5 Major AI Trends of 2018
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.
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.
