Latest Machine Learning Development Trends for 2020


Machine learning is accelerating with the availability of tools like TensorFlow, Google Cloud ML Engine, Apache Singa, Apache Spark MLLib and Amazon Machine Learning. Developers incorporating artificial intelligence and machine learning into their NexGen solutions for business, government, and healthcare find it easier to use these advanced tools. The result is a spurt in ML development and adoption in several key areas that are likely to be frontrunners in 2020 and beyond.
Conversational AI
Chatbots are must-have tools for online businesses but their Neanderthal capabilities are limiting, to say the least. Now we can look forward to conversational bots or con bots that will actually be able to carry on an intelligent conversation. Research and development activity focuses on dialog systems that lookup the conversation history and the context. Your bot in 2020 will be able to give varied responses and even recognize emotions. This is possible due to the ease with which massive amounts go into machine learning. Naturally, bots should be multilingual and here again machine learning developments focus on natural language processing.
Natural language processing
For businesses to grow they must be able to capture the widest possible market segments. For this to happen it is important to be able to serve people in the language of their choice. Machine learning developers fall back on tools such as CoVE, BERT, and other language models. Machines will translate and process language and even respond to callers. Human interpreters at conferences and political heads of state meetings may find this alarming but this technology still has some way to go given the number of languages and nuanced expressions. If speech and hearing are important in the overall vision of the future so is the vision, another area where ML development services are quite active.
Machine learning development powers solutions such as face recognition. This is crucial in several areas such as crime and access control, healthcare and manufacturing. The interesting thing to note is 3D is gaining importance as software can prepare 3D images from 2D image data.
Practical application areas of ML vision:
– Identify diseases through the analysis of visual clues in the healthcare segment
– Traffic control and better monitoring in transportation
– Security and crime prevention
Data lakes
Data warehousing is old and the future is data lakes that take in all sorts of unstructured data and let machine learning sort it out as well as deliver actionable intelligence. The cloud platform coupled with data lake will likely help even small businesses dip their fingers into AI/ML technology to further their business.
Talk of AI/ML and the talk invariably veers towards costs but with free and open-source tools, affordably powerful computing infrastructure and technology skills of ML developers bring the benefit of this technology to everyone. Speed can be crucial since it takes time to train machines but here again, ML developers may speed up matters with their access to huge amounts of relevant data.