14/09/2020 – AI3SD Online Seminar Series: On the Basis of Brain: Neural–Network–Inspired Changes in General Purpose Chips – Ms Ekaterina Prytkova & Dr Simone Vannuccini

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Presenting the paper: On the Basis of Brain: Neural–Network–Inspired Changes in General Purpose Chips. In this paper, we disentangle the changes that the rise of Artificial Intelligence Technologies (AITs) is inducing in the semiconductor industry. The prevailing von Neumann architecture at the core of the established “intensive” technological trajectory of chip production is currently challenged by the rising difficulty to improve product performance over a growing set of computation tasks. In particular, the challenge is exacerbated by the increasing success of Artificial Neural Networks (ANNs) in application to a set of tasks barely tractable for classical programs. The inefficiency of the von Neumann architecture in the execution of ANN-based solutions opens room for competition and pushes for an adequate response from hardware producers in the form of exploration of new chip architectures and designs. Based on an historical overview of the industry and on collected data, we identify three characteristics of a chip — (i) computing power, (ii) heterogeneity of computation, and (iii) energy efficiency — as focal points of demand interest and simultaneously as directions of product improvement for the semiconductor industry players and consolidate them into a techno– economic trilemma. Pooling together the trilemma and an analysis of the economic forces at work, we construct a simple model formalising the mechanism of demand distribution in the semiconductor industry, stressing in particular the role of its supporting services, the software domain. We conclude deriving two possible scenarios for chip evolution: (i) the emergence of a new dominant design in the form of a “platform chip” comprising heterogeneous cores; (ii) the fragmentation of the semiconductor industry into submarkets with dedicated chips. The convergence toward one of the proposed scenarios is conditional on (i) technological progress along the trilemma’s edges, (ii) advances in the software domain and its compatibility with hardware, (iii) the amount of tasks successfully addressed by this software, (iv) market structure and dynamics.

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09/09/2020 – AI3SD Online Seminar Series: Using Artificial Intelligence to Optimise Small-Molecule Drug Design – Dr Nathan Brown

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he concept of in silico molecular design goes back decades and has a long history of published approaches using many different algorithms and models. Major challenges involved in de novo molecular design are manifold, including identifying appropriate molecular representations for optimisation, scoring designed molecules against multiple modelled endpoints, and objectively quantifying synthetic feasibility of the designed structures. Recently, multiobjective de novo design, more recently referred to as generative chemistry, has had a resurgence of interest. This renaissance has highlighted a step-change in successful applications of such methods. This presentation will review the development of de novo design methods over the years including the author’s original work in this area from the early 2000s, to recent approaches that show great promise. Through this review, improvements in important components of de novo design, including machine learning model predictions and automated synthesis planning, will also be presented.

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04/09/2020 – AI3SD Online Seminar Series: Machine Learning for Early Stage Drug Discovery – Professor Charlotte Deane

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Professor Charlotte Deane from the University of Oxford speaks about some of the work her research group have done on Machine Learning for Early Stage Drug Discovery to give a flavour of the different kinds of approaches they have been looking at. These run from predicting whether molecules will bind or not bind to a given protein target, to trying to remove biases from that kind of work, to finally how do we generate novel molecules in the protein binding sites. 

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04/09/2020 – AI3SD Online Seminar Series: Machine Learning for Early Stage Drug Discovery – Professor Charlotte Deane

https://www.youtube.com/watch?v=GY0myVuhrCo&t=14s&ab_channel=AI4ScientificDiscovery Abstract: Professor Charlotte Deane from the University of Oxford speaks about some of the work her research group have done on Machine Learning for Early Stage Drug Discovery to…

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02/09/2020 – AI3SD Online Seminar Series: The Bluffers Guide to Symbolic AI – Dr Louise Dennis

https://www.youtube.com/watch?v=Gc7MGnQ4mEk&ab_channel=AI4ScientificDiscovery Abstract: Symbolic AI, sometimes referred to as Good Old-fashioned AI, has its roots in the earliest days of the AI project.  It seeks to represent reasoning using explicit data…

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02/09/2020 – AI3SD Online Seminar Series: The Bluffers Guide to Symbolic AI – Dr Louise Dennis

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Symbolic AI, sometimes referred to as Good Old-fashioned AI, has its roots in the earliest days of the AI project. It seeks to represent reasoning using explicit data structures often drawn from logic. Symbolic AI systems have the advantage of being comparatively easy to understand and analyse and potentially allow compact forms of representation and communication. Their disadvantages tend to include inflexibility, a high knowledge engineering cost, and difficulty handling non-symbolic, statistical and analogue processes such as vision and motion. This talk will cover a brief history of the field and current topics within it as well as looking at proposals for combining symbolic and non-symbolic reasoning.

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26/08/2020 – AI3SD Online Seminar Series: Smart Cleaning & COVID-19 – Dr Nicholas Watson

https://www.youtube.com/watch?v=o3TSkGgHI78&ab_channel=AI4ScientificDiscovery Abstract: Industrial Digital Technologies (IDTs) such as robotics, AI and IoT are transforming manufacturing worldwide with significant productivity, efficiency and environmental sustainability benefits. This digital revolution is often labelled…

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26/08/2020 – AI3SD Online Seminar Series: Smart Cleaning & COVID-19 – Dr Nicholas Watson

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Industrial Digital Technologies (IDTs) such as robotics, AI and IoT are transforming manufacturing worldwide with significant productivity, efficiency and environmental sustainability benefits. This digital revolution is often labelled Industry 4.0 and at its heart is the enhanced collection and use of data. The food and drink sector has been slow to adopt IDT’s for a variety of reasons including the availability of cost effective sensing technologies, capable of operating in production environments. This presentation will discuss the use of IDTs within the important task of food factory cleaning. It will cover the benefits and challenges of deploying robots, sensors and machine learning technologies for factory cleaning tasks in addition to the ever growing importance of effective factory cleaning during a global pandemic.

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19/08/2020 – AI3SD Online Seminar Series: Artificial Intelligence’s new clothes? From General Purpose Technology to Large Technical System – Dr Simone Vannuccini & Ms Ekaterina Prytkova

https://www.youtube.com/watch?v=WyB42XTI4VA&t=12s&ab_channel=AI4ScientificDiscovery Abstract: Artificial Intelligence (AI) is expected to be characterised by wide applicability; for this reason, it has been quickly labelled a General Purpose Technology (GPT). In this paper, we…

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19/08/2020 – AI3SD Online Seminar Series: Artificial Intelligence’s new clothes? From General Purpose Technology to Large Technical System – Dr Simone Vannuccini & Ms Ekaterina Prytkova

Artificial Intelligence (AI) is expected to be characterised by wide applicability; for this reason, it has been quickly labelled a General Purpose Technology (GPT). In this paper, we critically assess whether AI is really a GPT. Provided that the answer is ‘not exactly’, we suggest that an alternative framework – drawn from the literature on large technical systems (LTS) – could be useful to understand the nature of AI. AI, in its current understanding, is a ‘system technology’ – a collection of techniques built and enabled by the conjunction of many sub-systems. From this premise, we try the fundamental building blocks of LTS on AI to provide new insights on its nature, goal orientation, and the actors and factors playing a role in enabling or constraining its development. Thinking in terms of AI LTS can help researchers to identify how control is distributed, coordination is achieved, and where decisions take place, or which levers actors (among which policy makers) can pull to relax constraints or steer the evolution of AI.

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