Artificial Intelligence, a common word used in today’s era is the sole reason for Industrial 4.0. With AI software, the world is dreaming of connecting people with machines where a major part of our lifestyle will be dominated by AI built Robots.
AI is that part of Computer Science that replicates the human intelligence and automates this behavior. By making use of artificial artifacts simulations, AI executes its software programs on a computer.
Did you know that most of the diagnostic tools in the healthcare industry make use of AI built software? The knowledge-based models use rational intelligence and provide logical answers through formalized specialist knowledge. Hence, such models replicate the role of a subject matter expert and advise the team to take the right actions based on their expertise. By feeding the AI systems with MRI scans, CT scans, or eye images, the diagnostic results can be expected within seconds and the data can be made available across the departments without much delay.
We have all come across this model very frequently. Biometric scanning to enter a company, facial scanning as an identification tool, or fingerprint scanning used for security purposes use the Pattern analysis model. It deals with the analysis and recognition of numerous complex patterns and shapes. Apart from identifying manuscripts, faces, and fingerprints, this model can be used for speech synthesis and speech recognition.
Replicating repetitive human tasks is what this model takes care of. Its main focus is on manipulative intelligence that allows robots to work on critical and dangerous human activities. These intelligent robots help in optimizing the everyday tasks of humans by imitating human behavior.
Jeff Hawkins once quoted that Prediction is just not an activity of the brain but the foundation of every intelligence. The Pattern Prediction model can be thought of as an extension of a pattern recognition model where the software recognizes the object and also anticipates its next move. This model can be considered as the basis of hierarchical temporal memory. The most common example of such models can be found in developing online games like Chess.
The development and the research phase of AI should overlap with each other. This way all the AI-related knowledge can be implemented leading to cognition of human life becoming a part of AI research. This model can be considered as a sub-discipline where development, execution, and research co-exist with each other to successfully imitate human intelligence.
Have you ever thought about what would happen if RPA was combined with powerful analytics? This combination is nothing but intelligent automation. This cognitive technology allows companies to either assist people in increasing the performance of the non-routine tasks or completely automate a repetitive process within the organization.
Hence, Intelligent Automation promises to automate routine and non-routine tasks which include intuition, judgment, creativity, persuasion, or problem-solving related tasks. It is just not the performance of such models that are attractive in the market, but the decreasing costs of data storage and processing power are increasing the usage and development of such models in the field of AI. Such models help us to create human-like capabilities like recognizing handwriting, identifying images, and natural language processing.
Therefore, it is very evident that IA models are very beneficial for companies and their use can be considered as potentially limitless. If we provide extensive configuration and if specific machine learning algorithms are developed for the process, the IA model can easily handle complex scenarios it may encounter.
As we already mentioned in the above section, AI or Intelligent Automation makes use of NLP to replicate human behavior. Hence, NLP is the ability of a software program to understand how the human language is spoken or written and tries to automate activities related to such tasks.
The following subsections of Natural Language Processing are used for this:
When an RPA bot works in combination with the NLP model, it can read any unstructured format like emails and legal contracts just like a human would do. Once the data is read, it will find the required data from the existing database and convert the initial data into a structured format that can be easily used for other tasks.
The major part of AI is Machine Learning models. Such models continuously learn from the data and make predictions with future data. They are good at pattern recognition and hence quick adjustments in the data can be made as explicit programming is not required to cover all scenarios. Such models automate the analytical models which enable machines to identify new scenarios independently.
If we talk about integrating Robots with ML models, the result will be very satisfactory. For example, if we look at the claim processing task, ML will read all the accepted and rejected claims and create its algorithm. Hence, a bot can automate the claim processing task and ML algorithms allow the bot to handle any new scenario without much human-intervention, a goal worth pursuing in every industry.
The very popular sub-area of Machine learning or Artificial Intelligence is Deep Learning. You get to hear this concept time and again among the next-gen data scientists as they focus on creating artificial neural networks using a large amount of data. Deep learning precisely recognizes images or texts and makes decisions quickly as compared to other models of AI.
If we look at how an RPA bot can be benefitted from DL, the answer is simple. With DL, a bot can scan multiple layers of data and detect any suspicious transactions happening in the workplace. The moment it detects suspicious patterns, it can inform the concerned authority to take decisions immediately without much damage being caused.
Machine Vision is that part of AI which deals with images fed as inputs. These models then analyze such images and extract data for further processing. For example, if a bank cheque is fed to the system, the model will capture all the cheque details and send the data to the concerned department.
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