Informatics
Figure 3
IT infrastructure – both foundational and emerging
capabilities are required to be successful in AI
Data Experimentation Environment Computable Data Sources
Data Management Environment Compute
Network
Additional materials ‘The State of AI’, by Andrew Ng,
Deeplearning.ai; Stanford University14. Andrew Ng has authored or co-authored more than 100 research papers in machine learning, robotics, and related fields. Related: Coursera online course ‘Neural Networks and Deep Learning’15 If you want to break into cutting-edge AI, this course will help you do so. McAfee, A, Brynjolfsson, E (2017). Machine, Platform, Crowd: Harnessing Our Digital Future16. Andrew McAfee (@amcafee), a principal research scientist at MIT, studies how digital technologies are changing business, the economy and society. Erik Brynjolfsson is the director of the MIT Center for Digital Business and one of the most cited scholars in information systems and economics. Davenport, T, Kirby, J (2016). Only Humans Need Apply: Winners and Losers in the Age of Smart Machines17. Thomas H. Davenport and Julia Kirby reframe the conversation about automation, arguing that the future of increased productivity and business success isn’t either human or machine. It is both. The key is augmentation, utilising technology to help humans work better, smarter, and faster.
14 Data Visualisation Environment
Infrastructure AI infrastructure requirements fall into two cate- gories: (i) The foundational capabilities that most companies have today which might need enhanced capability or greater agility or more mature pro- cesses to be effective for AI management (shown in grey in Figure 3), and (ii) the emerging capabilities that are not yet as mature in most companies today and will require new investment to create (shown in blue in Figure 3). Much of the infrastructure cannot be achieved if
the enterprise does not first have a clear data strat- egy and data inventory as part of the data manage- ment environment.
and leverage IT skillsets such as information archi- tecture, systems architecture and knowledge repre- sentation. Data reporting and analysis should be the focus of embedded experts in the sub-disci- plines applying AI. It can be argued that both cen- tralised and decentralised approaches might increase the pace of adoption. For example, cen- tralisation encourages the recruitment and reten- tion of scarce skillsets, creates standards, shares use cases across functions, increases organisational commitment and the faster reuse of technology. However, decentralisation increases discipline agility and expertise, and discipline-level innova- tion keeps focus on value and with a lower gover- nance hurdle, enabling faster deployment of resources. Many capabilities founded on AI evolve into
something else as they become successful. Examples might include help desk automation, image analysis, imaging biomarkers, supply chain analytics, genomic data analysis, computa- tional chemistry, field force and promotion ana- lytics, clinical protocol authoring. By the time the system is working well, responsibility for using the technology is more amenable to decen- tralisation. An IT organisation should strive to create the
environment for domain experts to self-serve their reports and analytics and to be a partner for advice and implementation of new platforms and tech- nologies or to manage new types of data. This ensures less siloing of data and redundancy of plat- forms and builds enterprise level data assets.
Metrics What metrics will be most effective in measuring AI maturity and the success of AI implementation efforts? There are at least three broad categories for measuring the success of AI efforts: Experiments: A number of AI pilots, and within these pilots there should be cycle time reduction, cost savings or quality metrics for rapid assess- ment of value. There should be the rapid conclu- sion of pilots (both successes and failures) and then for production implementation of the suc- cessful pilots. Automation: Where we should see a high percent- age of the process becoming automated and human responsibilities becoming concentrated on the higher-level, decision making, organisation of per- sonnel and scientific-interpretation tasks. Decision quality: Metrics that show that AI- enabled decision-making increases decision quality. Setting accountabilities and goals around these
areas can provide the organisational incentives suc- cessfully to advance the AI agenda.
Summary AI is a potentially transformative technology for the biopharmaceutical and healthcare industries and has many applications for which a rapidly- evolving set of technology vendors and services is emerging. The PRISME Forum Technical Meeting in
November 2017 brought together R&D IT experts from across 30 top biopharma companies to map a path to successful adoption and to prioritise the actions that biopharma R&D IT organisations should take to be competitive in the adoption of AI. There are implications for all facets of R&D IT, viz: Skills, Data, Organisation, Infrastructure and Metrics. Of these, staffing with the right set of skills, redefining the role of IT and measuring success are
Drug Discovery World Spring 2018
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72
