5 Major AI Trends of 2018
5 Major AI Trends of 2018
In this article, we will explain five evolving trends around all these technologies and learn about their benefits.
Join the DZone community and get the full member experience.Join For Free
Humans have always been thrilled with the concept of human-like robots and Artificial Intelligence (A.I.). Hollywood movies and science fiction have perhaps inspired several scientists to start working towards this direction. Although the AI bubble has burst many times, significant developments and breakthroughs are now renewing public interest in this field. In 2017, Gartner placed general AI at the stage of early adoption in its hype cycle. It also placed deep learning and machine learning technologies at the peak of this hype cycle.
It is important to realize that AI is an umbrella term for several interlinked technologies. These include Natural Language Processing (NLP), machine learning, cognitive computing, neural network, computer vision, and robotics and its related technologies. In this article, we will explain five evolving trends around all these technologies and learn about their benefits.
1. The Democratization of Machine Learning Models
Machine learning aims to enable computers to learn from data and make improvements without any dependence on commands in a program. This learning could eventually help computers in building models such as those used in the prediction of weather. Here, we have covered some of the common applications that leverage machine learning:
The financial industry is evolving rapidly with Fintech startups challenging the incumbents. Many of these incumbents largely rely on traditional inefficient methods for advisory and distribution of standardized financial products. AI advancements have transformed this field with the introduction of Automated Advisory. Machine learning models are also replacing the traditional predictive analysis methods for gauging the market trends. These models can provide a higher level of accuracy and speed in predicting market swings as compared to the conventional investment models.
Machine learning is now also helping financial companies in the prevention of financial frauds. These models are especially adept at finding any anomaly based on historical data, and can easily identify and even predict fraudulent activity. Banks are using these models to alert the customer about any unusual activity in their accounts. Apart from fraud prevention, machine learning could play an even larger role in the field of risk management. These models can increase the accuracy of credit ratings and improve risk management for lending institutions.
Machine learning and Big Data hold the key to harnessing the massive potential medical data holds. New Applications built on machine learning models can help in identification of diseases and in providing a correct diagnosis of ailments. Machine learning can also help in gene-sequencing, clinical trials, drug discovery and R&D, and epidemic outbreak predictions.
For instance, Alibaba Cloud's ET Medical Brain recently brought algorithm scientists from all parts of the world to a common platform in Tianmart Precision Medical Competition. They were able to develop a predictive model for the personalized treatment of diabetes.
AI-based systems are also helping hospitals in the improvement of their operational workflows and management of data. It is also common for healthcare professionals to commit mistakes in reading dosage instructions or diagnostics data. Smart AI systems with image recognition and optical character recognition capabilities can double check all this data and ensure reduction of such errors.
Machine learning Algorithms are supporting a lot of applications covering the entire manufacturing lifecycle including product design, production planning, production optimization, distribution, and field service and reclamation. Several industries are now implementing AI and IoT based solutions on top of their siloed and fragmented SCADA (Supervisory Control and Data Acquisition) solutions for increased synergy.
Further, the use of robots and automated machines are not new to the manufacturing industry. Advanced IoT based systems now drive preventative maintenance and repair of plant equipment and machinery. Optimization of supply-chain operations with AI-based technologies is another evolving use case.
Most of us have witnessed IT setups where IT practitioners are often overburdened handling thousands of events on a daily basis. These analytics systems fail to harness the true potential of IT operational data. That’s why there is a shift towards developing smarter operational capabilities. Advanced AI algorithms in AIOps can automate the process of analyzing and correlating event data. Further, AIOps could reduce the frequency of such events using algorithms that can deduplicate, blacklist, and correlate event feeds in real-time.
2. Simplification of Human-Machine Interactions with Natural Language Processing
Natural Language Processing (NLP) is a fast evolving branch of AI, which focuses on analysis and understanding of human languages. NLP based applications do a better job of interacting with humans by understanding the finer nuances of speech, context, dialects, and pronunciations.
Moreover, NLP is helping computers develop reading and comprehension capabilities that surpass even humans. In January 2018, Alibaba Cloud scored better than humans in a Stanford University reading and comprehension test. Alibaba Cloud’s NLP and deep neural networks based AI machine answered more than 100,000 questions in this test.
Let’s have a look at some evolving trends that feature NLP and AI-based technologies:
Customer Service Chatbots
NLP can support numerous real-world customer service applications where humans have to handle routine customer queries, often in highly stressful work-conditions. The NLP based chatbots can improve the customer services by offering higher efficiency, reduced wait-times, standardized documentation, and better resolution of customer queries.
Amazon Echo, Alexa, Cortana, Google Assistant, and Siri are some of the most celebrated examples of NLP entering the consumer space. By understanding human voice requests, the AI technology is transforming the way we interact with machines. Virtual assistants have the potential to disrupt the traditional advertising business and transform the way we make purchase decisions.
NLP based recruitment portals are becoming increasingly common. Such portals help enterprises in handling mass hirings, where HR managers need to sort through thousands of resumes. NLP can swiftly find candidates by scanning reams of job-applications and matching it with hiring criteria. Unlike the portals in the past, these portals need not depend on keywords.
3. Augmentation of Customer Experience with Sentiment Analysis
Customers can get frustrated when they need to wait in an IVR queue before a customer service representative attends them. All of us have gone through this experience. Businesses lose customers due to such inefficient customer support processes. This is where sentiment analysis can offer a way forward. Sentiment analysis allows computers to understand the context or intent of a conversation, comment, or feedback. It gives them the ability to distinguish between opinions, suggestions, complaints, queries, and compliments.
Applications that utilize sentiment and emotion analysis can help businesses in understanding their customers’ needs better. Such applications can analyze numerous social media channels to improve social listening for brands.
With the ongoing developments in sentiment analysis, it is possible that in the future, virtual personal assistants and emotion sensing wearables would understand our emotional state and preferences. These systems would help marketing departments in providing contextualized and personalized experiences to their customers. According to Tractica, the worldwide revenue for similar software tools will reach $3.8 billion-mark by 2025.
Sentiment analysis is also playing a role in the field of healthcare and mental wellbeing. Emotion-sensing wearables could monitor mental health in addition to the other indicators regarding physical health. Mental health providers can also adopt psychotherapy chatbots like Karim and Woebot to help people manage their mental health.
Further, even automotive companies are now evaluating the scope of sentiment analysis. With advanced emotion detection systems deployed on a vehicle, the onboard computer will be able to gauge driver’s mood and attention levels to assist in driving. Also, automated vehicles in the future would be able to take complete control away from the driver on detecting emotions like anger, drowsiness, and anxiety to prevent accidents.
4. Development of Smart Cities
At present, most global cities are under-equipped to meet the demand of their exploding population. Providing water, electricity, easy transportation, and cleaner air is becoming an increasingly complex challenge for city administrators. Access to healthcare and public services is another major concern. Amidst all this, government organizations also need to maintain law and order within their limited resources.
Smart cities leverage AI, Big Data, and IoT to resolve most of these urban population challenges. Using a mix of these technologies, cities can better analyze their video camera feeds from across the city. Image and real-time video analysis can help in identifying accidents and traffic congestion. Administrators can utilize this information to centrally manage traffic on the roads. Further, they can depend on smart systems to automatically control traffic signals for allowing prioritized passage to VIPs, emergency response teams, and law enforcement agencies.
Alibaba Cloud ET City Brain provides most of these above-mentioned capabilities. China has run several successful pilot projects using ET City Brain. To know more about these developments, you can read our blog — How ET City Brain Is Transforming the Way We Live — One City at a Time.
Apart from general surveillance, facial recognition and emotion sensing abilities could be helpful for retail stores operating in a city. AI-based marketing systems could augment the geo-fencing and beacon based in-store marketing methods that presently rely on customer’s smartphone usage.
AI is also playing a major role in building design and construction activities. AI-based systems can not only manage construction assets but can also improve the selection of vertical framework systems, help in performance diagnostics, and assist in planning stages of construction with analysis of GIS data. In the future, AI would help in designing of customized construction material with nanotechnology. This means that in addition to steel and concrete, engineers will have a lot of new construction material to build environmentally sustainable buildings.
5. Unification of AI Tools and Development Platforms
The market for AI tools and platforms has numerous competing vendors offering varying capabilities in a fragmented ecosystem. Most of the AI developments are still in the infancy. While numerous business use cases have matured over the years, full-scale adoption of AI is still not common across all industries. This is where traditional cloud and distributed computing services providers hold a significant edge over the AI startups. Cloud service providers have a ready infrastructure, scale, and significant resources to develop Big Data and AI platforms for businesses of all sizes.
Alibaba Cloud’s ET Brain is one such platform. It combines multiple AI and Big Data capabilities and is powering breakthroughs across different industry verticals. ET Brain can help your organization in real-time decisions with reasoning algorithms and drive innovation with machine learning approaches. It offers multi-source large-scale processing that improves proactiveness in decision making. ET Brain will also help you leverage neural networks for increased agility. The cloud-based platform is already helping government organizations in improving their public services.
Developers can use ET Brain for creating apps that utilize voice recognition, facial recognition, image recognition, text recognition, natural language processing, machine learning, and other AI technologies. These applications can also leverage Alibaba Cloud’s Big Data platform DataWorks and MaxCompute for analyzing complex and large volumes of data in real-time.
It is easy to conclude that AI-based developments have now gone mainstream. Businesses are not only keen on improving their existing processes but also see the potential for new revenue streams with AI. That’s why AI holds strategic importance for CIOs. There is still a lot of room for innovation in this space. In the end, businesses who stay agile and readily adopt the latest advances in AI, Big Data, IoT, and Blockchain will be better placed to gain an early advantage.
Published at DZone with permission of Leona Zhang . See the original article here.
Opinions expressed by DZone contributors are their own.