7 ways AI will impact on the industrial automation
According to Gartner Data and Analytics, by 2020, 50% of companies will not have enough Artificial Intelligence tools and data literacy skills will be absolutely insufficient to achieve the real value of the business.
The other 50% of organizations within any industry will have data literacy, intelligent algorithms and a high capacity to compute resources to provide real value to their business.
Artificial Intelligence and Machine Learning will help any society to evolve into the 4th Industrial Revolution.
However, having advanced AI and machine learning models is not enough. The AI without data is nothing, and an AI without computational power is impossible.
Here you can find 7 ways AI will impact automation in 2020. These are the AI technologies that are already taking off, such as computer vision, collaborative robots or reinforcement learning, which in 2020 will have a major impact on the industry.
- Find out information from data with AI
- Improving services and products through computer vision
- Advanced Deep Learning on data for smart manufacturing
- Safer collaboration and productivity with cobots
- Improving decision-making robots with reinforcement learning
- Bring machine learning to the Edge with AI-enabled chips
- Predictive analytics powered by deep learning platforms at the source of data
Find out information from data with Artificial Intelligence
Why do we need tons of terabytes of data memory?
Data is practically useless if we do not make sense out of it. In fact, having a lot of data is important but alphabetizing data is much more important to unlock the real value of data. And the more data we have, the more valuable the analysis can be.
The industry generates tons of valuable data in a single day. Records from the production line, temperature sensors, video, audio, etc. With the right Artificial Intelligence models, all raw data can be transformed into valuable information. New information can lead engineers and designers to discover new ways to improve a product, the assembly line or even know trends.
Empowered by AI, professionals within any industry will be able to make better and faster decisions.
The following industries may benefit from the discovery of information:
- Mining, oil, and gas.
- Hospitality and food services
- Finance and Insurance
- Scientific research
Improving services and products through computer vision
Computer Vision, a field within AI, seeks to replicate the capabilities of human vision and extract valuable information from images and videos. It uses machine learning and neural networks to identify and process objects in a video or images.
In order to function, computer vision is based on 3 elements: visual data, intelligent algorithms and high-processing computers.
These are some of the most common improved services/products due to computer vision:
- Automatic machine driving: this technology helps machines to recognize what is happening around them. Cameras located around the machine transmit the data in real time to the central computer. This computer performs vision algorithms to process images and find obstacles on the road and read traffic signs, too.
- Facial recognition: a photo of the user is taken and sent to the computer to run vision algorithms on facial biometrics and then compare it with a database containing facial profiles. In addition to finding criminals, facial recognition can also be used to improve security in products and services.
- Mixed Reality AR/VR: Using applications such as Pokemon Go, Computer Vision is responsible for the overlapping of virtual objects in the physical world shown on the screen.
Advanced Deep Learning on data for smart manufacturing
It all started with the popular lean manufacturing techniques developed by the Toyota Production System (TPS). This system was based on continuous measurement and statistical modelling of a huge amount of processes.
Machine Learning and Deep Learning will help intelligent manufacturing systems to:
- Improve the quality control of the assembly line
- Improve product quality by detecting anomalies and monitoring the production process
- Improve machine maintenance with predictive analyses – Help with capacity planning
Safer collaboration and productivity with cobots
Amazon has made a big leap in the automation sector by purchasing in 2019 the robotics warehouse Canvas Technology. Amazon has been able to build new warehouse bots thanks to the computer vision powered by Canvas Technology.
The new autonomous system is already allowing Amazon warehouse workers to work safely alongside Collaborative Robots (Cobot).
These will not replace the human workforce, they will only help perform monotonous tasks that require high precision.
Cobots are autonomous systems that can collect and position objects, pack them, inject, analyze etc. The power of Cobots is limited to avoid accidents.
Improving decision-making robots with reinforcement learning
Reinforcement Learning (RL) is a state-of-the-art machine learning technique that seeks to create ML models for advanced decision-making and strategic learning.
Through reinforcement learning, the model uses the error to look for a solution to a problem. In other words, the machine is rewarded or punished for actions necessary to achieve the goal.
This machine learning technique has been widely adopted by the gaming industry.
The RL helps a machine to learn how to perform tasks without knowing much about how to do it at first. When reinforcement learning algorithms go through a huge number of problem-solving missions, the model/machine achieves incredible abilities.
In addition to the world of video games, the RL will also form other industries.
For example, a robot programmed with RL that is in an unknown labyrinthine location, will observe, navigate and learn through the process. The next time it will go through the maze it will be able to make automatic decisions previously learned.
Bring machine learning to the Edge with Artificial Intelligence enabled chips
The cloud has the infrastructure and services to run AI and ML algorithms on the data and then send the results. This is a great solution for those who have high-speed Internet access and a reliable connection, but for those who operate remotely this service is unattainable.
Instead of relying on the cloud, you are going to rely on a distributed, decentralized IT architecture: edge computing.
Edge computing will bring the power of the cloud closer to the user. All the data will be managed, processed and transmitted by tons of devices.
An example of a device that can bring machine learning to the Edge is Intel’s Movidius Neural Compute Stick (as small and simple as a USB drive). This device enables rapid prototyping and distribution of Deep Neural Network applications on the Edge network. It uses a Vision Processing Unit (VPU) architecture, which is an integrated chip optimized for AI to accelerate computing vision based on neural networks.
Predictive analytics powered by deep learning platforms at the source of data
Traditionally, an ML algorithm trains a model using a marked and structured data set. After some time, the model learns from the data and predicts the coming future data. Deep Learning adopts this same concept but uses unstructured data. Deep Learning models can also be data as images, videos or audio. That is why deep learning is crucial to recognizing the word or the image.
Deep Learning depends on 3 different factors: huge amounts of data, intelligent algorithms and GPU (Graphics Processing Unit) to accelerate learning.
The latter is a type of calculation that uses advanced graphics processing units and CPU to process intensive deep learning and analysis operations. It will collect all the data, analyze and predict trends.
Predictive maintenance will help inform you when certain machines need assistance.
This will be useful for Automotive, Manufacturing, Logistics and Transportation, Oil and Gas, Services industries.