20/05/2021 – ML4MC Summer School
We are pleased to announce that AI3SD have teamed up with the Directed Assembly Network to run a virtual summer school this summer (2021) on Machine Learning 4 Materials &…
We are pleased to announce that AI3SD have teamed up with the Directed Assembly Network to run a virtual summer school this summer (2021) on Machine Learning 4 Materials &…
Presenting the paper: On the Basis of Brain: NeuralâNetworkâInspired 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 âintensiveâ 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 â (i) computing power, (ii) heterogeneity of computation, and (iii) energy efficiency â as focal points of demand interest and simultaneously as directions of product improvement for the semiconductor industry players and consolidate them into a technoâ 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 âplatform chipâ 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 trilemmaâs 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.
In March 2020, right before our world got turned upside down and we went into lockdown, we managed to hold our big joint Network Conference: AIReact2020 in the beautiful setting…
This is a joint meeting between the Dial-a-Molecule, Directed Assembly and AI3SD (Artificial Intelligence and Augmented Intelligence for Automated Investigations for Scientific Discovery) Networks. The meeting will examine the state of the art and future opportunities in the use of Artificial Intelligence to predict the outcome of unknown chemical reactions, and consequently design optimum synthetic routes to desired molecules. A wide variety of AI approaches will be illustrated including expert systems, statistical methods, mechanism based and Machine Learning. The meeting will also consider: Data sourcing, sharing, and quality. Automated experimentation to generate reaction knowledge. Theoretical calculations to enrich or replace experimental data. The meeting will include talks to introduce the breadth of the area to all participants. Discussion sessions and opportunities to develop collaborations will be a key aspect of the meeting.