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
Lecture 1 -Technology, Innovation and Great Power CompetitionStanford University
acquisition, Mattis, 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, requirements, stanford, Steve blank, China,
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
COVID-19’s uneven trajectory has created a slower-than-expected rebound in urban travel worldwide. Some mobility modes, however, are poised to exceed pre-pandemic levels. BCG provides a breakdown of recovery levels in urban mobility by region and mode--and over time.
EY Price Point: global oil and gas market outlook, Q2 | April 2022EY
The theme for this quarter is rearrangement. The loss, or potential loss, of Russian oil and gas supplies is forcing producers, refiners and traders to rethink the flow of crude oil and refined products from the wellhead to the gas pump in light of sanctions, potential sanctions and the risk of reputational damage. Countries, companies and consumers will all be searching for ways to adapt, and the outcome of the race to bring alternatives to market could alter the global energy landscape for years to come.
It is likely crude oil and LNG prices will remain elevated for some time. The process of diverting Russian oil through countries unwilling to sanction it will take time and there is little indication OPEC members are willing (or able) to increase production to make up for the loss of Russian crude. Spare capacity sat at 3.7 mbpd at the end of 2021, just above where it was in January 2020. Currently, sanctioned Venezuelan and Iranian production (about 3 mbpd below their peak) could fill the gap, but political and commercial obstacles remain. At today’s prices, US shale production is attractive, but the fastest the industry has been able to grow is between 1mbpd and 2mbpd per year. The LNG infrastructure was already stretched before the war in Ukraine and there is little prosect of finding new supplies soon.
As the largest buyer of Russian energy, Europe will be the epicenter. There is a deeply embedded bias there in favor for renewable energy, and the current crisis is certain to result in an all-out effort to accelerate the build-out of wind and solar power. The capacity to add new green energy is limited though by the project pipeline and supply chains for solar panels and wind turbines, and it is likely that much of the shortfall will be made up with the new LNG infrastructure.
This presentation is on the theory of disruption by Clayton Christensen. While it is easy to explain why bad companies fail it is hard to understand how good companies, who are supposedly do everything right, fail in the end. The theory of disruption offers different ways of thinking to capture this phenomenon.
I explain the theoretical foundations and present a popular example of a new market disruption (the iphone). In the end the different key points of disruption are summarized.
A brief introduction of Quantum computers including including qu-bits and explained quantum mechanical phenomenon. Describing the need, applications and problems of a futuristic computer i.e. Quantum computer.
Global power and utilities deal value totaled $26.8 billion in Q2 2019, a 44% increase from Q1 2019. The Americas had the largest deal value at $13.1 billion. Financial investors dominated deal activity, seeking stability from integrated utility assets. Renewable energy continued to attract investment, driving interest in battery storage technologies. Utilities also invested in new technologies like blockchain, smart grids, and electric vehicles. Overall, utilities gained value in Q2 but slightly underperformed the market.
A.T. Kearney 2017 State of Logistics Report: Accelerating into UncertaintyKearney
2017 could be a pivotal year for logistics. Demand patterns are shifting, technological advances are altering industry economics, and new competitors are challenging old business models. This year could bring significant moves that reshape individual sectors and even the industry as a whole. Major business combinations, large-scale shifts in distribution flows, deep capacity cuts, massive infrastructure investments–anything is possible. Here are the ten key takeaways from the 2017 State of Logistics report, as well as the four potential scenarios for the future of logistics.
Para vencer nesse novo normal, as organizações devem prioritariamente aproveitar dados e análises para tomar melhores decisões nesse cenário de incerteza, entender seu cliente e adaptar-se rapidamente, e investir na automação como forma de ser mais eficiente e entregar mais valor.
Media-Morphosis Transforming Media and Entertainmentaccenture
This document discusses how digital technologies are transforming the media and entertainment industry. It outlines three phases for companies to undergo a digital transformation: 1) Tune Up - transform supply chains and collect richer data, 2) Tune In - put consumers at the heart of growth strategies and invest in technologies, 3) Take Off - design and scale new content, services, and partnerships to engage future consumers. The roles of media companies are evolving from content creators and distributors to intelligent enterprises that reinvent production and monetization using data and platforms.
In light of ChatGPT and the growing generative AI technologies, this presentation revisits my 2020 article on the need for Imagination Performance through the 2023 lens. Much has changed in three years in the technology front but nothing has changed in the human front. We still need meaning and to feel connected. This presentation surfaces the real human value proposition beyond generative AI tools.
Lydia Kostopoulos, PhD
LKCYBER.COM
Lifting the Barriers to Retail Innovation in ASEAN | A.T. KearneyKearney
The document discusses retail innovation in ASEAN countries. It notes that while ASEAN has over 600 million consumers and a $2.4 trillion GDP across 10 countries, the region lags global and regional competitors in innovation. Modern retail makes up less than 50% of ASEAN retail compared to 70-85% in developed markets. Private labels only account for 1-8% of ASEAN retail sales. E-commerce is growing but still below US and China levels. The report identifies four barriers to retail innovation in ASEAN - non-tariff barriers, access to talent, trade efficiency, and integration of innovators - and how to overcome them.
Brazilians have become more pessimistic about the duration of the pandemic, expecting it to last longer. However, expectations about the financial impact are now matching the real impact. Consumers' routines have remained stable since May, with more time spent on digital entertainment and less time working. Demand is falling compared to the start of the pandemic, with spending on essential categories expected to drop 5-25% long-term. Online spending continues to grow while offline spending declines.
A.T. Kearney Consolidation of the US Banking IndustryKearney
More and more banked consumers are migrating from small to large banks, flagging the accelerated consolidation of the retail banking industry in the years to come.
Global survey by BCG GAMMA and Ipsos finds overall optimism toward Artificial Intelligence in the workplace but large national differences and significant worries about privacy, job security, and economic equality.
BCG has launched its Telco Sustainability Index, designed to capture the four dimensions most relevant to a telco’s environmental strategy. The index tracks the company’s commitment to sustainability, its emissions intensity and that of its upstream and downstream partners, its elimination of waste, and its customer enablement.
Designing at the Intersection of HCI & AI: Misinformation & Crowdsourced Anno...Matthew Lease
This document summarizes a presentation about designing human-AI partnerships for fact-checking misinformation. It discusses using crowdsourced rationales to improve the accuracy and cost-efficiency of annotation tasks. It also addresses challenges in designing interfaces for automatic fact-checking models, such as integrating human knowledge and reasoning to correct errors and account for bias. The goal is to develop mixed-initiative systems where humans and AI can jointly reason and personalize fact-checking.
The Slow Growth of AI: The State of AI and Its ApplicationsJeffrey Funk
The failure of IBM Watson, disappointments of self-driving vehicles, slow diffusion of medical imaging, small markets for AI software, and scorching criticisms of Google’s research papers provide evidence for hype and disappointment in AI, which is consistent with negative social impact of Big Data and AI algorithms. There are some successes, but they are much smaller than the predictions, with virtual applications (advertising, news, retail sales, finance and e-commerce) having the largest success, building from previous Big Data usage in the past. Looking forward, AI will augment not replace workers just as past technologies did on farms, factories, and offices. Robotic process automation and natural language processing are likely to play important roles in this augmentation with RPA automating repetitive work, natural language processing summarizing information, and RPA also putting the information in the right bins for engineers, accountants, researchers, journalists, and lawyers. Big challenges include reductions in training time depending on faster computers, exponentially rising demands on computers for high accuracies in image recognition, a slowdown in supercomputer improvements, datasets riddled with errors, and reproducibility problems.
1) Quantum computing harnesses the laws of quantum mechanics to solve complex problems faster than classical computers. It uses quantum bits (qubits) that can exist in superpositions of states and become entangled in ways that normal computing cannot achieve.
2) Machine learning is a type of artificial intelligence that allows systems to learn from data and improve their abilities. Quantum machine learning combines quantum computing and machine learning to potentially solve problems like pattern recognition and optimization much faster.
3) Some challenges to quantum computing include qubit decoherence, error correction, scalability, and developing hardware and software. However, quantum computing shows promise for applications in fields like artificial intelligence, machine learning, cybersecurity, materials science, and pharmaceutical research
MIT's Poor Predictions About TechnologyJeffrey Funk
These slides analyze the 40 predictions of breakthrough technologies that were made betwee 2001 and 2005 by MIT’s Technology Review. Most of them are science-based technologies, and none of the science-based technologies predicted between 2001 and 2005 have markets larger than $10 billion. Among its 40 predictions, only four have markets larger than $10 billion and these technologies have little to do with recent advances in science and instead were enabled by Moore’s Law and improvements in Internet services. MIT also missed many technologies that have achieved market sales greater than $100 billion such as smart phones, cloud computing, and the Internet of Things and other technologies with sales greater than $50 billion such as e-commerce for apparel and tablet computers.
@microsoft Conversations on Education 10/29/2013atmicrosoft
This document summarizes a discussion on expanding access to computer science education. It discusses how computer science skills are important for many fields beyond just IT. However, the US is not keeping up with job demand, as there is a large gap between computer science degrees earned and related job openings. It also notes gaps in participation among female and minority students. The discussion promotes expanding K-12 computer science education and increasing support for teachers through curriculum, standards, and professional development to help prepare more students for 21st century jobs.
The Generative Artificial Intelligence Revolution and the Future of Academic ...Thomas Lancaster
How will teaching, learning and assessment look in a future world where the use of artificial intelligence is the norm, and where does academic integrity come in to the discussion? These slides, delivered as a keynote presentation, consider the issues and share plenty of examples.
The document summarizes seven megatrends - Cloud Computing, Artificial Intelligence, Mobility, Big Data, Robotics, Internet of Things, and Cybersecurity. It discusses the future of each trend, current trends, and open questions. Some benefits mentioned are improved collaboration, education, healthcare, safety, and business models. However, some risks include increased threats to privacy, job displacement, and security issues. The document recommends organizations consider how these trends could impact their operations and how to leverage new technologies while mitigating risks.
This document provides advice on continuing data science and AI education through various options like online courses, academic programs, and professional groups. It acknowledges challenges like a lack of data scientists in Mexico and firms implementing data science projects incorrectly. However, it argues Mexico must become a data science and AI hub to remain economically competitive as automation increases and other countries specialize. Specific recommendations include pursuing a full academic program, building an independent master's with MOOCs, and joining data science communities and groups in Mexico.
Data science is a multidisciplinary field that uses statistics, programming, and machine learning to extract knowledge and insights from large amounts of data. It has various applications like email spam detection, medical diagnosis, predicting stock prices, and self-driving cars. The document discusses how the size of data is rapidly increasing and will continue to do so, with an estimated 463 exabytes of new data generated daily by 2025. It also outlines common tasks performed by data scientists like understanding business problems, analyzing and visualizing data, making recommendations, and predicting future values. Theoretical and practical aspects of data science are also covered, along with examples of how it relates to other fields.
Michael Saylor gave a presentation on cyber security and STEM education. He discussed how technology can both improve lives but also be misused if it falls into the wrong hands. He provided examples of technologies that can enhance capabilities like medical implants but also be vulnerable to hacking. Saylor emphasized the need to inspire future generations to not only pursue careers in technology but also ensure the integrity of the technologies they develop. He highlighted issues with STEM education in the US, such as poor performance in math and science, declining enrollment and success in AP classes, and lack of qualified teachers, especially for female students. Saylor concluded by stressing the importance of improving how STEM is taught through better teachers to foster more interest and build a college
Using big data and implementing hadoop is a trend that people jump all to quickly to. Instead understanding the run time complexity of one's algorithms, reducing said complexity and managing the process from start to finish in a lean and agile way can yield massive cost savings - or save your organization.
The document discusses best practices for AI/ML projects based on past failures to understand disruptive technologies. It recommends (1) setting clear expectations and metrics, (2) assessing skills needed, (3) choosing the right tools based on cost, time and accuracy tradeoffs, (4) using best practices like iterative development, and (5) repeating until gains become irrelevant before moving to the next project.
Data science and quantum computing are two rapidly growing fields that can be combined to lead to major advances. Quantum computing uses quantum bits and quantum mechanics to solve problems too complex for classical computers, enabling more efficient problem solving, new algorithms, and tools for data scientists. However, quantum computing also faces challenges like noisy qubits and error correction. Collaboration between data scientists, quantum physicists, and domain experts is needed to fully realize the potential of quantum computing in data science.
This document provides an agenda for a presentation on AI and machine learning for financial professionals. The presentation will be given by Sri Krishnamurthy, founder and CEO of QuantUniversity. The agenda includes introductions of the speaker and an overview of QuantUniversity. It then covers key trends in AI/ML, the basics of machine learning in 30 minutes, building a machine learning application in 10 steps, and case studies of how AI/ML are used in finance from companies like Bank of America, Ravenpack, and Northfield.
This document discusses the history of innovations through six waves and upcoming trends until 2030. It outlines the key developments in each wave of innovation: 1) simple tools and textiles, 2) industrialization and faster communication, 3) automobiles, electrification, and mass transit, 4) analog electronics and digital computers, 5) internet and satellite technology, 6) potential for AI-based medical diagnosis, genetic engineering, quantum technologies, and batteries. The document emphasizes critical thinking skills for problem solving, using techniques like considering what if scenarios and implications. It provides game theory as an approach and discusses how the company helps children develop skills through computer games.
Artificial intelligence and automation will significantly impact future jobs. Many existing jobs will be displaced, but new jobs will also be created. Skills that are important today like driving trucks may no longer be needed if self-driving vehicles are widely adopted. However, AI will also drive innovation and create new industries and jobs. Education will need to focus on emerging technical skills like data science, cybersecurity and artificial intelligence to prepare students for these new types of jobs. Taylor's University is transforming its degree programs, like computer science, to offer specializations in high demand AI fields and opportunities for real-world work experience through internships and industry projects.
M2 l10 fairness, accountability, and transparencyBoPeng76
The document provides introductions for three lecturers:
- Adam Obeng is a research scientist studying experimental meta-analysis methods with a PhD in Sociology from Columbia University.
- Toby Farmer is a product manager at Facebook AI working on machine translation with a law degree and background in politics and tech entrepreneurship.
- The third section discusses fairness, accountability, transparency, and ethics (FAT*) in AI and provides an overview of why these issues are important and examples of problems that can arise.
Crowdsourced Data Processing: Industry and Academic PerspectivesAditya Parameswaran
This document provides a tutorial on crowdsourced data processing from both academic and industry perspectives. The tutorial is divided into three parts. Part 0 provides a background on crowdsourcing and surveys Parts 1 and 2. Part 1 surveys crowdsourced data processing algorithms from academia, discussing unit operations, cost models, error models, and examples like filtering and sorting. Part 2 surveys crowdsourced data processing in industry, finding that many large companies use internal platforms at large scale for tasks like categorization and content moderation, and that academic research is not yet widely used in industry.
A non-technical overview of Large Language Models, exploring their potential, limitations, and customization for specific challenges. While this deck is tailored for an audience from the financial industry in mind, its content remains broadly applicable.
(Note: Discover a slightly updated version of this deck at slideshare.net/LoicMerckel/introduction-to-llms.)
This document discusses how to succeed in the 4th Industrial Revolution. It begins with learning objectives about understanding the 4th IR, its impact, and how to apply it to biomedicine. It then discusses trends like globalization, technology, and managing millennials. Key technologies are outlined, like the Internet of Things, artificial intelligence, 5G, augmented reality, 3D printing, drones, biometrics, blockchain and quantum computing. Issues around unemployment, inequality, and ensuring AI safety are addressed. The document provides advice on how to empower societies to master technologies, prioritize futures through design rather than default, focus on key values, question algorithms, adapt skills, and protect data to succeed in the future.
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Team Quantum - 2022 Technology, Innovation & Great Power Competition
1. The Quantum Tech Problem
Problem Statement V1
China’s planned government
investment in quantum dwarfs
that of the U.S. by a factor of 10.
Problem Statement V4
The US quantum ecosystem does
not generate enough awareness of
opportunities to pursue careers in
quantum that could catalyze
industry growth.
30+
Bethanie Maples Brett Bornhoft Matt Kaplan Reed V.
Survey Responses
Interviews
235+
2. We had a lot of questions…
● What the hell is quantum and what are qubits?
● How many qubits are actually important?
● What can be done today with the current quantum
computers?
● Can allies do more?
● What do companies need?
● What do labs need?
● Should we, can we, simulate the centralized
approach that China has?
● What other quantum technologies exist? (sensors,
communication, etc…)
● How are algorithms different and how will these
computers function? Hybrid CPU/QPU?
3. Weeks 1 - 3: We searched for answers to these questions
Quantum
Technologies
Quantum Sensors
Quantum Computing
Quantum Information
/ Communications
● Encryption Breaking
● Optimization (ML/AI)
● Quantum chemistry
○ Materials
○ Pharma
● Cryptography
● Clock sync
● Networking
● Sensing
● Timing
● Imaging
What we learned: quantum has huge implications for science, security,
communication, and things we haven’t discovered
4. A closer look also revealed that the $ issue is complex
*QURECA March 10, 2022
But…
The Chinese government invests
more in quantum*
There are different leaders for different
parts of the industry
● The figure on the left also tells us nothing about private investment
● The US and its allies combined almost match Chinese government investment
5. Weeks 4-5: We explored joining forces with allies
1. We searched the
world for experts in
technology alliances
and quantum
computing
partnerships
2. We found
those people,
and read their
documents, and
interviewed
them.
3. The proposed tech
agreements were wide
ranching and in our
opinion, unlikely to align
the quantum industry in
the timeline we desired
(<5 years).
4. We asked multiple
founders and CEOs in
the quantum space
what would motivate
collaboration and
allyship. They said a
national grand
challenge would work.
6. Weeks 4-7: But we’re also hearing this is a talent issue
“Academic faculties only have a limited
number of quantum compute professors
with minimal labs”
“Not only are we lacking in specific
quantum computing expertise, but
we also need engineers with
cryogenic fluid backgrounds,
electronics, etc…., not necessarily
needing all Ph.D.’s”
“We need not only physicists to work
on quantum hardware but computer
security folks to work on quantum
communications and post-quantum
cryptography.”
“You don’t need a PhD to work with
quantum. There are lots of different
things to do in quantum even if you don’t
understand quantum mechanics.”
We talked to…
● Executives at major quantum companies (Rigetti, Google, QCWare, PsiQuantum, etc.)
● VCs and institutional investors (Founders Fund, Morgan Stanley)
● PhDs and researchers (Stanford, MIT Quanta, GIT)
● Government policy and researchers (White House OSTP, AFRL Information Dir, DOE Brookhaven NL)
● Higher ed admin (Stanford Fin Aid, Stanford Dept. of Physics)
7. Week 7: Maybe the market can’t recruit talent
But quantum companies pay well… and the industry is growing…exponentially
There doesn’t seem to be a market problem,
so what’s going on here?
Well…what does the talent pipeline think?
Source: indeed.com
8. Weeks 8-9: We surveyed over 235 STEM students
● How much do students know about quantum technologies, quantum careers?
● What motivates students to choose different problems to work on in school
and after graduation?
● How do different institutional approaches to quantum engineering affect
student awareness?
80% undergrad
20% graduate
> 10 engineering fields
(majority from physics, CS,
and mechanical engineering)
Stanford, MIT,
Georgia Tech
9. We wanted to see what students care about
Students care about impact and salary, and very few are outright
opposed to the idea of working in quantum. Quantum offers both
impact and good salaries…what’s going on here?
73.7%
17.4%
22.9%
48.7%
47%
28.8%
27.5%
59.7%
9.3%
4.7%
If a recruiter called you to invite you to work at their
Quantum company, what would convince you to work
there (check the top three)?
What motivates you regarding your chosen
academic track? (Please select your top three)
69.5%
80.5%
6.8%
41.9%
26.7%
24.2%
22%
10. What students don’t know can hurt US national security
94.1% of STEM students who took our survey have heard of quantum
computing…. but …
Are you aware of opportunities to work in the
quantum technology industry?
Have you considered pursuing an education
related to a Quantum Technology?
11. Students don’t know what quantum is or how they could participate
There is “no money” in quantum, a “lack of opportunities” in the field
Quantum companies mainly seek people who study physics and math
Quantum is “what Jane Street does” and where math majors “sellout”
The quantum industry lacks job security
It is unclear “what the work would be”
12. Students don’t know what quantum is or how they could participate
There is “no money” in quantum, a “lack of opportunities” in the field
Quantum companies mainly seek people who study physics and math
Quantum is “what Jane Street does” and where math majors “sellout”
The quantum industry lacks job security
It is unclear “what the work would be”
This is a market communication problem
13. We propose a Quantum University Technology Initiative
Grand Challenges /
Competitions
● Sponsored by gov’t and
industry
● Marketable events to
excite new talent
Scholarships /
Fellowships
● Targeted funding for all
higher education levels
with focus on quantum
technologies
● Required internships
each summer with
quantum tech company
Marketing / Educational
Programming
● Remove the stigma that
quantum is “too-hard”
● Focus on educating
STEM students on
quantum
Working with OSTP to:
● Provide memo on workforce development program – NSTC, OSTP want surveys like this
● Expand survey to different schools and analyze different responses
We started thinking we needed to find a breakthrough in quantum techno
*along with Intel, Global Founderies
Bethanie
Technology Alliance between allied nations
More lean than existing suggestions - paired down
Who to include, what to include, timeframe, funding…
Boost workforce pipeline
NCO programs
PhD endowments
Questions: which universities, cost and timeframe, labs to target, types of institute to focus on in order to direct a mandate
High School and Undergrad project prizes in STEM, annual, sponsored by local companies
Grand Challenge - market-making multi-year buying
An annual presidential challenge
An annual industry / academic / govt challenge - get all the uni students working on analogous projects
These together comprise: diplomatic, educational, and economic solutions
Details and budget.
This needs revisions, but the idea of gathering quotes from related players is a good idea.
- let’s attribute these and
Indeed.com and statista
7) Possible next steps for team-let us know if there’s any interest in continuing with the problem post class