If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
On February 9, 2017, two technology market leaders made announcements: SAP unveiled its next-generation intelligent ERP system, and Nvidia announced that demand for artificial intelligence (AI) applications was driving demand for its graphics platform. On the face of it, these announcements were business as usual--routine sound bites that proliferate in the tech news landscape. Look a bit deeper, though, and you realize that this day marked a profound shift in both the way businesses use technology and the implications for the rest of us. For decades, developing a computer that could think has been the Holy Grail of technology. And while we have made tremendous progress in our ability to process vast amount of data, the "thinking" part has remained mostly elusive.
A new platform from cybersecurity research firm ERPScan uses machine and deep learning to cover all aspects of SAP security – predictive, preventive, detective and responsive capabilities – in a single solution. The platform, released in Las Vegas last week, introduces three features important for SAP security: threats and anomaly detection with a user-defined interface and functionality; the integration of all SAP security areas (including platform security, code security and segregation of duties); and support for SAP cybersecurity requirements from Gartner's PPDR (Predict, Prevent, Detect, Respond and Monitor) framework. ERPScan founder and chief technology officer, Alexander Polyakov, said the solution is a real breakthrough for the company. "We spent the last two years developing a solution that would be able to not only cover all areas of SAP cybersecurity, but also be intuitive by adding machine learning and adaptive interfaces. Our secret team of data scientists and machine learning experts battled with the experienced Research team and taught the system to detect advanced attacks and anomalous user behaviour.
I'm excited about what AI will help us do in the future. How it will open up a realm of limitless possibilities for consumers, the economy, and society as a whole, such as helping reduce electricity use or quickly moving life-saving supplies to the people who need them most. During the recent European Big Data Value Forum, I had the opportunity to highlight the critical role and tremendous business opportunities that AI will play for European enterprises. In today's digital world, we know that data is coming at us from everywhere. It's fuel for machine learning – the branch within the area of artificial intelligence (AI) that helps computers to learn from data and discover patterns without being explicitly programmed.
SAP's Christian Boos had a light-bulb moment this year at SAPPHIRE NOW. The global business development expert for machine learning was due to hold a 20-minute meeting with a long-time SAP customer, but 20 minutes soon turned into 90. "They just kept on reeling off the use cases for machine learning at their company," Boos says. This and many other conversations at the event fed his conviction that, far from being "just another bandwagon," machine learning is a topic that will help decide many companies' future. While the consumer market is already brimming with highly advanced artificial intelligence (AI) and machine learning products, many enterprises are only just starting to embrace these technologies.
The view of the industrial Internet of Things (IoT) as billions of sensors connected into intelligent systems distracts from its true role: digital transformation. The gargantuan task and investment of making all these connections, reliably and securely, is pointless unless there is a solid business reason -- and there are two very good ones. The first reason big companies invest in IoT is they are worried about being digitally disrupted by a Tesla, Uber, or the next AirBnb in their industries -- disrupt or be disrupted. The second reason: a company's slowing growth and the need for increased productivity. BCG's Olivier Scalabre explained that driver during a 2016 TED Talk.
To execute on such a bold vision, SAP delivers an expanding portfolio of application and services that help all companies embed machine learning into their business processes. But to pave an optimal path forward for these businesses, SAP is committed to establishing an ecosystem of leaders with a variety of domain expertise in order to complement and expand its offerings. This collaboration is essential to enable customers to make a smooth transition to the intelligent enterprise. Opportunities to partner with SAP on machine learning are manifold, and so are the use cases customers are currently exploring as they inject intelligence into their value chains. To facilitate a close collaboration with partners and generate mutual value, SAP has created a machine learning partner program, which runs as an overlay to existing partner programs offered through SAP PartnerEdge.
When launching SAP Leonardo Machine Learning Foundation, SAP started on a mission to overcome these challenges and help all customers transition to the intelligent enterprise, no matter their level of digital maturity and AI expertise. Now, SAP expands the capabilities of its machine learning platform, including the training of image classification services with training data belonging to the customer, the opportunity for customers to deploy their own models on SAP Leonardo Machine Learning Foundation, and new ready-to-use services. To help customers and partners address a larger number of use cases, SAP has opened the enterprise-class model training capability of SAP Leonardo Machine Learning Foundation. It is now possible for customer to tailor services to their business needs by training services on their unique data. This new functionality is enabled via a secure extraction of data via SAP Cloud Platform and predefined training routines.
How is the company doing that? How do its solutions become intelligent through machine learning? And which use cases are suitable? "We'll never run out of ideas for compelling machine learning use cases," says Daniel Dahlmeier, the Singapore-based machine learning expert. At the SAP Innovation Center Network location in Singapore, Dahlmeier leads a team that works on machine learning solutions for sales and service.
Over the past few years I've delved into the topic of customer experience, uncovering how new technologies are empowering businesses to know more about their customers than ever before. While there's nothing wrong with beer getting smarter at the shelf (and the data that is responsibly collected), it's obviously not a good idea for businesses to take things too far. No one likes that "close talker" at a cocktail party. They can be downright creepy. But avoiding the creep factor isn't easy, especially when powerful capabilities like machine learning and artificial intelligence (AI) are on the guest list--and usually depicted as creepy forces in the world of sci-fi (I'm looking at you Blade Runner and Terminator).
Even though predictive analytics has been around for quite some time, interest around this topic has increased over the last couple of years. It is no longer enough for a company to accurately record what has happened. Today, an organization's success depends on its ability to reliably predict what will happen – be it predictions about what a customer is likely to buy next, an asset that could require maintenance, or the best action to take next in a business process. Predictive analytics uses (big) data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data, enabling both optimization and innovation. Existing processes can be improved – for example by forecasting sales and spikes in demand and enabling the required adjustments to the production planning.