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.


The technical scale: learning from AI history

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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.

<<Our conviction is that there will be disappointments, which is obvious, but the current standardization of methodologies and vast availability of tools let us foresee a clear economic impact, a democratization of AI>>. 

Lire plus: “Le nouvel âge d’or de l’IA“, Tom Morisse, Faber Novel

<<Machine Learning has recently evolved into Deep Learning mainly due to these two factors: 1. Access to much faster and more powerful computers resulting in much bigger processing power 2. Availability of vast amounts of data resulting in much better training for the computer>>

Read more: “Deep Learning – A Non-Technical Introduction“, Alfred Pong, SlideShare

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.

<<Applying Federated Learning requires machine learning practitioners to adopt new tools and a new way of thinking: model development, training, and evaluation with no direct access to or labeling of raw data, with communication cost as a limiting factor.>>

Read more: “Federated Learning: Collaborative Machine Learning without Centralized Training Data“, Brendan McMahan and Daniel Ramage, Google Research Blog

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.


Read more: “The Democratization of Machine Learning: What it means for Tech Innovation“, Knowledge@Wharthon


The human scale: adding sense to intelligence

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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. Yet other experts remind the currently low-added value of work replaced by machines, hence emphasizing the human creativity and ingenuity still needed to make sense of data-based intelligence.

<<I feel like machines just haven’t made progress on those kinds of things. They have made progress on, for example, speech recognition. But that’s not language understanding; that’s just transcription.>>

Read more: “NYU’s Gary Marcus is an artificial intelligence contrarian“, April Joyner,

Other experts go further and explain how technologies such as AI can actually empower and emphasize human creativity and problem-solving capabilities. In their opinion, automating part of intelligence related activities would generate even more innovation and creativity. As a way to encourage the birth of opportunities out of apparent crisis, inspiring thought leaders share their experience of learning from machines, reviving their own passion for intelligence.

<<People whose jobs are on the chopping block of automation are afraid that the current wave of tech will impoverish them, but they also depend on the next wave of technology to generate the economic growth that is the only way to create sustainable new jobs.>>

Read more: “Learning to Love Intelligent Machines“, Garry Kasparov, The Wall Street Journal

What human thinking would inspire such artificial intelligence? Scientists and senior leaders remind the need to further understand cultural differences in human interactions, as well as the systemic shift to undertake for companies and governments to anticipate any future combination of AI and economic models. Providing an ethical framework around business and scientific expertise involved in scaling appropriate AI components is therefore identified as an innovation opportunity.

<< Have we really thought about ethics for this type of technologies? Not mentioning cultural differences which infer that communication, whether with a human or a machine, does not react to same criteria from a country to another. Such elements are not always taken into account in developments.>>

Lire plus: “#SommetStartup: l’éthique en intelligence artificielle, un tremplin pour innover“, Arnaud Devillard, Sciences et Avenir


Read more: “The changing face of business – and the part artificial intelligence has to play“, Paul Daugherty, World Economic Forum



The business scale: AI on the long term

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Shaping the right culture necessitates to tell the right story. Beyond technical challenges brought by AI, business experts outline the need for meaningful storytelling to back-up AI and Machine Learning narratives. An idea mentioned by experts might be to provide a “simpler”, sensible definition of AI value to people-centric businesses needs. 

<<They dove deeply into their knowledge of the topic, the future of AI, and what they are doing in the space. Most had some trouble telling the story of AI and Machine Learning. While the audience was a mix of experts, novices like me, and business folks, storytelling is lacking big time in AI and ML.>>

Read more: “Artificial Intelligence and Machine Learning needs simplicity“, Anthony Onesto, LinkedIn

Behind the marketing aspect of AI, technical experts remind the necessity of accessing appropriate, enterprise-grade and interoperable platforms to process holistic datasets through appropriate algorithms. They also remind the unknown (because unmet) cyber-security challenges linked to machine-learning and artificial intelligence, which demand adaptation and investments on top of cultural shifts to grow horizontal models within organizations and industries. The hence defined systemic challenge to solve drives new open innovation opportunities.

<<Among the requirements would be a cloud platform capable of handling high data volume that is derived from multiple sources.>>

Read more: “How Machine Learning is Revolutionizing Digital Enterprises“, Ronald Van Loon, Data Science Central


Read more: “AI Adds a New Layer to Cyber Risk“, Greg Bell, Cliff Justice, Tony Buffomante, Ken Dunbar, HBR

Open innovation is far from being the only business driver identified for AI. As journalists suggest, potential economic growth can be expected from  digital initiatives  increasing investments into AI startups, indicating a growing interest from investors in this specific innovation sector. Another question mentioned by experts throughout this story may contribute to better articulate a long term perspective for AI: what skills, platforms and cultures, which ethical, social values, would sustain such developments on the long term?


Read more: “Here are 50 Companies Leading the AI Revolution“, Brian O’Keefe, Nicolas Rapp, Fortune




Through our latest loop, we have defined an action for innovation practitioners to partner with the digital crowd through substantial and intentional content.


By clarifying systemic threats and opportunities brought by AI, this is how technology experts and business specialists help making sense of innovation:

  • What substantial content is created to be consistent enough across technologies to inspire the right actions from communities? Data scientists and academic experts reframe the definition of Artificial Intelligence, Machine Learning and Deep Learning for innovation professionals to understand their current achievements and development potential in a long-term approach.


  • Who are those innovation experts? They are AI-enthusiasts and AI-contrarian seeking to best define the value of human creativity and “machine” productivity to generate further innovation.


  • Why is there a need to understand the background of shared conversations? AI, ML and DL are driving horizontal transformations that need in-depth human thinking for cultural, ethical, social and organizational transformation, as well as systemic adaptation to sustain economic models on the long term.


  • How do data-analytics tools contribute to clarify human and technological sense? Experts argue automated intelligence only performs basic operations which need human creativity to make sense, or on the contrary that such powerful in-depth automated learning inspires more human intelligence and creativity.


  • When do technological advances bring systemic changes? Although AI possesses a long history of developments and achievements, recent turns and conjunction with other technological, connectivity, business disruption accelerates part of the decision-making left to companies and governments deciding upon our digital future.


  • Where do multiple channels exist to tailor messages and tone? AI requires further simplicity and storytelling for marketing purposes, secured and open platforms from a technological viewpoint, a horizontally driven ecosystem for long-term and economic strategies. writes the daily story of change as thought, created and shared by thousands of experts and passionate readers in more than 140 countries. You can also browse our Knowledge Library.

Photograph: Nirina Photography

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