The human questions behind AI business models

Although AI brings a strong appetite to market, the dependencies surrounding the delivery of adequate services and solutions seem to outweigh the business model maturity. With an on-going debate on long term value add of such services with regards to the economic changes AI may generate, and questions on human place in technology, experts and analysts provide a dynamic market overview of automation, machine learning and neural network researches, with the Internet of Things and Big Data as key enabler for future AI services.

End to End Model Analysis

End to End Model Analysis
End to End Model Analysis
  1. “In order to enable the system to distinguish different object categories, (such as buildings and furniture), researchers have contacted 2,500 people who submitted 50,000 answers. As an average, people were able to identify 85% of objects. Using this data, the system was able to obtain similar results, identifying the correct category for 90% of objects submitted.” Julien Bergounhoux, Usine Digitale
  2. “One thing that the last few years has shown with technology as a whole is that tech companies are starting to get each others backs with matters that concern the well being and safety of the population, and I am glad to see that once again it is happening with something so important as this.” Adam Milton-Barker, Tech Bubble Info
  3. “Recurrent neural nets, or RNNs, can not only recognize complex moving images, but automatically generate detailed captions for online photos and videosimprove online services that translate from one language to another, and more. They’re pushing into companies like Facebook and Baidu as well as Google, and in recent weeks, this burgeoning technology received another shot in the arm with the arrival of a new startup called Nnaisense.” Cade Metz, WIRED
  4. “For Musks and his colleagues, AI has now reached a level enabling to foresee the arrival of (military) machines in the coming years, “not even decades”, and that these machines will become the third revolution in the “arts of war”, “after the invention of gun powder and nuclear weapons”. Oliver Lascar, Sciences et Avenir
  5. “Because of advances in two of the crucial areas of computing – natural language learning (computers are now able to derive meaning from human language) and machine learning (they are able to learn from data rather than merely respond to human programming) – a new generation of AI is challenging for the top jobs in law, pharmaceuticals, medicine, even government.” Michael Odell, Raconteur

WAI Comment: The spread of A.I. technologies is such that any sector or business form can be impacted in a near future. Yet humanity remains unable to properly control that impact, raising awareness on critical areas that could harm humans instead of protecting them. While major companies are involved in developing in depth research on neural networks and ways develop AI solutions for a variety of sector and changing business models, customers and markets increasingly realize they need to rethink models and prepare for a cross sector revolution that could leave part of humanity aside, if not in danger. The initial grade for business model maturity reaches 3 out of 5.

Strategic Initiatives Impact

Strategic Initiatives Impact
Strategic Initiatives Impact
  1. In manufacturing, robotics and artificial intelligence (AI) will be the most adopted technologies over the next five years. In fact, we can already see it happening. For example, rather than subcontract manufacturing to Asia, Zara built 14 highly automated factories in Spain, where robots work around the clock cutting and dyeing fabrics, allowing the company to adopt a just in time inventory approach.” Mayuri Ghosh, Sichuan Li, World Economic Forum
  2. “If a singularity really is coming, it’s beyond our ability understand it. Machines might become conscious – they may be already – but the odds are, we won’t be able to recognize it. If the singularity is not coming, then it’s just empty dogmatism. Hence our task is always more practical — to bring a machine’s functionality, as we comprehend it, to bear on our world and our projects, answering “What does it mean to us?” rather than puzzling about what we might mean to them.” JC Spender, HBR
  3. “The bottom line is that regular computers have to solve one problem at a time in sequence, but quantum computers can solve multiple problems at the same time. That kind of speed as the potential to revolutionize entire industries.” Kelly Dickerson, World Economic Forum
  4. Economist Brian Arthur has coined the term “Second Economy” to describe transactions that take place between two computers, with no input from a worker. Arthur estimates that the Second Economy will be as large as the first by 2025 — removing the need for as many as 100 million jobs.” Bernard Marr, World Economic Forum
  5. “So are EAs primed for resurgence now? It is quite possible that, by virtue of their distributability, EAs emerge as the next big thing in AI, allowing us to tackle more ambitious problems.” Babak Hodjat, Huffington Post

WAI comment: Developments are occurring at all levels of AI value-chains, showing an on-going investment and research phase alongside already advanced initiatives and trials. This way, benefits are clearly outlined under productivity terms while risks keep being registered as human-threatening. Technological advances allow experts to foresee new market opportunities, including those which do not exist yet and may add further unidentified risks to humanity. In such a polarized conversation, AI appears as a fascinating science about to disrupt our daily lives in the near future without the certainty it will improve them in the long term but with the evidence that many will try to make money out of it. The intermediate grade is therefore lowered to 2.5 out of 5.

The Business Case

Business Case
Business Case
  1. “For entrepreneurs and investors, this is an exciting time to innovate and place new bets in enterprise software. BCC Research predicts the machine learning market will reach $15.3 billion by 2019, with an average annual growth rate of 19.7 percent. One of the early growth categories is predictive analytics software, which is expected to reach $6.5 billion worldwide in 2019, up from $2 billion in 2012, according to Transparency Market Research”. Ajay Agarwal, TechCrunch
  2. “New research from Parks Associates reports that more than 6 percent of U.S. broadband homes currently own a robotic vacuum cleaner and adoption will exceed 11 percent by 2020.  Home robots are emerging as a new category within the connected home and Internet of Things by focusing on single tasks that provide a compelling value proposition for consumers. ” Barbara Kraus, Twice
  3. On June 4, 2015, the United Kingdom government invested $475 million through the Hartree Centre to deliver a new era of cognitive, data-centric computing using IBM Watson, big data and servers from theOpenPOWER Foundation. Along with the cognitive computing technology, IBM will contribute 24 researchers to deliver accelerated innovation to academia and businesses across a number of UK industries.” Moya Karin Brannan, The ISV Hub
  4. “Gartner predicts that at least 10 percent of potentially life-threatening activities will be performed by smart systems by 2024” Siemens
  5. “”By 2020, all products costing more than $100 should have sensors embedded in them and should offer services on top of the products,” says Peter Sondergaard, Senior Vice President and head of research at Gartner.” Siemens
  6. “Investors seeking to capitalise on the current and future artificial intelligence gold rush have invested substantial amounts of funds in AI startups, including Rethink Robotics ($127 million) and Sentient Technologies ($144 million).” Seeking Alpha
  7. “According to a new report from Tractica, the market for enterprise AI systems will increase from $202.5 million in 2015 to $11.1 billion by 2024.” Tractica

WAI comments: The forecasted market sizes for AI components and the investment activity around them reflect the same “uncertain enthusiasm” with regards to solutions launched to market. Compared to IoT market forecasts over similar periods, machine learning potential revenues are still nascent and show that from a business planning point of view, many other digital components will need to generate substantial benefits to enable AI full market potential. The on-going investments and discussions around AI oriented projects keep the topic on experts mind although consumer-led IoT and Big Data topics seem to currently outrun AI discussions, leaving them in a debate with high dependencies. The final business model maturity grade for AI is 2 out of 5.

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