AI and Machine Learning grew up and matured in the world of gaming -- pitting computational power and algorithmic mastery against the best human players up for the challenge of taking on a machine. It may have taken decades, but machines eventually asserted their dominance.
In 1997 IBM’s Big Blue defeated chess master Garry Gasparov; In 2011 IBM Watson defeated two of Jeopardy's greatest champions; and most recently, Google’s AlphaGo bested reigning champ Lee Sedol in the game of GO, a 2,500-year-old game that’s exponentially more complex than chess and, form the human perspective, requires intuition as well as calculation to execute a winning strategy.
Machine Learning owes its current prowess to gaming in a literal sense. The holy grail of computer science has always been to create intelligent machines that can perceive the world as we do, understand our language, and learn from examples.
In a breakthrough driven by gaming, large-scale use of Graphic Processing Units (GPUs) quickly gave rise to deep learning algorithms. Together with quantum leaps in computing power, multi-core architectures, in-memory databases, and of course, big data (growing exponentially via IoT), extremely efficient implementations of machine learning algorithms are rapidly becoming the newest new thing in enterprise applications.
Machine Learning in business – the next digital imperative
Check out these expert predictions and insights into Machine Learning's impact on business:
- Tractica forecasts the market for AI systems for enterprise applications will increase from $202.5 million in 2015 to $11.1 billion by 2024, expanding at a compound annual growth rate of 56.1%.
- As reported in WSJ, IDC predicts the worldwide market for cognitive software platforms and applications to grow to $16.5 billion in 2019 from $1.6 billion in 2015 with a CAGR of 65.2%.
- Deloitte Global predicts that by end-2016 more than 80 of the world’s 100 largest enterprise software companies by revenues will have integrated cognitive technologies into their products, a 25% increase on the prior year. By 2020, the number will rise to about 95 of the top 100.
- Since 2011, Deloitte reports, US-based start-ups that develop or apply cognitive technologies to enterprise applications have raised nearly $2.5 billion, suggesting that the biggest near-term opportunity for cognitive technologies is in using them to enhance business practices. The cognitive technologies that will be the most important in the enterprise software market will be: machine learning, natural language processing (NLP), and speech recognition.
- As reported in Forbes, IDC predicts that by 2020, 50% of all business analytics software will include prescriptive analytics built on cognitive computing functionality and that cognitive services will be embedded in new apps. Embedded data analytics will provide U.S. enterprises $60+ billion in annual savings by 2020.
- According to a recent MIT Sloan Management Review article, a survey of enterprises with at least $500M in sales that are targeting higher sales growth with machine learning found that 76% say they are targeting higher sales growth with machine learning; at least 40% of companies surveyed are already using machine learning to improve sales and marketing performance; and 38% credited machine learning for improvements in sales performance metrics.
SAP bringing intelligence to an enterprise app near you
Leveraging the SAP HANA Cloud Platform to embed Machine Learning into enterprise applications was a major theme at this year’s SAPPHIRE NOW in Orlando. SAP CEO Bill McDermott predicted that over the next five to 10 years machine learning, artificial intelligence, and augmented reality will play a major role in application development, business-process redesign, and business model creation.
"I think very strongly that intelligent applications will fundamentally change the way you do work in the enterprise and the way you collaborate with your trading partners outside of the enterprise," McDermott said during his keynote.
SAP’s Machine-Learning strategy is customer driven and application led. Starting with a business problem (or opportunity) and then lining up the appropriate ML services and supporting technologies is a sensible, pragmatic approach that spurs ideation and accelerates application development in a rapidly emerging field that is literally boundless.
SAP’s push to make all apps intelligent includes piloting applications such as Automated SalesForecast, CV Matching, Invoice Matching, Social Media Customer Service, yaaS Recommender, and the recently announced SAP Vehicle Insights.
These apps are just the tip of the Machine-Learning iceberg made possible by underlying SAP solutions. SAP Predictive Analysis, including its extensive library of statistical and data mining techniques and the SAP HANA predictive analytic library, combined with the SAP HANA Cloud Platform for IoT and SAP HANA Vora software provides a solid foundation for a new breed of Machine-Learning enterprise applications that can predict the future (and take appropriate action) in real-time.
Partners needed to unlock Machine Learning’s potential across industries
As stated earlier, the potential for Machine Learning applications in the enterprise is boundless and extends to all industries: predictive maintenance or condition monitoring in manufacturing; inventory planning, recommendations, and upsell/cross-channel marketing in retail; alerts, diagnostics, proactive management in healthcare; aircraft maintenance and scheduling, customer-complaint resolution and dynamic pricing in travel & hospitality; risk analytics, fraud detection, customer segmentation in financial services; smart grid management and power-usage analytics in energy & utilities…
Given the literally limitless potential for ML, only by tapping its expansive partner ecosystem can SAP hope to drive adoption of this category among its global customers.
In just one example, recently reported in the Dallas Morning News, NTT Data and Toray Industries recently developed a line of products that use ML and IoT technologies to promote good health. The project began by developing a smart shirt for IndyCar racer Tony Kanaan to monitor his heart rate, breathing and muscle activity while competing in grueling races, but NTT DATA has plans to mass produce its high-tech fabric in the U.S. for hospitals and other health providers – cutting costs, improving outcomes, optimizing care and promoting general wellness.
Contact NTT DATA today to validate concepts and develop new use cases powered by Machine Learning.
Fecha de publicación: 04/08/2016