Top 7 Machine Learning And AI Trends In 2020

Top 7 Machine Learning And AI Trends In 2020

Artificial Intelligence and machine learning has changed the way of working that we have continued for many years. A good example is the rise of chatbots that are taking over the businesses to manage the customer queries. Machine learning has helped in analysis of large data sets within minutes. There are a lot of innovative uses for Artificial intelligence and machine learning. AI powered virtual nurses like “Angel” and “Molly” are already saving lives and cost while robots are helping with everything, from less invasive procedures to Open heart surgery. Considering the rise in demand and interest in artificial intelligence and machine learning, lots of new trends are emerging. Let us see some of these.

Machine Learning And AI Trends In 2020-

1. Automation-

Intelligent Process Automation(IPA), is a process of ensuring automation of manual tasks with the assistance of artificial intelligence. All organizations have bottlenecks in different business measures. IPA will assist them with distinguishing the trend and foresee future bottlenecks by allowing the management to enhance decision making effectively. Automation is a beneficial development for any business to drive its tasks. For example, automation can help to prevent cyber attacks by recognizing unusual user requests and the frequency of these requests.In such cases, the system can notify the administrator, allowing them to take the necessary actions. Next automation is automated testing tool for developers.  Because of automation testing tool, programmers can focus on development by saving the time of testing smart systems and debugging.

2. Virtual Gaming-

The AI games that are available today, don’t have a robust environment for users. The reason behind this is the lack of data storage required to create such environments. The recent upsurge in AI technology is the push that virtual gaming required. The upcoming virtual games will be very realistic and  interactive. Through machine learning, games can evolve in the future based on character development taken by the user. Game developers are expected to adapt new skills in AI so as to stay aware of the demands of its users who no longer stay content with the visualization. Their desires are to enjoy games as close to real life as possible by using virtual reality and technology, such as, 3D augmentation. Also, mobile game developers have the opportunity to present their skills in such gaming apps.

3. Conversational AI-

Conversational AI is becoming an integral part of businesses. Many companies are adopting the advantages of chatbots to bring customer service, sales and marketing. Although chatbots are an important asset  for business, their performance is still a long way from human. Researchers from big institutions and tech leaders have explored the way to improve the performance of Dialog systems:

  • Dialog systems are improving at tracking long-term aspects of conversation. The main aim is to improve the system’s ability to understand complex relationships in conversation by using the conversation history and context.
  • Most of the current chatbots generate boring and repetitive responses. The next aim to generate diverse and relevant responses is on the way.
  • Emotion recognition is an important feature for open-domain chatbots. So experts are searching the best ways to include empathy into dialog systems. 

4. Computer Vision-

Computer vision systems have revolutionized industries and business functions with apps in security, healthcare, retail, transportation, agriculture and so on. Recently introduced architectures and approaches like EfficientNet and SinGAN improves the perspective and generative capacities of visual systems. The latest research topics in computer vision are-

  • Currently 3D is a leading research area in CV. The google research team introduced a novel approach for the generation of depth map of natural scenes.
  • There is an increase in the popularity of unsupervised learning methods. For instance, research team of Stanford University introduced a promising Local Aggregation approach to object detection and recognition with unsupervised learning.
  • Computer vision research is successfully combined with NLP. Recent research advances enable robust change captioning between two images in natural language, vision-language navigation in 3D environments and learning hierarchical vision-language representation  for better image caption retrieval and visual grounding. 

5. AI and Humans-

As the use of ML and AI has been advancing very rapidly and will proceed further, a need emerges to adapt ourselves to work with digital workers. AI can deal with complex tasks without need of human supervision. It can deal with numerous functions at the same time. In spite of the advantages, AI is still not advanced enough to be able to use creativity, imagination, and add human emotions to its work. AI can help humans by generating analytical reports according to the data sets in the system via machine learning. AI systems gives productivity of 99.9%. It is also good at maintaining focus on work without distractions. These characteristics are very advantageous for the human world’s evolution to the more advanced system. 

6. Reinforcement Learning-

Reinforcement Learning is still important for business apps. It is successfully applied in the cases where huge amounts of simulated data can be generated such as games and robotics.  RL is a promising path towards Artificial general intelligence(AGI) or true intelligence. Si researchers are exploring the ways to make RL algorithms more efficient and stable. Have a look at trending research topics in RL-

  • Off-policy evaluation and off-policy learning are very important for future RL applications. The recent steps in this research area  include new solutions for batch policy learning under different limitations, combining parametric and non-parametric models, and introducing a novel class of off-policy algorithms to force agent towards acting close to on-policy.
  • There are new efficient exploration methods with distributional RL, maximum entropy exploration, and a security condition to manage the gap effect in RL.

7. Cybersecurity-

Network security is always a priority in all organizations. Throughout the years, large organizations had faced a lot of issues about their customer data privacy. Thus, it is expected to see these companies invest lots of their assets in developing ways to improve data security. Improving data security measures will allow consumers to control and ownership their data, unlike in the past. Captcha was the first case to avoid robots hacking into the system. AI will allow the detection of account holders and protect users. With the upcoming AI trends, it is likely for rivals to get more brilliant with time and come up with better approaches to fight AI and hack into systems. Enterprises are also planning to fight tech with tech. Progressed AI security will empower quick steps to secure all leakages immediately.

Final Words-

As time passes, many new fraud attacks can occur, so there is need to advance your data security or organizational security with the new trends.  These are some of the important AI and machine learning trends in 2020 that you can adapt. If you are thinking of incorporating it for your organization, consult with Solace experts. We are here to help you through development and consultation. You can hire AI developers of solace teams for an effective development. Connect with Solace and get a free quote for Machine learning and AI development. We will be happy to help you.

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