What we need to understand is: AI is a general term for several interrelated technologies. It includes: Natural Language Processing (NLP), machine learning, cognitive computing, neural networks, computer vision, robotics and related technologies. In this article, we will explain the five major development trends of all these technologies and understand the benefits they bring.
1. Popularization of machine learning models
The purpose of machine learning is to enable computers to learn from data and make improvements without relying on program commands. This kind of learning can eventually help computers build models, such as those used to predict weather. Here, we introduce some common applications that utilize machine learning:
Financial application
With the rapid development of the financial industry, various start-up Fintech (financial technology) companies have emerged in large numbers. Practitioners in this industry have relied excessively on traditional inefficient methods to consult and distribute their standardized financial products. Nowadays, various advanced AI technologies have transformed the field through automated consulting and other methods. At the same time, machine learning models have also replaced the traditional predictive analysis methods commonly used in quantifying market trends. Compared with traditional investment models, these models can predict various market fluctuations with higher accuracy and speed.
In addition, machine learning can now also help financial companies prevent various financial frauds. These models are particularly good at discovering any anomalies based on historical data, and can easily identify and even predict a certain fraudulent activity. Once any unusual activity occurs in a customer's account, their bank can use these models to alert them. In addition to preventing fraud, machine learning can also play a greater role in the field of risk management. These models can improve the various risk management of lending institutions by improving the accuracy of credit ratings.
Medical application
Machine learning and big data are both key factors for mastering massive amounts of potential medical data. Through new applications built on top of machine learning models, we can identify various diseases and improve the accuracy of disease diagnosis. At the same time, machine learning can also contribute to gene sequencing, clinical trials, drug discovery and research and development, and outbreak prediction of epidemics.
In addition, AI-based systems can also help hospitals improve their operational processes and data management. In view of the fact that healthcare professionals will inevitably make mistakes in reading dose instructions or diagnostic data, the intelligent AI system performs a secondary check on all data through the functions of image recognition and optical character recognition to reduce the frequency of such errors.
Industrial application
Machine learning algorithms are now able to support a large number of applications and cover the entire manufacturing life cycle. It includes: product design, production planning, production optimization, distribution, field service and regeneration. At present, some industries are implementing AI-based and Internet of Things (IoT) solutions to their original, isolated and fragmented SCADA (Supervisory Control and Data Acquisition) solutions to give full play to the integration. Enhance synergy.
Although the use of robots and automated machinery and equipment is nothing new to the manufacturing industry, today, various advanced, IoT-based systems have been able to implement preventive maintenance and repair of equipment and machinery. In addition, the optimization of supply chain operations based on AI technology is another evolving industrial use case.
AIOps platform
Most of us have had this experience: IT personnel need to deal with thousands of system events at full capacity every day, and the analysis system at hand cannot make use of the data that is really useful for IT operations. They urgently need to develop systems with more intelligent operation capabilities. The advanced AI algorithm in AIOps can automate the analysis and correlation process of event data. In addition, AIOps can also use various algorithms to perform operations such as de-duplication, blacklist management, and associated event feedback in real time to reduce the frequency of such events.
2. Use natural language processing to simplify human-computer interaction
Natural language processing (NLP) is a rapidly developing branch of artificial intelligence, which focuses on the analysis and understanding of human language. NLP-based applications can better interact with humans by understanding the nuances of language, context, dialects, and pronunciation.
In addition, NLP can help computers continue to develop reading and comprehension abilities, even surpassing humans. In January 2018, in Stanford University's reading comprehension test, Alibaba Cloud scored higher than humans. In this test, Alibaba Cloud's NLP and AI-based deep neural network successfully answered more than 100,000 questions.
Let's take a look at some of the development trends of NLP and AI-based technologies:
Customer service chatbot
In traditional applications, people must handle various routine queries from customers in a highly stressful work environment. NLP can support many real-world customer service applications. Those NLP-based chatbots can improve the quality of customer service by providing higher efficiency, shorter waiting time, standardized documents, and better resolution of customer queries.
Virtual assistant
Amazon Echo, Alexa, Cortana (Cortana), Google Assistant, and Siri are some famous cases of NLP entering the consumer field. By understanding human voice requests, AI technology is changing the way we interact with machines. Of course, virtual assistants also have the ability to interfere with traditional advertising businesses and can change the way we make various purchase choices.
Recruitment portal
Various NLP-based recruitment portal platforms are becoming more and more common. These portal platforms can sort thousands of resumes for HR managers to assist companies in handling a large number of recruitment tasks. NLP can quickly find suitable candidates by scanning a large number of job applications and matching them with recruitment criteria. Unlike previous portal platforms, these portals will no longer need to rely on various keyword matching.
3. Enhance customer experience through sentiment analysis
I believe we have all had this experience: customers often have to wait in an IVR (Translator’s Note: Interactive Voice Response) queue in despair before receiving a call from a customer service representative. Companies will also lose a large number of customers because of such inefficient support processes. Today, AI provides a new way of sentiment analysis. By allowing the computer to understand the context, and intentions of a certain conversation, comment, and feedback, AI can distinguish different opinions, suggestions, complaints, queries, and praise.
The application of sentiment analysis can help companies better understand customer needs. This application can be used to analyze a large number of social media sources to improve the brand's ability to listen to society.
With the continuous development of sentiment analysis, virtual personal assistants and wearable devices with emotional awareness capabilities will be able to understand our emotional state and preferences. These systems will help marketing departments provide customers with contextualized and personalized experiences. According to data from Tractica (translator's note: a market research and consulting organization), by 2025, the global revenue of such software tools will reach $3.8 billion.
In addition, sentiment analysis also plays a huge role in healthcare and mental health. Wearable devices with emotional awareness capabilities can not only display indicators related to physical health, but also monitor psychological health. Therefore, mental health providers can also use chat robots like Karim and Woebot to develop treatment plans to help people manage their mental health.
At present, some auto companies have also introduced sentiment analysis. By deploying advanced emotion detection systems on the vehicles, they allow those on-board computers to detect the driver's emotions and attention levels to assist driving. In addition, future automated vehicles will be able to detect the driver’s anger, sleepiness, anxiety and other emotions, and control the vehicle to avoid accidents.
4. The development of smart cities
At present, the infrastructure of most cities in the world cannot meet the demand of population surge. Water, electricity, easy travel and cleaner air are all complex challenges that city managers face daily. People's access to health care and public services is another major issue. At the same time, government organizations also need to maintain corresponding laws and order within these limited resources.
Smart cities use AI, big data, and the Internet of Things to solve most of the challenges faced by the urban population. Using a combination of these technologies, cities can better analyze the information fed back by their cameras everywhere. For example: Through the analysis of various images and real-time video, it can assist in identifying accidents and traffic jams. Managers can also use this information to centrally manage traffic on different roads. In addition, they can also rely on intelligent systems to automatically control traffic signals and other equipment so that VIPs, emergency response teams, and law enforcement personnel have priority in passing.
In addition to general monitoring, facial recognition and emotion perception capabilities can also assist in the daily operations of retail stores in cities. AI-based marketing systems can enhance store marketing based on geo-fencing and coordinate models. This method currently mainly relies on the use of smart phones by customers.
AI also plays an important role in architectural design and construction activities. AI-based systems can not only manage building assets, but can also improve the choice of vertical frame systems, which facilitates performance diagnosis and assists through the analysis of GIS (Translator’s Note: Geographic Information System) data Planning and construction. In the future, AI will help design and customize building materials with nanotechnology. This will mean that in addition to reinforced concrete, engineers will have more new building materials to adapt to different construction environments.
5. Unify various AI tools and development platforms
In a fragmented ecosystem and market, many competitive vendors provide AI tools and platforms with different capabilities. Most of these AI development products are still in their infancy. Over the years, although business use cases in many areas have matured, it is still too early to fully adopt AI in all industries. This is also the advantage of traditional cloud and distributed computing service providers over AI startups. Those cloud service providers have ready-made infrastructure, scale and a lot of resources, and can develop big data and AI application platforms for enterprises of all sizes.
in conclusion
In summary, we can easily draw the conclusion: Today, AI-based development has become the mainstream. Various companies are not only keen to improve their existing processes, but also see the potential growth points that AI brings to them. This is why CIOs should pay attention to the strategic significance of AI and the space for its innovation and development. In short, those companies that can quickly embrace new technologies such as AI, big data, Internet of Things, and blockchain will surely seize an advantage in the competition.
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