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Singapore experiments with smart government
January 22, 2018 | FT | by John Thornhill.

Singapore has a reputation as a free-trading entrepôt, beloved of buccaneering Brexiters. ....But stiff new challenges confront Singapore, just as they do all other countries, in the face of the latest technological upheavals. Is the smart nation, as it likes to style itself, smart enough to engineer another reboot?.....Singapore is becoming a prime test bed for how developed nations can best manage the potentially disruptive forces unleashed by powerful new technologies, such as advanced robotics and artificial intelligence...Naturally, Singapore’s technocratic government is well aware of those challenges and is already rethinking policy and practice. True to its heritage, it is pursuing a hybrid approach, mixing free market principles and state activism.

Rather than passively reacting to the technological challenges, the island state is actively embracing them....“The real skill of Singapore has been to reverse engineer the needs of industry and to supply them in a much more cost-effective way than simply writing a cheque,” says Rob Bier, managing partner of Trellis Asia, which advises high-growth start-ups...To take one example, the country has become an enthusiastic promoter of autonomous vehicles. The government has created one of the most permissive regulatory regimes in the world to test driverless cars.....GovTech’s aim is to help offer seamless, convenient public services for all users, creating a truly digital society, economy and government. To that end, the government is acting as a public sector platform, creating a secure and accessible open-data infrastructure for its citizens and companies. For example, with users’ permission, Singapore’s national identity database can be accessed by eight commercial banks to verify customers with minimal fuss. A public health service app now allows parents to keep check of their children’s vaccinations.

By running with the technological wolves, Singapore is clearly hoping to tame the pack.
Singapore  autonomous_vehicles  dislocations  traffic_congestion  aging  smart_government  disruption  robotics  automation  artificial_intelligence  test_beds  reboot  city_states  experimentation  forward-thinking  open-data  privacy  reverse_engineering 
january 2018 by jerryking
Artificial intelligence is too important to leave unmanaged
September 26, 2016 | FT | John Thornhill.

Investors are scrambling to understand how technology will enable wealth to be created and destroyed

In the 60-year history of AI, the technology has experienced periodic “winters” when heightened expectations of rapid progress were dashed and research funding was cut. “It’s not impossible that we’re setting ourselves up for another AI winter,” says the co-founder of one San Francisco AI-enabled start-up. “There is a lot of over-promising and a real risk of under-delivering.”
One of the more balanced assessments of the state of AI has come from Stanford University as part of a 100-year study of the technology. The report, which brought together many of AI’s leading researchers, attempted to forecast the technology’s impact on a typical US city by 2030......Apart from the social impact, investors are scrambling to understand how such applications of AI will enable wealth to be created — and destroyed.
Suranga Chandratillake, a partner at Balderton Capital, a London-based venture capital firm, says “AI is the big question of the now” for many investors. The clue, he suggests, is to identify those companies capable of amassing vast pools of domain specific data to run through their AI systems that can disrupt traditional business models. [Large data sets with known correct answers serve as a training bed and then new data serves as a test bed]
artificial_intelligence  boom-to-bust  investors  disruption  data  training_beds  test_beds  massive_data_sets  wealth_creation  wealth_destruction  social_impact  venture_capital 
march 2017 by jerryking
The Sensor-Rich, Data-Scooping Future - NYTimes.com
APRIL 26, 2015 | NYT | By QUENTIN HARDY.

Sensor-rich lights, to be found eventually in offices and homes, are for a company that will sell knowledge of behavior as much as physical objects....The Internet will be almost fused with the physical world. The way Google now looks at online clicks to figure out what ad to next put in front of you will become the way companies gain once-hidden insights into the patterns of nature and society.

G.E., Google and others expect that knowing and manipulating these patterns is the heart of a new era of global efficiency, centered on machines that learn and predict what is likely to happen next.

“The core thing Google is doing is machine learning,” Eric Schmidt....The great data science companies of our sensor-packed world will have experts in arcane reaches of statistics, computer science, networking, visualization and database systems, among other fields. Graduates in those areas are already in high demand.

Nor is data analysis just a question of computing skills; data access is also critically important. As a general rule, the larger and richer a data set a company has, the better its predictions become. ....an emerging area of computer analysis known as “deep learning” will blow away older fields.

While both Facebook and Google have snapped up deep-learning specialists, Mr. Howard said, “they have far too much invested in traditional computing paradigms. They are the equivalent of Kodak in photography.” Echoing Mr. Chui’s point about specialization, he said he thought the new methods demanded understanding of specific fields to work well.

It is of course possible that both things are true: Big companies like Google and Amazon will have lots of commodity data analysis, and specialists will find niches. That means for most of us, the answer to the future will be in knowing how to ask the right kinds of questions.
sensors  GE  GE_Capital  Quentin_Hardy  data  data_driven  data_scientists  massive_data_sets  machine_learning  automated_reasoning  predictions  predictive_analytics  predictive_modeling  layer_mastery  core_competencies  Enlitic  deep_learning  niches  patterns  analog  insights  latent  hidden  questions  Google  Amazon  aftermath  physical_world  specialization  consumer_behavior  cyberphysical  arcane_knowledge  artificial_intelligence  test_beds 
april 2015 by jerryking
A 25-Question Twitter Quiz to Predict Retweets - NYTimes.com
JULY 1, 2014 | NYT | Sendhil Mullainathan.

how “smart” algorithms are created from big data: Large data sets with known correct answers serve as a training bed and then new data serves as a test bed — not too differently from how we might learn what our co-workers find funny....one of the miracles of big data: Algorithms find information in unexpected places, uncovering “signal” in places we thought contained only “noise.”... the Achilles’ heel of prediction algorithms--being good at prediction often does not mean being better at creation. (1) One barrier is the oldest of statistical problems: Correlation is not causation.(2) an inherent paradox lies in predicting what is interesting. Rarity and novelty often contribute to interestingness — or at the least to drawing attention. But once an algorithm finds those things that draw attention and starts exploiting them, their value erodes. (3) Finally, and perhaps most perversely, some of the most predictive variables are circular....The new big-data tools, amazing as they are, are not magic. Like every great invention before them — whether antibiotics, electricity or even the computer itself — they have boundaries in which they excel and beyond which they can do little.
predictive_analytics  massive_data_sets  limitations  algorithms  Twitter  analytics  data  data_driven  Albert_Gore  Achilles’_heel  boundary_conditions  noise  signals  paradoxes  correlations  causality  counterintuitive  training_beds  test_beds  rarity  novelty  interestingness  hard_to_find 
july 2014 by jerryking
Ping - How Google Decides to Pull the Plug - NYTimes.com
February 14, 2009 NYT article By VINDU GOEL on how Google
evaluates budding projects, its key tests for continued incubation, its
use of its own employees as a test bed, and its use of product-specific
blogs to communicate and listen to, the public.
attrition_rates  stage-gate  Daniel_Pink  Freshbooks  decision_making  business  innovation  Google  exits  trial_&_error  commercialization  projects  kill_rates  test_beds  assessments_&_evaluations  Communicating_&_Connecting  testing  blogs  new_products  Michael_McDerment  culling 
february 2009 by jerryking

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