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jerryking : machine_learning   50

Jeff Bezos’ family office invests in Chilean plant-based food start-up
March 1, 2019 | Financial Times | by Leila Abboud in Paris.

The family office of Jeff Bezos is among the investors in a $30m funding round for a Chile-based start-up that uses machine learning to create vegetarian alternatives for animal-derived products such as mayonnaise and ice cream.

Four-year old NotCo on Friday announced the financing round led by The Craftory, a fund co-founded by consumer industry veteran Elio Leoni Sceti, as well as Bezos Expeditions.....The funds will be used to finance product development and help NotCo expand to Mexico and the US later this year. It sells its plant-based mayonnaise, which is made with chickpeas, in grocery stores in Chile......NotCo has developed a software platform that analyses the molecular structure of foods, such as beef or milk, so as then to derive combinations of plant-based alternatives that most closely resemble the original in taste, colour, and texture. The technology seeks to map the similarities between the genetic properties of plants and their corollaries in animals, so as to more accurately mimic the properties.....“The potential is massive because NotCo is not just a meat-replacement company or a milk-replacement company,”.....The technology can be applied to all foods derived from animals,” he said, adding that if successful, the opportunity was there to create a major food company to compete with the likes of Nestlé and Danone......the approach of analysing the molecular structure of foods to engineer vegetarian versions of meats, cheeses and dairy products is similar to that of US-based start-up Just Inc, formerly known as Hampton Creek.....The company changed its name after a series of setbacks, including an alleged food safety issue that led to it losing distribution at retailer Target. Nevertheless, Just Inc is well-funded; it has said that it has raised $220m from investors.....Venture capital investors have been pouring money into start-ups to create plant-based or lab-grown alternatives to traditional meat and dairy. Impossible Foods — which is backed by Bill Gates and Alphabet’s GV, formerly Google Ventures, among others — has raised $387.5m,
Chile  Chileans  Danone  family_office  flexitarian  food  Jeff_Bezos  machine_learning  Nestlé  plant-based  start_ups  vegetarian  vc  venture_capital 
march 2019 by jerryking
How to Navigate Investing in A.I., From Someone Who’s Done It
March 2, 2019 | The New York Times | By Katie Robertson.

Reid Hoffman, the co-founder of LinkedIn and a prominent venture capitalist, said at The New York Times’s New Work Summit in California that he looked very carefully at A.I. ventures to see how they were making new, interesting things possible and how he could bet on them early. He said current machine learning techniques, which are transforming fundamental industries, gave an amazing glimpse of the future.

“My ideal investing is stuff that looks a little crazy now and in three years is obvious or five years is obvious,” Mr. Hoffman said.....voiced some concerns around how A.I. could transform the global landscape, likening it to the shift from the agricultural age to the industrial age.

“You’ll see enormous changes from where the bulk of people find jobs and employment,” he said. “The first worry is what does that transition look like. That intervening transition is super painful.”....Mr. Hoffman recently released the book “Blitzscaling: The Lightning-Fast Path to Building Massively Valuable Companies,” which details his theory that the rapid growth of a company — above almost all else — is what leads to its success.
artificial_intelligence  blitzscaling  books  competitive_landscape  machine_learning  Reid_Hoffman  scaling  Silicon_Valley  start_ups  vc  venture_capital 
march 2019 by jerryking
The robot-proof skills that give women an edge in the age of AI
February 11, 2019 | Financial Times |by Sarah O’Connor.

in a world of algorithms and artificial intelligence, communication skills and emotional intelligence — traditionally seen as female strengths — could prove key.

The latest panic about artificial intelligence is that it will deal a blow to women in the workplace..... The concerns are legitimate enough, but they fail to appreciate the big ways in which the world of work is going to change. In fact, it is quite possible the age of AI will belong to women. Men are the ones in danger of being left behind....Some AI tools may be biased against women — a risk for any group that has been historically under-represented in the workplace. Because machine learning tends to learn from historical data, it can perpetuate patterns from the past into the future......It is right to pay attention to these problems and work on solutions. Algorithms shouldn’t be given power without transparency, accountability, and human checks and balances. Top AI jobs should be held by a more diverse set of smart people.....As machines become better at many cognitive tasks, it is likely that the skills they are relatively bad at will become more valuable. This list includes creative problem-solving, empathy, negotiation and persuasion. As Andy Haldane, chief economist at the Bank of England, has put it, “the high-skill, high-pay jobs of the future may involve skills better measured by EQs (a measure of emotional intelligence) than IQs”..... increasing demand in these jobs for supplementary skills such as emotional intelligence, which has given women an edge.....as the AI era dawns, it is the right moment to overhaul the way we value these skills, and the way we teach them. With an eye on the demands of the future, we are trying to persuade girls that coding is not just for boys. So why aren’t we also trying to persuade boys that empathy is not just for girls?

We could start by changing the language we use. For too long we have talked about “soft skills”, with connotations of femininity and a lack of rigour. Let’s call them what they are: “robot-proof skills” that neither men nor women can afford to face the 21st century
21st._century  algorithms  artificial_intelligence  biases  checks_and_balances  dark_side  emotional_intelligence  EQ  future-proofing  gender_gap  machine_learning  soft_skills  smart_people  under-representation  women  workplaces  pay_attention 
february 2019 by jerryking
Amazon offers cautionary tale of AI-assisted hiring
January 23, 2019 | Financial Times | by Andrew Hill.

the task of working out how to get the right people on the bus has got harder since 2001 when Jim Collins first framed it, as it has become clearer — and more research has underlined — that diverse teams are better at innovation. For good reasons of equity and fairness, the quest for greater balance in business has focused on gender, race and background. But these are merely proxies for a more useful measure of difference that is much harder to assess, let alone hire for: cognitive diversity. Might this knotty problem be solved with the help of AI and machine learning? Ming is sceptical. As she points out, most problems with technology are not technology problems, but human problems. Since humans inevitably inherit cultural biases, it is impossible to build an “unbiased AI” for hiring. “You simply have to recognise that the biases exist and put in the effort to do more than those default systems point you towards,” she says...........What Amazon’s experience suggests is that instead of sending bots to crawl over candidates’ past achievements, companies should be exploring ways in which computers can help them to assess and develop the long term potential of the people they invite to board the bus. Recruiters should ask, in Ming’s words, “Who will [these prospective candidates] be three years from now when they’re at their peak productivity inside the company? And that might be a very different story than who will deliver peak productivity the moment they walk in the door.”
heterogeneity  Amazon  artificial_intelligence  hiring  Jim_Collins  machine_learning  recruiting  teams  Vivienne_Ming  cautionary_tales  biases  diversity  intellectual_diversity  algorithms  questions  the_right_people 
january 2019 by jerryking
Company led by Google veterans uses AI to ‘nudge’ workers toward happiness - The Globe and Mail
The startup, Humu, is based in Google’s hometown and it builds on some of the people-analytics programs pioneered by the internet giant, which has studied things including the traits that define great managers and how to foster better teamwork.

Humu wants to bring similar data-driven insights to other companies. It digs through employee surveys using artificial intelligence to identify one or two behavioural changes that are likely to make the biggest impact on elevating a work force’s happiness. Then it uses e-mails and text messages to “nudge” individual employees into small actions that advance the larger goal.

At a company where workers feel that the way decisions are made is opaque, Humu might nudge a manager before a meeting to ask the members of her team for input and to be prepared to change her mind. Humu might ask a different employee to come up with questions involving her team that she would like to have answered.

At the heart of Humu’s efforts is the company’s “nudge engine” (yes, it’s trademarked). It is based on economist Richard Thaler’s Nobel Prize-winning research into how people often make decisions because of what is easier rather than what is in their best interest, and how a well-timed nudge can prompt them to make better choices.

Google has used this approach to coax employees into the corporate equivalent of eating their vegetables, prodding them to save more for retirement, waste less food at the cafeteria and opt for healthier snacks.

Using machine learning, Humu will tailor the timing, content and techniques of the messages it delivers based on how employees respond.

“Often we want to be better people,” said Laszlo Bock, Humu’s chief executive and Google’s former leader of what the company calls people operations, or human resources
Asha_Isaacs  artificial_intelligence  Google  happiness  machine_learning  Richard_Thaler  nudge  behavioural_economics  Laszlo_Bock 
january 2019 by jerryking
Canada’s IP strategy is not in step with our innovation and commercialization goals - The Globe and Mail
JIM HINTON AND PETER COWAN
CONTRIBUTED TO THE GLOBE AND MAIL
PUBLISHED 57 MINUTES AGO
UPDATED NOVEMBER 25, 2018
Jim Hinton is a principal at Own Innovation and Peter Cowan is a principal at Northworks IP

There is a global arms race for artificial intelligence-related intellectual property. The United States and China are amassing thousands of patent filings related to AI and machine learning.....The hype surrounding R&D funding has not translated to commercialization of AI outside of a small handful of domestic high-growth companies, such as Hatch and Sightline Innovation. This confirms what we already know: Innovation and IP funding announcements alone are not a strategy for growth. What Canada needs is a strategy to own its AI innovations and turn them into prosperity engines for the Canadian economy.

Lost in the hype around Canada becoming an AI hub is an absolute lack of follow-through to ensure intellectual property (IP) rights are preserved for current and future Canadian commercialization needs. There is currently no strategy in any of the taxpayer-funded programs ensuring IP ownership is maintained for the benefit of the Canadian economy. ......Companies such as Alphabet, Huawei and others will continue to partner with Canadian universities and use Canadian taxpayer-funded technology to their global advantage: Of the 100 or so machine learning-related patents that have been developed in Canada over the past 10 years, more than half have ended up in the hands of foreign companies such as Microsoft and IBM.......

.........To reverse the status quo, Canada’s IP strategy must include at least four key tactics: (1) IP generation, ensuring that Canadian firms own valuable IP and data stocks; (2) IP retention; (3) freedom to operate strategies for our innovative high-growth companies; and (4) alignment of the national IP strategy with the national data strategy.
artificial_intelligence  Canada  innovation  intellectual_property  machine_learning  property_rights  arms_race  commercialization  Jim_Balsillie 
november 2018 by jerryking
Computer vision: how Israel’s secret soldiers drive its tech success
November 20, 2018 | Financial Times | Mehul Srivastava in Tel Aviv.
.... those experiences that have helped such a tiny country become a leader in one of the most promising frontiers in the technology world: computer vision. Despite the unwieldy name it is an area that has come of age in the past few years, covering applications across dozens of industries that have one thing in common: the need for computers to figure out what their cameras are seeing, and for those computers to tell them what to do next.........Computer vision has become the connecting thread between some of Israel’s most valuable and promising tech companies. And unlike Israel’s traditional strengths— cyber security and mapping — computer vision slides into a broad range of different civilian industries, spawning companies in agriculture, medicine, sports, self-driving cars, the diamond industry and even shopping. 

In Israel, this lucrative field has benefited from a large pool of engineers and entrepreneurs trained for that very task in an elite, little-known group in the military — Unit 9900 — where they fine-tuned computer algorithms to digest millions of surveillance photos and sift out actionable intelligence. .........The full name for Unit 9900 — the Terrain Analysis, Accurate Mapping, Visual Collection and Interpretation Agency — hints at how it has created a critical mass of engineers indispensable for the future of this industry. The secretive unit has only recently allowed limited discussion of its work. But with an estimated 25,000 graduates, it has created a deep pool of talent that the tech sector has snapped up. 

Soldiers in Unit 9900 are assigned to strip out nuggets of intelligence from the images provided by Israel’s drones and satellites — from surveilling the crowded, chaotic streets of the Gaza Strip to the unending swaths of desert in Syria and the Sinai. 

With so much data to pour over, Unit 9900 came up with solutions, including recruiting Israelis on the autistic spectrum for their analytical and visual skills. In recent years, says Shir Agassi, who served in Unit 9900 for more than seven years, it learned to automate much of the process, teaching algorithms to spot nuances, slight variations in landscapes and how their targets moved and behaved.....“We had to take all these photos, all this film, all this geospatial evidence and break it down: how do you know what you’re seeing, what’s behind it, how will it impact your intelligence decisions?” .....“You’re asking yourself — if you were the enemy, where would you hide? Where are the tall buildings, where’s the element of surprise? Can you drive there, what will be the impact of weather on all this analysis?”

Computer vision was essential to this task....Teaching computers to look for variations allowed the unit to quickly scan thousands of kilometres of background to find actionable intelligence. “You have to find ways not just to make yourself more efficient, but also to find things that the regular eye can’t,” she says. “You need computer vision to answer these questions.”.....The development of massive databases — from close-ups of farm insects to medical scans to traffic data — has given Israeli companies a valuable headstart over rivals. And in an industry where every new image teaches the algorithm something useful, that has made catching up difficult.......“Computer vision is absolutely the thread that ties us to other Israeli companies,” he says. “I need people with the same unique DNA — smart PhDs in mathematics, neural network analysis — to tell a player in the NBA how to improve his jump shot.”
Israel  cyber_security  hackers  cyber_warfare  dual-use  Israeli  security_&_intelligence  IDF  computer_vision  machine_learning  Unit_9900  start_ups  gene_pool  imagery  algorithms  actionable_information  geospatial  mapping  internal_systems  PhDs  drones  satellites  surveillance  autism 
november 2018 by jerryking
Commodity trading enters the age of digitisation
July 9, 2018 | Financial Times | by Emiko Terazono.

Commodity houses are on the hunt for data experts to help them gain an edge after seeing their margins squeezed by rivals......commodity traders are seeking ways of exploiting their information to help them profit from price swings.

“It is really a combination of knowing what to look for and using the right mathematical tools for it,” ........“We want to be able to extract data and put it into algorithms,” .......“We then plan to move on to machine learning in order to improve decision-making in trading and, as a result, our profitability.” The French trading arm is investing in people, processes and systems to centralize its data — and it is not alone.

“Everybody [in the commodity world] is waking up to the fact that the age of digitisation is upon us,” said Damian Stewart at headhunters Human Capital.

In an industry where traders with proprietary knowledge, from outages at west African oilfields to crop conditions in Russia, vied to gain an upper hand over rivals, the democratisation of information over the past two decades has been a challenge......the ABCDs — Archer Daniels Midland, Bunge, Cargill and Louis Dreyfus Company — all recording single-digit ROE in their latest results. As a consequence, an increasing number of traders are hoping to increase their competitiveness by feeding computer programs with mountains of information they have accumulated from years of trading physical raw materials to try and detect patterns that could form the basis for trading ideas.......Despite this new enthusiasm, the road to electronification may not come easily for some traders. Compared to other financial and industrial sectors, “they are coming from way behind,” said one consultant.

One issue is that some of the larger commodities traders face internal resistance in centralising information on one platform.

With each desk in a trading house in charge of its profit-and-loss account, data are closely guarded even from colleagues, said Antti Belt, head of digital commodity trading at Boston Consulting Group. “The move to ‘share all our data with each other’ is a very, very big cultural shift,” he added.

Another problem is that in some trading houses, staff operate on multiple technology platforms, with different units using separate systems.

Rather than focusing on analytics, some data scientists and engineers are having to focus on harmonising the platforms before bringing on the data from different parts of the company.
ADM  agribusiness  agriculture  algorithms  artificial_intelligence  Bunge  Cargill  commodities  data_scientists  digitalization  machine_learning  traders  food_crops  Louis_Dreyfus  grains  informational_advantages 
july 2018 by jerryking
JetBlue Tech Execs Tap Startups To Help Airline Innovate - CIO Journal. - WSJ
As digital technology transforms business, enterprises can be at a disadvantage relative to newcomers. One solution is to work with startups, but that can be tricky because of security, regulatory and policy requirements at large companies. CIO Journal spoke to the top technology executives at JetBlue Airways about how they make the relationship work through a corporate venture arm, JetBlue Technology Ventures.

The Silicon Valley-based venture group looks for technology that could add business value within 18 months, as well as that which may have longer, 5- to 10-year payoffs. It has made early and mid-stage investments in 18 startups since 2016.

“Being part of the Silicon Valley innovation ecosystem is very important for us,” Eash Sundaram, JetBlue’s chief digital and technology officer, tells CIO Journal.
+++++++++++++++++++++++++++++++++++++++++++++++++
Ms. Simi said JetBlue may have never come across the startups in the venture arm’s portfolio if they had simply made a request for proposals for a specific technology project. Instead, the dedicated venture team vets startups, makes strategic investments and works alongside them to match technologies to JetBlue’s current and future needs.

“It’s been hugely helpful at JetBlue in terms of keeping our thinking fresh and innovative,”
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JetBlue  brands  large_companies  airline_industry  innovation  start_ups  CIOs  machine_learning  blockchain 
july 2018 by jerryking
The Morning Download: Computing’s Future Lies at Edge of Network, Just Before the Cloud - CIO Journal. - WSJ
By Steve Rosenbush
Jun 20, 2018

For years, computing has been centralized in one place or another. First, the data center, and later massive clouds. Now, networks are taking a more decentralized structure, with power located at the so-called edge, be it a retail environment, an oil rig or an automobile. On Tuesday, Hewlett Packard Enterprise Co. said it will invest $4 billion during the next four years to accelerate innovation in what HPE calls “the intelligent edge.”

Edge of opportunity. “We see significant areas for growth … (as) more assets and ‘things’ come online and the amount of data generated continues to grow exponentially,” HPE CEO Antonio Neri told CIO Journal’s Sara Castellanos in an email. The number of devices connected to the internet will reach 20.4 billion by 2020, up from 8.4 billion in 2017, according to Gartner Research Inc. By 2021, 40% of enterprises will have an edge computing strategy in place, up from about 1% in 2017, Gartner says.

The payoff. Stewart Ebrat, CIO at bridal gown and fashion company Vera Wang Co., an HPE customer, maintains that with data analytics and Bluetooth-enabled beacon devices at the edge, a salesperson could know more about a prospective customer’s preferences as soon as they walk into a brick-and-mortar store. Says Mr. Ebrat: “The customer has always been number one (at Vera Wang), but technology is going to enhance that experience even further.”
cloud_computing  decentralization  edge  future  Industrial_Internet  IT  artificial_intelligence  centralization  machine_learning  HPE  HP  data_centers 
june 2018 by jerryking
The future of computing is at the edge
June 6, 2018 | FT | by Richard Waters in San Francisco.

With so much data being produced, sending it all to cloud does not make economic sense.

The economics of big data — and the machine learning algorithms that feed on it — have been a gift to the leading cloud computing companies. By drawing data-intensive tasks into their massive, centralised facilities, companies such as Amazon, Microsoft and Google have thrived by bringing down the unit costs of computing.

But artificial intelligence is also starting to feed a very different paradigm of computing. This is one that pushes more data-crunching out to the network “edge” — the name given to the many computing devices that intersect with the real world, from internet-connected cameras and smartwatches to autonomous cars. And it is fuelling a wave of new start-ups which, backers claim, represent the next significant architectural shift in computing.....nor.ai, an early-stage AI software start-up that raised $12m this month, is typical of this new wave. Led by Ali Farhadi, an associate professor at University of Washington, the company develops machine learning algorithms that can be run on extremely low-cost gadgets. Its image recognition software, for instance, can operate on a Raspberry Pi, a tiny computer costing just $5, designed to teach the basics of computer science......That could make it more economical to analyse data on the spot rather than shipping it to the cloud. One possible use: a large number of cheap cameras around the home with the brains to recognise visitors, or tell the difference between a burglar and a cat.

The overwhelming volume of data that will soon be generated by billions of devices such as these upends the logic of data centralisation, according to Mr Farhadi. “We like to say that the cloud is a way to scale AI, but to me it’s a roadblock to AI,” he said. “There is no cloud that can digest this much data.”

“The need for this is being driven by the mass of information being collected at the edge,” added Peter Levine, a partner at Silicon Valley venture capital firm Andreessen Horowitz and investor in a number of “edge” start-ups. “The real expense is going to be shipping all that data back to the cloud to be processed when it doesn’t need to be.”

Other factors add to the attractions of processing data close to where it is collected. Latency — the lag that comes from sending information to a distant data centre and waiting for results to be returned — is debilitating for some applications, such as driverless cars that need to react instantly. And by processing data on the device, rather than sending it to the servers of a large cloud company, privacy is guaranteed.

Tobias Knaup, co-founder of Mesosphere, another US start-up, uses a recent computing truism to sum up the trend: “Data has gravity.”....Nor are the boundaries between cloud and edge distinct. Data collected locally is frequently needed to retrain machine learning algorithms to keep them relevant, a computing-intensive task best handled in the cloud. Companies such as Mesosphere — which raised $125m this month, taking the total to more than $250m — are betting that this will give rise to technologies that move information and applications to where they are best handled, from data centres out to the edge and vice versa...Microsoft unveiled image-recognition software that was capable of running on a local device rather than its own data centres.
cloud_computing  edge  future  Industrial_Internet  IT  low-cost  artificial_intelligence  centralization  machine_learning  data_centers  decentralization  Microsoft  computer_vision  Richard_Waters 
june 2018 by jerryking
Google and Repsol team up to boost oil refinery efficiency
June 3, 2018 | Financial Times | Anjli Raval in London YESTERDAY

Repsol will use Cloud ML, Google’s machine learning tool, to optimise the performance of its 120,000 barrel-a-day Tarragona oil refinery on the east coast of Spain, near Barcelona.

A refinery is made up of multiple divisions, including the unit that distils crude into various components to be processed into fuels such as gasoline and diesel and the entity that converts heavy residual oils into lighter, more valuable products.

Google’s technology will be used to analyse hundreds of variables that measure pressure, temperature, flows and processing rates among other functions for each unit at Tarragona. Repsol hopes this will boost margins by 30 cents per barrel at the facility and plans to roll out the technologies across its five other refineries.

Energy companies are increasingly looking to use the type of analytics often employed by companies such as Google and Amazon for consumer data across their operations, from boosting the performance of drilling rigs to helping to deliver greater returns from refineries.

“Until very recently, [oil and gas] companies have not had the tools or the capabilities needed to operate these assets at their maximum capacity,” McKinsey, the professional services firm, said in a recent report. “Analytics tools and techniques have advanced far and fast.”
artificial_intelligence  efficiencies  energy  Google  oil_industry  oil_refiners  Silicon_Valley  Repsol  tools  machine_learning 
june 2018 by jerryking
Inside Amazon Go, a Store of the Future - The New York Times
Jan. 21, 2018 | NYT | By Nick Wingfield

....Amazon’s store of the future hits you right at the front door. It feels as if you are entering a subway station. A row of gates guard the entrance to the store, known as Amazon Go, allowing in only people with the store’s smartphone app......Every time customers grab an item off a shelf, Amazon says the product is automatically put into the shopping cart of their online account. If customers put the item back on the shelf, Amazon removes it from their virtual basket. The only sign of the technology that makes this possible floats above the store shelves — arrays of small cameras, hundreds of them throughout the store. Amazon won’t say much about how the system works, other than to say it involves sophisticated computer vision and machine learning software. Translation: Amazon’s technology can see and identify every item in the store, without attaching a special chip to every can of soup and bag of trail mix. ........Amazon Go, checking out feels like — there’s no other way to put it — shoplifting. ......A big unanswered question is where Amazon plans to take the technology. It won’t say whether it plans to open more Amazon Go stores, or leave this as a one-of-a-kind novelty. A more intriguing possibility is that it could use the technology inside Whole Foods stores, though Ms. Puerini said Amazon has “no plans” to do so.

There’s even speculation that Amazon could sell the system to other retailers, much as it sells its cloud computing services to other companies.
Amazon_Go  Amazon  cashierless  computer_vision  convenience_stores  customer_experience  grocery  machine_learning  one-of-a-kind  supermarkets  retailers  Whole_Foods 
january 2018 by jerryking
Algos know more about us than we do about ourselves
NOVEMBER 24, 2017 | Financial Time | John Dizard.

When intelligence collectors and analysts take an interest in you, they usually start not by monitoring the content of your calls or messages, but by looking at the patterns of your communications. Who are you calling, how often and in what sequence? What topics do you comment on in social media?

This is called traffic analysis, and it can give a pretty good notion of what you and the people you know are thinking and what you are preparing to do. Traffic analysis started as a military intelligence methodology, and became systematic around the first world war. Without even knowing the content of encrypted messages, traffic analysts could map out an enemy “order of battle” or disposition of forces, and make inferences about commanders’ intentions.

Traffic analysis techniques can also cut through the petabytes of redundant babble and chatter in the financial and political worlds. Even with state secrecy and the forests of non-disclosure agreements around “proprietary” investment or trading algorithms, crowds can be remarkably revealing in their open-source posts on social media.

Predata, a three-year-old New York and Washington-based predictive data analytics provider, has a Princeton-intensive crew of engineers and international affairs graduates working on early “signals” of market and political events. Predata trawls the open metadata for users of Twitter, Wikipedia, YouTube, Reddit and other social media, and analyses it to find indicators of future price moves or official actions.

I have been following their signals for a while and find them to be useful indicators. Predata started by creating political risk indicators, such as Iran-Saudi antagonism, Italian or Chilean labour unrest, or the relative enthusiasm for French political parties. Since the beginning of this year, they have been developing signals for financial and commodities markets.

The 1-9-90 rule
1 per cent of internet users initiate discussions or content, 9 per cent transmit content or participate occasionally and 90 per cent are consumers or ‘lurkers’

Using the example of the company’s BoJ signal. For this, Predata collects the metadata from 300 sources, such as Twitter users, contested Wikipedia edits or YouTube items created by Japanese monetary policy geeks. Of those, at any time perhaps 100 are important, and 8 to 10 turn out to be predictive....This is where you need some domain knowledge [domain expertise = industry expertise]. It turns out that Twitter is pretty important for monetary policy, along with the Japanese-language Wiki page for the Bank of Japan, or, say, a YouTube video of [BoJ governor] Haruhiko Kuroda’s cross-examination before a Diet parliamentary committee.

“Then you build a network of candidate discussions [JK: training beds] and look for the pattern those took before historical moves. The machine-learning algorithm goes back and picks the leads and lags between traffic and monetary policy events.” [Jk: Large data sets with known correct answers serve as a training bed and then new data serves as a test bed]

Typically, Predata’s algos seem to be able to signal changes in policy or big price moves [jk: inflection points] somewhere between 2 days and 2 weeks in advance. Unlike some academic Twitter scholars, Predata does not do systematic sentiment analysis of tweets or Wikipedia edits. “We only look for how many people there are in the conversation and comments, and how many people disagreed with each other. We call the latter the coefficient of contestation,” Mr Shinn says.

The lead time for Twitter, Wiki or other social media signals varies from one market to another. Foreign exchange markets typically move within days, bond yields within a few days to a week, and commodities prices within a week to two weeks. “If nothing happens within 30 days,” says Mr Lee, “then we say we are wrong.”
algorithms  alternative_data  Bank_of_Japan  commodities  economics  economic_data  financial_markets  industry_expertise  inflection_points  intelligence_analysts  lead_time  machine_learning  massive_data_sets  metadata  non-traditional  Predata  predictive_analytics  political_risk  signals  social_media  spycraft  traffic_analysis  training_beds  Twitter  unconventional 
november 2017 by jerryking
We Survived Spreadsheets, and We’ll Survive AI - WSJ
By Greg Ip
Updated Aug. 2, 2017

History and economics show that when an input such as energy, communication or calculation becomes cheaper, we find many more uses for it. Some jobs become superfluous, but others more valuable, and brand new ones spring into existence. Why should AI be different?

Back in the 1860s, the British economist William Stanley Jevons noticed that when more-efficient steam engines reduced the coal needed to generate power, steam power became more widespread and coal consumption rose. More recently, a Massachusetts Institute of Technology-led study found that as semiconductor manufacturers squeezed more computing power out of each unit of silicon, the demand for computing power shot up, and silicon consumption rose.

The “Jevons paradox” is true of information-based inputs, not just materials like coal and silicon......Just as spreadsheets drove costs down and demand up for calculations, machine learning—the application of AI to large data sets—will do the same for predictions, argue Ajay Agrawal, Joshua Gans and Avi Goldfarb, who teach at the University of Toronto’s Rotman School of Management. “Prediction about uncertain states of the world is an input into decision making,” they wrote in a recent paper. .....Unlike spreadsheets, machine learning doesn’t yield exact answers. But it reduces the uncertainty around different risks. For example, AI makes mammograms more accurate, the authors note, so doctors can better judge when to conduct invasive biopsies. That makes the doctor’s judgment more valuable......Machine learning is statistics on steroids: It uses powerful algorithms and computers to analyze far more inputs, such as the millions of pixels in a digital picture, and not just numbers but images and sounds. It turns combinations of variables into yet more variables, until it maximizes its success on questions such as “is this a picture of a dog” or at tasks such as “persuade the viewer to click on this link.”.....Yet as AI gets cheaper, so its potential applications will grow. Just as better weather forecasting makes us more willing to go out without an umbrella, Mr. Manzi says, AI emboldens companies to test more products, strategies and hunches: “Theories become lightweight and disposable.” They need people who know how to use it, and how to act on the results.
artificial_intelligence  Greg_Ip  spreadsheets  machine_learning  predictions  paradoxes  Jim_Manzi  experimentation  testing  massive_data_sets  judgment  uncertainty  economists  algorithms  MIT  Gilder's_Law  speed  steam_engine  operational_tempo  Jevons_paradox  decision_making 
august 2017 by jerryking
Art market ripe for disruption by algorithms
MAY 26, 2017 | Financial Times | by John Dizard.

Art consultants and dealers are convinced that theirs is a high-touch, rather than a high-tech business, and they have arcane skills that are difficult, if not impossible, to replicate..... better-informed collectors [are musing about] how to compress those transaction costs and get that price discovery done more efficiently.....The art world already has transaction databases and competing price indices. The databases tend to be incomplete, since a high proportion of fine art objects are sold privately rather than at public auctions. The price indices also have their issues, given the (arguably) unique nature of the objects being traded. Sotheby’s Mei Moses index attempts to get around that by compiling repeat-sales data, which, given the slow turnover of particular works of art, is challenging.....Other indices, or value estimations, are based on hedonic regression, which is less amusing than it sounds. It is a form of linear regression used, in this case, to determine the weight of different components in the pricing of a work of art, such as the artist’s name, the work’s size, the year of creation and so on. Those weights in turn are used to create time-series data to describe “the art market”. It is better than nothing, but not quite enough to replace the auctioneers and dealers.....the algos are already on the hunt....people are watching the auctions and art fairs and doing empirics....gathering data at a very micro level, looking for patterns, just to gather information on the process.....the art world and its auction markets are increasingly intriguing to applied mathematicians and computer scientists. Recognising, let alone analysing, a work of art is a conceptually and computationally challenging problem. But computing power is very cheap now, which makes it easier to try new methods.....Computer scientists have been scanning, or “crawling”, published art catalogues and art reviews to create semantic data for art works based on natural-language descriptions. As one 2015 Polish paper says, “well-structured data may pave the way towards usage of methods from graph theory, topic labelling, or even employment of machine learning”.

Machine-learning techniques, such as software programs for deep recurrent neural networks, have already been used to analyse and predict other auction processes.
algorithms  disruption  art  art_finance  auctions  collectors  linear_regression  data_scientists  machine_learning  Sotheby’s  high-touch  pricing  quantitative  analytics  arcane_knowledge  art_market 
june 2017 by jerryking
China gifts luxury a reprieve
29 April/30 April 2017 | FT Weekend | by Harriet Agnew and Tom Hancock

Chinese consumers, the drivers of global luxury for more than a decade, once travelled overseas to the European fashion capitals of Paris, London and Milan to take advantage of lower prices. Now they are increasingly inclined to spend at home. Last year Chinese consumers made two-thirds of their personal luxury goods purchases domestically, compared with roughly a third in 2013, according to the Boston Consulting Group.
.............In an era of lower growth, brands are trying to adapt to changing consumer demands and the disruption of digital while keeping the creative process at the heart of it. “Creativity and audacity is what allows you to elicit desire [and therefore sales] over the long run, telling a story that people want to discover, chapter after chapter,” says François-Henri Pinault, chairman and chief executive of Kering.
......Yet brands can no longer rely on opening lots of new stores to fuel growth. Instead they have to keep costs down, revamp their existing stores to make them more profitable, and seek new customers through avenues like digital.

“The business model of luxury has completely changed,” says Erwan Rambourg, global co-head of consumer and retail at HSBC in New York. “Either brands understand that and make the changes themselves, or they don’t and they leave themselves open to activism or M&A.”
.......Compared with other consumer brands, luxury has been late to the digital party. Phoebe Philo, the then creative director at fashion house Céline, told Vogue in 2013 that “the chicest thing is when you don’t exist on Google”. But that view now looks unsustainable.

Six out of 10 sales are digitally influenced, says BCG, which estimates that online commerce will grow from 7 per cent of the global personal luxury market today to 12 per cent by 2020.

Within digital, the holy grail is so-called omnichannel — the ability to offer a seamless experience to customers that blends digital and bricks-and-mortar stores, and includes initiatives like click-and-collect. “Blending the physical and the digital is the future of the online flagship stores,” says Federico Marchetti, chief executive of the YOOX Net-a-Porter Group.

The emphasis is on the customer experience. Net-a-Porter is launching a same-day delivery service in September for its top clients in London called, “You try, we wait.” Customers will be able to try on their online order at home or in the office while the delivery van waits outside.
......As e-commerce gathers steam and groups collect more and more data on their clients, the next stage is machine learning and artificial intelligence, believes Mr Marchetti. In this vision of the future algorithms will act as virtual shopping assistants, suggesting items that the customer might like, “enabling us to speak to each customer on an individual basis rather than to the whole customer base”, he says.

Luxury brands are also increasingly using blogs, online “influencers” and social media platforms such as Instagram to generate visibility and lure potential buyers.

All of this is happening at a time when the definition of what constitutes luxury is expanding beyond physical possessions to include experiences both as a competitor to, and opportunity for, the traditional houses.

“Luxury brands are now competing with the plastic surgeon and the luxury travel agent,” says Mr Rambourg. “For a similar price you can have a Louis Vuitton handbag, a facelift or a trip to the Maldives.”
....“Our pulse is the Chinese customer,” says LVMH’s Mr Guiony: “It made the sector worse a couple of years ago and it has made it better now. We have to be aware of that. Trees don’t grow to the sky.”
/
luxury  brands  China  Chinese  China_rising  consumers  digital_disruption  e-commerce  travel_agents  BCG  growth  LVMH  watches  noughties  Yoox  customer_experience  WeChat  Burberry  digital_influencers  creativity  audacity  storytelling  omnichannel  artificial_intelligence  machine_learning  virtual_assistants  same-day 
may 2017 by jerryking
Explosion in data ushers in new high-tech era.pdf
December 5, 2016 | Financial Times | Ian Whylie.

However, one of the consequences of the introduction of AI into consulting will be greater clarification of consulting methodologies, predicts Harvey Lewis, Deloitte's UK artificial intelligence lead in technology consulting. There will be repeatable, common approaches that are supported by machines, and then a class of essentially human approaches for dealing with more varied, wide-ranging and uncertain problems........However, AI could also have a significant impact on the way strategy consultants do their job.

“Consulting firms have a lot of intellectual property locked up inside their consultants’ heads, which, if codified and converted into algorithms, can be used by computers instead,” he says. “This will allow computers to work on the repeatable consulting tasks by following prescribed methodologies, while the human consultants are freed to work on those projects where inputs, outputs and outcomes are more uncertain or which require greater creativity, subjectivity, social interaction and perceptiveness or human judgment.”

Clients want us to arrive, ready to load in their data, and provide insights on the first day of the project
Paul Daugherty. If consulting can be codified then the cost of performing certain types of consulting work is likely to fall, says Mr Lewis. This means that consulting can be offered to more organisations, such as start-ups, small and medium enterprises and charities that might not previously have been able to afford consulting services.

“The days of old-style consulting, where the work was centred around a bunch of people mulling over a PowerPoint presentation and analysis for the client, are either dead or dying fast,” says Mr Daugherty. “Increasingly, strategy consulting is moving to fast-paced database analysis, supported by machine learning. Clients will want us to arrive, ready to load in their data, understand the situation and particular dynamics of their business and provide insights on the first day of the project.”
Accenture  artificial_intelligence  automation  data  fast-paced  insights  machine_learning  management_consulting  PowerPoint  situational_awareness  virtual_agents 
april 2017 by jerryking
Nick Bostrom: ‘We are like small children playing with a bomb’
Sunday 12 June 2016 | Technology | The Guardian | by Tim Adams.

Sentient machines are a greater threat to human existence than climate change, according to the Oxford philosopher Nick Bostrom.

Bostrom, a 43-year-old Swedish-born philosopher, has lately acquired something of the status of prophet of doom among those currently doing most to shape our civilisation: the tech billionaires of Silicon Valley. His reputation rests primarily on his book Superintelligence: Paths, Dangers, Strategies, which was a surprise New York Times bestseller last year and now arrives in paperback, trailing must-read recommendations from Bill Gates and Tesla’s Elon Musk. (In the best kind of literary review, Musk also gave Bostrom’s institute £1m to continue to pursue its inquiries.)
artificial_intelligence  dangers  books  Oxford  risks  machine_learning  deep_learning  catastrophic_risk  existential 
march 2017 by jerryking
Machine learning, algorithms drive this advertising company’s growth - The Globe and Mail
MARK BUNTING
Special to The Globe and Mail
Published Wednesday, Mar. 08, 2017

What is programmatic advertising?

Canadian company AcuityAds Holdings Inc. (AT-X) is at the forefront of that transformation. It specializes in what’s called programmatic advertising where algorithms are used to allow advertisers to target, connect with, and accumulate data about their campaigns and their audiences. One of AcuityAds’ co-founders has a PhD in machine learning and algorithms. It’s one of the reasons the company believes its patented technology stands out from its peers.....
A happy advertiser spends more money

“The whole idea was build the algorithm in a way that delivers a positive ROI for clients,” Mr. Hayek says. “As long as they get a positive ROI, they’re going to spend more with us. And that’s proven itself to be a very good concept because we deal with advertisers. When they make money using our system, they’re very happy and they spend more money on our systems.”

Risks

Is Mr. Hayek concerned that in the fast-growing, rapidly changing sector in which AcuityAds operates, a new technology or unforeseen competitor could emerge to disrupt its model?

“Digital advertising is an $83-billion (U.S.) market place. $51-billion out of that is already programmatic,” Mr. Hayek explains. “All the pipes are already built, it was a fundamental shift that this is how we do this kind of business.”
machine_learning  algorithms  ad-tech  advertising  programmatic  risks 
march 2017 by jerryking
As Goldman Embraces Automation, Even the Masters of the Universe Are Threatened
February 7, 2017 | MIT Technology Review | by Nanette Byrnes.

Automated trading programs have taken over cash equities trading function at Goldman Sachs. A job that once employed 600 people in 2000, is now in 2017 being done by 2 people, with the rest of the work, supported by 200 computer engineers. Marty Chavez, the company’s deputy chief financial officer and former chief information officer, explained all this to attendees at a symposium on computing’s impact on economic activity held by Harvard’s Institute for Applied Computational Science last month.....Chavez, who will become chief financial officer in April, says areas of trading like currencies and even parts of business lines like investment banking are moving in the same automated direction that equities have already traveled.....Complex trading algorithms, some with machine-learning capabilities, first replaced trades where the price of what’s being sold was easy to determine on the market, including the stocks traded by Goldman’s old 600.

Now areas of trading like currencies and futures, which are not traded on a stock exchange like the New York Stock Exchange but rather have prices that fluctuate, are coming in for more automation as well. To execute these trades, algorithms are being designed to emulate as closely as possible what a human trader would do,.....Goldman’s new consumer lending platform, Marcus, aimed at consolidation of credit card balances, is entirely run by software, with no human intervention, Chavez said. It was nurtured like a small startup within the firm and launched in just 12 months,
automation  Goldman_Sachs  Martin_Chavez  CFOs  CIOs  risk-assessment  platforms  human_intervention  Marcus  software  algorithms  machine_learning  job_displacement 
february 2017 by jerryking
Winton Capital’s David Harding on making millions through maths
NOVEMBER 25, 2016 | Financial Times | by Clive Cookson.

Harding’s career is founded on the relentless pursuit of mathematical and scientific methods to predict movements in markets. This is a never-ending process because predictive tools lose their power as markets change; new ones are always needed. “We have 450 people in the company, of whom 250 are involved in research, data collection or technology,” he says. That is equivalent to a medium-sized university physics department....Harding's approach to making money is to exploit failures in the efficient market theory...the problem with the EMT is that “It treats economics like a physical science when, in fact, it is a human or social science. Humans are prone to unpredictable behaviour, to overreaction or slumbering inaction, to mania and panic.”...The Winton investment system is based instead on “the belief that scientific methods provide a good means of extracting meaning from noisy market data. We don’t make assumptions about how markets should work, rather we use advanced statistical techniques to seek patterns in huge data sets and base all our investment strategies on the analysis of empirical evidence...Harding emphasises the breadth and volume of investments involved, covering bonds, currencies, commodities, market indices and individual equities. The aim is to exploit a large number of weak predictive signals, he says: “We don’t expect to find any strong relationships between data and the price of the market. That may sound counter-intuitive but if there are strong relationships, someone else is going to be exploiting those. Weak relationships are where we have a competitive advantage.” Weather strategies are one feature of Winton research, including analysis of cloud cover and soil moisture levels to predict the prices of agricultural commodities. Other important indicators, for which maths can uncover value not fully reflected in market prices, include seasonal factors and inventory levels across supply chains....When I ask Harding about the use of machine learning and artificial intelligence to guide investment decisions, he bristles slightly. “There is a sudden upsurge of excitement about AI,” he says, “but we have used techniques that would be described as machine learning for at least 30 years.”

Essentially, he says, quantitative investing, self-driving cars and speech recognition are all applications of “information engineering”....he heads off to a lecture by German psychologist Gerd Gigerenzer, who runs the Harding Centre for Risk Literacy in Berlin
communicating_risks  mathematics  hedge_funds  investment_research  financiers  Winton_Capital  physics  Renaissance_Technologies  James_Simons  moguls  quantitative  panics  overreaction  massive_data_sets  philanthropy  machine_learning  signals  human_factor  weak_links  JumpMath 
november 2016 by jerryking
Universities’ AI Talent Poached by Tech Giants - WSJ
By DANIELA HERNANDEZ and RACHAEL KING
Nov. 24, 2016

Researchers warn that tech companies are draining universities of the scientists responsible for cultivating the next generation of researchers and who contribute to solving pressing problems in fields ranging from astronomy to environmental science to physics.

The share of newly minted U.S. computer-science Ph.D.s taking industry jobs has risen to 57% from 38% over the last decade, according to data from the National Science Foundation. Though the number of Ph.D.s in the field has grown, the proportion staying in academia has hit “a historic low,” according to the Computing Research Association, an industry group.

Such moves could have a long-term impact on the number of graduates available for teaching positions because it takes three to five years to earn a doctorate in computer science. ....The squeeze is especially tight in deep learning, an AI technique that has played a crucial role in moneymaking services like online image search, language translation and ad placement,
Colleges_&_Universities  poaching  Alphabet  Google  Stanford  artificial_intelligence  Facebook  machine_learning  talent_pipelines  research  PhDs  deep_learning  war_for_talent  talent 
november 2016 by jerryking
Inside the mind of a venture capitalist | McKinsey & Company
August 2016 | McK | Steve Jurvetson is a partner at Draper Fisher Jurvetson. Michael Chui,
(1) entrepreneurs who have infectious enthusiasm.
(2) sector of the economy believed to be experiencing rapid growth/ massive disruptive change.
(3) wide range of industries, from synthetic biology to rockets to electric cars to a variety of sectors that weren’t ripe for venture investment in prior decades but now are becoming software businesses.
(4) attributes and people somewhat similar to what I look for in the team at work: enough self-confidence to be humble about what it’s proposing and respect for the team over individuals
How should large companies respond? The large companies that are most exciting to me are the ones that innovate outside their core. big companies need to innovate outside their core businesses. The biggest start-up: Space.
Steve_Jurvetson  McKinsey  DFJ  venture_capital  teams  vc  disruption  space  large_companies  software  core_businesses  Moore's_Law  machine_learning  passions  Elon_Musk  accelerated_lifecycles  space_travel  innovation  self-confidence  humility 
august 2016 by jerryking
Artificial Intelligence Swarms Silicon Valley on Wings and Wheels
JULY 17, 2016 | - The New York Times | By JOHN MARKOFF.

Funding in A.I. start-ups has increased more than fourfold to $681 million in 2015, from $145 million in 2011, according to the market research firm CB Insights. The firm estimates that new investments will reach $1.2 billion this year, up 76 percent from last year.
machine_learning  Silicon_Valley  deep_learning  artificial_intelligence  funding  venture_capital  vc 
july 2016 by jerryking
Your Lawyer May Soon Ask This AI-Powered App for Legal Help | WIRED
DAVEY ALBA BUSINESS DATE OF PUBLICATION: 08.07.15.
08.07.15

ROSS Intelligence is a voice recognition app powered by IBM Watson, the machine learning service based on the company’s Jeopardy-playing cognitive system, that doles out legal assistance.

The app is yet another example of the ways machine learning is infiltrating our everyday lives. These days, it’s not just AI algorithms themselves that have improved, but the ability to deliver them across the Internet that has made so many new applications possible.....Asking Natural Questions
Ross works much like Siri. Users can ask it any question the same way a client might—for instance, “If an employee has not been meeting sales targets and has not been able to complete the essentials of their employment, can they be terminated without notice?” The system sifts through its database of legal documents and spits out an answer paired with a confidence rating. Below the answer, a user can see the source documents from which Ross has pulled the information; if the response is accurate, you can hit a “thumbs up” button to save the source. Select “thumbs down” and Ross come up with another response.
technology  law  lawtech  lawyers  law_firms  machine_learning  voice_recognition  voice_interfaces  virtual_assistants  artificial_intelligence  Siri  IBM_Watson 
june 2016 by jerryking
Microsoft banks on bots to restore company’s mobile relevance - The Globe and Mail
SHANE DINGMAN - TECHNOLOGY REPORTER
The Globe and Mail
Published Wednesday, Mar. 30, 2016

Mr. Nadella to describe how bots and machine learning tools are going to create a new “distributed computing fabric” that will vault Microsoft back into relevance on mobile platforms that are built and owned by rivals at Apple and Google. The theory is that if the App Store is owned by the phone makers, you go around the store with bots that live inside other popular mobile services....Everyone from Facebook and Slack to Amazon and Google are already vying to build the best hosts for these new bot services. Canadian messaging company Kik is among those making major investments in this bot-driven future that foresees commands to semi-artificially intelligent interactive chatbots expanding into everything from physical commerce (buying stuff at a shop with your phone, essentially) to controlling Internet of Things devices (texting your coffee machine to make an espresso). Microsoft showed off similar concepts on Wednesday, including a cupcake shopbot and a Domino’s Pizza bot that can deliver food to your location.
bots  Microsoft  platforms  Kik  CEOs  Satya_Nadella  distributed_computing  machine_learning  Azure  cloud_computing  software  intelligent_agents  chatbots 
march 2016 by jerryking
The Language Barrier Is About to Fall - WSJ
By ALEC ROSS
Jan. 29, 2016

Universal machine translation should accelerate the world’s growing interconnectedness. While the current stage of globalization was propelled in no small part by the adoption of English as the lingua franca for business—to the point that there are now twice as many nonnative English speakers as native speakers—the next wave will open up communication even more broadly by removing the need for a shared language. Currently, when Korean-speaking businesspeople speak with Mandarin-speaking executives at a conference in Brazil, they converse in English. There will no longer be this need, opening the door of global business for nonelites and a massive number of non-English speakers.
languages  globalization  machine_learning  translations 
january 2016 by jerryking
Looking Beyond the Internet of Things
JAN. 1, 2016 | NYT | By QUENTIN HARDY.

Adam Bosworth is building what some call a “data singularity.” In the Internet of Things, billions of devices and sensors would wirelessly connect to far-off data centers, where millions of computer servers manage and learn from all that information.

Those servers would then send back commands to help whatever the sensors are connected to operate more effectively: A home automatically turns up the heat ahead of cold weather moving in, or streetlights behave differently when traffic gets bad. Or imagine an insurance company instantly resolving who has to pay for what an instant after a fender-bender because it has been automatically fed information about the accident.

Think of it as one, enormous process in which machines gather information, learn and change based on what they learn. All in seconds.... building an automated system that can react to all that data like a thoughtful person is fiendishly hard — and that may be Mr. Bosworth’s last great challenge to solve....this new era in computing will have effects far beyond a little more efficiency. Consumers could see a vast increase in the number of services, ads and product upgrades that are sold alongside most goods. And products that respond to their owner’s tastes — something already seen in smartphone upgrades, connected cars from BMW or Tesla, or entertainment devices like the Amazon Echo — could change product design.
Quentin_Hardy  Industrial_Internet  data  data_centers  data_driven  machine_learning  Google  Amazon  cloud_computing  connected_devices  BMW  Tesla  Amazon_Echo  product_design  Michael_McDerment  personalization  connected_cars 
january 2016 by jerryking
How Stanford Took On the Giants of Economics - The New York Times
SEPT. 10, 2015 | NYT | By NEIL IRWIN.

Stanford’s success with economists is part of a larger campaign to stake a claim as the country’s top university. Its draw combines a status as the nation’s “it” university — now with the lowest undergraduate acceptance rate and a narrow No. 2 behind Harvard for the biggest fund-raising haul — with its proximity to many of the world’s most dynamic companies. Its battle with Eastern universities echoes fights in other industries in which established companies, whether hotels or automobile makers, are being challenged by Silicon Valley money and entrepreneurship....reflection of a broader shift in the study of economics, in which the most cutting-edge work increasingly relies less on a big-brained individual scholar developing mathematical theories, and more on the ability to crunch extensive sets of data to glean insights about topics as varied as how incomes differ across society and how industries organize themselves....The specialties of the new recruits vary, but they are all examples of how the momentum in economics has shifted away from theoretical modeling and toward “empirical microeconomics,” the analysis of how things work in the real world, often arranging complex experiments or exploiting large sets of data. That kind of work requires lots of research assistants, work across disciplines including fields like sociology and computer science, and the use of advanced computational techniques unavailable a generation ago....Less clear is whether the agglomeration of economic stars at Stanford will ever amount to the kind of coherent school of thought that has been achieved at some other great universities (e.g. Milton Friedman's The Chicago School neoclassical focus on efficiency of markets and the risks of government intervention and M.I.T.’s economics' Keynesian tradition)
economics  economists  empiricism  in_the_real_world  Stanford  MIT  Harvard  Colleges_&_Universities  recruiting  poaching  movingonup  rankings  machine_learning  cross-disciplinary  massive_data_sets  data  uChicago  microeconomics  Keynesian  Chicago_School 
september 2015 by jerryking
Canada a new technology hotbed? If so, we need to commit to it - The Globe and Mail
KHANJAN DESAI
Contributed to The Globe and Mail
Published Friday, Aug. 14, 2015

the end goal should be about making Canada the centre of gravity for another ecosystem.

In the words of Wayne Gretzky, we need to skate to where the puck is going to be, not where it has been.

The hardware opportunity has already become mainstream, and other ecosystems have already pounced on it, but Canada isn’t far behind. We are creating companies to solve complex problems in the health-medical and wearable-technology spaces, and applying complex nanotechnologies to revolutionize conventional markets.

Nanotechnology engineering graduates from the University of Waterloo are now starting companies at the same pace as any other program at the university, and a venture fund for innovations exclusively in the quantum domain was just created in Waterloo. Wearable-technology and machine-learning startups are booming, with the University of Toronto alumni leading the charge, and we’re just getting started. The Creative Destruction Lab is launching a separate stream to support machine-learning startups and Velocity recently launched the Velocity Foundry program to house startups that build physical products.

If Canada is going to become the hotbed for wearable technology or create a Quantum Valley in the Waterloo region, we need to commit to it. It’s much better to be extremely good at one thing than be mediocre at many things.
Neverfrost  start_ups  uWaterloo  uToronto  Silicon_Valley  CDL  Canada  Y_Combinator  ecosystems  wearables  nanotechnology  machine_learning  Velocity  Pablo_Picasso  widgets  Kitchener-Waterloo  quantum_computing  complex_problems 
august 2015 by jerryking
What to Learn in College to Stay One Step Ahead of Computers - NYTimes.com
MAY 22, 2015 | NYT | By ROBERT J. SHILLER.

The successful occupations, by this measure, shared certain characteristics: People who practiced them needed complex communication skills and expert knowledge. Such skills included an ability to convey “not just information but a particular interpretation of information.” They said that expert knowledge was broad, deep and practical, allowing the solution of “uncharted problems.”

These attributes may not be as beneficial in the future. But the study certainly suggests that a college education needs to be broad and general, and not defined primarily by the traditional structure of separate departments staffed by professors who want, most of all, to be at the forefront of their own narrow disciplines.....In a separate May 5 statement, Prof. Sean D. Kelly, chairman of the General Education Review Committee, said a Harvard education should give students “an art of living in the world.”

But how should professors do this? Perhaps we should prepare students for entrepreneurial opportunities suggested by our own disciplines. Even departments entirely divorced from business could do this by suggesting enterprises, nonprofits and activities in which students can later use their specialized knowledge....I continue to update the course, thinking about how I can integrate its lessons into an “art of living in the world.” I have tried to enhance my students’ sense that finance should be the art of financing important human activities, of getting people (and robots someday) working together to accomplish things that we really want done.
Robert_Shiller  Yale  Harvard  college-educated  education  students  automation  machine_learning  Colleges_&_Universities  finance  continuing_education  continuous_learning  Communicating_&_Connecting  indispensable  skills  Managing_Your_Career  21st._century  new_graduates  interdisciplinary  curriculum  entrepreneurship  syllabus  interpretation  expertise  uncharted_problems 
may 2015 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
Amazon to Sell Predictions in Cloud Race Against Google and Microsoft - NYTimes.com
By QUENTIN HARDY APRIL 9, 2015

Amazon Web Services announced that it was selling to the public the same kind of software it uses to figure out what products Amazon puts in front of a shopper, when to stage a sale or who to target with an email offer.

The techniques, called machine learning, are applicable for technology development, finance, bioscience or pretty much anything else that is getting counted and stored online these days. In other words, almost everything.
Quentin_Hardy  Amazon  Google  machine_learning  cloud_computing  AWS  Microsoft  Azure  predictions  predictive_analytics  predictive_modeling  automated_reasoning 
april 2015 by jerryking
Wealth Managers Enlist Spy Tools to Map Portfolios - NYTimes.com
AUG. 3, 2014 | NYT | QUENTIN HARDY.

Karen White, Addepar’s president and chief operating officer, says a typical customer has investments at five to 15 banks, stockbrokers or other investment custodians.

Addepar charges based on how much data it is reviewing. Ms. White said Addepar’s service typically started at $50,000, but can go well over $1 million, depending on the money and investment variables involved.

And in much the way Palantir seeks to find common espionage themes, like social connections and bomb-making techniques, among its data sources,[jk: traffic_analysis] Mr. Lonsdale has sought to reduce financial information to a dozen discrete parts, like price changes and what percentage of something a person holds.

As a computer system learns the behavior of a certain asset, it begins to build a database of probable relationships, like what a bond market crisis might mean for European equities. “A lot of computer science, machine learning, can be applied to that,” Mr. Lonsdale said. “There are lessons from Palantir about how to do this.”
wealth_management  software  valuations  Quentin_Hardy  Addepar  Palantir  money_management  social_connectivity  machine_learning  correlations  portfolio_management  investment_custodians  tools 
august 2014 by jerryking
Sponsor Generated Content: The State of the Data Economy
June 23, 2014

Where the Growth is
So for many companies right now, the core of the data economy is a small but growing segment—the information two billion-plus global Internet users create when they click "like" on a social media page or take action online. Digital customer tracking—the selling of “digital footprints” (the trail of information consumers leave behind each time they surf the Web)—is now a $3 billion segment, according to a May 2014 Outsell report. At the moment, that's tiny compared to the monetary value of traditional market research such as surveys, forecasting and trend analysis. But digital customer tracking "is where the excitement and growth is," says Giusto.

Real-time data that measures actions consumers are actually taking has more value than study results that rely on consumer opinions. Not surprising, businesses are willing to pay more for activity-based data.

Striking it Richer
Outsell Inc.'s analyst Chuck Richard notes that the specificity of data has a huge affect on its value. In days past, companies would sell names, phone numbers, and email addresses as sales leads. Now, data buyers have upped the ante. They want richer data—names of consumers whose current "buying intent" has been analyzed through behavioral analytics. Beyond the “who,” companies want the “what” and “when” of purchases, along with “how” best to engage with prospects.
"Some companies are getting a tenfold premium for data that is very focused and detailed," Richard says. "For example, if you had a list of all the heart specialists in one region, that’s worth a lot."

Tapping into New Veins
Moving forward, marketers will increasingly value datasets that they can identify, curate and exploit. New technology could increase the value of data by gleaning insights from unstructured data (video, email and other non-traditional data sources); crowdsourcing and social media could generate new types of shareable data; predictive modeling and machine learning could find new patterns in data, increasing the value of different types of data.

Given all this, the data economy is sure to keep growing, as companies tap into new veins of ever-richer and more-specific data.
data  data_driven  SAS  real-time  digital_footprints  OPMA  datasets  unstructured_data  data_marketplaces  value_creation  specificity  value_chains  intentionality  digital_economy  LBMA  behavioural_data  predictive_modeling  machine_learning  contextual  location_based_services  activity-based  consumer_behavior 
july 2014 by jerryking
Davos diary: A new angst settles over the world's elites - The Globe and Mail
John Stackhouse - Editor-in-Chief

Davos, Switzerland — The Globe and Mail

Published Friday, Jan. 24 2014,

Another machine revolution is upon us. There is a new wave forming behind the past decade’s surge of mobile technology, with disruptive technologies like driverless cars and automated personal medical assistants that will not only change lifestyles but rattle economies and change pretty much every assumption about work....For all the talk of growth, though, the global economy is also in an employment morass that has the smartest people in the room humbled and anxious. The rebound is not producing jobs and pay increases to the degree that many of them expected. Most governments are tapped out, fiscally, and can only call on the private sector – “the innovators” – to do more....If a 3-D printer can kneecap your construction industry, or an AI-powered sensor put to pasture half your nurses, what hope is there for old-fashioned job creation?

The new digital divide – it used to be about access, now it’s about employment – stands to further isolate the millions of long-term jobless people in Europe and North America, many of whom have left the workforce and won’t be getting calls when jobs come back.... Say’s Law--a theory that says successful products create their own demand.
Davos  John_Stackhouse  Say’s_Law  Eric_Schmidt  digital_disruption  joblessness  fault_lines  Google  McKinsey  creative_destruction  Joseph_Schumpeter  unemployment  machine_learning  disruption  autonomous_vehicles  bots  chatbots  artificial_intelligence  personal_assistants  virtual_assistants  job_creation  global_economy 
january 2014 by jerryking
Why Monsanto Spent $1 Billion on Climate Data - Modern Farmer
By Dan Mitchell on October 2, 2013

Climate Corporation doesn’t limit itself to weather data. As politicians, pundits, and people on the Internet continue to argue over whether climate change is real, the insurance industry has for years been operating under the assumption that it is. So Climate Corporation uses data from major climate-change models — the very ones that are under constant assault by doubters — in its calculations.

Climate Corporation manages an eye-popping 50 terabytes of live data, all at once. Besides climate-change models, data is collected from regular old weather forecasts and histories, soil observations, and other sources. The company collects data from 2.5 million separate locations. Given these numbers, it shouldn’t be surprising that Climate Corporation is basically alone in this market. The barriers to entry are immense.

The company makes use of “machine learning” —a kind of artificial intelligence. That’s the technology behind, for example, determining which of your incoming email messages are spam —except in this case the tech is much, much more sophisticated. Each new bit of data that’s entered into the system — rainfall in Douglas County Nebraska, say, or the average heat index in Louisiana’s Winn Parish —helps it learn, and more accurately forecast what will happen in the future.
Monsanto  Climate_Corporation  weather  crop_insurance  insurance  massive_data_sets  data_driven  machine_learning  artificial_intelligence 
october 2013 by jerryking
G.E. Looks to Industry for the Next Digital Disruption - NYTimes.com
By STEVE LOHR
Published: November 23, 2012

G.E. resides in a different world from the consumer Internet. But the major technologies that animate Google and Facebook are also vital ingredients in the industrial Internet — tools from artificial intelligence, like machine-learning software, and vast streams of new data. In industry, the data flood comes mainly from smaller, more powerful and cheaper sensors on the equipment.

Smarter machines, for example, can alert their human handlers when they will need maintenance, before a breakdown. It is the equivalent of preventive and personalized care for equipment, with less downtime and more output.... Today, G.E. is putting sensors on everything, be it a gas turbine or a hospital bed. The mission of the engineers in San Ramon is to design the software for gathering data, and the clever algorithms for sifting through it for cost savings and productivity gains. Across the industries it covers, G.E. estimates such efficiency opportunities at as much as $150 billion.

Some industrial Internet projects are already under way. First Wind, an owner and operator of 16 wind farms in America, is a G.E. customer for wind turbines. It has been experimenting with upgrades that add more sensors, controls and optimization software.

The new sensors measure temperature, wind speeds, location and pitch of the blades. They collect three to five times as much data as the sensors on turbines of a few years ago, said Paul Gaynor, chief executive of First Wind. The data is collected and analyzed by G.E. software, and the operation of each turbine can be tweaked for efficiency. For example, in very high winds, turbines across an entire farm are routinely shut down to prevent damage from rotating too fast. But more refined measurement of wind speeds might mean only a portion of the turbines need to be shut down. In wintry conditions, turbines can detect when they are icing up, and speed up or change pitch to knock off the ice.

Upgrades on 123 turbines on two wind farms have so far delivered a 3 percent increase in energy output, about 120 megawatt hours per turbine a year. That translates to $1.2 million in additional revenue a year from those two farms, Mr. Gaynor said.

“It’s not earthshaking, but it is meaningful,” he said. “These are real commercial investments for us that make economic sense now.” ...
breakdowns  GE  Industrial_Internet  disruption  Steve_Lohr  sensors  artificial_intelligence  machine_learning  digital_disruption  downtime 
november 2012 by jerryking

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