week 11 - digital trends Flashcards
what is the location of silicon valley?
- 1854 sq miles.
- 2.9 million people.
- 1.4 million jobs.
- equiv to the worlds 19th largest economy.
- San Francisco bay area.
what is the origin of silicon valley?
a hub for innovation + tech in the mid 20th century san francisco bay area. Traces back to 1930’s when stanford uni encouraged students + faculty to commercialise their research.
what are some key early milestones for silicon valley?
1939: hewlett-packard was founded in a palo alto garage (the birthplace of silicon valley).
1956: william shockley, co-inventor of the transistor, set up shockley semiconductor laboratory in mountain view, attracting talent + spawning lots of tech startups.
Eight of his employees (the traitorous eight), left + formed Fairchild semiconductor which became the breeding ground for future tech leaders.
1970’s-80’s: the region exploded with semiconductor, computer + internet innovations, giving rise to companies like intel, apple, google + facebook.
what are some key ingredients to silicon valley?
Research capacity: top research uni’s, US government policy, major national laboratories + research labs.
Industry actors: venture capital firms + angel investors, law firms + accountants.
Access to funding: venture capitalists raise a large amount of capital from institutional investors + angel investors are well connected + use their own money.
Socio-economic: inviting location for high skilled talent, incentives + culture of employee ownership, highly dynamic labour market + the nature of the industry (tech sector).
Socio-cultural: flat structure, regenerative ability, highly networked yet competitive social environment, lineage of role models.
what are some current challenges for silicon valley?
- increasingly unaffordable housing.
- certain company practices called into question (taxation).
- more critical perceptions of digi techs + their impact on society.
- lead in high tech start ups are eroding.
what is a digital twin?
virtual model of a physical object, process or system, continuously receives real time data from its physical counterpart.
what do digital twins enable?
- simulation.
- analysis.
- optimisation of business operations.
what are the applications of digital twins?
Manufacturing: predictive maintenance, process optimisation, simulating scenarios before physical changes are made.
Construction + urban planning: monitor progress + safety on sites, model infrastructure behaviour under stress or climate scenarios.
Supply chains: real time visibility of logistics networks, identify bottlenecks + simulate contingency plans.
what is green IT?
enivronmentally sustainable computing, minimising environmental impacts across the lifecycle of IT systems.
what is sustainability technology?
tech that enables businesses to measure, reduce or offset environmental impact.
often tied to esg goals (environmental, social + governance).
what are some key areas of green IT?
Energy-efficient data centers: use of liquid cooling, ai for power optimisation, renewable powered facilities.
Carbon-aware software development: optimising code to reduce computer cycles + energy use.
Device lifecycle management: refurbishment, recycling, modular upgrades instead of replacements.
Cloud computing with a green edge: carbon aware workload scheduling across data centres.
what are some key green IT initiatives?
Ai optimised cooling: google deep mind ai reduced cooling energy use by up to 40% in data centres by adjusting fan speeds, windows etc.
Custom energy-efficient servers: designed in house hardware to be energy efficient + optimised for their workloads.
Power purchase agreements: long term deals with wind + solar farms to match energy use with clean energy production.
Carbon aware workload scheduling: moves compute tasks to data centres where the electricity is cleanest at the moment.
what are generative technologies?
systems powered by ai that can autonomously create novel + coherent content such as text, images, video, audio or code by learning patterns from large datasets + producing outputs that resemble those patterns without simply coding them.
what are some challenges + risks of generative technology?
- bias in output from biased training data.
- misinformation + fake content.
- ip + copyright: who owns it?
- overdependence: risk of reducing human creativity + critical thinking.
- regulation: eu ai act, corporate governance on ai use.
