23/09/2020 - AI3SD Online Seminar Series: AI for Science: Transforming Scientific Research - Professor Tony Hey

posted 17 Aug 2020, 08:56 by Samantha Kanza   [ updated 22 Sep 2020, 07:51 ]

Eventbrite Link: https://www.eventbrite.co.uk/e/ai3sd-online-seminar-series-ai-4-science-transforming-scientific-research-tickets-117289242281

Description:
This seminar forms part of the AI3SD Online Seminar Series that will run across the summer. Please sign up to register for this event, and the weblink for the seminar will be sent to you the day before the event. A recording of this seminar will be made available on the AI3SD YouTube Channel. 

Abstract:
There is now broad recognition within the scientific community that the ongoing deluge of scientific data is fundamentally transforming academic research. Turing Award winner Jim Gray referred to this revolution as “The Fourth Paradigm: Data Intensive Scientific Discovery’. Researchers now need tools and technologies to manipulate, analyze, visualize, and manage vast amounts of research data. This talk will begin by reviewing the challenges posed by the explosive growth of experimental and observational data generated by large-scale facilities such as the Diamond Synchrotron and the CryoEM Facilities at the Rutherford Appleton Laboratory. Increasingly, scientists are beginning to use sophisticated machine learning and other AI technologies both to automate parts of the data pipeline and also to find new scientific discoveries in the deluge of experimental data. In particular, ‘Deep Learning’ neural networks have already transformed several areas of computer science and research scientists are now exploring their use in analyzing their ‘Big Scientific Data’. The talk concludes with a vision of how this ‘AI for Science’ agenda can be truly transformative for experimental scientific discovery.

Biography:
Tony is the Chief Data Scientist at the Science and Technology Facilities Council. Tony’s original background was in Physics, completing his undergraduate degree and subsequent post-docs at the University of Oxford in the UK and then CalTech and CERN in the USA. He worked at the University of Southampton in the Physics Department originally before transferring to the Electronics and Computer Science Department where he created a leading research group in parallel computing. He was the director of the UK’s e-Science initiative (2001-2005) and then became the Vice President in Microsoft Research afterwards.

16/09/2020 - AI3SD Online Seminar Series: From molecular self-association to novel weapons in the fight against antimicrobial resistance – the next target for AI/Machine Learning? - Dr Jennifer Hiscock

posted 17 Aug 2020, 08:49 by Samantha Kanza   [ updated 26 Aug 2020, 03:21 ]

Eventbrite Link: https://www.eventbrite.co.uk/e/ai3sd-seminar-supramolecular-antimicrobials-the-next-target-for-aiml-tickets-118272188299 

Description:
This seminar forms part of the AI3SD Online Seminar Series that will run across the summer. Please sign up to register for this event, and the weblink for the seminar will be sent to you the day before the event. A recording of this seminar will be made available on the AI3SD YouTube Channel. 

Abstract:
Since the 1980’s the development of novel antibiotics has dramatically reduced. This, combined with the ever-increasing prevalence of antibiotic resistance in bacteria, means that some bacterial strains have now been identified that are resistant to treatment with all known classes of antibiotic currently available. Supramolecular Self-associating Amphiphiles (SSAs) are a novel class of amphiphilic salts that contain an uneven number of covalently linked hydrogen bond donating and accepting groups, meaning that they are ‘frustrated’ in nature. The hydrogen-bonded, self-associative properties for members of this class of over 70 compounds synthesised to date have been extensively studied in the gas phase, solution state, solid state and in silico. Through these studies we have shown correlations between certain physicochemical properties that maybe predicted by simple, low-level, high-throughput, easily accessible computational modelling. In addition, members from this class of compound have been shown to kill a variety of different bacteria, including those with known antibiotic resistance (e.g. Methicillin Resistant Staphylococcus aureus (MRSA)). These initial studies have highlighted within the supramolecular chemistry community a vast amount of experimental data, not yet accessed by AI/machine learning. Could data sets such as these be the next targets of interest for this community? Is there room for a consortium or community led approach to solving predictive modelling within this branch of chemistry.

Biography:
Jennifer obtained her PhD from the University of Southampton (UK) under the supervision of Prof. Philip A. Gale in 2010 studying supramolecular host:guest chemistry. She continued her post-doctoral research between this group and Dstl (Porton Down - UK) until 2015 when she moved to the University of Kent (UK) as the Caldin research fellow. In 2016 she was awarded a permanent lectureship position at that same institution, which has since been followed by her promotion to Reader in Supramolecular Chemistry and Director of Innovation and Enterprise for the School of Physical Sciences in 2019. In 2020 she was awarded a UKRI Future Leaders Fellowship, developing novel cell surface active therapeutics and drug adjuvants. Her research currently focuses on an interdisciplinary approach to applying supramolecular chemistry to solve real-world problems.

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

posted 17 Aug 2020, 08:45 by Samantha Kanza   [ updated 26 Aug 2020, 08:41 ]

Eventbrite Link: https://www.eventbrite.co.uk/e/ai3sdda-seminar-neuralnetworkinspired-changes-in-general-purpose-chips-tickets-118301148921 

Description:
This seminar forms part of the AI3SD Online Seminar Series that will run across the summer. This particular seminar will be run in conjunction with the Directed Assembly Network. Please sign up to register for this event, and the weblink for the seminar will be sent to you the day before the event. A recording of this seminar will be made available afterwards on our YouTube Channel. 

Abstract:
Presenting the paper: On the Basis of Brain: Neural–Network–Inspired Changes in General Purpose ChipsIn 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.

Biographies:
Simone Vannuccini is a Lecturer in the Economics of Innovation at the Science Policy Research Unit (SPRU), University of Sussex Business School. At the University of Sussex, Dr Vannuccini co-convenes the Research Mobilisation Group on Artificial Intelligence, is the Deputy director of the Future of Work Hub, and the convenor of the SPRU Freeman Seminars. Dr Vannuccini is also an Associated Fellow of the Graduate College 'The Economics of Innovative Change', Friedrich Schiller University Jena (Germany) and has been Adjunct Professor of Economics of Innovation at the University of Insubria (Italy), where currently is a Faculty Board Member of the PhD Program in Methods and Models for Economic Decisions. He also collaborates with the Center for Studies on Federalism in Turin (Italy). Before joining SPRU in 2018, Dr Vannuccini has been working as Research Fellow (Post-doc) at the Friedrich Schiller University Jena (Germany), where he also obtained his PhD in a joint programme with the Max Planck Institute of Economics. Dr Vannuccini's research focuses on microeconomics of innovation and more precisely on the 'regular irregularities' of technical change: in particular, he studied the nature of 'general-purpose technologies' and their impact on industrial dynamics. More recently, he is working on the economics of artificial intelligence and in particular on the current AI-driven trajectories in the semiconductor industry; further ongoing themes of interest are the general-purposeness of AI, the economics of digitalisation and the industrial organisation of multi-sided platforms, and the modelling of industry life-cycles.

Ekaterina Prytkova is a Doctoral candidate at the Department of Economics and Business Administration of the Friedrich Schiller University Jena (Germany) and the Graduate College 'The Economics of Innovative Change'. She is a recipient of the Landegraduietertstipendium, a State scholarship supporting excellence research projects, and has been the Programme Coordinator for the Double Degree MSc in Economics between the Universities of Jena and Insubria (Italy). From September to December 2019, she has been Visiting Research Fellow at SPRU, University of Sussex Business School (UK). Ms Prytkova has been designing and teaching modules on Economics of Innovation, Introduction to the software R, and Productivity and Efficiency Analysis. Ms Prytkova's research focuses on the Economics of Technological Change and Industrial Dynamics. In particular, she has been working on the nature and diffusion of ICTs, digital infrastructure, and artificial intelligence (AI). Her current work is dedicated to understanding the trajectories and scenarios for the semiconductor industry given the adoption of AI technologies and tracing patterns of technological reliance on evolving ICT cluster among industries using text mining techniques and network analysis.

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

posted 17 Aug 2020, 08:38 by Samantha Kanza   [ updated 8 Sep 2020, 07:50 ]

Eventbrite Link: https://www.eventbrite.co.uk/e/ai3sd-online-seminar-series-ai-4-optimising-small-molecule-drug-design-tickets-117753272207

Description:
This seminar forms part of the AI3SD Online Seminar Series that will run across the summer. Please sign up to register for this event, and the weblink for the seminar will be sent to you the day before the event. A recording of this seminar will be made available on the AI3SD YouTube Channel. 

Abstract:
The 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.

Biography:
Nathan is recognised as a global thought-leader in Chemoinformatics and computational drug discovery, and is the inventor of the first multiobjective de novo molecular design system. He joined BenevolentAI in 2017 from The Institute of Cancer Research, London where he founded and led the In Silico Medicinal Chemistry team for over ten years, with significant scientific impact on drugs in active clinical trials, and the development of new algorithms for drug discovery. Nathan has published over 40 papers and three books; is a Fellow of The Royal Society of Chemistry; and is the 2017 recipient of the Corwin Hansch Award.

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

posted 17 Aug 2020, 08:21 by Samantha Kanza   [ updated 7 Sep 2020, 08:47 ]

Eventbrite Link: Coming Soon

Description:
This seminar forms part of the AI3SD Online Seminar Series that will run across the summer. Please sign up to register for this event, and the weblink for the seminar will be sent to you the day before the event. A recording of this seminar will be made available on the AI3SD YouTube Channel. 

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 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. 

Biography:
Charlotte is Professor of Structural Bioinformatics at the Department of Statistics, University of Oxford and Deputy Executive Chair of the Engineering and Physical Sciences Research Council (EPSRC). At Oxford, Charlotte leads the Oxford Protein Informatics Group, who work on diverse problems across protein structure, interaction networks and small molecule drug discovery; combining theoretical and empirical analysis with special interest in AI. She collaborates with experimentalists in academia and industry in experiment design to leverage the power of computation for biological insight. Her work focusses on the development of novel algorithms, tools and databases that are openly available to the community. Examples include SAbDab, SAbPred, PanDDA and MEMOIR. These tools are widely used web resources and are also part of several Pharma drug discovery pipelines. Charlotte has consulted extensively with industry and has set up a consulting arm within her own research group as a way of promoting industrial interaction and use of the group’s software tools.

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

posted 29 Jul 2020, 10:47 by Samantha Kanza   [ updated 10 Aug 2020, 02:51 ]

Eventbrite Link: https://www.eventbrite.co.uk/e/ai3sd-online-seminar-series-the-bluffers-guide-to-symbolic-ai-tickets-116121184589 

Description:
This seminar forms part of the AI3SD Online Seminar Series that will run across the summer. Please sign up to register for this event, and the weblink for the seminar will be sent to you the day before the event. A recording of this seminar will be made available afterwards to AI3SD members. 

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 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.

Biography:
Louise is a senior lecturer at the University of Manchester where she is part of the Autonomy and Verification group. She is a member of the IEEE Global Initiative for Ethical Considerations in the Design of Autonomous Systems and the IEEE Standards working group for Transparency for Autonomous Systems (P7001). She is currently co-investigator on two EPSRC Hubs for Robotics in Extreme and Challenging Environments: Future AI and Robotics for Space (FAIR-SPACE) and Robotics and AI for Nuclear (RAIN). Her expertise is in the development and verification of autonomous systems with interests in rational agent programming languages, and architectures for autonomous systems, with a particular emphasis on ethical machine reasoning and creating verifiable systems.

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

posted 29 Jul 2020, 10:42 by Samantha Kanza   [ updated 31 Jul 2020, 02:46 ]

Eventbrite Link: https://www.eventbrite.co.uk/e/ai3sd-ioft-online-seminar-smart-cleaning-covid-19-tickets-115359290744

Description:
This seminar forms part of the AI3SD Online Seminar Series that will run across the summer. This particular seminar will be run in conjunction with the Internet of Food Things Network. Please sign up to register for this event, and the weblink for the seminar will be sent to you the day before the event. A recording of this seminar will be made available afterwards on our YouTube Channel. 

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 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.

Biography:
Nik is an Associate Professor of Chemical Engineering. He has a PhD in engineering and spent four years as a researcher in the School of Food Science and Nutrition at the University of Leeds before joining Nottingham in 2014. Nik’s research is focussed on digital manufacturing within the food and drink sector and his team develops intelligent sensor technologies to tackle some of the biggest challenges around sustainability, food safety, hygiene and productivity. A focus of Nik’s research is developing sensor and data analysis methods that work effectively in challenging food production environments and can be integrated with other key industrial digital technologies such as AI, Robotics and the Industrial Internet of Things. Nik has led projects investigating how sensors and data analytics can be used to reduce the cost and environmental impact of industrial cleaning processes and unit operations such as fermentation and mixing. Nik is currently a member of the EPSRC Early Career Forum in Manufacturing Research, on the Food Standards Agency register of experts and a Co-Investigator on the EPSRC digital manufacturing network: Connected Everything 2.

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

posted 29 Jul 2020, 06:41 by Samantha Kanza   [ updated 5 Aug 2020, 07:01 ]

Eventbrite Link: https://www.eventbrite.co.uk/e/ai3sd-online-seminar-series-artificial-intelligences-new-clothes-tickets-115197839840 

Description:
This seminar forms part of the AI3SD Online Seminar Series that will run across the summer. Please sign up to register for this event, and the weblink for the seminar will be sent to you the day before the event. A recording of this seminar will be made available afterwards to AI3SD members. 

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 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.

Biographies:
Simone Vannuccini is a Lecturer in the Economics of Innovation at the Science Policy Research Unit (SPRU), University of Sussex Business School. At the University of Sussex, Dr Vannuccini co-convenes the Research Mobilisation Group on Artificial Intelligence, is the Deputy director of the Future of Work Hub, and the convenor of the SPRU Freeman Seminars. Dr Vannuccini is also an Associated Fellow of the Graduate College 'The Economics of Innovative Change', Friedrich Schiller University Jena (Germany) and has been Adjunct Professor of Economics of Innovation at the University of Insubria (Italy), where currently is a Faculty Board Member of the PhD Program in Methods and Models for Economic Decisions. He also collaborates with the Center for Studies on Federalism in Turin (Italy). Before joining SPRU in 2018, Dr Vannuccini has been working as Research Fellow (Post-doc) at the Friedrich Schiller University Jena (Germany), where he also obtained his PhD in a joint programme with the Max Planck Institute of Economics. Dr Vannuccini's research focuses on microeconomics of innovation and more precisely on the 'regular irregularities' of technical change: in particular, he studied the nature of 'general-purpose technologies' and their impact on industrial dynamics. More recently, he is working on the economics of artificial intelligence and in particular on the current AI-driven trajectories in the semiconductor industry; further ongoing themes of interest are the general-purposeness of AI, the economics of digitalisation and the industrial organisation of multi-sided platforms, and the modelling of industry life-cycles.

Ekaterina Prytkova is a Doctoral candidate at the Department of Economics and Business Administration of the Friedrich Schiller University Jena (Germany) and the Graduate College 'The Economics of Innovative Change'. She is a recipient of the Landegraduietertstipendium, a State scholarship supporting excellence research projects, and has been the Programme Coordinator for the Double Degree MSc in Economics between the Universities of Jena and Insubria (Italy). From September to December 2019, she has been Visiting Research Fellow at SPRU, University of Sussex Business School (UK). Ms Prytkova has been designing and teaching modules on Economics of Innovation, Introduction to the software R, and Productivity and Efficiency Analysis. Ms Prytkova's research focuses on the Economics of Technological Change and Industrial Dynamics. In particular, she has been working on the nature and diffusion of ICTs, digital infrastructure, and artificial intelligence (AI). Her current work is dedicated to understanding the trajectories and scenarios for the semiconductor industry given the adoption of AI technologies and tracing patterns of technological reliance on evolving ICT cluster among industries using text mining techniques and network analysis.

12/08/2020 - AI3SD Online Seminar Series: Quantum Computing: A Guide for the Perplexed - Professor Andy Stanford-Clark

posted 14 Jul 2020, 02:26 by Samantha Kanza   [ updated 15 Jul 2020, 01:27 ]


Description:
This seminar forms part of the AI3SD Online Seminar Series that will run across the summer. Please sign up to register for this event, and the weblink for the seminar will be sent to you the day before the event. A recording of this seminar will be made available afterwards to AI3SD members. Andy will introduce the mind-bending principles of quantum computing, give some history of the technology, and describe potential application areas for quantum computers. He will take us on tour inside a real quantum computer, and explain how you can get free hands-on experience of IBM's quantum computer, and start to learn how to program these exciting new machines.

Abstract:
We experience the benefits of classical computing every day. However, there are challenges that today’s systems will never be able to solve. For problems above a certain size and complexity, we don’t have enough computational power on Earth to tackle them. To stand a chance at solving some of these problems, we need a new kind of computing.  Quantum computers could spur the development of new breakthroughs in science: Medications to save lives, machine learning methods to diagnose illnesses sooner, materials to make more efficient devices and structures, financial strategies to live well in retirement, and algorithms to quickly direct resources such as ambulances. IBM Q is the world's most advanced quantum computing initiative, focused on propelling the science and pioneering commercial applications for quantum advantage. An industry first initiative to build universal quantum computers for business, engineering and science. This effort includes advancing the entire quantum computing technology stack and exploring applications to make quantum broadly usable and accessible. 

Biography:
Andy Stanford-Clark is the Chief Technology Officer for IBM in UK and Ireland. He is an IBM Distinguished Engineer and Master Inventor with more than 40 patents. Andy is based at IBM's Hursley Park laboratories in the UK, and has a long background in Internet of Things technologies. He has a BSc in Computing and Mathematics, and a PhD in Computer Science. He is a Visiting Professor at the University of Newcastle, an Honorary Professor at the University of East Anglia, an Adjunct Professor at the University of Southampton, and a Fellow of the British Computer Society.

Interview:
Andy was interviewed by Michelle Pauli at our Network Conference in 2019 on his thoughts on AI. This interview can be found here: https://eprints.soton.ac.uk/441867/

05/08/2020 - AI3SD Online Seminar Series: Dimensionality in chemistry: using multidimensional data for machine learning - Dr Ella Gale

posted 14 Jul 2020, 02:21 by Samantha Kanza   [ updated 21 Jul 2020, 06:50 ]


Description:
This seminar forms part of the AI3SD Online Seminar Series that will run across the summer. Please sign up to register for this event, and the weblink for the seminar will be sent to you the day before the event. A recording of this seminar will be made available afterwards to AI3SD members. This is a general interest seminar, and by the end the audience should have gained some intuition for dealing with big, multidimensional data in machine learning approaches to chemical problems.

Abstract:
In the last hundred years mankind has fully absorbed the idea of multi-dimensional space, starting with 4D space time. Due to the increase in computational power, scientists can now manipulate molecules in 4D (3D vibrating molecules in VR) and work with multidimensional datasets, which are needed to utilize big data and machine learning. However, our intuition from 3D space can fall down when dealing with higher dimensions and a lack of intuition can lead to mistakes in analysis. In this talk I will discuss how to think about the best dimensional space to use to describe chemical problems, how multi-dimensional space is different, techniques for using it and analysing the outputs of machine learning. 

Biography:
Dr. Ella M Gale is the machine learning subject specialist attached to the Technology Enhanced Chemical Synthesis Centre for Doctoral Training at the University of Bristol. She has a PhD in Computational Chemistry from Imperial College London. In her career since she has worked in a set of diverse areas: neural networks, AI, cellular automata, unconventional computing, machine learning, memristors, computer vision, nanotechnology, materials science and supports colleagues in chemical engineering and synthetic chemistry. Her current research interests are applying machine learning techniques to de novo drug design and retrosynthesis and applying computer vision techniques to chemistry lab monitoring.

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