Win more, win faster with sales automation
•Download as PPTX, PDF•
0 likes•325 views
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against developing mental illness and improve symptoms for those who already have a condition.
Report
Share
Report
Share
Recommended
Let's banish "it works on my machine"
Between spending hours (or days!) making sure you can code and test locally and the difficulties of keeping remote environments up to date, sometimes we find ourselves falling back on "It works on my machine!". Getting rid of the difficulties in making new development environments and maintaining testing infrastructure is really key to banishing the dreaded phrase. In this session, we'll take you through some of the recent tools and techs that will not only make your life easier but will mean you never have to say "works on my machine" ever again.
How to build brilliant managers.pdf
Too often, we chuck talented technologists into a promotion to the role of team lead or manager without any training or support. This can lead to a shaky foundation in their management style, many new managers quitting to go back to tech, and people being mis-handled as managers learn. In this session, I'll take you through some of the concepts, frameworks, and support systems I think help a company grow great leaders. This is a great session for existing leaders but useful for those who are "management-curious" and want to see what's involved.
Working with data using Azure Functions.pdf
Azure Functions are great for a wide range of scenarios, including working with data on a transactional or event-driven basis. In this session, we'll look at how you can interact with Azure SQL, Cosmos DB, Event Hubs, and more so you can see how you can take a lightweight but code-first approach to building APIs, integrations, ETL, and maintenance routines.
Developer Velocity
Join Steph and Chris as they run through Microsoft's transformation from shipping boxed products to always-on online services. Developer Velocity is a critical part of that journey to ensure the teams can keep delivering value to their end-users at scale. In this session, you will learn about some of the tips & tricks that Microsoft used along the way.
https://www.youtube.com/watch?v=d_4i0lxKtr0
The Microsoft Well Architected Framework For Data Analytics
With more than a decade of organizations running large data & analytics workloads in the cloud, Microsoft have extended their architecture framework to provide best practices and guidance for businesses. In this session, we’ll introduce the 'Well Architected Framework', go into detail about effective data architectures, and give you concrete next steps you can take whether you already have a cloud data architecture or are planning your first implementation.
Sustainable manufacturing with AI
Sustainable manufacturing with AI
Improve your processes:
Defect detection to reduce waste
Predictive maintenance to improve energy efficiency
Generative design to reduce materials used in products
Process optimisation to improve energy usage
Inventory optimisation to reduce materials held
Improve your IT:
Go paperless
Move to carbon neutral clouds
Adopt green software products
Optimise your compute usage
Improve with Nightingale HQ
We’re doing bespoke and pilot projects with manufacturers and adjacent industries. Make your business more sustainable.
bit.ly/nhqaichat
Wrangling data like a boss
Effective data wrangling starts with data collection. Make sure to think about Front-end validation, limiting free text, GDPR, security, accessibility, inclusion, and designing to minimise the amount of data collected.
The next part is effective data storage. You can store data in relational or non-relational data stores. Relational data solutions in Azure include Azure SQL Database, Azure Database for MariaDB, and Azure Database for PostgreSQL. Non-relational data solutions include Azure Storage and Azure Cosmos DB.
You might wrangle data during a data movement process between systems. A batch process moves multiple records, typically on a schedule. A streaming process operates on each individual event/message as it happens. In ETL (Extract Transform Load) you run a process to retrieve data from a system, process it, and then insert it into a new data store. In ELT (Extract Load Transform) you run a process to retrieve data from a system, insert it into a new data store, and the process it.
The modern data warehouse stack (used to combine data from multiple sources) includes Azure Data Factory, Azure Databricks, Azure Synapse Analytics and Azure Analysis Services.
Power Query is a useful language for processing data and can be used in Excel, Power BI, and Azure Data Factory.
Power BI is a self-service data modelling and visualisation tool that can connect to data stored in lots of different places. It has a data modeling area where Power Query can be used to build a dataset. The reports allow you make multi-page analysis of a dataset. A dashboard combines visuals from one or more reports to provide a broader and simpler view.
SQL is a language that works with many relational and non-relational systems. You can use it interact with data, data structures, and the access model for the data objects.
Finally you can write code to wrangle data with Python, R, and dotnet being common data wrangling languages.
Digitalisation from the back office to the factory floor
AI is a huge set of tools for making computers behave intelligently - Andrew Ng
70% of implementations fail to meet their stated aims. Following the holistic triple transformation approach taken by ‘lighthouses’ seems like a sensible approach to take. Industry 4.0: Reimagining manufacturing operations after COVID-19 McKinsey
Get started with AI:
- Start small with pilot projects to gain momentum
- Strong business case
- Build a team around this
- Provide broad training
- Longer-term develop an AI strategy that aligns with your business objectives
Recommended
Let's banish "it works on my machine"
Between spending hours (or days!) making sure you can code and test locally and the difficulties of keeping remote environments up to date, sometimes we find ourselves falling back on "It works on my machine!". Getting rid of the difficulties in making new development environments and maintaining testing infrastructure is really key to banishing the dreaded phrase. In this session, we'll take you through some of the recent tools and techs that will not only make your life easier but will mean you never have to say "works on my machine" ever again.
How to build brilliant managers.pdf
Too often, we chuck talented technologists into a promotion to the role of team lead or manager without any training or support. This can lead to a shaky foundation in their management style, many new managers quitting to go back to tech, and people being mis-handled as managers learn. In this session, I'll take you through some of the concepts, frameworks, and support systems I think help a company grow great leaders. This is a great session for existing leaders but useful for those who are "management-curious" and want to see what's involved.
Working with data using Azure Functions.pdf
Azure Functions are great for a wide range of scenarios, including working with data on a transactional or event-driven basis. In this session, we'll look at how you can interact with Azure SQL, Cosmos DB, Event Hubs, and more so you can see how you can take a lightweight but code-first approach to building APIs, integrations, ETL, and maintenance routines.
Developer Velocity
Join Steph and Chris as they run through Microsoft's transformation from shipping boxed products to always-on online services. Developer Velocity is a critical part of that journey to ensure the teams can keep delivering value to their end-users at scale. In this session, you will learn about some of the tips & tricks that Microsoft used along the way.
https://www.youtube.com/watch?v=d_4i0lxKtr0
The Microsoft Well Architected Framework For Data Analytics
With more than a decade of organizations running large data & analytics workloads in the cloud, Microsoft have extended their architecture framework to provide best practices and guidance for businesses. In this session, we’ll introduce the 'Well Architected Framework', go into detail about effective data architectures, and give you concrete next steps you can take whether you already have a cloud data architecture or are planning your first implementation.
Sustainable manufacturing with AI
Sustainable manufacturing with AI
Improve your processes:
Defect detection to reduce waste
Predictive maintenance to improve energy efficiency
Generative design to reduce materials used in products
Process optimisation to improve energy usage
Inventory optimisation to reduce materials held
Improve your IT:
Go paperless
Move to carbon neutral clouds
Adopt green software products
Optimise your compute usage
Improve with Nightingale HQ
We’re doing bespoke and pilot projects with manufacturers and adjacent industries. Make your business more sustainable.
bit.ly/nhqaichat
Wrangling data like a boss
Effective data wrangling starts with data collection. Make sure to think about Front-end validation, limiting free text, GDPR, security, accessibility, inclusion, and designing to minimise the amount of data collected.
The next part is effective data storage. You can store data in relational or non-relational data stores. Relational data solutions in Azure include Azure SQL Database, Azure Database for MariaDB, and Azure Database for PostgreSQL. Non-relational data solutions include Azure Storage and Azure Cosmos DB.
You might wrangle data during a data movement process between systems. A batch process moves multiple records, typically on a schedule. A streaming process operates on each individual event/message as it happens. In ETL (Extract Transform Load) you run a process to retrieve data from a system, process it, and then insert it into a new data store. In ELT (Extract Load Transform) you run a process to retrieve data from a system, insert it into a new data store, and the process it.
The modern data warehouse stack (used to combine data from multiple sources) includes Azure Data Factory, Azure Databricks, Azure Synapse Analytics and Azure Analysis Services.
Power Query is a useful language for processing data and can be used in Excel, Power BI, and Azure Data Factory.
Power BI is a self-service data modelling and visualisation tool that can connect to data stored in lots of different places. It has a data modeling area where Power Query can be used to build a dataset. The reports allow you make multi-page analysis of a dataset. A dashboard combines visuals from one or more reports to provide a broader and simpler view.
SQL is a language that works with many relational and non-relational systems. You can use it interact with data, data structures, and the access model for the data objects.
Finally you can write code to wrangle data with Python, R, and dotnet being common data wrangling languages.
Digitalisation from the back office to the factory floor
AI is a huge set of tools for making computers behave intelligently - Andrew Ng
70% of implementations fail to meet their stated aims. Following the holistic triple transformation approach taken by ‘lighthouses’ seems like a sensible approach to take. Industry 4.0: Reimagining manufacturing operations after COVID-19 McKinsey
Get started with AI:
- Start small with pilot projects to gain momentum
- Strong business case
- Build a team around this
- Provide broad training
- Longer-term develop an AI strategy that aligns with your business objectives
The fundamentals of regression
Understand how machine learning, regression, and linear regression works. Learn about the assumptions made and how to interpret models.
Practical AI & data science ethics
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
Help There’s Too Many [Something]Ops!
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/
Reproducible machine learning
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
AI monitoring in the workplace
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
Working with relational data in Microsoft Azure
This slide deck overviews the key technologies businesses considering moving to the cloud may benefit from, and how they can apply strong governance and cost controls to build a more secure data environment.
Build or buy AI?
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
AI in manufacturing - a technical perspective
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/
The historian and AI
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.
AI for marketers
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
AI in manufacturing
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
More Related Content
More from Stephanie Locke
The fundamentals of regression
Understand how machine learning, regression, and linear regression works. Learn about the assumptions made and how to interpret models.
Practical AI & data science ethics
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
Help There’s Too Many [Something]Ops!
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/
Reproducible machine learning
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
AI monitoring in the workplace
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
Working with relational data in Microsoft Azure
This slide deck overviews the key technologies businesses considering moving to the cloud may benefit from, and how they can apply strong governance and cost controls to build a more secure data environment.
Build or buy AI?
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
AI in manufacturing - a technical perspective
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/
The historian and AI
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.
AI for marketers
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
AI in manufacturing
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