Audience: Project managers, business analysts, subject matter experts
Author: Mark Muir l SAP BTS, SAP S/4HANA RIG Americas
Welcome to a new blog series brought to you by the global SAP S/4HANA Regional Implementation Group, more commonly known as the SAP S/4HANA RIG. A key differentiator supporting early adoption customers the RIG has supported many S/4HANA on premise and cloud implementations.
Caught up in the minutiae helping customers successfully implement S/4HANA you lose sight of the awesomeness and tangible value SAP S/4HANA delivers to our customer’s. This blog series is an extension of lessons learned pulling from select content focused on management, business owners, analysts and subject matter experts.
Let us start with the history of Artificial Intelligence, SAP’s Intelligent Enterprise Strategy and Terminology then jump into Solving Unique Challenges in Your Business.
A Brief History in Time
Not wanting to recreate the wheel Alan Turing started the ball rolling with his Turing Test, “can machines think”. Continuing your journey read the Forbes publicationA Short History of Machine Learning -- Every Manager Should Read by Bernard Marr highlights milestone achievements and the startling pace of innovation.
SAP Intelligent Enterprise Strategy
The Journey to the Intelligent Enterprise starts here, understand the SAP Vision, Mission and Strategy from Value Discovery to Delivery.
Intelligent Suite to enable our customers to automate their day-to-day business processes and better interact with their customers, suppliers, and employees through applications that have intelligence embedded
Manufacturing & Supply Chain
Network & Spend Management
Digital Platform to facilitate the collection, connection, and orchestration of data as well as the integration and extension of processes in our integrated applications
Intelligent Technologies to enable our customers to leverage their data to detect patterns, predict outcomes, and suggest actions
Artificial Intelligence / Machine Learning
Internet of Things
The evolution of research areas and software comes with its challenges, marketing and branding is one of them. Terms and definitions associated with AI can vary, if I were to pick an elevator pitch below are definitions you could use.
Artificial Intelligence (AI)
Artificial Intelligence is the intelligence exhibited by machines and broadly defined to include any simulation of human intelligence. This includes robotics, rule-based reasoning, natural language processing (NLP), knowledge representation techniques (knowledge graphs), …
Artificial Intelligence areas of research
Machine Learning [branch to] Deep Learning
Natural Language Processing [branch to] Translation
Speech [branch to] Text to Speech, Speech to Text
Robotics [branch to] Autonomous Vehicles
Machine Learning is enabling computers to do things without being explicit programed for. The key area to enable this is by leveraging the information that is already available and using some math to derive conclusions and rules that will describe a desired outcome. Machine learning uses sophisticated algorithms to “learn” from massive volumes of Big Data. The more data the algorithms can access, the more they can learn. In S/4 HANA Machine Learning capabilities and predictive analytics are embedded into core business processes to help organizations stay competitive in a rapidly changing business environment.
Includes robotics, rule-based reasoning, natural language processing (NLP), knowledge representation techniques (knowledge graphs), …
Deep learning describes a revival of neural networks. Neural networks take inspiration from the human brain: they consist of small neuron-like computing units resembling the synapses of the brain. These networks can learn complex, non-linear problems from the input data. Deep learning networks derive their name from their “deep architectures” with several hidden layers. Deep learning networks have led to breakthroughs in several machine learning tasks and are currently the best bet in getting us closer to some of the goals of AI, for example making computers see and understanding language.
Also known as predictive analytics describe the widely-used analytics methods, where tools or users explicitly train exploratory models on given and well prepared data and features, in order to apply such models on new data to predict the respective pattern classification or values. Moreover, forecasting extends the concept by predicting a time series of values about the future. Many predictive analytics methods use machine learning to make their predictions.
Algorithmic and computational techniques and tools for handing large data sets
Increasingly focused on preparing and modeling data for ML & DL tasks
Encompasses statistical methods, data manipulation and streaming technologies (e.g. Spark, Hadoop)
Data mining is a multi-disciplinary field, the origins of which grew out of database technology, machine learning, artificial intelligence and statistics. It is a field included in the Data Science umbrella.
Data mining is the process of extracting hidden and previously unknown patterns from raw data and relationships between variables. Once you find these insights, you validate the findings by applying the detected patterns to new subsets of data.
The ultimate goal of data mining is prediction - and predictive data mining is the most common type of data mining and one that has the most direct business applications.
Big Data is an umbrella term for technology that can process data with high volume, velocity, and variety, beyond what traditional databases can offer.
The availability of Big Data is one of the driving forces behind the progress in machine learning in recent years. But not every aspect of Big Data is about machine learning.
Analytics is concerned with the analysis and interpretation of patterns in data and is a term mostly used in industry.
Internet of Things
The Internet of things (IoT) is the inter-networking of physical devices (“things”) to collect and exchange data. Thus, IoT generates massive volumes of data. This represents a great opportunity for machine learning to turn this data into value-creating assets.
For example, in predictive maintenance machine learning is used to predict machine failure before it happens.
Thank you for your interest
Further reading in this Machine Learning blog series