Artificial Intelligence: what is the long term story?
Artificial Intelligence as an innovation trend nourishes an on-going discussion followed by many experts on social networks. One could consider AI talks remain a forward-looking, scientific research field gradually affecting algorithms, business information and analytics. Yet recent political and economic debates remind the concrete impact AI, and beyond it digital economies, already drives in tech innovation reality. As an intent to help readers make sense of latest AI informations, I have written this new InnoGraph from viewpoints recently exchanged by experts and specialists. They help foresee long-term developments and expectations currently putting AI, Machine Learning and Deep Learning evolution into perspective with a business and human environment already pressured to adapt to new models.
Artificial Intelligence has been developed for several decades already, along with an on-going questioning of its threats and opportunities for human work and creativity. The recent data-rich and connected developments occurring across sectors are shedding a new light to AI, Machine Learning and Deep Learning, providing an essential reminder of the long-term evolution of machines potential to help humans make sense of information. Understanding such an evolution through a human and business scope on top of a technical approach to AI enables to foresee the cultural and systemic adaptations experts seek to instigate. In addition, technology specialists outline the creativity required to align developments with a human environment.
As it gains traction through economic, political, business and social viewpoint, AI necessitates a constant review of its very definition, a story that started decades ago and is seeing critical turns in recent experts discussion. The evolution from machine learning to deep learning drives experts’ debates about the place of technology in defining intelligence for humans. Advances and strategic investments into specific approaches of AI sets the trajectory of the systemic impact machines productivity and human creativity can potentially deliver. By reframing their definitions of AI, experts outline the development history forming the basis of a potential data-oriented future.
One of the reasons leading to assess current definition of AI components, from a human and technological viewpoint, is to best understand the transformation needed to sustain an economic model rapidly shifting towards technologically driven intelligence. Beyond the skills, knowledge and culture questions, experts remind the strategic importance of AI for businesses. They develop their own approach of machine learning and outline requirements to generate more innovation.
Another reason driving conceptual discussions can be found in the horizontal impact that AI leads with a set of business, economic and social models being redefined. Experts intend to provide a holistic understanding of changes likely inferred by machine-learning and cloud-based business information. Whereas the business and market opportunity is clearly identified, academic specialists outline the efforts needed to achieve coordination and standardization of open platforms adding value to AI.
Contrarian analyses of AI help understand what are current limits and achievements of technological innovation from a realistic (some say pessimistic) viewpoint. AI enthusiasts and supporters keep outlining scientific breakthroughs and technical challenges solved, which already have business and process applications, as well as economic and social impact.
