Meaning of AI and their Lifecycle stages
We can define AI as a kind of information or knowledge acquired by a state to accomplish a task. Work on AI and automation has started around the 1950s. The AI in the 1950s focussed on manipulating symbols to abstract thinking. Heuristic search algorithms used to solve many problems in the Classical AI era. In the classical AI era, Eliza (the first Chatbot) came into existence to help humans, which people thought to be real. It used to reply with canned responses with basic grammar. But the classical AI era was hindered with limitations like the computational power of the machines. In the classical AI era, the programs developed were only able to solve trivial queries, and non- trivial questions were challenging to solve as these problems grow exponentially. It led to the first AI winter.
Then in the 1980s came Knowledge-based AI systems. AI systems were able to answer questions and solve problems using the limited domain of knowledge. AI systems were using inference engines to deduce to a conclusion like Human Beings are mortal, John is a human being, and it could infer that John is mortal. Based on this AI technology, AI systems like Mycin (Stanford University) and inventory control systems were built, which were fruitful initially but were not a success as the knowledge was acquired from AI-based systems expert and constrained. It has led to the second AI winter.
Now we are in the Data-based AI era. Most of the developments in SAP and other domain are on data-driven AI systems.
Figure has been derived after analysis from https://en.wikipedia.org/wiki/Artificial_intelligence
Automation/AI based developments in SAP
What Automation/AI in SAP has to offer in AI/Automation world.
Automation/AI is provided by SAP and other parties in this domain. There are automation/AI systems provided by Cloud Service providers like Microsoft Azure, Google Cloud Platform, AWS. Also, there are AI-driven techniques are used by companies like RedHat and Suse Linux. Where does SAP stand, and what is the impact SAP has in AI/Automation industry? Though the AI/automation for SAP is also Data-driven, companies like Microsoft Azure and Google Cloud platforms have AI/automation mainly focused on Machine Learning and Automated Machine Learning. Automation/AI in cloud service helps to build tailor-made VMs which have OS platform, Networking, and Data storage in a few minutes. Machine learning platform for training purposes and Fully automated machines where the user has to provide data to achieve desired outcomes.
SAP has a cutting edge in this area with SAP Leonardo. SAP Leonardo is a combination of IoT, Machine learning, Analytics, Big Data, Blockchain, Data Intelligence, Design Thinking. SAP Leonardo uses advanced Deep Learning and other techniques to be a leader in Automation/AI market. Around 60% of world technology connects with SAP is also a considerable advantage for SAP led automation.
- Internet of Things connects with people and makes infrastructure and market connect with everything.
- Big Data provides insights into the business.
- Machine Learning offers ways to use data for predicting outcomes.
- Analytics provides new processes and applications based on insights.
- Design Thinking help to innovate and offer the opportunity to excel
- Data intelligence provides trusted, real-time benchmarks and decision-making scenarios.
- Blockchain services provide trust in peer to peer transactions, full visibility of good provenance, increased audibility, and decreased fraud.
Some of the used cases for SAP Leonardo are
- Monitor temperature data of a person using a thermal scanner into SAP Leonardo IoT
- Intelligent water intake tracking with SAP Leonardo IoT
- Intelligent Corporate Dashboard
- Smart client-centric portal
- Intelligent manufacturing line Etc
Figure has been derived after analysis of ebook DLD100_EN_Col01
SAP also provides other automation and AI techniques like focus run in solution manager powered by SAP HANA and S/4 HANA with focus run in Solution manager 7.2, we get
Advanced System management- These systems help us to monitor hosts, databases, and operating systems for the whole solution landscape. Metric Forecasting and the System Anomaly Analysis helps in the prediction of specific systems behavior. It provides automated health Checks for analysis of critical situations.
Advanced User Monitoring- This use case consists of two applications. Synthetic User Monitoring (SUM) and Real User Monitoring (RUM) helps us to monitor user scenarios based on synthetic probes by robots which can be deployed in different customer locations and also provides the measurement of different user request types, such as HTTP(S), SAPUI5, RFC, and SAPGUI.
Advanced Integration Monitoring – provides monitoring of data exchange processes between different systems within a solution landscape.
Advanced Event and Alert Management – It is a central access point to handle alerts from different use cases like system monitoring, user monitoring, and integration monitoring with a dashboard driven approach. It helps to integrate alerts coming from external monitoring systems into AEM (inbound integration) and forward alerts to an external event and alert management or ticket systems (outbound integration). In system monitoring, automated alert reactions for production down like situation is also possible.
Also, Advanced Configuration Monitoring and Advanced Root Cause Analysis (ARA) is present for SAP run Experience-centric Intelligent Enterprise.
Modern AI/Automation is focussed on Data-driven technology. Clients will not only use data to predict what will be beneficial for their business. But Data will drive their business also.
Hope this helps AI Community experts find this Blog post useful in any scenario.
Any comments will be appreciated.