Artificial Intelligence (AI) and Machine Learning (ML) are two trending buzzwords in the technology world that everyone has heard about. We are in fact surrounded by AI/ML-run gadgets in our day to day life. Amazon’s Alexa to IBM’s Watson and everything else in between has adopted AI/ML to a large extent.
AI and ML are providing cutting edge solutions to enhance our lives. We have smart machines that can think better and can even predict better than humans in some scenarios.
Let’s view five trends in AI/ML that are sure to shape 2021.
Is there a possibility of automating almost anything inside a company, for example, legacy business processes? Hyperautomation harnesses the power of multiple technologies including Robotic Process Automation (RPA), AI, and ML.
Hyperautomation is a term coined by Gartner. It refers to as digital process automation by Forrester and Intelligent process automation by IDC.
A key attribute of hyperautomation is the ability to link humans to the process. With technology and humans working together, employees can begin to train automation tools, and with Machine Learning, get to a state of AI-enabled decision making.
By minimizing manual tasks, employees get to focus on more impactful work such as planning and strategizing.
Using hyperautomation, you can model and automize your product for both gaining revenues and improving employee productivity. With the help of AI consultancy services, you can create a product meeting customer needs and understanding customer behavior better.
Overall, hyperautomation is a solution for businesses that want to build products for the future.
The amalgamation of AI and IoT
New machines used in the healthcare industry, such as X-ray machines, and CT-scanners are AI-enabled. Personal health care assistants are AI-enabled as well. These medical devices with AI can predict illnesses and help physicians prescribe the right treatment.
In the Banking industry, AI is used for tracking fraudulent transactions. In the retail industry, virtual shopping capabilities offer personalized recommendations and purchase options.
Internet of Things, on the other hand, enables devices to work without human help. With IoT, fridges can automatically order food when stock is low, while cars can drive themselves. IoT-driven devices are used across many industries including healthcare, retail, and manufacturing.
Combining AI and IoT brings major benefits:
- Avoid unplanned downtime by predicting failure of equipment well ahead so that maintenance of the equipment can be scheduled.
- Increase operational efficiency by predicting operating conditions and identifying parameters to be adjusted on the fly, to maintain ideal output.
- Enhanced risk management by predicting a variety of risks that can be managed in advance to mitigate a mishap, and also help automate a rapid response.
Augmented Intelligence is a subsection of AI/ML, developed to enhance human intelligence, rather than operate independently or outrightly replace it. Augmented Intelligence improves human decision-making and by extension, actions taken in response to improved decisions.
Augmented Intelligence includes the best of both worlds by combining the capabilities of humans and technology. For example, in a Big Data environment, Augmented Intelligence extends the power of human intelligence by analyzing large amounts of data that people can barely grasp, and by providing found patterns or dependencies as a basis for decision-making.
Focus on better data security and regulations
In our modern world, data is the most vital and valuable entity. Data is required to be secured as much as possible. Many nations have passed stringent laws to guard customer data. Privacy laws such as the GDPR and the California Consumer Privacy Act, carry strict penalties for violating data privacy laws.
AI and ML can help prevent and flag violations automatically and ensure that organizations remain compliant with these laws.
Reinforcement Learning (RL) is a domain of Machine Learning (ML) where the intelligent agent self learns by a sequence of actions and results, to maximize reward. In RL, the AI agent faces simulated situations, where it learns by trial and error to come up with a solution to the problem.
An example of RL is the training of self-driving cars. These cars do not get any instructions on how to drive. The cars are enabled to make their own decisions to maximize their reward.
The most vital task in RL is to prepare an appropriately simulated environment. For example, when training a self-driving car, it is crucial to building a realistic simulation of the road and traffic conditions to help improve learning, before letting the car drive on the road.
We overviewed only crucial and emerging trends that will be prevalent in 2021 in the realm of Artificial Intelligence and Machine Learning. Considering these advantages, you can grow your business, experiment with new approaches, or build an entirely new product of the future.