Tuesday, January 31, 2023

7 Approaches to Artificial Intelligence

Artificial Intelligence

Artificial Intelligence (AI) has become a trend for companies. Many “C-level” executives are reporting progress in their companies due to the application of AI in their business units. However, AI covers a wide range of approaches to problem solving.

Along these lines, multinational technology company Stratesys has defined a digital hub between Europe and the US. Key AI Approaches That Were in Trend During 2022 and Will Continue to Evolve in 2023,

  • Automated Machine Learning (AutoML). Anyone can access the AutoML platform with the added benefit of reducing human error and accelerating the democratization of AI. Almost every step of the AI ​​modeling cycle is automated. This is a big step because we don’t spend much time finding effective AI models. With the help of semi-supervised and self-supervised learning, at least 3 times more models can be built than using traditional planning, reducing costs and democratizing AI model development across the company.
  • No-code machine learning and low-code machine learning development, No-code and low-code are becoming more and more popular among companies. Different platforms allow businesses to function without the need for an engineer or developer. This is possible because users can create their own tools with a “drag and drop” interface, rather than requiring complex coding to do so. It saves money and time due to less technical skills and less coding required. Since business analysts do not have the required software programming and coding skill set, the need for these applications by businesses is increasing rapidly.
  • Machine Learning Operations Management (MLOps), It includes a set of practices focused on enabling companies to implement and maintain AI models reliably and efficiently. First, you go through a phase of continuous development (DevOps) where the model is tested and developed in isolated experimental systems. When they are approved by the business, they move to the deployment or production phase (MLOps). In this final phase, the goal is to enhance automation and improve the quality of deployed models, focusing on regulatory and commercial requirements.
  • reinforcement learning. A few years ago, this approach was closely linked to robotics, as it uses reward and punishment systems to reinforce learning. For a long time, it has been used for problems related to robotic interaction devices (spiders, drones, robots, etc.). However, with the explosion of the world of process mining and process simulation, this approach has gained a new field of application by finding the best possible path within a variety of possibilities to execute the same process.
  • Robotic Process Automation (RPA) and Process Mining. For one thing, RPA allows a system to automate any process that might be repetitive, making it possible for people to spend their time working on other projects that require more important human resources. Thinking skills are required. However, all steps must be well predetermined before the “RPA bot” processes it, as it may fail due to uncontrolled deviations. On the other hand, Process Mining manages to find out the processes of the company where the most time is consumed. In addition, having the simulation capability allows preparing for scenarios that were not considered that may arise suddenly (COVID19) and with this, see the impacts on the life cycle of the process and how to deal with them.
  • Generative AI. This approach is capable of producing text, speech and images; Blog posts ranged across program code, poetry and artwork (and even controversially winning contests). Generative AI builds complex AI models to predict the next word based on a sequence of previous words, or to guess the next image based on words that describe previous images. Today its power can be seen through various platforms such as GPT for text, DALL-E for images, Whisper for voice and Copilot for generating code in various programming languages.
  • Tiny ML. The aim of this approach is to develop AI models that use hardware-constrained machinery, such as microcontrollers. The algorithms are optimally designed and developed to consume the least amount of resources while maintaining high efficiency. Data does not need to be processed in the cloud, showing independence and self-learning. With Tiny ML, printers, televisions and automobiles will be able to perform tasks that previously only computers and smartphones were able to handle.

“An AI-first company must adopt and develop each of these AI approaches to offer quality services and streamline processes”Understand Octavio Loyola-González, Executive Manager and Head of Machine Learning n Stratesys,

Nation World News Desk
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