Wednesday, 27 May 2020
Internet of Things Challenges
There are two main challenges
Tuesday, 26 May 2020
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.
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 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.
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.
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
Thursday, 21 May 2020
Deepgram - To Realize Speech Recognition And Text Transcription Using Artificial Intelligence For Business
Tuesday, 19 May 2020
Source : Uipath
Friday, 15 May 2020
Big data has 5v characteristics.
Thursday, 14 May 2020
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.
Tuesday, 12 May 2020
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.
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
Note: WeChat public account is equivalent to the Facebook Page
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.
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 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.
Source : AI Process Invoices
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.
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
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 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.
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.
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
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 miningCollect 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.
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 miningBasic technology
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.
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 miningText mining is widely used in business, government, healthcare and other fields.
Customer service, market research, risk management, business intelligence, strategic analysis, financial insurance, retail
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.
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.
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.
#Textmining #naturallanguageprocessing #machinelearning #wordfrequency #informationextraction
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