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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------
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> AI is just whatever computational task is hard to achieve right now. If it’s become “off-the-shelf”, it isn’t AI.
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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
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