive work for consumers and businesses, and improves efficiency.

#artificialintelligence #wechat #beauty

Perfect Corp

Note: WeChat public account is equivalent to the Facebook Page

Definition of Fintech According to Customer’s Experience

What is fintech, so far there is probably no clear definition, in the article of "What is fintech? Fintech?" the author explained the so-called "thousand-faced fintech" at the beginning, meaning people in different aspects giving different definitions and interpretations of financial technology.

To sum up as follows, from the perspective of bankers, fintech is something that improves service efficiency. Entrepreneurs associated with financial services believe that P2P platforms are a manifestation of fintech, while financial giants believe that IT and other related technologies empower finance and come to the fore. This is fintech.

The author of this article believes that these views have some truth, but they are not comprehensive.

Customer-Centric Definition of Fintech

To provide a standard and comprehensive definition for fintech, it should be defined from the perspective of increasing the use value for users, that is, to be “customer-centric”. Therefore, the author defines fintech as "fintech should be a solution that integrates technology, customer insights, financial scenarios, and product operations, and helps financial institutions adapt to new changes in users' financial consumption habits."

This can extend the following 4 conclusions

1. Can't ignore the financial scene to talk about financial technology
Whether it is the use of technical personnel, or the development and application of technology, it should be firmly integrated with financial scenarios, events, and problems.

2. Same Feature empowerment is not all about fintech
The strategic layout of various institutions for financial technology is very similar, but the end user experience should be different.

3. Fintech will eventually become platformized
In an ecosystem built with financial technology as the core, institutions that provide various services will eventually survive and develop in the form of platforms.

4. Small and medium-sized financial institutions can also use fintech
Fintech has facilitated the participation of small and medium-sized financial institutions, which will lead to a more complete market competition mechanism, which will be more beneficial to consumers.

In conclusion

Artificial intelligence, cloud computing, and big data can collect and analyze user behaviors to develop customers more accurately, provide products and services, and do more accurate market analysis.

Fintech companies make full use of big data analysis in customer analysis, which can provide customers with personalized financial services and improve the level of scene service.

Fintech is a combination of finance and technology. The ultimate goal is to enhance the user experience.

#fintech #BigData #cloudcomputing #artificialintelligence

What is fintech? Fintech?

Friday, 8 May 2020

Artificial Intelligence Processing Invoices Significantly Reduces Costs

Artificial intelligence is used in the accounting and finance department. A typical example is the processing of invoices.


Invoices are processed by every company. According to the traditional processing method, a series of processes such as certification, entry, cost sharing, and accounting vouchers will consume a lot of manpower. Now, invoices are entering a new era of artificial intelligence processing. No matter from large enterprises to small and medium-sized enterprises, the application of artificial intelligence in the field of financial management is becoming more and more in-depth and extensive. Artificial intelligence will solve the repetitiveness of data collection and collation in the invoice processing process, and the error-prone work.


In addition to these conventional tasks, artificial intelligence can also perform other tasks, just like manual operations. This includes cross-checking invoices, purchase orders and inventory, and forwarding unclear invoices to accountants for further verification.

Artificial intelligence processing invoices saves 80% -90% of labor costs


Look at the following examples

On the employee side, the entry of an invoice can be shortened from 1 minute to 2 ~ 4 seconds. The audit work on the company accounting side can improve the efficiency by about 50%.


Take pictures and make accounts to save customer delivery costs and time costs, and prevent the loss of bills. Upgrade to cloud storage, at the same time, it can be connected to the ERP system in real time, and the processing efficiency of the accounting documents and bills image inspection has been improved by more than 30 times at any time.

Self-service bill settlement service, scanning identification and automatic order filling can be carried out 24 hours a day, 7 days, saving more than 80% of the workload in points, authenticity identification and information verification.


Artificial intelligence allows the computer to better simulate the human eye and brain to recognize various types of bills, thus becoming an "expert" in bill recognition.


The use of artificial intelligence to process invoices is only a task of the accounting and finance department. It can be expected that with the further development of artificial intelligence, more functions will be developed and applied, for example, fraud prevention, auditing, financial evaluation, bankruptcy estimates, etc.


In Conclusion


Artificial intelligence will replace some intensive and repetitive tasks in the accounting and finance department. But on the other hand, it has also become a powerful assistant for companies and other organizations, saving a lot of costs and reducing errors.


Accountants will have an intelligent tool to fill out forms faster so that they can focus on higher-level tasks such as budgeting, and the manpower saved will be able to work in other emerging industries.


#artificialIntelligence #AI #finance #accounting #invoice


Wednesday, 6 May 2020

5 Business Commonly Used Visual Analytics Tool

During the operation of a company, a large amount of data is collected, which is difficult to read without processing. After analysis and processed data, it is meaningful and valuable to companies. One of the analysis methods is Data Visualization, which involves statistics, data mining and other work and technologies. 

Data Visualization analysis also uses artificial intelligence and machine learning to make the data clear, organized and simple to understand, so that company managers, sales team, marketing team, customer service team, these non-data analysis professionals can also easily read, understand and help them know the status of operations, sales and services, as well as their own companies advantages and disadvantages, so as to make more correct decisions.

Basic Functions and Features of Visual Analytics Tools

General visual analytics tools have the following parts

Data Visualization

Data visualization refers to acquiring data and displaying information in the form of graphs, tables, etc.


Here you can see graphics, tables, etc., so that various teams and managers can see key information, such as KPI and timely information, such as inventory management, timely adjustment of inventory can enable companies to more effectively use funds and increase sales.


The visualization software can be integrated with various data sources of a company in order to collect and process data. These data sources include databases and other software for sales, services, inventory, marketing, human resources, etc.


This part of the visualization software enables companies and partners to share these visualized data, add comments, increase interaction, discuss issues such as supply of goods, and marketing.

5 Commonly Used Visual Analytics Tools


Allows users to display data in different predefined charts and animations to visualize changes in multiple groups or time periods.

Supports geospatial interaction.

Perform specific analysis, draw trend lines, and make predictions.

Microsoft Power BI

Various chart suggestions and animations are provided to represent the data in a customized and descriptive manner.

Power View can share insights with others through rich presentation capabilities.

Provide specific analysis and predictive analysis.


Real-time data can be connected to the graph to display the latest data.

Personalization features can be added to the chart, like video, images, which can add content to interactive media.

Google Charts

The biggest benefit of Google's software is user-friendliness.

Charts are interactive and scalable, giving users the opportunity to interact with the data and ultimately make them stay on the page longer.


It has the ability to control chart customization to achieve maximum efficiency. The chart can be made simple or complex according to the user's wishes.

Personalized elements can be added to the chart to increase interaction.

Integrate multiple data sources.

In Conclusion

Each of these visual analytics tools has its own characteristics, making data processing and visualization easier and more versatile, allowing users to have more choices, and can choose tools according to their own needs, so as to better understand the analysis results for the team and companies, and help them make more accurate decisions.

#datavisualization #dataanalysis #business

Monday, 4 May 2020

Basic Steps, Techniques and Applications of Text (Data) Mining

Text (data) mining (also called text analysis) is an artificial intelligence (AI) technology that uses Natural Language Processing (NLP) to convert unstructured text data in documents and databases into structured text data to make them Suitable for analysis.

For enterprises, a large amount of data is obtained through analysis of emails, product reviews, social media posts, customer feedback and other channels, but these data are unstructured and unprocessed, meaningless to the enterprise.

Basic steps of text mining

Collect unstructured data from multiple data sources (such as web pages, emails).

Perform preprocessing to detect and eliminate anomalies in the data. Data cleansing allows you to extract and retain valuable information hidden in the data, and helps identify the roots of specific words.

Relevant information extracted from unstructured data is converted into a structured format.

Analyze the patterns in the data through a management information system (MIS).

Store the analyzed and valuable information in a secure database.

So far, the data obtained by the enterprise is helpful for decision-making.

Text mining tools

The tool used for text mining is natural language processing. This process is to convert these unstructured data into machine-understandable information and classify it. Text mining technology enables companies to process large amounts of unstructured data and reduce repetitive work.

Natural Language Processing
By simulating human's ability to understand natural language (such as English, Chinese) to help the machine "read" text (or other input, such as speech). Natural language processing includes natural language understanding and natural language generation, which simulates humans creating natural language texts, for example, summarizing information or participating in conversations. Google's voice search uses natural language processing to understand and respond to user requests.

Machine Learning
It is an artificial intelligence (AI) technology that enables the system to automatically learn from experience without programming, and can help solve complex problems. Natural language processing can extract the clean, structured data needed to drive machine learning without the need for time-consuming and laborious manual annotation.

Characteristics of current natural language processing systems
Ability to analyze large amounts of text-based data
Understand complex semantics and maintain continuity
Extract key facts and provide a summary

The technology used for text mining

Basic technology

Word frequency
Can be used to identify the most frequently used terms and nouns in the data. When analyzing customer reviews and feedback, they are often used, such as cheap prices, good service, and timely response. These similar words often appear, indicating that customers are satisfied with these items of the company.

Advanced technology

It is the process of extracting meaningful information from large amounts of text data. This technique focuses on identifying attributes and their relationships from semi-structured or unstructured text. Then, store the extracted information in the database for future access.

The process of assigning categories or labels to unstructured text data. Natural language processing makes it easy to construct complex texts and turn them into meaningful data.

It is to browse multiple text sources, make a text summary containing a lot of information in a concise format, and keep the overall meaning of the original document basically the same.

Application fields of text mining

Text mining is widely used in business, government, healthcare and other fields.

Customer service, market research, risk management, business intelligence, strategic analysis, financial insurance, retail

Customer Service
Software applied to customer service can improve the customer experience through analysis of text data from various sources, such as solving complaints, reducing phone waiting, and shortening response time.

Business Intelligence
Using text mining technology, companies can understand the strengths and weaknesses of competitors to formulate corresponding strategies. For the analysis of their customers' behavior, habits and preferences, they can develop personalized products and implement more effective sales strategies.

In Conclusion

Text mining uses natural language processing, machine learning and other technologies to effectively process large amounts of unstructured data quickly, turning them into valuable information, thereby improving the efficiency of various industries.

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