Machine learning and the larger world of artificial intelligence (AI) are no longer the stuff of science fiction. They’re here – and many businesses are already taking advantage. As a new breed of software that is able to learn without being explicitly programmed, machine learning (and deep learning) can access, analyze, and find patterns in Big Data in a way that is beyond human capabilities. The business advantages are huge, and the market is expected to be worth $47 billion by 2020.
Artificial intelligence, machine learning, and deep learning are often used interchangeably, but they’re not the same. In a nutshell, AI is the broader concept of machines that can act intelligently. Machine learning and deep learning are sub-sets of AI based on the idea that given access to large volumes of data, machines can learn for themselves. Read on for more about deep learning vs. machine learning, and other important terms.
The simplest machine learning definition? The practice of teaching a computer how to spot patterns and make connections by showing it a massive volume of data. So rather than programming software to accomplish a specific task, the machine uses Big Data and sophisticated algorithms to learn how to perform the task itself. Machine learning allows applications to “think” and independently make a determination or prediction – going beyond what predictive analytics and Big Data analytics can do, and often beyond what humans can do. A popular consumer example of machine learning is a recommendation engine in an online retail environment.
What is deep learning?
Deep learning, sometimes known as cognitive computing, is a form of advanced machine learning. It uses multi-layered (aka deep) neural networks to simulate human thought processes. These networks are made up of small computing nodes that mimic the synapses of the human brain. Using input data sets and sophisticated algorithms, machines can help solve complex, non-linear problems. Deep learning is responsible for breakthroughs such as speech and image recognition and natural language processing. Some popular deep learning examples include:
Facial recognition software
Smart home automation devices
Supervised vs unsupervised learning
There are three main ways that machines can “learn”:
Supervised learning – in this approach humans label the inputs and outputs and then the model figures out the rules for connecting the two.
Semi-supervised (or reinforcement) learning – the machine is
rewarded or penalized for actions it takes through trial and error, and the algorithm adjusts accordingly.
Unsupervised learning – algorithms are left to discover patterns in the data (which is sometimes clustered) on their own.
Regardless of the type of training used, the machine is able to learn from the data on its own, absorbing new behaviors and functions over time. The result is a model which can be used to predict outcomes based on data, and which is regularly retrained for accuracy.
Why is machine learning advancing so rapidly?
Machine learning is not a new concept, but it has recently gained fresh momentum. Why? Processing power and storage are now more affordable than ever, and the explosion of Big Data from various sources – such as text, images, and IoT devices – is making it easier for machines to “train” and learn.
Hear from customers already using our machine learning technology to leverage all their data – for faster, more personalized customer service in the travel industry, HR recruiting without bias, speedy, low-cost invoice payment clearing, and more.
Machine learning can automate and prioritize routine decision making processes – so you can achieve best outcomes sooner. For example, when coupled with the Internet of Things, it can help you decide what to fix first in your manufacturing plant.
Your data is constantly being updated, which means your machine learning models will be too – much faster than humans can currently develop them. This lets you quickly discover and process new insights to adapt to rapidly changing business environments.
Innovation and growth
An “algorithmic business” uses advanced algorithms to drive process automation and improved decision making. Making the shift can accelerate overall knowledge harvesting and pave the way for innovative business models, products, and services.
One of the most exciting uses for machine learning is to understand patterns in Big Data in a way that humans currently can’t – and then trigger concrete actions. For example, it can predict potential sales opportunities and then recommend actions to close deals.
With machine-aided business processes and faster overall workflows, you can optimize business operations and your product and service offerings – so you can do and sell more while lowering back-office costs and TCO.
AI and machine learning help to eliminate human error, improve the quality of outputs, and bolster cybersecurity – a must for financial service and other companies that need to protect sensitive information and comply with regulations.
The most popular examples of machine learning in action are consumer applications like recommendation engines and smart devices. But the technology also holds great promise for business-to-business (B2B) use cases. Here are two key areas we think the technology will really shine:
Machine learning shifts traditional rules-based processes to intelligent ones that can discover new patterns in large, unstructured data sets – and make strategic predictions all on their own. It can also take on highly repetitive tasks such as checking invoices and travel expenses for accuracy.
Digital assistants and bots
Advances in AI technology suggest that self-learning algorithms may soon come to their own conclusions within certain parameters and develop context-sensitive behavior. Devices will be able to schedule meetings, translate documents, and take on other routine business tasks.
The idea of machines taking over our lives and livelihoods has made for some great movies, but the reality is far less dramatic. That’s not to say that we should put blind faith in outcomes uncovered through the machine learning process. Here’s how you can keep AI on track to produce reliable results:
Conduct a proof of concept so you feel confident in the decisions that are being made
Supervise processes and results and make adjustments as needed
Adjust confidence levels by applying business rules in algorithms
Include feedback mechanisms in your machine learning training process
Machine learning is already being welcomed into offices around the world. Its role? To assist humans with routine tasks. Advances in machine learning technology are opening up countless new scenarios, opportunities, and business models that will:
Result in higher paid jobs that emphasize creativity, problem solving, and knowledge work
Automate boring, repetitive tasks to make jobs more interesting (and fun!)
Always allow humans to retain control over the most strategic tasks and priorities
In the past, you needed specialized talent to put machine learning into action: “Quants” who are educated in the language and methods and “translators” who could bridge the disciplines of data, machine learning, and decision making to reframe complex results into executable insights.
Not anymore. Now modern business applications take on the roles of data scientists. They feature user-friendly interfaces and integrated AI technology that let business users reap the rewards of machine learning – without extensive training, and often with the push of a few buttons.
Even though machine learning in B2B applications is still in its infancy, companies are already using it to automate transactions, detect fraud, further medical research, and save time and money. And when AI is built into cloud platforms and applications, you won’t need a costly custom build to get up and running.
SAP touches more than 70 percent of the world’s business transactions, and we want to infuse them with even greater intelligence. Our goal is to build machine learning technology into all our software, across every line of business and industry we serve. And we’re doing it with SAP Clea – machine learning intelligence embedded into our cloud platform and applications. This will make it easier to become an algorithm-driven business that lets you discover unprecedented insights, make more accurate predictions, and automate routine tasks so that employees can focus on higher-value work.
Get a glimpse into the future with our first wave of machine learning applications, tools, and services, developed in collaboration with co-innovation customers across diverse industries.
SAP's vision for machine learning is to focus on solving real business problems that will have huge business impact.
Juergen Mueller, Chief Innovation Officer
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Machine learning is ideal for scenarios with complex rules and unknown elements, for making predictions on new rather than historical data, and for automating highly repetitive tasks. But if you know the exact conditions in which your system should execute all instructions, rules-based programming is sufficient.
Take a crash course
Still unsure how to apply machine learning in an enterprise context? Our free openSAP course will guide you through your next steps – from identifying the right problems to preparing data in complex landscapes. It also takes you through use cases and prototypes, components for building AI products, and much more.
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