In this episode of our Inside the Strategy Room podcast, senior partner Tamim Saleh cuts through the hype around artificial intelligence (AI) and offers clear guidance for executives looking to make precise strategic decisions about where and how to employ AI in their businesses. Tamim shares insights on the impact of machine vision on AI, the future of voice recognition, and the latest developments in advanced analytics, virtual assistants, and robotics. He outlines the challenges companies face when adopting AI and the steps CEOs can take to overcome them. Tamim is a senior partner in our London office, and he is with me at our Global CFO Forum, where he's speaking about AI and machine learning. Tamim, one of the things you've talked about is the notion of five different developments of AI. Tamim Saleh: Machine learning and AI are limited by the fact that when we input data as humans, first of all we are slow, and we make mistakes. One of the fastest-growing technologies is capturing data through image analytics and cameras. And the beauty of this is, cameras don't make the same mistakes we do, because they capture things the way they are, and they don't see the world the same way that we do. In fact, the spectrum is much wider than what we see. It includes infrared, et cetera. So there are a lot of business problems [that image technology can help].

When Lighthouse's intelligent security camera finally went on sale just over a year ago, the company believed that with AI recognition and 3D sensing technology, it offered something truly unique over its rivals. However, the promise of automatic face detection didn't really resonate with consumers, and the hardware maker shut down less than 10 months later. While the company slipped quietly into the night, its technology didn't, with none other than Apple ghosting in to acquire its innovations. Intellectual Property publication IAM reports that Apple snapped up a total of eight patents and patent applications very soon after Lighthouse ceased operations. Some cover typical security camera features -- like two-way communication between a camera and other devices -- but others specifically relate to the 3D depth-sensing technology that allowed Lighthouse's camera to distinguish between pets and people, as well as to recognize faces.

In this work, we enrich a formalism for argumentation by including a formal characterization of features related to the knowledge, in order to capture proper reasoning in legal domains. We add meta-data information to the arguments in the form of labels representing quantitative and qualitative data about them. These labels are propagated through an argumentative graph according to the relations of support, conflict, and aggregation between arguments.

Argumentation theory is a powerful paradigm that formalizes a type of commonsense reasoning that aims to simulate the human ability to resolve a specific problem in an intelligent manner. A classical argumentation process takes into account only the properties related to the intrinsic logical soundness of an argument in order to determine its acceptability status. However, these properties are not always the only ones that matter to establish the argument's acceptability---there exist other qualities, such as strength, weight, social votes, trust degree, relevance level, and certainty degree, among others.

This paper examines five key questions surrounding computer generated art. Driven by the recent public auction of a work of "AI Art" we selectively summarise many decades of research and commentary around topics of autonomy, authenticity, authorship and intention in computer generated art, and use this research to answer contemporary questions often asked about art made by computers that concern these topics. We additionally reflect on whether current techniques in deep learning and Generative Adversarial Networks significantly change the answers provided by many decades of prior research.

Bipolar Argumentation Frameworks (BAFs) admit several interpretations of the support relation and diverging definitions of semantics. Recently, several classes of BAFs have been captured as instances of bipolar Assumption-Based Argumentation, a class of Assumption-Based Argumentation (ABA). In this paper, we establish the complexity of bipolar ABA, and consequently of several classes of BAFs. In addition to the standard five complexity problems, we analyse the rarely-addressed extension enumeration problem too. We also advance backtracking-driven algorithms for enumerating extensions of bipolar ABA frameworks, and consequently of BAFs under several interpretations. We prove soundness and completeness of our algorithms, describe their implementation and provide a scalability evaluation. We thus contribute to the study of the as yet uninvestigated complexity problems of (variously interpreted) BAFs as well as of bipolar ABA, and provide the lacking implementations thereof.

In this paper, we introduce a formalism for single-agent decision making that is based on Dynamic Argumentation Frameworks. The formalism can be used to justify a choice, which is based on the current situation the agent is involved. Taking advantage of the inference mechanism of the argumentation formalism, it is possible to consider preference relations and conflicts among the available alternatives for that reasoning. With this formalization, given a particular set of evidence, the justified conclusions supported by warranted arguments will be used by the agent's decision rules to determine which alternatives will be selected. We also present an algorithm that implements a choice function based on our formalization. Finally, we complete our presentation by introducing formal results that relate the proposed framework with approaches of classical decision theory.

Artificial Intelligence represents many things: a new market to conquer or a quality label for tech companies, a threat for traditional industries, a menace for democracy, or a blessing for our busy everyday life. The press abounds in examples illustrating these aspects, but one should draw not hasty and premature conclusions. The first successes in AI have been a surprise for society at large-including researchers in the field. Today, after the initial stupefaction, we have examples of the system reactions: traditional companies are heavily investing in AI, social platforms are monitored during elections, data collection is more and more regulated, etc. The resilience of an organization (i.e. its capacity to resist to a shock) relies deeply on the perception of its environment. Future problems have to be anticipated, while unforeseen events occurring have to be quickly identified in order to be mitigated as fast as possible. The author states that this clear perception starts with a common definition of AI in terms of capacities and limits. AI practitioners should make notions and concepts accessible to the general public and the impacted fields (e.g. industries, law, education). It is a truism that only law experts would have the potential to estimate IA impacts on judicial system. However, questions remain on how to connect different kind of expertise and what is the appropriate level of detail required for the knowledge exchanges. And the same consideration is true for dissemination towards society. Ultimately, society will live with decisions made by the "experts". It sounds wise to involve society in the decision process rather than risking to pay consequences later. Therefore, society also needs the key concepts to understand AI impact on their life. This was the purpose of the trial of an IA that took place in October 2018 at the Court of Appeal of Paris: gathering experts from various fields to expose challenges in law and science towards a general public.

We study model-agnostic copies of machine learning classifiers. We develop the theory behind the problem of copying, highlighting its differences with that of learning, and propose a framework to copy the functionality of any classifier using no prior knowledge of its parameters or training data distribution. We identify the different sources of loss and provide guidelines on how best to generate synthetic sets for the copying process. We further introduce a set of metrics to evaluate copies in practice. We validate our framework through extensive experiments using data from a series of well-known problems. We demonstrate the value of copies in use cases where desiderata such as interpretability, fairness or productivization constrains need to be addressed. Results show that copies can be exploited to enhance existing solutions and improve them adding new features and characteristics.

Utility functions or their equivalents (value functions, objective functions, loss functions, reward functions, preference orderings) are a central tool in most current machine learning systems. These mechanisms for defining goals and guiding optimization run into practical and conceptual difficulty when there are independent, multi-dimensional objectives that need to be pursued simultaneously and cannot be reduced to each other. Ethicists have proved several impossibility theorems that stem from this origin; those results appear to show that there is no way of formally specifying what it means for an outcome to be good for a population without violating strong human ethical intuitions (in such cases, the objective function is a social welfare function). We argue that this is a practical problem for any machine learning system (such as medical decision support systems or autonomous weapons) or rigidly rule-based bureaucracy that will make high stakes decisions about human lives: such systems should not use objective functions in the strict mathematical sense. We explore the alternative of using uncertain objectives, represented for instance as partially ordered preferences, or as probability distributions over total orders. We show that previously known impossibility theorems can be transformed into uncertainty theorems in both of those settings, and prove lower bounds on how much uncertainty is implied by the impossibility results. We close by proposing two conjectures about the relationship between uncertainty in objectives and severe unintended consequences from AI systems.