Category: Big-Data

The 5-Es of AI potential: What do executives and investors need to think about when evaluating Artificial Intelligence?

Dr Will VentersLeave a comment

I spent last week in Berlin as part of a small international delegation of AI experts convened by the Konrad-Adenauer Foundation[1]. In meetings with politicians, civil servants and entrepreneurs, over dinners, conferences and a meeting in the Chancellery[2], we discussed in detail the challenges faced in developing AI businesses within Germany.

A strong theme was the difference between AI as a “thing” and as “component”. Within most commercial sales-pitches AI is a “thing” developed by specialist AI businesses to be evaluated for adoption. Attention is focused on what I will term efficacy. Such efficacy aligns with pharmacology definitions as “the performance of an intervention under ideal and controlled circumstances” and is contrasted with effectiveness which “refers to its performance under ‘real-world’ conditions.”[3]. AI efficacy is demonstrated through sales-pitch presentations based on specially tagged data or upon human-selected data-sets honed for the purpose.

As a “component” however, AI only becomes when it is incorporated into real-world consequential and ever evolving business processes. To be effective not just efficacious, AI must bring together real-data sources in real-world physical technology (usually involving cloud services, complex networking and physical devices) for consequential action. AI “components” then become part of a complex digital ecosystem within the Niagara-like flow of real businesses rather than a “thing” isolated from it. Since business processes, data-standards, sensors and devices, evolve and change so the AI must evolve as well while continuing to meet the needs of this flow.

To be effective (not efficacious) AI components must also be:

  • efficient in terms of energy and time (providing answers sufficiently quickly as to be useful.
  • economic in terms of cost-benefit for the company (particularly as the cost of human tagging of training data can be extreme),
  • ethical by making correct moral judgements in the face of bias in data and output, ensuring effective oversight of the resultant process, and transparency of the way the algorithm works. For example resent research shows image classifier algorithms may work by unexpected means (for example identifying horse pictures from copyright tags or train types by the rails). This can prove a significant problem when new images are introduced.
  • established in that it will continue to run long-term without disruption for real world business processes and data-sets.

The final two of these Es are particularly important: Ethics because business data is usually far from pure, clean and will likely include many biases. And Established because any process delay or failure can cause a pile-ups and overflow to other processes, and thus cause disaster. In my opinion, only if all these 5Es are achieved should a business move AI into core business processes.

For business leaders seeking to address these Es the challenges will not be in acquiring PhDs in AI-algorithms but instead in (1) hiring skilled business analysts with knowledge of AI’s opportunity but also knowledge of real-world IT challenges, (2) hiring skilled Cloud-AI implementors who can ensure these Es are met in a production environment and (3) appointing AI ethics people to focus on ensuring bias, data-protection laws, and poor data quality do not lead to poor ineffective AI. Given the significant competition for AI skills, digital transformation skills and for cloud-skills [4] this will not be easy.

So while it is fun to see interesting wizz-bang demos of AI products at industry AI conferences like those in Berlin this week, in my mind executives should remain mindful that really harnessing the potential of AI represents a much deeper form of digital transformation. Hopefully my 5Es will aid those navigating such transformation.

(C) 2019 W. Venters

[1] https://www.kas.de/ also https://www.kas.de/veranstaltungen/detail/-/content/international-perspectives-on-artificial-intelligence

[2] https://en.wikipedia.org/wiki/Federal_Chancellery_(Berlin)

[3] Efficacy : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3912314/  Singal AG, Higgins PD, Waljee AK. A primer on effectiveness and efficacy trials. Clin Transl Gastroenterol. 2014;5(1):e45. Published 2014 Jan 2. doi:10.1038/ctg.2013.13. I acknowledge drawing on Peter Checkland’s SSM for 3Es (Efficacy ,Efficiency and Effectiveness) in systems thinking.

[4] Venters, D. W., Sorensen, C., and Rackspace. 2017. “The Cost of Cloud Expertise,” Rackspace and Intel.

[5] https://en.wikipedia.org/wiki/Industry_4.0

Anti-competitive Artificial Intelligence (AI) – [FT.com]

Dr Will Venters1 Comment

Yesterday’s FT provides a fascinating article (available here) on the role algorithms may increasingly plan in price-rigging and collusion. While previously humans have colluded to fix prices, today’s algorithms which seek profit maximization may end up colluding in a way which is hard to detect and difficult to stop. Indeed a recent OECD report states:

“Finding ways to prevent collusion between self-learning algorithms might be one of the biggest challenges that competition law enforcers have ever faced… [Algorithms and Big Data] “may pose serious challenges to competition authorities in the future, as it may be very difficult, if not impossible, to prove an intention to co-ordinate prices, at least using current antitrust tools”.

While algorithmic trading has proliferated in financial services (reported in many popular books such as “Dark Pools”), it is their increasing use in consumer marketplaces which concerns the article’s authors – airline booking, hotels, and online retailing.

The problem for regulation is that “All of the economic models are based on human incentives and what we think humans rationally will do.” (Terrell McSweeny US FTC) while an AI algorithm which “learns” that its most profitable course of action is price coordination are poorly represented in our understanding.

“What happens if the machines realise it is in their interest to systematically and quickly raise prices in a co-ordinated way without deviating?” (Terrell McSweeny)

Indeed we might ask whether an algorithm which uses huge databases of historical demand and supply data, and detailed data of the competitive marketplace, to arrive at its most profitable price in the milliseconds of a webpage loading is acting competitively in keeping with market principles or against the consumer (who could never undertake similar analysis and therefore faces huge information asymmetry challenges).

An interesting example in the article is an App to track petrol pricing whereby, because the app highlights instantly to competitors that a price has been cut (and they can match the price cut before demand shifts), so it removes the incentive for anyone to discount.

The article even states: “the availability of perfect information, a hallmark of free market theory, might harm rather than empower consumers”

 

(Image (cc) Keith Cooper – thanks)

ITOe – Speakers – Nordic Innovation & Agility

Dr Will VentersLeave a comment

I’ll be talking about cloud computing and outsourcing at the Nordic Innovation and Agility forum  in Stockholm in April…

ITOe – Speakers – Nordic Innovation & Agility.

The title of my talk will be “The business of cloud computing – innovation and agility” with my focus on the way cloud computing can support innovation and drive agility in businesses. Along the way I will (probably) discuss cloud computing and the Large Hadron Collider, Smart-cities and Big-data – exploring how high capacity and agile computing can support agile business practices and innovation.

I hope you can make it!