Recently I participated in the ThinkX program sponsored by SAP Innovation Partnership Program and Singularity University. The origins of ThinkX touch the xPrize competitions and other design events which seek to leverage exponential learning. Over the past 5 years, SAP and SU have hosted over 800 SAP executives, leaders and employees to events around the world. In this second article of a three-part series I will explore the big trends, themes and take-aways – including how they may impact industry and society in the coming years.
Advances in cognitive science, particularly in areas such as Artificial Intelligence (AI) and blockchain will increase at an accelerated rate. Processes leveraging cognitive tools such as AI and blockchain will deliver vastly greater value to organizations, people and society than traditional approaches.
Blockchain-enabled processes alone are poised to generate over $3T in value globally in the next 30 years (SAP & Singularity University). As a disruptive technology, blockchain removes the central controlling figure in a large system or institution – banking being the classic example – which can lead to greater process efficiency and control. Supply chain applications based on private blockchains – tuna harvested in the Pacific Ocean or vehicles assembled in Mexico – offer greater accuracy, control and traceability than traditional logistics approaches. In a growing geopolitical world, these blockchains are also border-less, providing a greater flexibility to leverage data and process across what would be traditionally high-risk business environments.
Beyond blockchain, AI represents yet another vast and potentially limitless source of value. While many technologies exist based on AI algorithms – vehicle Advanced Driver Assist Systems (ADAS) renders the age-old art of parallel parking nearly obsolete – the growth and maturity of these systems will only accelerate.
AI is undergoing six levels of development, which began with rules-based systems in the 1950s with the first computer. Since that time predictive analytics have leverage patterns to rules to create highly-likely potential scenarios that could be acted upon. Today’s cognitive computing have applications not only in transportation systems such as ADAS but also in agriculture (robo-farming) and healthcare (robo-surgery) just as examples. The key point in this transition stage is the move from AI learning what “not to do” to a state of AI learning “what to do.” This only allows for greater advances in the area of so-called “Creative Machines” in the next decade as well early stage physical embodiment and eventual sentience of bio-robots and androids into the 2030s and beyond.
In the end it will be determined by how our machines of today will create the machines of tomorrow – AI creating the next AI – without the trepidation of Skynet and other human oriented fears portrayed in Hollywood movies.
This is the second of a three-part story. Part three: Leading for Exponential Organizations may be found here after it publishes. This article is cross-posted on LinkedIn, Medium and WordPress by the author.