2018 five major artificial intelligence trends

Humans have always been very curious about the concept of robotics and artificial intelligence (AI). Hollywood films and science fiction may have inspired some scientists to start working in this direction. Although the artificial intelligence bubble has appeared many times, the current major developments and breakthroughs are re-inviting public interest in this field.

In 2018 we need to focus on the relevant areas of AI, as change is slowly coming, including natural language processing (NLP), machine learning, cognitive computing, neural networks, computer vision and robotics and related technologies. In this article, we will explain five evolving trends around all of these technologies and understand their benefits.

1. Democratization of machine learning models

Machine learning is designed to enable computers to learn from the data and make improvements without relying on commands in the program. This learning can ultimately help the computer build models, such as models for predicting weather. Here, we introduce some common applications that use machine learning:

1.1 Financial application

As financial technology startups challenge existing businesses, the financial industry is growing rapidly. Many of these existing businesses rely primarily on traditional inefficient methods to provide advice and business on standardized financial products. Advances in artificial intelligence are transforming this area by introducing automated consulting. The machine learning model also replaces traditional predictive analytics to measure market trends. These models provide a higher level of accuracy and predict market volatility than traditional investment models.

Machine learning now also helps financial companies prevent financial fraud. These models are particularly good at finding anomalies based on historical data and can easily identify and even predict fraudulent activity. Banks are using these models to alert customers to any unusual activity in their accounts. In addition to preventing fraud, machine learning can play a greater role in risk management. These models can improve the accuracy of credit ratings and improve the risk management of lending institutions.

1.2 Medical applications

Machine learning and big data can take advantage of a large amount of potential medical data, and new applications built on machine learning models can help identify diseases and provide the right diagnosis of disease. Machine learning can also help humans with gene sequencing, clinical trials, drug discovery and development, and prediction of epidemics.

For example, Alibaba Cloud's ET Medical Brain, a recent algorithmic scientist from around the world, will use their intelligence to conduct precision medical competitions on this platform, and they will develop predictive models around personalized treatment for diabetes.

AI-based systems also help hospitals improve their operational workflows and data management. It is worth noting that health care professionals also make mistakes when reading dose instructions or diagnostic data. Intelligent AI systems with image recognition and optical character recognition can double check these data and ensure that such errors are reduced.

1.3 Industrial applications

Machine learning algorithms support many applications covering the entire manufacturing lifecycle, including product design, production planning, production optimization, distribution, field service, and recycling. Several industries are now implementing solutions based on artificial intelligence and the Internet of Things, and achieving greater synergies on top of their isolated and decentralized SCADA (monitoring and data acquisition) solutions.

In addition, the use of robots and automated machines is no stranger to manufacturing. Advanced systems based on the Internet of Things are now driving preventive maintenance and repair of plant equipment and machinery, and the use of AI-based technology to optimize supply chain operations is also evolving.

1.4 AIOps platform

Most of us have seen the process setup of IT operations, where IT practitioners are often overburdened and handle thousands of events every day. These analytics systems are unable to take advantage of the true potential of IT operational data, which is why they are turning to smart systems with higher operational capabilities. The advanced AI algorithm in AIOps automates the process of analyzing and correlating event data. In addition, AIOps can reduce the frequency of such events using algorithms that can be deduplicated in real time, blacklisted, and associated event feeds.

2. Simplify human-computer interaction with natural language processing

Natural Language Processing (NLP) is a rapidly evolving branch of artificial intelligence that focuses on analyzing and understanding human languages. NLP-based applications interact with humans by understanding speech, context, dialect and pronunciation, and more nuances.

In addition, NLP is helping computers develop reading and understanding skills beyond humans. In January 2018, the model designed by Alibaba Cloud's NLP team scored higher than humans in Stanford's reading and comprehension tests. The ALP model of the Alibaba Cloud team is based on a deep neural network AI machine, which answered more than 100,000 questions in this test.

Let's take a look at the trends in NLP and AI-based technologies:

2.1 Customer Service Chat Robot

NLP can support a wide range of real-world customer service applications, where people typically have to deal with regular customer queries under highly stressed working conditions. NLP-based chat bots can improve customer service by providing greater efficiency, reducing wait times, and standardizing documents to better resolve customer queries.

2.2 Virtual Assistant

Amazon Echo, Alexa, Cortana, Google Assistant, and Siri are some of the most famous examples of NLP entering the consumer space. By understanding human voice requests, AI technology is changing the way we interact with machines. Virtual assistants are likely to break our traditional advertising business model and prompt us to make purchasing decisions.

2. 3 Recruitment Portal

NLP-based recruitment portals are becoming more common. These portals help companies deal with large-scale recruitment, and HR managers need to distribute thousands of resumes in these recruits. NLP can quickly find candidates by scanning a large number of job applications and matching them to recruitment criteria. Unlike past portals, these portals do not need to rely on keywords.

3. Enhance customer experience through sentiment analysis

Customers may be frustrated if they need to wait for an IVR queue before a customer service representative arrives. All of us have experienced this experience, and because of this inefficient customer support process, companies lose customers. This is where emotional analysis can provide improvement, and sentiment analysis allows the computer to understand the background or intent of a conversation, comment or feedback. It enables them to distinguish between opinions, suggestions, complaints, inquiries and compliments.

Apps that use sentiment analysis can help companies better understand customer needs, and such applications can analyze many social media channels to improve the brand's social listening.

As sentiment analysis continues to evolve, future virtual personal assistants and sentiment-sensitive wearable devices may understand our emotional state and preferences. These systems will help the marketing department provide contextual and personalized experiences for its customers. According to Tractica, global revenues from similar software tools will reach $3.8 billion by 2025.

Emotional analysis also plays an important role in health care and mental health. In addition to other indicators of physical health, emotional sensing wearables can also monitor mental health. Mental health service providers can also use psychotherapy chat bots like Karim and Woebot to help people manage their mental health.

In addition, even car companies are now evaluating the scope of sentiment analysis. By deploying an advanced sentiment detection system on the vehicle, the onboard computer will be able to measure the driver's mood and level of attention to help drive. Future autonomous vehicles will be able to completely replace drivers by detecting emotions such as anger, lethargy and anxiety to prevent accidents.

4. The development of smart cities

Currently, most cities do not have the capacity to meet the needs of their explosive population. Providing water, electricity, transportation and cleaner air to a large urban population is becoming an increasingly complex challenge for urban managers, and access to health care and public services is another major problem. Among them, government organizations also need to maintain law and order within their limited resources.

Smart cities can use artificial intelligence, big data and the Internet of Things to solve most urban population challenges. By mixing these technologies, cities can better analyze camera data from across the city, and images and real-time video analysis can help identify accidents and traffic congestion. Administrators can use this information to centrally manage traffic on the road. In addition, they can rely on intelligent systems to automatically control traffic signals for priority adoption of VIP: emergency response teams and law enforcement agencies.

Alibaba Cloud ET City Brain provides most of the above functions, and several successful pilot projects have been carried out in China using ET City Brain. To learn more about these developments, you can read our blog, how ET City Brain changes our way of life - one city at a time.

In addition to general monitoring, facial recognition and sentiment perception capabilities may be helpful for retail stores operating in the city. The artificial intelligence-based marketing system can enhance the geographic location and beacon-based in-store marketing methods currently relying on the use of customer smartphones.

Artificial intelligence also plays an important role in architectural design and construction activities. The AI-based system not only manages building assets, but also improves the selection of vertical frame systems, aids in performance diagnosis, and helps plan the construction phase through GIS data analysis. In the future, artificial intelligence will help design custom building materials for nanotechnology. This means that in addition to steel and concrete, engineers will also have a large amount of new building materials to build environmentally sustainable buildings.

5. Unification of AI tools and development platforms

The artificial intelligence tools and platform market has many competing vendors that offer different capabilities in a decentralized ecosystem. Most artificial intelligence development platforms are still in their infancy, and while many business use cases have matured over the years, the full adoption of AI is still uncommon in all industries. This is where traditional cloud and distributed computing service providers play an important role in AI startups. Cloud service providers have an off-the-shelf infrastructure, scale and critical resources to develop big data and artificial intelligence platforms for businesses of all sizes.

Alibaba Cloud's ET Brain is such a platform. It combines multiple artificial intelligence and big data capabilities and delivers breakthroughs in verticals across industries. ET Brain can help your organization make real-time decisions through inference algorithms and drive innovation through machine learning. It has multi-source large-scale processing capabilities and can increase decision-making initiative. Currently, cloud-based platforms are already helping government organizations improve their public services.