The Way Forward: 15 AI-ML Trends for 2020
- March 30, 2020
- Posted by: Subham Paul
- Category: Blogs
Paying at shops? Listening to music? Booking a cab? Tracking a courier package? Look around yourself and almost everything is handled digitally. Technology is the virtual life support system of mankind because our ways of living have got extremely fast and easier with its perusal along with Artificial Intelligence and Machine Learning. This calls for a constant inspection of areas to improve, which brings you to read this article on 15 AI-ML trends to watch out in 2020.
While AI enables us to build autonomous systems, it needs an evolving pool of data to keep learning with changing situations and environments. In this respect, conventional methodologies and algorithms might not always be the most optimized approach. The internet is replete with the latest trends in this field which proves that advancements in the field of Machine Learning have been tremendous lately. So here are the 15 AI-ML trends that have a huge potential of being helpful in industrial and commercial applications this year:
- Basic Tasks Automation in the Workplace – tasks such as data entry, customer service, network monitoring, etc. can be automated that can help startups save on staffing costs.
- Improved AI System Assistance – Siri, Alexa, and Cortana are just the openers in this game. There are a lot more opportunities that shall develop diverse AI assistants across industries.
- Controllable Generative Models – making predictions beyond the training data that might be text, numbers, pictures, audio, video, and others.
- Digital Workers – there shall be entities in the workspace that can completely substitute human efforts, thereby cutting down company costs.
- Machine learning in the wild – to develop models that can quickly adapt to rapidly changing data environments, utilizing sophisticated model compression and real-time processing.
- Integration of Blockchain, IoT, and Artificial Intelligence – for enhancing the activation, regulation, security, and scalability of systems connected by IoT.
- Synthetic data/simulations – this shall be essential where training data is extremely limited in applications such as autonomous driving.
- Accuracy in Facial Recognition Features – better-designed ML models can certainly improve the accuracy of such systems and enhance the performance of their parent systems.
- Deep Active Learning and Human-in-the-Loop Learning – models shall have intelligent data collection as an essential component and utilize self-supervision.
- AI in Media and Entertainment Industry – AI has great potential in aiding creators in generating ideas, scripts, etc.
- AI Marketing – optimizing digital marketing programs and enhancing customer service and retention.
- Secured Privacy and Policy – the talks about policy requirements in AI and data privacy and likely to continue, paving the way for newer standards and compliances.
- Cybersecurity Powered by AI Technology – monitoring the behavior of systems and users to detect suspicious activities and raise alarms, thereby saving time and minimizing losses.
- Hybrid deep Learning with Symbolic or Causal Reasoning – cross-domain knowledge will have to collaborate with the available data and constraints of the problem.
- Conversational AI – customer service and psychological support systems shall involve more chatbots that can leverage the power of AI to improve customer satisfaction and operational efficiency.
To follow or not to follow these trends depends a lot on the existing technology frameworks and requirements of organizations. However, it shall be interesting to see how these trends shape the AI scene and take us forward in our constant journey of improving lives.