AI monitoring in the workplace

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If you're thinking about using AI monitoring in the workplace to help ensure appropriate measures are taken against COVID-19 you need to be thinking about some of the practical implications of using AI. Supporting article: https://blog.nightingalehq.ai/ethics-considerations-for-ai-monitoring-in-the-post-covid-workplace

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The ethical implications of our work can be staggering but how do we balance commercial needs, ethical requirements, and productivity? Taking a pragmatic approach through starting with simple checklists evolving to the use of automation and structured processes, I look at how we can make our work more robust from an ethical perspective. Step 0: Get alignment Step 1: Make others think before you start Step 2: Work robustly Step 3: Maintain vigilance

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Developer Velocity Series in association with Quest DevOps: A compound of development (Dev) and operations (Ops), DevOps is the union of people, process, and technology to continually provide value to customers. DataOps: DataOps is an automated, process-oriented methodology, used by analytic and data teams, to improve the quality and reduce the cycle time of data analytics. MLOps: MLOps […] enables data science and IT teams to collaborate and increase the pace of model development and deployment via monitoring, validation, and governance of machine learning models. DevSecOps: DevSecOps automatically bakes in security at every phase of the software development lifecycle, enabling development of secure software at the speed of Agile and DevOps. ChatOps: ChatOps is a collaboration model that connects people, tools, process, and automation into a transparent workflow. This flow connects the work needed, the work happening, and the work done in a persistent location staffed by the people, bots, and related tools. NoOps: NoOps is the idea that the software environment can be so completely automated that there’s no need for an operations team to manage it. GitOps: GitOps is a way of implementing Continuous Deployment for cloud native applications. It focuses on a developer-centric experience when operating infrastructure, by using tools developers are already familiar with, including Git and Continuous Deployment tools. Developer Velocity is the Grand Unified Theory Developer velocity: The ability to drive transformative business performance through software development Top DVI companies are stronger financially - 5x compound annual growth rate - 60% more shareholder returns - 20% higher operating margins >Companies in the top quartile of the Developer Velocity Index (DVI) outperform others in the market by four to five times. Top-quartile companies also have 60 percent higher total shareholder returns and 20 percent higher operating margins. Critical areas of focus - People Product management Product management function Product telemetry Culture Psychological safety Collaboration and knowledge sharing Continuous improvement culture Talent management Incentives Capability building - Processes Working practices Compliance practices Security practices Organisational enablement Autonomous scoped teams Dependency management Culture Continuous improvement Talent management Recruiting Team health management - Tooling Planning tools Collaboration tools Development tools DevOps tools Cloud Video at: https://www.quest.com/event/steph-lockes-developer-velocity-series-8148798/

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Reproducible machine learning Steph Locke Reproducible if Data=Same + Analysis=Same Replicable if Data=Different + Analysis=Same Robust if Data=Same + Analysis=Different Generalisable if Data=Different + Analysis=Different It’s reproducible if… With the same ✨ environment With the raw ✨ data With the unmodified ✨ code = Produces exactly the same results Benefits for You/Team - Fewer headaches around environments and data - Less rework of code - Clear standards - Easier operationalisation Stakeholders - Stable results - Auditable - Maintainable / correctable - Easier operationalisation Recommendations FAIR data Findable - Unique name / ID - Documented Accessible - Common access methods - Open protocol - Metadata stored even after data may be removed Interoperable - Common standards - Terms defined Reusable - License and use rights specified - Provenance documented What to use Use - Logging - Version control - Fixed seeds* - Dependency tracking Framework examples - MLFlow - {drake} - Azure ML - FairML

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What should you think about when determining whether your AI strategy should be to Build, Buy, or codify It Depends? The makings of a business decision Value Alignment Intellectual property Capabilities Compliance Ethics Value The _____ line Return on Investment Differentiation against the competition Value over time Alignment Overall Product AI Intellectual property Core vs peripheral IP stance Investors Capabilities Existing Hiring Ongoing overhead Peripheral skills Outsourcing approach Bus factor Compliance Oversight GDPR Broader laws Risk-appetite Regulators Ethics Historic data bias General bias Misuse considerations Internal awareness Public awareness Framework & handling

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AI in Manufacturing – a Technical Perspective (#AIFightsBack series) Presented by Steph Locke, CEO @ Nightingale HQ T: @theStephLocke Li: /stephanielocke Covers: - Overview of AI: AI performs “cognitive” tasks - Key areas of AI + Machine learning & data science + Robots may be AI - AI & ML: What techniques are we most likely to use? + Core AI tasks + Computer vision + Speech + Language + Core ML tasks + Classification + Common classification methods + Decision trees: Identify ways to split data to get cleanest outcome groups + Regression: Predicts the chances of something happening + Neural networks: Uses multiple iterations to predict the chances + Anomaly detection + Anomaly detection types + Point anomalies: Unusual inside the whole dataset + Contextual anomalies: Unusual compared to neighbouring values + Collective anomalies: Connected records that are unusual + Patterns + K-means clustering: Group records based on “distance” + Hierarchical clustering: A multi-level grouping of records + Associations: Identify co-occurrences and correlations - Critical infrastructure: What do we need to have in place? + Data + Data lake - Conclusion + What should I do next? + Key areas of AI + The process Resources - 7 Quick-win AI Projects paper https://cdn2.hubspot.net/hubfs/5410772/7 Quick Wins/7 Quick Win AI Projects-1.pdf - AI in Manufacturing article https://blog.nightingalehq.ai/ai-in-manufacturing - More AI in manufacturing webinars https://nightingalehq.eventbrite.com/ - McKinsey on the future of pharma QC https://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/digitization-automation-and-online-testing-the-future-of-pharma-quality-control - Otis ONE https://www.otis.com/en/hk/otis-signature-service/otis-one/ - ZEISS investment https://www.zeiss.com/corporate/int/innovation-and-technology/zeiss-ventures/investment-strategy/artificial-intelligence-and-image-data.html - Amgen manufacturing deviation https://www.bioprocessonline.com/doc/how-amgen-uses-ai-tools-to-improve-manufacturing-deviation-investigations-0001 - Edera Safety with Autodesk https://www.autodesk.com/solutions/generative-design/manufacturing?wvideo=0fgcc0xfxw - Speedy Hire inventory management https://peak.ai/hub/success-story/speedy/

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AI for manufacturing has huge potential. As well as clear AI use cases like robotics and automation, the wealth of data being consolidated into industrial time series via historian appliances presents an opportunity for further AI applications. Using the data being consolidated, we can build early warning systems for critical issues, optimise maintenance programs, and improve processes. Attend to learn about the use cases and the key AI terms you need to start identifying how much AI could save your business.

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Overview of AI ------ What is AI? - AI is just whatever computational task is hard to achieve right now. If it’s become “off-the-shelf”, it isn’t AI. A cynical view - AI performs “cognitive” tasks AI usecases Chatbots - Provide new interaction route for potential customers Integrate into omnichannel experience - Use to inform or support activity - Letterbox Lab case study Content repackaging - Turn content from one format to another intelligently - Blog -> Video - Video / voice -> Transcription Lumen5.com - Transform blog posts into videos Metadata enrichment - Discover new tags, topics, key points in text to generate stronger metadata to support search inside the site and beyond. Microsoft Text Analytics AI - Using Microsoft Text Analytics we can easily grab information about our content and even recommended links to sources. Social listening - Identify the direct and indirect mentions and conversation opportunities for you to engage with Using RPA tools - There are many social listening paid tools out there but tools like Microsoft Power Automate and Zapier can be used to bootstrap your social listening process by integrating off-the-shelf AI APIs. Hyper-personalization - Use extracted insights from a number of sources to generate content and funnel processes targeted at the individual to improve conversion rates. CrystalKnows.com - Get personality insights based on social and email contents to tailor communication. Implementing AI quickly How do I use AI now? - Address demand - Start small - Gather your data Technologies showcased: - Microsoft Power Automate - Microsoft Cognitive Services - Lumen5 - Crystal - Chatfuel

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AI in manufacturing (#AIFightsBack series) Watch on YouTube: https://youtu.be/8CJb14vMXjw You have been hit particularly hard during these times. In this webinar Steph will focus on a set of practical ways to help you cope. Learn how AI can save you time and money in manufacturing, from optimising processes, predicting maintenance, or enforcing quality control. AI is already having a significant impact on manufacturing and those who are getting it right will reap real benefits. It’s estimated that there is a 4-10% EBITDA increase from predictive maintenance AI solutions alone. AI is set to become a key differentiator in manufacturing processes, and you need to stay ahead of the competition. This webinar will give you robust practical insights and real use cases. # Overview of AI ## What is AI? > AI is just whatever computational task is hard to achieve right now. If it’s become “off-the-shelf”, it isn’t AI. (A cynical view) ## AI performs “cognitive” tasks - Reasoning: Learning and forming conclusions from imperfect data - Understanding: Interpreting the meaning of data including text, voice, and images - Interacting: Engaging with people in natural ways, such as speech ## ZEISS Investments ZEISS call out AI in Healthcare and Manufacturing, especially quality control as key technologies they are looking to invest in as part of their corporate strategy. # Key areas of AI ## Expert systems or data-driven? Experts - Understands domain - Has already learnt rules or developed them - Can provide rules to handle the future Data - Represents the domain - Includes past processes and consequences - Assumes future is like the past ## Machine learning & data science - Arificial Intelligence – Cognitive functions - Machine Learning – Learning from data - Deep Learning – Adaptive learning from data ## Robots may be AI - Does it do the same thing every time? - Can it handle variation? - Does it “see” and vary it’s actions based on inputs? - How autonomous is it? # AI usecases ## Usecases - Quality control - Generative design - Procurement + Stock forecasting + Supply chain analytics + Demand prediction - Production + Predictive Maintenance + Process control & optimisation - HR + Recruiting automation - Finance + Automated accounting + Asset allocation + Reporting and forecasting - Multi-function + Robotic Process Automation + Accessible Meetings ## Quality control - Use data to uncover signals that lead to poor output - Monitor for signals and identify products for QC - Use AI to perform some or all QC checks

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