The document discusses EdgeAI, a startup developing an AI chip with a custom machine learning accelerator and new embedded memory technologies targeting low-power, high-performance edge applications. In early stages, EdgeAI aimed to enable AI vision on battery-powered cameras but faced challenges competing with GPUs. It later found product-market fit enabling solar-powered security cameras by developing a chip that performs inference with no idle power consumption. EdgeAI will validate this approach with pilots and plans to fabricate a second silicon chip and raise seed funding to develop end-to-end prototypes.
1. The document describes the journey of a startup called Hard Hats in developing a product for the construction industry. It details their initial broad goal, multiple pivots as they learned more, and iterations to arrive at an MVP focused on automating permit applications.
2. Through interviews with over 100 stakeholders, they narrowed in on problems with the permitting process and developed an MVP called Auto-Check to automatically check permit applications for errors.
3. They secured pilot programs with two cities to test Auto-Check and were getting positive feedback that it could significantly speed up the initial review process for permit applications.
business model, business model canvas, mission model, mission model canvas, customer development, lean launchpad, lean startup, stanford, startup, steve blank, entrepreneurship, I-Corps, Stanford
business model, business model canvas, mission model, mission model canvas, customer development, hacking for defense, H4D, lean launchpad, lean startup, stanford, startup, steve blank, pete newell, bmnt, entrepreneurship, I-Corps, Security, NSIN, NSA, cybersecurity, Joe Felter
29 SETTEMBRE 2021 – Aula Magna – Corso Duca degli Abruzzi, 24 – Politecnico di Torino
Ricerca, trasferimento tecnologico e supporto alle aziende sui temi fondamentali dei Big Data, Intelligenza Artificiale, la robotica e la rivoluzione digitale
business model, business model canvas, mission model, mission model canvas, customer development, lean launchpad, lean startup, stanford, startup, steve blank, entrepreneurship, I-Corps, Stanford
The document discusses EdgeAI, a startup developing an AI chip with a custom machine learning accelerator and new embedded memory technologies targeting low-power, high-performance edge applications. In early stages, EdgeAI aimed to enable AI vision on battery-powered cameras but faced challenges competing with GPUs. It later found product-market fit enabling solar-powered security cameras by developing a chip that performs inference with no idle power consumption. EdgeAI will validate this approach with pilots and plans to fabricate a second silicon chip and raise seed funding to develop end-to-end prototypes.
1. The document describes the journey of a startup called Hard Hats in developing a product for the construction industry. It details their initial broad goal, multiple pivots as they learned more, and iterations to arrive at an MVP focused on automating permit applications.
2. Through interviews with over 100 stakeholders, they narrowed in on problems with the permitting process and developed an MVP called Auto-Check to automatically check permit applications for errors.
3. They secured pilot programs with two cities to test Auto-Check and were getting positive feedback that it could significantly speed up the initial review process for permit applications.
business model, business model canvas, mission model, mission model canvas, customer development, lean launchpad, lean startup, stanford, startup, steve blank, entrepreneurship, I-Corps, Stanford
business model, business model canvas, mission model, mission model canvas, customer development, hacking for defense, H4D, lean launchpad, lean startup, stanford, startup, steve blank, pete newell, bmnt, entrepreneurship, I-Corps, Security, NSIN, NSA, cybersecurity, Joe Felter
29 SETTEMBRE 2021 – Aula Magna – Corso Duca degli Abruzzi, 24 – Politecnico di Torino
Ricerca, trasferimento tecnologico e supporto alle aziende sui temi fondamentali dei Big Data, Intelligenza Artificiale, la robotica e la rivoluzione digitale
business model, business model canvas, mission model, mission model canvas, customer development, lean launchpad, lean startup, stanford, startup, steve blank, entrepreneurship, I-Corps, Stanford
Here is a petal leaf diagram describing our potential competition in the work from anywhere relocation services space:
Relocation Services
- Corporate relocation companies (moving large # of employees)
- Regional moving companies
Temporary Housing Marketplaces
- Airbnb
- VBRO
- FurnishedFinder
Storage & Shipping Companies
- Uhaul
- PODS
- United Van Lines
Coworking Space Marketplaces
- WeWork
- Industrious
- Knotel
Town & City Economic Development Orgs
- Compete for remote workers with incentives
- Help new residents get settled
Digital Nomad Platforms
- Remote Year
- Outsite
- J
business model, business model canvas, mission model, mission model canvas, customer development, hacking for defense, H4D, lean launchpad, lean startup, stanford, startup, steve blank, pete newell, bmnt, entrepreneurship, I-Corps, Security, NSIN, NSA, disposable infrastructure, cyber, Joe Felter, DOD
business model, business model canvas, mission model, mission model canvas, customer development, hacking for defense, H4D, lean launchpad, lean startup, stanford, startup, steve blank, pete newell, bmnt, entrepreneurship, I-Corps, Security, NSIN, NSA, cybersecurity, Joe Felter, ATO, DOD
business model, business model canvas, mission model, mission model canvas, customer development, hacking for defense, H4D, lean launchpad, lean startup, stanford, startup, steve blank, pete newell, bmnt, entrepreneurship, I-Corps, Security, NSIN, wearable sensors, DOD
The document describes an off-grid tech solution called Eye-Dentify that aims to make patient tracking and data collection more accurate and efficient during medical outreach campaigns to optimize patient care. It consists of (1) a $5-12 patient bracelet that identifies and tracks patients, storing and transferring their data between stations and (2) a digitized patient record accessed on a portable device. The solution aims to address inefficiencies in paper-based systems like lost records and time spent re-collecting data. It seeks funding and partnerships to pilot the solution and further test its value proposition and business model.
business model, business model canvas, mission model, mission model canvas, customer development, hacking for defense, H4D, lean launchpad, lean startup, stanford, startup, steve blank, pete newell, bmnt, AI, Machine Learning, ML
business model, business model canvas, mission model, mission model canvas, customer development, hacking for defense, H4D, lean launchpad, lean startup, stanford, startup, steve blank, pete newell, bmnt, entrepreneurship, I-Corps, Security, NSIN, DPAS, Vehicle Fleet Readiness, Joe Felter, Air Force, DOD
The document summarizes the work of a team called Sea++ over 10 weeks. In the first 3 weeks, they interviewed cyber engineers to understand barriers to testing ship navigation systems safely. They developed an initial MVP of a collaboration tool for commanding officers and engineers to plan cyber tests. In weeks 4-7, they sought to understand why integrated cyber testing does not occur on ships post-acquisition. They identified issues such as lack of tools, documentation, and cultural barriers. In weeks 8-10, they focused on what commanding officers need to know about cyber threats and developed a way to convey essential mission impact information to them. Going forward, they see opportunities to expand their work to other maritime sectors.
business model, business model canvas, mission model, mission model canvas, customer development, hacking for defense, H4D, lean launchpad, lean startup, stanford, startup, steve blank, pete newell, bmnt, entrepreneurship, I-Corps
Team Omniscient aims to help intelligence analysts review large quantities of imagery data more efficiently. They identified detecting North Korean AN-2 aircraft in satellite images as a specific problem their computer vision tools could help solve. This would allow analysts to more rapidly locate and identify objects of interest, focusing their time on higher-level tasks. The team outlined key partners, resources, activities, and impact factors to execute their solution and measure its success at achieving the mission.
Beyond the Knowledge Base: Turning Data into Wisdom - an ITSM Academy WebinarKaren Skiles
Many organizations live perceiving Knowledge Management begins and ends with a Knowledge Base. However, a more robust Knowledge Management process exists. The KM process is a pipeline to Continual Service Improvement. This presentation provides insight and methods for developing and implementing a more comprehensive Knowledge Management process leading to improvement throughout the enterprise. This presentation covers design of the KM process, DIKW and its usages, the KM-CSI connection, knowledge repositories and much more.
business model, business model canvas, mission model, mission model canvas, customer development, hacking for defense, H4D, lean launchpad, lean startup, stanford, startup, steve blank, pete newell, bmnt, entrepreneurship, I-Corps, Security, NSIN, stress, biosensor, DOD, Joe Felter
How INOVVO Delivers Analysis that Leads to Greater User Retention and Loyalty...Dana Gardner
Transcript of a sponsored discussion on how advanced analytics drawing on multiple data sources provides wireless operators improved interactions with their subscribers and enhances customer experience through personalized insights.
How Texmark Chemicals Pursues Analysis-Rich, IoT-Pervasive Path to the ‘Refin...Dana Gardner
Transcript of a discussion on how a Texas chemical company combines the best of operational technology with IT and now Internet of Things to deliver data-driven insights that promote safety, efficiency, and unparalleled sustained operations.
business model, business model canvas, mission model, mission model canvas, customer development, hacking for defense, H4D, lean launchpad, lean startup, stanford, startup, steve blank, pete newell, bmnt, entrepreneurship, I-Corps, Security, NSIN, SOCOM, sensors, Joe Felter
Dominic Bortolussi's Lean startup lessons Jason Gendreau
What are important things to consider when trying to run a lean startup? Dominic Bortolussi, founder of The Working Group, shares his strategy in a slide show presented during the 2014 Funding Roadshow.
This document discusses challenges with estimating "epics" or large chunks of work in Agile projects. It summarizes a project at Siemens Healthcare from 2012-2014 that demonstrated these challenges. The key lessons were that uncertainty increases with epic size, dependencies between epics increase risk, and flexibility is needed to adjust plans when estimates are inaccurate. It recommends shorter release cycles to reduce risk, comparing epics to historical data to set expectations, and having fallback options when estimates are off.
Digi-Flow was formed in 2020 through the merger of Solindus and ESAS, which were both originally founded in the early 2000s to work in industrial automation and IT services. Digi-Flow's mission is to help industrial companies transform into smart industries through automation and digitalization solutions that improve efficiency, flexibility, safety, and productivity for both companies and employees. They offer consulting, engineering, maintenance, and turnkey solutions across various industries.
Here is a petal leaf diagram describing our potential competition in the work from anywhere relocation services space:
Relocation Services
- Corporate relocation companies (moving large # of employees)
- Regional moving companies
Temporary Housing Marketplaces
- Airbnb
- VBRO
- FurnishedFinder
Storage & Shipping Companies
- Uhaul
- PODS
- United Van Lines
Coworking Space Marketplaces
- WeWork
- Industrious
- Knotel
Town & City Economic Development Orgs
- Compete for remote workers with incentives
- Help new residents get settled
Digital Nomad Platforms
- Remote Year
- Outsite
- J
business model, business model canvas, mission model, mission model canvas, customer development, hacking for defense, H4D, lean launchpad, lean startup, stanford, startup, steve blank, pete newell, bmnt, entrepreneurship, I-Corps, Security, NSIN, NSA, disposable infrastructure, cyber, Joe Felter, DOD
business model, business model canvas, mission model, mission model canvas, customer development, hacking for defense, H4D, lean launchpad, lean startup, stanford, startup, steve blank, pete newell, bmnt, entrepreneurship, I-Corps, Security, NSIN, NSA, cybersecurity, Joe Felter, ATO, DOD
business model, business model canvas, mission model, mission model canvas, customer development, hacking for defense, H4D, lean launchpad, lean startup, stanford, startup, steve blank, pete newell, bmnt, entrepreneurship, I-Corps, Security, NSIN, wearable sensors, DOD
The document describes an off-grid tech solution called Eye-Dentify that aims to make patient tracking and data collection more accurate and efficient during medical outreach campaigns to optimize patient care. It consists of (1) a $5-12 patient bracelet that identifies and tracks patients, storing and transferring their data between stations and (2) a digitized patient record accessed on a portable device. The solution aims to address inefficiencies in paper-based systems like lost records and time spent re-collecting data. It seeks funding and partnerships to pilot the solution and further test its value proposition and business model.
business model, business model canvas, mission model, mission model canvas, customer development, hacking for defense, H4D, lean launchpad, lean startup, stanford, startup, steve blank, pete newell, bmnt, AI, Machine Learning, ML
business model, business model canvas, mission model, mission model canvas, customer development, hacking for defense, H4D, lean launchpad, lean startup, stanford, startup, steve blank, pete newell, bmnt, entrepreneurship, I-Corps, Security, NSIN, DPAS, Vehicle Fleet Readiness, Joe Felter, Air Force, DOD
The document summarizes the work of a team called Sea++ over 10 weeks. In the first 3 weeks, they interviewed cyber engineers to understand barriers to testing ship navigation systems safely. They developed an initial MVP of a collaboration tool for commanding officers and engineers to plan cyber tests. In weeks 4-7, they sought to understand why integrated cyber testing does not occur on ships post-acquisition. They identified issues such as lack of tools, documentation, and cultural barriers. In weeks 8-10, they focused on what commanding officers need to know about cyber threats and developed a way to convey essential mission impact information to them. Going forward, they see opportunities to expand their work to other maritime sectors.
business model, business model canvas, mission model, mission model canvas, customer development, hacking for defense, H4D, lean launchpad, lean startup, stanford, startup, steve blank, pete newell, bmnt, entrepreneurship, I-Corps
Team Omniscient aims to help intelligence analysts review large quantities of imagery data more efficiently. They identified detecting North Korean AN-2 aircraft in satellite images as a specific problem their computer vision tools could help solve. This would allow analysts to more rapidly locate and identify objects of interest, focusing their time on higher-level tasks. The team outlined key partners, resources, activities, and impact factors to execute their solution and measure its success at achieving the mission.
Beyond the Knowledge Base: Turning Data into Wisdom - an ITSM Academy WebinarKaren Skiles
Many organizations live perceiving Knowledge Management begins and ends with a Knowledge Base. However, a more robust Knowledge Management process exists. The KM process is a pipeline to Continual Service Improvement. This presentation provides insight and methods for developing and implementing a more comprehensive Knowledge Management process leading to improvement throughout the enterprise. This presentation covers design of the KM process, DIKW and its usages, the KM-CSI connection, knowledge repositories and much more.
business model, business model canvas, mission model, mission model canvas, customer development, hacking for defense, H4D, lean launchpad, lean startup, stanford, startup, steve blank, pete newell, bmnt, entrepreneurship, I-Corps, Security, NSIN, stress, biosensor, DOD, Joe Felter
How INOVVO Delivers Analysis that Leads to Greater User Retention and Loyalty...Dana Gardner
Transcript of a sponsored discussion on how advanced analytics drawing on multiple data sources provides wireless operators improved interactions with their subscribers and enhances customer experience through personalized insights.
How Texmark Chemicals Pursues Analysis-Rich, IoT-Pervasive Path to the ‘Refin...Dana Gardner
Transcript of a discussion on how a Texas chemical company combines the best of operational technology with IT and now Internet of Things to deliver data-driven insights that promote safety, efficiency, and unparalleled sustained operations.
business model, business model canvas, mission model, mission model canvas, customer development, hacking for defense, H4D, lean launchpad, lean startup, stanford, startup, steve blank, pete newell, bmnt, entrepreneurship, I-Corps, Security, NSIN, SOCOM, sensors, Joe Felter
Dominic Bortolussi's Lean startup lessons Jason Gendreau
What are important things to consider when trying to run a lean startup? Dominic Bortolussi, founder of The Working Group, shares his strategy in a slide show presented during the 2014 Funding Roadshow.
This document discusses challenges with estimating "epics" or large chunks of work in Agile projects. It summarizes a project at Siemens Healthcare from 2012-2014 that demonstrated these challenges. The key lessons were that uncertainty increases with epic size, dependencies between epics increase risk, and flexibility is needed to adjust plans when estimates are inaccurate. It recommends shorter release cycles to reduce risk, comparing epics to historical data to set expectations, and having fallback options when estimates are off.
Digi-Flow was formed in 2020 through the merger of Solindus and ESAS, which were both originally founded in the early 2000s to work in industrial automation and IT services. Digi-Flow's mission is to help industrial companies transform into smart industries through automation and digitalization solutions that improve efficiency, flexibility, safety, and productivity for both companies and employees. They offer consulting, engineering, maintenance, and turnkey solutions across various industries.
The new dominant companies are running on data SnapLogic
The cost of Digital Transformation is dropping rapidly. The technologies and methodologies are evolving to open up new opportunities for new and established corporations to drive business. We will examine specific examples of how and why a combination of robust infrastructure, cloud first and machine learning can take your company to the next level of value and efficiency.
Rich Dill, SnapLogic's enterprise solutions architect, at Big Data LDN 2017.
The Energy Management Application enables plant personnel to monitor real-time energy flow and automatically notifies operators, supervisors and cost accountants of energy inefficiencies and non-compliance of configured policies. Application functionality includes recording consumption and demand at main and sub-meters for a wide range of energy types, including power, water, chill, gas, air and steam. For more information, please go through the slide.
iARMS-EMS/PMS is developed by Envision Enterprise Solutions
Envision Enterprise Solutions is one of the fast growing global technology and innovation companies offering services in IT consulting, software implementation, system integration and development, with presence in USA, UAE, Singapore and India. Envision has solutions portfolio that range from Enterprise Asset Management, Port Automation Solutions, ERP Solutions, Application Services, Enterprise Mobility Solutions, Cloud Solutions, Performance Optimization and Real Time Monitoring Solutions. Envision is partnered with leading technology giants like IBM, SAP, Oracle and Microsoft.
Big Data LDN 2017: The New Dominant Companies Are Running on DataMatt Stubbs
The document discusses solutions for deriving value from data through data integration and analytics. It describes three approaches companies have taken: 1) Building a custom machine learning platform like Uber's Michelangelo. 2) Developing custom integrations for a large multinational corporation with many technologies. 3) Implementing a cloud-first enterprise data stack for a 360-degree view of customers. The cloud-first approach provides benefits like scalability, collaboration, and reduced maintenance costs.
9 Hyperion Performance Myths and How to Debunk ThemDatavail
In this presentation, we explore and debunk 9 misconceptions of Hyperion Performance Management that are keeping you from effectively managing your platform.
GE Healthcare required the fast performance of a DynamoDB-compatible database, but to run on-premises to ensure patient data remained confidential. Find out how they employed Scylla's DynamoDB-compatible API to provide precision medical diagnoses in their Edison AI solution.
Digital Transformation through Product and Service Innovation - Session Spons...Amazon Web Services
Today large enterprises are under pressure to innovate faster than ever, drive down costs, and deliver increased value to their organisations through more responsive and flexible IT. Organisations that are shifting to a data-driven, insight-powered culture will be in the best position to defend, differentiate and disrupt in their respective industries, potentially expanding their business with new products and revenue sources. Learn how some leading companies are leveraging data – from IoT sources, social sources, enterprise, partners, competitors, and consumers – to unlock new sources of insight.
Speaker: Amit Bansal, Digital Delivery & Analytics Lead, APAC, Accenture
Advanced Analytics for Asset Management with IBMPerficient, Inc.
Extreme volumes of machine data, such as process control logs, asset inspection records and part quality data are created each day. Companies must analyze patterns and interactions within this data to create a total view of their operational processes.
In this webinar, our experts discussed how IBM is enabling companies to proactively address maintenance and asset management challenges with advanced analytics solutions:
-Determine root causes of failure based on usage and wear characteristics
-Analyze component and environmental data to reduce time to identify quality issues
-Identify conditions to optimize maintenance and inventory management
-Monitor, maintain and optimize assets for better supply chain availability
Our webinar covered an introduction to predictive maintenance, analytics use cases for asset management, and IBM Maximo integration with analytics solutions.
Intech Corporation provides executive search, business consulting, and software products and services. It has a presence in major cities across India, Southeast Asia, the Middle East, Africa, Europe, the US and UK. Intech's team of experienced professionals in executive search and business consulting aims to help clients gain a competitive advantage. The company also partners with RAMCO Systems to provide HR, ERP, logistics, and aviation software solutions, along with analytics capabilities. Intech supports clients across various industries with technical services and product development.
Human Sensor Conference Opening- Future of Real Estate TechKevin Kononenko
This document discusses the CrowdComfort mobile software, which allows employees to provide feedback on workplace conditions via their mobile devices. It aggregates this feedback to create heat maps and contentment maps that identify opportunities for improvement. Customers report benefits like improved operational performance and workflow efficiency. The software provides better data to drive results, with examples showing increased KPI performance, participation rates, and issue response times. It is described as giving managers insights into asset management and space planning. Breakout session topics also covered include productivity, energy/comfort, safety, and workforce engagement.
Dr. Stefan Radtke gave a presentation on the journey to big data analytics. He discussed how analytics is affecting many industries and the evolution of analytic questions from descriptive to predictive to prescriptive. He emphasized the need to collect all potential data from both traditional and new sources. A strategic approach was presented that aligns business and IT goals, identifies strategic opportunities, prioritizes use cases, and recommends an analytics roadmap. Dell EMC offers various services to help customers with their big data and analytics initiatives and solutions.
2018 McRock Capital IIoT Symposium: Antonio Pietri, AspentechMTechHub
Antonio Pietri, President and CEO of Aspen Technology, presented at the McRock IIoT Symposium on June 20, 2018. Aspen Technology is a global leader in asset optimization software for capital-intensive industries, with over 2,100 blue-chip customers globally. The presentation outlined Aspen Technology's strengths in the market, their focus on extending optimization throughout the entire asset lifecycle, and their strategy around leveraging IIoT technologies to provide predictive and prescriptive maintenance insights.
AppSphere 15 - Smoke Jumping with AppDynamicsAppDynamics
IHS experienced the raw power of AppDynamics upon their first installation by immediately gaining insight into their applications problems in production. With the help of AppDynamics, IHS was about to increase collaboration between the operations and development teams in an effort to fix performance issues. The various IHS teams were able to benefit from having tangible evidence and metrics to pinpoint the exact root cause for clearer communication on performance problems.
In this talk, you'll learn how IHS:
- Built a bridge between the operations and development workflows
- Used custom dashboards for multiple teams throughout their organization
- Reduced confusion across teams on performance root cause
- Monitors multiple environments to filter potential problems early
This deck was originally presented at AppSphere 2015.
Case Study: Increasing Produban's Critical Systems Availability and PerformanceCA Technologies
The Santander Group is a Spanish banking group and the largest bank in the Eurozone by market value. It is also one of the largest banks in the world in terms of market capitalization. Produban is Santander’s group company responsible for Santander's entire IT infrastructure. The Produban challenge was to monitor - proactively and in real time - all transactions running in critical systems and being able to take action before major problems happen. Considering this scenario, Produban adopted CA Core APM (CA Introscope) in order to count with alerts that permit to the technical team to detect problems before they impact business.
For more information on DevOps solutions from CA Technologies, please visit: http://bit.ly/1wbjjqX
Big Data LDN 2018: THE PATH TO ENTERPRISE AI: TALES FROM THE FIELDMatt Stubbs
Date: 14th November 2018
Location: AI Lab Theatre
Time: 11:50 - 12:20
Speaker: Romain Fouache
Organisation: Dataiku
About: Enterprise AI is a target state where every business process is AI-augmented and every employee is an AI beneficiary. But is that really attainable? And, if so, what is the path to get there? In this talk, Kurt Muehmel, VP Sales Engineering at Dataiku, will share learnings from the field, describing how companies of different sizes and across different sectors have begun this journey. Some are farther along than others, and by making the right decisions now and avoiding stumbling blocks, you can to supercharge your quest to this AI-fuelled future.
Infodream, is the European leader in the quality software for 27 years. We are specialized in monitoring and improving quality in manufacturing.
Qual@xy Suite is an integrated group of quality and production software modules. This MES software is totally an innovative and cost effective solution.
Infodream provides a unique insight of manufacturing process allowing you to plan and manage quality whilst increasing plant efficiency and productivity.
Introduction about the concept of a digital workspace and the challenges going forward, physical-virtual-cloud, security, automation, identity and access management
Open Source and the New Economics of IT - Ingres CIO Doug HarrAlfresco Software
http://blogs.alfresco.com/wp/webcasts
Open source ECM is proven to :
* Lower Total Cost of Ownership
* Eliminate licensing fees and vendor lock-in
* Deliver faster proofs-of-concept
* Provide a complete solution for managing all enterprise content
Many companies are already leveraging open source ECM to take control of their ever growing business content at a fraction of the cost of proprietary ECM market solutions and without the danger of vendor lock-in.
The Ingres ECM Bundle for Alfresco enables innovative document management, team collaboration, and knowledge management applications.
Basing the ECM solution on Ingres Database guarantees unique high availability features that make compliance with auditing requirements an easier task, and cost much less.
Ingres CIO Doug Harr shares examples on how he uses content management solutions from Alfresco.
He also discusses the significant trends affecting the IT market today.
Embracing The New Economics of IT by adopting open source ECM will help companies to:
* better maintain their systems during the economic downturn,
* keep essential projects alive, and
* pursue innovation that can help guarantee a competitive advantage when conditions improve.
Similar to Lemnos engr 245 lean launchpad stanford 2019 (20)
Team Networks - 2022 Technology, Innovation & Great Power CompetitionStanford University
Technology Innovation and Great Power Competition,TIGPC, Gordian knot Center, DIME-FIL, department of defense, dod, intlpol 340, joe felter, ms&e296, raj shah, stanford, Steve blank, AI, ML, AI/ML, china, networks
Team LiOn Batteries - 2022 Technology, Innovation & Great Power CompetitionStanford University
Technology Innovation and Great Power Competition,TIGPC, Gordian knot Center, DIME-FIL, department of defense, dod, intlpol 340, joe felter, ms&e296, raj shah, stanford, Steve blank, AI, ML, AI/ML, china, LiOn Batteries
Team Quantum - 2022 Technology, Innovation & Great Power CompetitionStanford University
Technology Innovation and Great Power Competition,TIGPC, Gordian knot Center, DIME-FIL, department of defense, dod, intlpol 340, joe felter, ms&e296, raj shah, stanford, Steve blank, AI, ML, AI/ML, china, Quantum
Team Disinformation - 2022 Technology, Innovation & Great Power CompetitionStanford University
Technology Innovation and Great Power Competition,TIGPC, Gordian knot Center, DIME-FIL, department of defense, dod, intlpol 340, joe felter, ms&e296, raj shah, stanford, Steve blank, AI, ML, AI/ML, china, Disinformation
Team Wargames - 2022 Technology, Innovation & Great Power CompetitionStanford University
Technology Innovation and Great Power Competition,TIGPC, Gordian knot Center, DIME-FIL, department of defense, dod, intlpol 340, joe felter, ms&e296, raj shah, stanford, Steve blank, AI, ML, AI/ML, china, Wargames
Team Acquistion - 2022 Technology, Innovation & Great Power Competition Stanford University
Technology Innovation and Great Power Competition,TIGPC, Gordian knot Center, DIME-FIL, department of defense, dod, intlpol 340, joe felter, ms&e296, raj shah, stanford, Steve blank, AI, ML, AI/ML, china, Acquistion
Team Climate Change - 2022 Technology, Innovation & Great Power Competition Stanford University
Technology Innovation and Great Power Competition,TIGPC, Gordian knot Center, DIME-FIL, department of defense, dod, intlpol 340, joe felter, ms&e296, raj shah, stanford, Steve blank, AI, ML, AI/ML, china, climate
The document describes a team's efforts to commercialize a new protein quantification technology called PLA-Seq. After initially thinking the technology's value propositions of lower cost, faster throughput, and lower sample volume would appeal to pharmaceutical and personalized health companies, the team conducted customer interviews and learned accuracy was more important than cost to most customers. They also found their target markets should be preclinical biotech and academia rather than personalized health or CROs. The team incorporated their business and pivoted their marketing strategy and funding plans accordingly based on learnings outside of the building.
The document summarizes the development of Invisa Bio over 10 weeks as they pivoted between different medical applications and solutions for their self-assembling medical device technology. They initially focused on manufacturing and delivery but shifted to leveraging drug delivery mechanisms. They considered applications in cardiology, neurology, and orthopedics before focusing on brain aneurysms based on feedback from physicians. The company incorporated, raised funding, and began shadowing doctors to further develop their technology to address unmet needs in difficult to reach areas.
(1) The document describes the journey of a team developing a saffron supplement product to address mental health issues like anxiety and depression.
(2) It started with the goal of targeting adults aged 18-40, but through customer interviews and testing, they learned that teenagers were more interested in an anti-anxiety gummy product.
(3) Key lessons included the challenges of building the right team, navigating advice, knowing when enough customer feedback has been received, and setting individual and project milestones. The team is now continuing work over the summer to further develop the product.
Team Army venture capital - 2021 Technology, Innovation & Great Power Competi...Stanford University
Technology, Innovation and Great Power Competition,TIGPC, Gordian knot Center, DIME-FIL, department of defense, dod, intlpol 340, joe felter, ms&e296, raj shah, stanford, Steve blank, AI, ML, AI/ML, china, unmanned, autonomy, Army venture capital
Team Army venture capital - 2021 Technology, Innovation & Great Power Competi...Stanford University
Technology, Innovation and Great Power Competition,TIGPC, Gordian knot Center, DIME-FIL, department of defense, dod, intlpol 340, joe felter, ms&e296, raj shah, stanford, Steve Blank, Army Venture capital
Team Catena - 2021 Technology, Innovation & Great Power CompetitionStanford University
Technology, Innovation and Great Power Competition,TIGPC, Gordian knot Center, DIME-FIL, department of defense, dod, intlpol 340, joe felter, ms&e296, raj shah, stanford, Steve blank, AI, ML, AI/ML, china, unmanned, autonomy, economic coercion,
Team Apollo - 2021 Technology, Innovation & Great Power CompetitionStanford University
Technology, Innovation and Great Power Competition,TIGPC, Gordian knot Center, DIME-FIL, department of defense, dod, intlpol 340, joe felter, ms&e296, raj shah, stanford, Steve blank, AI, ML, AI/ML, china, unmanned, autonomy, space force
Team Drone - 2021 Technology, Innovation & Great Power CompetitionStanford University
Technology, Innovation and Great Power Competition,TIGPC, Gordian knot Center, DIME-FIL, department of defense, dod, intlpol 340, joe felter, ms&e296, raj shah, stanford, Steve blank, AI, ML, AI/ML, china, unmanned, autonomy, c3i, command and control
Team Short Circuit - 2021 Technology, Innovation & Great Power CompetitionStanford University
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Team Aurora - 2021 Technology, Innovation & Great Power CompetitionStanford University
Technology, Innovation and Great Power Competition,TIGPC, Gordian knot Center, DIME-FIL, department of defense, dod, intlpol 340, joe felter, ms&e296, raj shah, stanford, Steve blank, AI, ML, AI/ML, china, unmanned, autonomy, Army venture capital
Team Conflicted Capital Team - 2021 Technology, Innovation & Great Power Comp...Stanford University
Technology, Innovation and Great Power Competition,TIGPC, Gordian knot Center, DIME-FIL, department of defense, dod, intlpol 340, joe felter, ms&e296, raj shah, stanford, Steve blank, AI, ML, AI/ML, china, unmanned, autonomy, venture capital
Lecture 8 - Technology, Innovation and Great Power Competition - CyberStanford University
Technology, Innovation and Great Power Competition,TIGPC, Gordian knot Center, DIME-FIL, department of defense, dod, hacking for defense, intlpol 340, joe felter, ms&e296, raj shah, stanford, Steve blank, AI, ML, AI/ML, china, unmanned, autonomy, Michael Sulmeyer, cybercom,USCYBERCOM
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.pptHenry Hollis
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...indexPub
The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...EduSkills OECD
Andreas Schleicher, Director of Education and Skills at the OECD presents at the launch of PISA 2022 Volume III - Creative Minds, Creative Schools on 18 June 2024.
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
1. LEMNOS
ALISSA ORLANDO
hustler
MBA
DANE RENNER
picker
MBA
FERDINAND LEGROS
designer
CS + MS&E
MAXIME VOISIN
hacker
CS + MS&E
IAN TAYLOR
hustler
MSX
CRAIG SEIDEL
mentor
110
INTERVIEWS
TODAYDAY ONE
Reduce production
interruptions
in Oil & Gas plants
Increase production
throughput in plants
EXISTING MARKET – BETTER PERFORMANCE
1
2. Setting the Scene
What is a plant, and how big is it?
200k
equipment
items
150
people
$170M
operational
budget
EXAMPLE: PLUTO PLANT, AUSTRALIA
2
3. Setting the Scene
Who are the people?
They have four objectives:
- Increasing throughput
- Reducing production interruptions
- Reducing costs
- Safety
3
Executive
Sign-off authority >$5mm
Plant Manager
Sign-off authority >$0.5mm
Maintenance Manager
Sign-off authority <$0.5mm
Engineer
Sign-off authority <$0.5mm
4. We were a group with AI expertise
looking for a problem to solve...
Week
1
4
Executive
Sign-off authority >$5mm
Plant Manager
Sign-off authority >$0.5mm
Maintenance Manager
Sign-off authority <$0.5mm
Engineer
Sign-off authority <$0.5mm
They have four objectives:
- Increasing throughput
- Reducing production interruptions
- Reducing costs
- Safety
5. We got out of the building to test our first value proposition Week
1
Value Proposition
Increase throughput in plants and factories
using AI
Oil & Gas, Chemicals, Food & Bev,
Pharma, etc etc.
Customer Segment
5
6. … and we received good signals, but we hit a roadblock Week
1
6 agreed 3 agreed 2 agreed
“This sounds really exciting…”
(Engineer @ Shell)
“...I would hesitate to approve it
because of safety risks…”
(Engineer @ Woodside)
“We have done this… it takes a lot
of engineering hours…”
(Executive @ Woodside)
Consulting model
(difficult to scale)
6
7. Many interviewees suggested we apply our AI expertise to
predict when equipment fails
Week
2-3
“Predictive maintenance would be super
useful and has far fewer implementation
risks.”
(Engineer @ BASF)
8 agreed
7
8. So we pivoted to apply AI to a different problem! Week
2-3
“Predictive maintenance would be super
useful and has far fewer implementation
risks.”
(Engineer @ BASF)
8 agreed
8
They have four objectives:
- Increasing throughput
- Reducing production interruptions
- Reducing costs
- Safety
9. We got out of the building with our new value proposition Week
2-3
Value Proposition
Predict when equipment fails to
reduce plant downtime using AI
9
10. … and we received good signals,
but we hit another roadblock
Week
2-3
DEMAND FROM CUSTOMERS
FEASIBLE TECHNOLOGY
AVAILABLE DATA
“We had three separate contractors try to
build predictive models ... there simply isn’t
enough data”
(Maintenance Manager @ Nihar)
2 agreed
10
11. We learnt to embrace a problem-first approach...
not a technology-first approach...
Week
4
“I have no visibility on how
we are going”
“SAP’s user interface sucks”
“Our data is kept in different
places, and they don’t talk to
each other”
“I’m hearing a lot about
predictive analytics!”
11
13. Identity Crisis Interlude
Our divide and conquer interview
style was creating confusion
Competitors
Equipment
Manufacturers
Oil and Gas
Operators
Chemicals
Other
Manufacturing
13
Week
4-6
Week
4-6
14. We heard that a new risk-based way of managing maintenance
has emerged to improve on the classical approach
Week
7
CLASSICAL
MAINTENANCE
PARADIGM
RISK-BASED
MAINTENANCE
PARADIGM
“I maintain all pumps
every 6 months”
“ I maintain all pumps
based on their
current condition & the
consequence of failure
for each”
14
15. We discovered that risk-based maintenance teams
have no modern software solution!
Week
7
(spreadsheet hack)
X
CUSTOMERS :
SOFTWARE :
OPPORTUNITY
CLASSICAL
MAINTENANCE
PARADIGM
RISK-BASED
MAINTENANCE
PARADIGM
15
16. So we pivoted to software for risk-based maintenance.
We got out of the building with our new value proposition
Week
8
Plants in the Oil & Gas industry which
are already hacking solutions
Customer Segment
Software for risk-based maintenance
activity selection to improve
production and safety performance
Value Proposition
16
17. Our interviewees got really excited! Week
8
“This is exciting… I’d like to test it when it’s up
and running.”
(Plant Manager @ Chevron)
“I am actively looking for this!”
(Maintenance Manager @ DuPont)
“We had to develop our own tool in Excel”
(Maintenance Engineer @ ExxonMobil)
17
18. We developed other elements of the business model,
here’s our potential sales process for a plant…
Week
7-9
IDENTIFY
CHAMPION
GATHER
INFORMATION
ENGAGE
USERS
ENGAGE
IT FUNCTION
ADDRESS
DETRACTORS
CLOSE THE
DEAL
OPERATIONAL
INTEGRATION
Org Chart
18
19. The company will continue, with Dane taking the lead Week
10
ALISSA
ORLANDO
DANE
RENNER
FERDINAND
LEGROS
MAXIME
VOISIN
IAN
TAYLOR
CONTINUING MOVING ON TO NEW PROJECTS
19
21. We learnt to solve one problem for a specific customer
subsegment, before solving more problems for them!
Week
8-9
TODAY
+PREDICTIVE
MAINTENANCE
+REPLACE
SOFTWARE
ADD-ONS
+CHEMICALS
+NUCLEAR &
POWER
+ETC.
NEW INDUSTRIES
NEWPRODUCTS
Bottom-up
TAM
$300M
Bottom-up
TAM
$6-8B
21
22. We learnt that sales cycles are 9-12 month-long.
This impacts our fundraising & operational plan!
Week
9
Q1 Q2 Q3 Q4
2019 2020 2021 2022
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
Cashreserves
5M
10M
20M
30M
Seed
$2M
Series A
$5M
Start sales
process
Finalize
product
Complete first
sale
22
Editor's Notes
[same script as the video?]
Team
Maxime + Ferdinand, Andrew Ng’s lab, AI for traditional industries [NOT DEFINED]
Alissa [future of work] + Taylor [tech investor] GSB
Dane [industry expert]
Idea Genesis:
spoke to people in traditional industries
produce commodities. it’s crucial to make production efficient
sparked an idea: squeeze as much out of plants as possible
Plant… equipment count, budget, authority.
@Steve: adapt diagram with equipment count / budget / authority
What is a plant / what does it do / industries [emphasize on diversity of plants?]
We will look mostly at these specific plants: [oil and gas?]
How is it set up [nb of pieces of equipment…]
E.g. Pluto
There are X plants like Pluto in the world
Who are the key people running a plant? What are their problems? What are their authority [budget…]
[Put a picture of us in a plant?]
Plant… equipment count, budget, authority.
@Steve: adapt diagram with equipment count / budget / authority
What is a plant / what does it do / industries [emphasize on diversity of plants?]
We will look mostly at these specific plants: [oil and gas?]
How is it set up [nb of pieces of equipment…]
E.g. Pluto
There are X plants like Pluto in the world
Who are the key people running a plant? What are their problems? What are their authority [budget…]
[Put a picture of us in a plant?]
We start with technology.
Looking for a problem!
....... So we got out of the building…..
We were keen to apply AI to industries that are largely untouched by innovation
An early interviewee [use the same title as the character we introduced before?] explained how they [were using Noodle.AI to] adjust inputs to his process to get consistent output quality using AI (tobacco plant)
[tobacco story -> keep it grounded in real life example]
[ great, makes sense to use AI and historical data to optimise production…. but it’s tobacco…. we’re never getting into LLP… then we realized it applies to a lot of industries!]
...... And here's our insight!
- the problem is valid: plants want to optimize production
- the solution is feasible
- but difficult to turn it into a product
Good signals
Some push back on technology “blackbox”
But customization
[are we allowed to use the word “process control”?]
Good signals
Some push back on technology “blackbox”
But customization
[are we allowed to use the word “process control”?]
We keep our AI expertise.
We focus on a different problem: reducing downtime in plants!
We were super excited, we had found a problem where we can apply our solution!
Good signals
Some push back on technology “blackbox”
But customization
[are we allowed to use the word “process control”?]
We were keen to apply AI to industries that are largely untouched by innovation
An early interviewee [use the same title as the character we introduced before?] explained how they [were using Noodle.AI to] adjust inputs to his process to get consistent output quality using AI (tobacco plant)
[tobacco story -> keep it grounded in real life example]
[ great, makes sense to use AI and historical data to optimise production…. but it’s tobacco…. we’re never getting into LLP… then we realized it applies to a lot of industries!]
Have impact today + start building trust with clients who don’t trust startups
Here’s our insight:
So….. we have a problem (reduce downtime)
We have a solution: predict when equipment will fail
But we cannot build the solution!
There was good tech & demand, but insufficient data
We couldn’t add value in the short term so parked this opportunity
So we zoomed out of AI technology, gathered our interview notes,
and realized there are many ways outside AI to reduce plant downtime!
Here’s our insight:
We hit a roadblock with our “where can we apply AI” approach
However, we have found a good problem: reducing downtime in plants!
We learnt that AI is not the solution
That’s fine, because we learnt that there are many other ways to reduce downtime in plants
→ We decide to build whatever it takes will solve the problem (reduce downtime in plants), even if it does not involve AI…
→ We shifted from technology-first to problem-first
We hit a roadblock with our “where can we apply AI” approach
Began considering where else problems were being experienced in plant maintenance
There were a lot of them!
Here we are going to step out of the facts and into our feelings for a moment
We were feeling frustrated and fatigued by two big pivots and a string of micro-pivots
Many on the team were feeling disillusioned by the path that we were on - we’d moved so far away from AI & the industries were hard work!
We came together and spent three hours talking about our feelings on the matter,
We found that there was little overlap …
…
...
We were each coming back from interviews with different insights and were struggling to reconcile
We eventually realized that there was little overlap between the types of
Many people in maintenance follow a classical paradigm:
“Currently their model for maintenance is: I replace this pump every 6 months because that’s when it breaks on average”
Our insight: in a perfect world, people would do risk-based maintenance:
“ TODO describe”
They have in-house hacks
They are looking for software!
Predict when equipment fails
to reduce plant downtime
using AI
So, from AI to optimize throughput at the outset,
we’ve landed on software for Risk-Based Maintenance!
Mention that we had validation from 5-7 plants
And that 2 people wanted to quit their job to build this software with us
We learnt X…
We learnt X…
We learnt X...
We were keen to apply AI to industries that are largely untouched by innovation
An early interviewee explained how they were using Noodle.AI to adjust inputs to his process to get consistent output quality using AI
This triggered a thought… we could explore AI for process optimization… there’s a lot of underutilized data in this space!
We were faced with skepticism and push back from many interviewees
We were keen to apply AI to industries that are largely untouched by innovation
An early interviewee explained how they were using Noodle.AI to adjust inputs to his process to get consistent output quality using AI
This triggered a thought… we could explore AI for process optimization… there’s a lot of underutilized data in this space!
We were faced with skepticism and push back from many interviewees
We were keen to apply AI to industries that are largely untouched by innovation
An early interviewee explained how they were using Noodle.AI to adjust inputs to his process to get consistent output quality using AI
This triggered a thought… we could explore AI for process optimization… there’s a lot of underutilized data in this space!
We were faced with skepticism and push back from many interviewees