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Google and Harvard release COVID-19 prediction models

August 3, 2020 Big Data

In partnership with the Harvard Global Health Institute, Google today released the COVID-19 Public Forecasts, a set of models that provide projections of COVID-19 cases, deaths, ICU utilization, ventilator availability, and other metrics over the next 14 days for U.S. counties and states. The models are trained on public data such as those from Johns Hopkins University, Descartes Lab, and the United States Census Bureau, and Google says they’ll continue to be updated with guidance from its collaborators at Harvard.

The COVID-19 Public Forecasts are intended to serve as a resource for first responders in health care, the public sector, and other affected organizations preparing for what lies ahead, Google says. They allow for targeted testing and public health interventions on a county-by-county basis, in theory enhancing the ability of those who use them to respond to the rapidly evolving COVID-19 pandemic. For example, health care providers could incorporate the forecasted number of cases as a datapoint in resource planning for PPE, staffing, and scheduling. Meanwhile, state and county health departments could use the forecast of infections to help inform testing strategies and identify areas at risk of outbreaks.

To create the COVID-19 Public Forecasts, Google says its researchers developed a novel time-series machine learning approach that combines AI with a clever epidemiological foundation. By design, the models are trained on public data and leverage an architecture that allows researchers to dive into relationships the models have identified and interpret why they make certain forecasts. They’ve also been evaluated to ensure predictions with respect to people of color — who have been hardest hit by COVID-19, with disproportionately high rates of cases and deaths — aren’t wildly skewed or otherwise misleading.

“We observe that our models produce meaningfully lower absolute error and normalized (relative) error as compared to the comparison model across predominantly African American, Hispanic, and white counties,” Google researchers wrote in a fairness analysis of the COVID-19 prediction models. “Our models optimize for high accuracy across all U.S. counties to provide the best overall forecast for most communities.”

The COVID-19 Public Forecasts are free to query in BigQuery as part of the service’s 1TB-per-month free tier or to download as comma-separated value files (CSVs). Additionally, they’re available through Google’s Data Studio dashboard and its National Response Portal.

All bytes processed in queries against the data set will be zeroed out, Google says, but data joined with the data set will be billed at the normal rate to prevent abuse. After September 15, queries over the forecast sets will revert to the normal Google Cloud billing rate.

The release of the COVID-19 Public Forecasts follows the launch of Google’s COVID-19 Public Datasets program, which hosts a repository of public data sets relating to the crisis and makes them easier to access and analyze. Corpora within the COVID-19 Public Datasets program includes the Johns Hopkins Center for Systems Science and Engineering (JHU CSSE) data set, Global Health Data from the World Bank, and OpenStreetMap data, all of which are stored for at no cost on Google Cloud.

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Big Data – VentureBeat

AI Weekly: Big Tech’s antitrust reckoning is a cautionary tale for the AI industry

August 3, 2020 Big Data

This week, as the heads of four of the largest and most powerful tech companies in the world were called before a virtual congressional antitrust hearing to answer inquiries into how they built and run their respective behemoths, you could see that the bloom on the rose of Big Tech has faded.

Facebook’s Mark Zuckerberg, once the rascally college dropout boy genius you loved to hate, still doesn’t seem to grasp the magnitude of the problem of globally destructive misinformation and hate speech on his platform. Tim Cook struggles to defend how Apple takes a 30% cut from some of its app store developers’ revenue — a policy he didn’t even establish that is a vestige of Apple’s mid-2000s vise grip on the mobile app market. The plucky young upstarts who founded Google are both middle-aged and have stepped down from executive roles, quietly fading away while Alphabet and Google CEO Sundar Pichai runs the show. And Jeff Bezos wears the untroubled visage of the world’s richest man.

Amazon, Apple, Facebook, and Google all created tech products and services that have undeniably changed the world, some in ways that are undeniably good. But as these tech titans moved fast and broke things, they also largely excused themselves from asking difficult ethical questions, from how they built their business empires to the impacts their products and services have on the people who use them.

As AI continues to lead the next wave of transformative technology, skating over these difficult questions is a mistake the world can’t afford to repeat. What’s more, AI technologies won’t actually work properly unless companies address the issues at their heart.

Smart and ruthless was the tradition of Big Tech, but AI requires people to be smart and wise. Those working in AI have to not only ensure the efficacy of what they make, but holistically understand the potential harms for people AI tech impacts. That’s a more mature and just way of building world-changing technologies, products, and services. Fortunately, many prominent voices in AI are leading the field down that path.

This week’s best example was the widespread reaction to a service called Genderify, which promised to use natural language processing (NLP) to help companies identify customers’ gender using only their name, username, or email address. The entire premise is absurd and problematic, and when AI folks got ahold of it to put it through its paces, they predictably found it to be terribly biased (which is to say, broken).

Genderify was such a bad joke that it almost seemed like some kind of performance art. In any case, it was laughed off the internet. Just a day or so after it was launched, the Genderify site, Twitter account, and LinkedIn page were gone.

It’s frustrating to many in the field that such ill-conceived and poorly executed AI offerings keep popping up. But the swift and wholesale deletion of Genderify illustrates the power and strength of this new generation of principled AI researchers and practitioners.

The burgeoning AI sector is already experiencing the kind of reckoning Big Tech is only facing after decades. Other recent examples include an outcry over a paper that promised to use AI to identify criminality from people’s faces (really just AI phrenology), which led to the paper being withdrawn from publication. Landmark studies on bias in facial recognition have led to bans and moratoriums on the technology’s use in several U.S. cities, as well as a raft of legislation to eliminate or combat its potential abuses. Fresh research is finding intractable problems with bias in well-established data sets like 80 Million Tiny Images and the legendary ImageNet — and leading to immediate (if overdue) change. And there’s more.

Although advocacy groups play a role in pushing for changes and posing tough questions, the authority for such inquiry and the research-based proof is coming from people inside the field of AI — ethicists, researchers looking for ways to improve AI techniques, and actual practitioners.

There is, of course, an immense amount of work to be done and many more battles ahead as AI fuels the next dominant set of technologies. Look no further than problematic AI in surveillance, military, the courts, employment, policing, and more.

But seeing tech giants like IBM, Microsoft, and Amazon pull back on massive investments in facial recognition is a sign of progress. It doesn’t actually matter whether their actions are narrative cover for a capitulation to other companies’ market dominance, a calculated move to avoid potential legislative punishment, or just a PR stunt. For whatever reason, these companies acknowledged the value of slowing down and reducing damage rather than continuing to “move fast and break things.”

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Big Data – VentureBeat

Parameter estimation using LogLikelihood

August 2, 2020 BI News and Info

I’m trying to understand Likelihood methods with parameter estimation in mind. To this end I am trying to construct small examples that I can play with. Let’s say I have some data which I know (or suspect follows the function)
$ $ f(x) = (x + x_0)^{2}$ $
and I want to find out the value of the parameter $ x_{0}$ and the associated error using likelihood methods.

Let us then make some pretend experimental data:

f[x0_, x_] := (x + x0)^2
  
ExperimentData = Table[{x, f[-1.123, x] + RandomVariate[NormalDistribution[0, 0.25]]}, {x, 0, 3, 0.1}];

Then let us construct some test data where I “guess” my parameter $ x_{0}$ . I replace $ x_{0}$ with the parameter $ \theta$ to represent my test value:

TestData = 
Table[
        {\[Theta], Table[{x, f[\[Theta], x]}, {x, 0, 3, 0.1 }]},
        {\[Theta], 0.5, 1.6, 0.1}
     ];

How can I use LogLikelihood to make to make a parameter estimation of $ x_{0}$ . Using my TestData? The motivation is if I cannot construct a pure function, for example if I generate my test data from a numeric intergeneration.

My approach so far is to maximise the log-likelihood of the “residuals”

X = ExperimentData[[All, 2]];
MLLTest = 
  Table[
        \[Theta] = TestData[[i, 1]];        
        F = TestData[[i, 2]][[All, 2]];
        MLL = 
    FindMaximum[
      LogLikelihood[NormalDistribution[\[Mu], \[Sigma]], 
       X - F], {{\[Mu], 0}, {\[Sigma], 0.25}}][[1]];
        {\[Theta], MLL},
        {i , 1, Length[TestData]}
    ];

Then if I plot the Maximum Log-Likelihood as a function of my guess parameter $ \theta$ .

However this is clearly wrong, so I think I misunderstand something about the Log-Likeihood in this context.

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Recent Questions – Mathematica Stack Exchange

Antitrust experts weigh in on breaking up Amazon, Apple, Facebook, and Google

August 2, 2020 Big Data

Gary Reback is perhaps best known as the lawyer who helped convince the U.S. Department of Justice to bring an antitrust lawsuit against Microsoft in the 1990s. He watched the majority of the House Judiciary Committee hearing earlier this week with Amazon CEO Jeff Bezos, Apple CEO Tim Cook, Google CEO Sundar Pichai, and Facebook CEO Mark Zuckerberg. Reback still remembers a Senate hearing that put pressure on the government and led to the filing of United States v. Microsoft Corporation in 1998. That’s why he focused more on lines of questioning this week than on answers from CEOs of companies that have amassed unprecedented wealth and power.

“To me, the core turning point was right at the beginning, basically,” he said, pointing to sharp and detailed questioning in reference to a range of documents obtained in the course of the committee’s assessment.

The question on Wednesday was whether tech giants have grown too powerful, and if you watched the hearings, many members of Congress leave no doubt that they believe the answer is yes. In his opening remarks, for example, U.S. House Antitrust subcommittee chair David Cicilline (D-RI) said tech giants enjoy the power to pick winners and losers in the private market and that consumers have “no escape from surveillance” because there are no alternatives. He added “What’s at stake is whether we let ourselves be governed by private monopolies.”

VentureBeat spoke with Reback and other antitrust experts about what stood out during the hearing and what should happen next. Each favors some form of action or legislation to address the litany of anticompetitive practice allegations that emerged over nearly six hours of testimony.

‘We may have run out of options’

During the hearing, a number of Democratic lawmakers argued that Washington needs to take steps to ensure a fair market. Rep. Jim Sensenbrenner (R-WI) insisted that existing antitrust law is fine but needs enforcement. Reback pointed out that convoluted antitrust law makes it difficult to bring cases to court. He said Sensenbrenner was correct in the sense that new legislation might not have been necessary if action had been taken earlier, but he stressed that there isn’t much antitrust enforcement happening today.

Reback blamed the outsized growth of tech giants in part on the Obama administration’s failure to take action. Nobody who understands antitrust law would suggest implementing restrictions cavalierly, Reback said, but regulators have already waited so long that it’s tough to see any viable alternative to reform.

“By not doing anything for so long, we may have run out of options,” Reback said. “The way anticompetitive practices work, if you deal with them quickly, if you deal with them in a rifle shot, you fix it and the companies go on competing and everything’s fine. But if you don’t fix it, then market power builds up and builds up and, in the case we have here, there are no competitors basically for at least three of these companies. They bought their competitors, put them out of business, whatever. And so that then is a problem in terms of how you expect a free market to fix this.”

For context: Apple reported nearly $ 60 billion in profits and became the world’s most valuable company on Friday. On Thursday, Amazon reported revenue up 40% to $ 88.9 billion, while Google’s parent company Alphabet and Facebook reported revenue above analysts’ estimates.

Each of these companies has been accused of anticompetitive practices that harm democracy, consumers, and small businesses. And each enjoys majority control in a number of industry verticals in the U.S. (and much of the world). Areas of dominance include:

Facebook’s control of social media platforms Facebook and Instagram
Apple and Google’s control of mobile app markets
Google’s control in search
Facebook and Google’s control of online advertising
Amazon’s online shopping platform, which records nearly 75% of online sales
Facebook’s and Google’s control of digital advertising, which has hurt journalism industry revenue

Stacy Mitchell is codirector of the Institute for Local Self-Reliance (ILSR), a nonprofit organization that champions distributed, local control over corporate power. She found the committee to be very knowledgeable about how the four companies conduct business and argued that Amazon has a monopolistic hold on small businesses. Last week, Mitchell resigned her position as a fellow at the Yale University Thurmond Arnold Project, a group studying antitrust issues, after finding out that director Fiona Scott Morton is a paid advisor to Amazon and Apple. ILSR also researches Amazon’s anticompetitive practices and advocates breaking the ecommerce giant into separate companies.

Mitchell watched the hearing with interest.

“I found it thrilling, really,” she said. “Like, ‘Oh, this is what it looks like when a democratic government actually addresses fundamental issues of power and control and … takes an aggressive stance toward CEOs and really in some ways alters the power dynamics.’”

She said an exchange between Rep. Mary Gay Scanlon (D-PA) and Jeff Bezos during this week’s antitrust hearing demonstrated the need for this kind of intervention. Under questioning, Bezos confirmed that the Buy Box algorithm favors products shipped with Prime and Amazon fulfillment services. Bezos also said Amazon ties the use of its fulfillment services to winning the Buy Box. Mitchell highlighted the obvious pressure this puts on sellers. “This is a pretty serious matter because effectively what Amazon has done is it has compelled sellers to use Amazon’s fulfillment services in order to get the kind of placement on the site that actually results in sales.”

Scanlon also cited a report ILSR released last week that found Amazon pockets about 30% of sales from independent sellers, up from 19% five years ago. The fee brought in nearly twice the revenue Amazon Web Services did in 2019, and Mitchell said Amazon uses the money to subsidize other company divisions and dominate new industry verticals. That’s part of why ILSR supports breaking major Amazon divisions into individual companies.

“Much of its power, and abuse of its power, comes from it being able to leverage one part of its operation to compel someone to do something in another part of its operation, like we were talking about with the fulfillment. A lot of the abuse lives in the in-between, and I think that’s a pretty powerful case,” Mitchell said.

In testimony Wednesday, Cicilline noted that Amazon internally refers to third-party sellers as “internal competitors.”

Bezos said Wednesday that he “cannot guarantee” Amazon employees have not used data about independent sellers to create the company’s own products, which would be a violation of company policy. Lawmakers argued this practice undercuts small businesses that have no option but to sell their goods on Amazon’s online marketplace. The Wall Street Journal reported in April that Amazon employees used data about individual sellers to create competing Amazon products.

Anticompetitive behavior accusations have also been lobbed at Amazon by startups who participated in its Alexa Fund, including Nucleus, which says Amazon stole its smart display and home intercom concept. AI startup DefinedCrowd and more than two dozen startup founders detailed similar practices last week. Rep. Joe Neguse (D-CO) referred to this practice as an “innovation kill zone” that puts fear into smaller businesses and makes fair competition impossible.

During the hearing, Apple, Facebook, and Google were also accused of stealing ideas from other companies. (Each of the tech giants has growing investment fund arms.)

“Three of the companies [Amazon, Facebook, and Google] basically have the same set of accusations against them, which is that competitors come on your platform, you take their information, you preference your own results, compete against them, drive them out of business, or buy them cheap,” Reback said.

Big tech too big for free markets?

Both Mitchell and Reback repeatedly emphasized the contrast between the tone of the hearing Wednesday and congressional hearings held in 2018 following the Cambridge Analytica scandal with Mark Zuckerberg. In that earlier hearing, members of Congress demonstrated an inability to understand technology or how Facebook applications work.

What seemed to be missing back then, Mitchell and Reback said — but could be seen in exchanges Wednesday — was the ability to ask followup questions. Without a grasp of how a company operates, or its products and services, any line of questioning will yield little beyond the initial question. CEOs can then run out a portion of the five minutes allotted to each elected official by explaining technology or offering platitudes about how much their companies value things like privacy.

Mitchell said the recent antitrust hearing had a different tone. “This clearly was proceeding as part of an investigation … and sort of had the feel of ‘We’ve gathered all this data or information, and the process is that we’re going to give you a chance to answer for it,’” Mitchell said.

Sally Hubbard is director of enforcement strategy at the Open Markets Institute and testified before a House Judiciary subcommittee on antitrust at its first big tech hearing in June 2019.

Last summer, Hubbard testified that Facebook and Google use their digital advertising monopoly in multiple ways to disadvantage independent journalism and newspapers that are deemed competitors. Both companies directly compete with the free press for attention while controlling the main traffic highways of search engines, smartphones, and social media. She said the hearing Wednesday in part restored her faith in democracy and institutions and gave her hope that meaningful action is possible.

“To me, it was the most impressive hearing that I can remember seeing in my lifetime of the members of Congress being so highly prepared, so in-the-know about complex issues, and willing to take on the most powerful companies in America and the world,” Hubbard said.

Break up big tech?

Hubbard said she could see the rollback of acquisitions like Google’s AdMob and DoubleClick or Facebook’s acquisition of Instagram and WhatsApp as part of potential solutions, but she pointed out that such measures aren’t the only option. She wants lawsuits filed when anticompetitive behavior like the kind described in the hearing occurs, ongoing monitoring of exclusionary conduct, and scrutiny of mergers, especially acquisitions of small companies that may be viewed as a competitive threat. She also recommended passing legislation to support Senator Elizabeth Warren’s (D-MA) suggestion that you can own a platform or sell goods on it, but you can’t do both.

Reback agrees.

“If we can’t get antitrust enforcement on [these companies] … we need legislation like the type Elizabeth Warren is suggesting, where if you own the platform then you can’t own anything on it. That would be a big change, but if there’s no way to police the [current] situation so that competitors get a fair shot and you don’t run everybody else out of business by using their data against them, if there’s no way to police that, then you don’t have any alternative but new legislation,” he said.

On the topic of the digital ad market’s negative impact on journalism in the United States, Rep. Pramila Jayapal (D-WA) talked Wednesday with Google and Alphabet CEO Sundar Pichai about Google’s AdExchange operating in a way that resembles insider trading, adding in the context of independent news outlets.

There’s nothing revolutionary about stopping a single company from controlling both sides of the market to prevent adverse outcomes that resemble insider trading, and it’s past time to bring the same concepts to the digital economy, Hubbard said.

“The point that I like to make a lot is that the online world somehow has avoided regulation, but it’s not the cute little internet of the ’90s anymore. The online world has basically eaten the offline world, so if we don’t have any rules or regulations governing the online world or fair competition, then you don’t have them in the offline world either,” Hubbard said, adding that the hearing will add momentum to other antimonopoly efforts and will help educate the public.

Mitchell concluded that the hearing itself was just one step in the process and the committee’s final report will convey the actual substance of the debate.

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Big Data – VentureBeat

Teradata Supports Volkswagen Industrial Cloud as Partner

July 31, 2020 BI News and Info

SAN DIEGO and MUNICH, GERMANY – July 23, 2020 – Teradata (NYSE: TDC), the cloud data and analytics company, today announced that it will be a partner of the Volkswagen Industrial Cloud.

The Industrial Cloud – which is being developed by Volkswagen together with Amazon Web Services (AWS) – is an open IoT platform combining the data of all machines, plants and systems from all facilities of the Volkswagen Group. Teradata will support Volkswagen by providing cloud-based data analytics to optimize production processes and drive productivity increases in the plants.

“We are delighted to partner with Volkswagen as part of this open industry platform, joining other leading companies in shaping the future of digital production,” says Sascha Puljic, Vice President, Central Europe at Teradata. “Our cloud-based data and analytics solution provides comprehensive data intelligence and supports Volkswagen in fully leveraging the value of data in production to increase efficiency and quality.”

The aim of the Industrial Cloud is to integrate companies from the entire value chain of the automotive industry, creating a vast partner network that shares data and services so that all partners benefit – including suppliers, technology providers, system integrators, independent software vendors (ISVs), logistics providers and manufacturers.

In the future, all sensor data and other information will be integrated in the cloud-based platform and analyzed with intelligent algorithms. Relevant data will be made available to partners in the Industrial Cloud, creating enormous potential for optimizing production and logistics processes.

To this endeavor Teradata brings its extensive experience in industrial IoT data analysis, as well as its analytics platform – Teradata Vantage – which is available on AWS. Data analysis from the cloud is used throughout production at Volkswagen to, for example: optimize yield, implement data-based quality assurance and enable predictive maintenance.

Optimizing Production and Driving Productivity

One initial use case that showcases Teradata’s role in supporting Volkswagen’s efforts to improve production processes, is analyzing data from welding procedures in manufacturing:

First, spot welding data and additional metadata is integrated and analyzed in Teradata Vantage.
Using analytical models that include machine learning, Teradata classifies and refines the data based on various quality characteristics.
Based on the results, the downstream inspection processes can be optimized, and required rework can be specifically assigned.
The outcome is confident, documented quality of the manufactured parts as well as increased efficiency of the production and quality assurance processes.

Another example of Teradata’s future contribution comes from a set of preliminary projects being explored at the Volkswagen Group: Teradata was able to identify additional efficiency potential in production processes, using AI (artificial intelligence). The team analyzed sequence optimization for specific production plants, while also developing quality predictions for manufacturing processes and products.

Such use cases, driven by Teradata expertise in the manufacturing space, demonstrate the power of integrating data to elevate production processes and increase productivity.

About Volkswagen Group

The Group comprises twelve brands from seven European countries: Volkswagen Passenger Cars, Audi, SEAT, ŠKODA, Bentley, Bugatti, Lamborghini, Porsche, Ducati, Volkswagen Commercial Vehicles, Scania and MAN. In addition, the Volkswagen Group offers a wide range of financial services, including dealer and customer financing, leasing, banking and insurance activities, and fleet management.

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Teradata United States

Researchers examine the ethical implications of AI in surgical settings

July 31, 2020 Big Data

A new whitepaper coauthored by researchers at the Vector Institute for Artificial Intelligence examines the ethics of AI in surgery, making the case that surgery and AI carry similar expectations but diverge with respect to ethical understanding. Surgeons are faced with moral and ethical dilemmas as a matter of course, the paper points out, whereas ethical frameworks in AI have arguably only begun to take shape.

In surgery, AI applications are largely confined to machines performing tasks controlled entirely by surgeons. AI might also be used as a clinical decision support system, and in these circumstances, the burden of responsibility falls on the human designers of the machine or AI system, the coauthors argue.

Privacy is a foremost ethical concern. AI learns to make predictions from large data sets — specifically patient data, in the case of surgical systems — and it’s often described as being at odds with privacy-preserving practices. The Royal Free London NHS Foundation Trust, a division of the U.K.’s National Health Service based in London, provided Alphabet’s DeepMind with data on 1.6 million patients without their consent. Separately, Google, whose health data-sharing partnership with Ascension became the subject of scrutiny last November, abandoned plans to publish scans of chest X-rays over concerns that they contained personally identifiable information.

Laws at the state, local, and federal levels aim to make privacy a mandatory part of compliance management. Hundreds of bills that address privacy, cybersecurity, and data breaches are pending or have already been passed in 50 U.S. states, territories, and the District of Columbia. Arguably the most comprehensive of them all — the California Consumer Privacy Act — was signed into law roughly two years ago. That’s not to mention the Health Insurance Portability and Accountability Act (HIPAA), which requires companies to seek authorization before disclosing individual health information. And international frameworks like the EU’s General Privacy Data Protection Regulation (GDPR) aim to give consumers greater control over personal data collection and use.

But the whitepaper coauthors argue measures adopted to date are limited by jurisdictional interpretations and offer incomplete models of ethics. For instance, HIPAA focuses on health care data from patient records but doesn’t cover sources of data generated outside of covered entities, like life insurance companies or fitness band apps. Moreover, while the duty of patient autonomy alludes to a right to explanations of decisions made by AI, frameworks like GDPR only mandate a “right to be informed” and appear to lack language stating well-defined safeguards against AI decision making.

Beyond this, the coauthors sound the alarm about the potential effects of bias on AI surgical systems. Training data bias, which concerns the quality and representativeness of data used to an AI system, could dramatically impact a preoperative risk stratification prior to surgery. Under-representation of demographics might also cause inaccurate assessments, driving flawed decisions such as whether a patient is treated first or offered extensive ICU resources. And contextual bias, which occurs when an algorithm is employed outside the context of its training, could result in a system ignoring non-trivial caveats like whether a surgeon is right- or left-handed.

Methods to mitigate this bias exist, including ensuring variance in the data set, sensitivity to overfitting on training data, and having humans-in-the-loop to examine new data as it’s deployed. They coauthors advocate the use of these and transparency broadly to prevent patient autonomy from being undermined. “Already, an increasing reliance on automated decision-making tools has reduced the opportunity of meaningful dialogue between the healthcare provider and patient,” they wrote. “If machine learning is in its infancy, then the subfield tasked with making its inner workings explainable is so embryonic that even its terminology has yet to recognizably form. However, several fundamental properties of explainability have started to emerge … [that argue] machine learning should be
simultaneous, decomposable, and algorithmically transparent.”

Despite AI’s shortcomings, particularly in the context of surgery, the coauthors argue the harms AI can prevent outweigh the adoption cons. For example, in thyroidectomy, there’s risk of permanent hypoparathyroidism and recurrent nerve injury. It might take thousands of procedures with a new method to observe statistically significant changes, which an individual surgeon might never observe — at least not in a short time frame. However, a repository of AI-based analytics aggregating these thousands of cases from hundreds of sites would be able to discern and communicate those significant patterns.

“The continued technological advancement in AI will sow rapid increases in the breadths and depths of their duties. Extrapolating from the progress curve, we can predict that machines will become more autonomous,” the coauthors wrote. “The rise in autonomy necessitates an increased focus on the ethical horizon that we need to scrutinize … Like ethical decision-making in current practice, machine learning will not be effective if it is merely designed carefully by committee – it requires exposure to the real world.”

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Big Data – VentureBeat

Alphabet revenue dropped in Q2 2020, the first decline since going public

July 31, 2020 Big Data

(Reuters) — Google parent Alphabet’s quarterly sales fell for the first time in its 16 years as a public company, but the decline was less than expected as many advertisers stuck with the most popular online search engine during the pandemic.

Shares of Alphabet fell 1.2% to $ 1,518.85 after it released the second-quarter results. The stock had rebounded early Thursday to this year’s pre-pandemic high of about $ 1,525.

With its mostly free tools for web browsing, video watching and teleconferencing, Google unit has become a larger part of many consumers’ lives during the pandemic as lockdown orders force people to rely on the internet for work and entertainment.

But advertisers on Google have suffered mass layoffs and other cutbacks during the pandemic, and marketing budgets are often the first to get slashed especially by big clients like travel search engines, airlines and hotels.

Google’s ads business has long trended with the broader economy, and the U.S. economy contracted at its steepest pace since the Great Depression in the second quarter, the Commerce Department said on Thursday.

Google appeared to weather the slowdown better than before, as the pandemic has made the internet more attractive to advertisers than TV, radio and other avenues.

“This quarter, we saw the early signs of stabilization, as users returned to commercial activity online,” Alphabet Chief Executive Sundar Pichai told analysts on Thursday. “Of course, the economic climate remains fragile.”

Alphabet’s overall second-quarter revenue was $ 38.3 billion, down 2% from the year-ago period. Analyst’s tracked by Refinitiv, on average, had estimated a 4% decline to $ 37.367 billion.

The sales decline was the first since the company went public in 2004 and the worst performance since its 2.9% growth during the Great Recession in 2009.

About 66% of Alphabet’s revenue came from Google search and YouTube ads, 12% from ads sold on partner properties online, 8% from its cloud business and 14% from its mobile app store and about a dozen other smaller businesses.

It has adjusted by slowing expense growth. Alphabet’s total costs and expenses rose about 7% from a year ago to $ 31.9 billion in the second quarter, compared with a 12% jump a quarter ago.

Alphabet’s quarterly profit was $ 6.96 billion, or $ 10.13 per share, compared with the analysts’ average estimate of $ 5.645 billion, or $ 8.29 per share.

New data privacy laws, including one that went into effect this month in Google’s home state of California, are also depressing ad prices.

Antitrust regulators in countries across the Americas, Europe and Asia are weighing whether Google has stifled competition on its way to dominating search, mobile software and other businesses, with some bodies even considering forcing it divest parts of its ad operations.

About 2,000 employees last month petitioned Google’s emerging cloud business to scuttle deals with some police agencies, citing racial discrimination concerns. And whether a massive hiring spree will win other cloud clients is uncertain.

Investors may be shifting toward less ad-reliant rivals. Entering Thursday, Amazon and Microsoft, which have smaller ad businesses than Google but bigger cloud units, were trading at 145 times and 35 times their respective earnings over the last 12 months. Alphabet shares were at 30 times earnings over the last year.

(Reporting by Paresh Dave in Oakland, Calif. and Munsif Vengattil in Bengaluru; Editing by Shailesh Kuber and Richard Chang)

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Big Data – VentureBeat

Google releases Model Card Toolkit to promote AI model transparency

July 30, 2020 Big Data

Google today released the Model Card Toolkit, a toolset designed to facilitate AI model transparency reporting for developers, regulators, and downstream users. It’s based on Google’s Model Cards framework for reporting on model provenance, usage, and “ethics-informed” evaluation, which aims to provide an overview of a model’s suggested uses and limitations.

Google launched Model Cards publicly over the past year, which sprang from a Google AI whitepaper published in October 2018. Model Cards specify model architectures and provide insight into factors that help ensure optimal performance for given use cases. To date, Google has released Model Cards for open source models built on its MediaPipe platform as well as its commercial Cloud Vision API Face Detection and Object Detection services.

The Model Card Toolkit aims to make it easier for third parties to create Model Cards by compiling the necessary information and aiding in the creation of interfaces for different audiences. A JSON schema specifies the fields to include in a Model Card; using the model provenance data stored with ML Metadata (MLMD), the Model Card Toolkit automatically fills the JSON with information including data class distributions and performance statistics. It also provides a ModelCard data API to represent an instance of the JSON schema and visualize it as a Model Card.

Google releases Model Card Toolkit to promote AI model transparency

Above: An example of a Model Card.

Image Credit: Google

Model Card creators can choose which metrics and graphs to display in the final Model Card, including stats that highlight areas where the model’s performance could deviate from its overall performance. Once the Model Card Toolkit has populated the Model Card with key metrics and graphs, developers can supplement this with information regarding the model’s limitations, intended usage, trade-offs, and ethical considerations otherwise unknown to model users. If a model underperforms for certain slices of data, the Model Cards’ limitations section offers a place to acknowledge that along with mitigation strategies to help address the issues.

“This type of information is critical in helping developers decide whether or not a model is suitable for their use case, and helps Model Card creators provide context so that their models are used appropriately,” wrote Google Research software engineers Huanming Fang and Hui Miao in a blog post. “Right now, we’re providing one UI template to visualize the Model Card, but you can create different templates in HTML should you want to visualize the information in other formats.”

The idea of Model Cards emerged following Microsoft’s work on “datasheets for datasets,” or datasheets intended to foster trust and accountability through documenting data sets’ creation, composition, intended uses, maintenance, and other properties. Two years ago, IBM proposed its own form of model documentation in voluntary factsheets called ” “Supplier’s Declaration of Conformity” (DoC) to be completed and published by companies developing and providing AI. Other attempts at an industry standard for documentation include Responsible AI Licenses (RAIL), a set of end-user and source code license agreements with clauses restricting the use, reproduction, and distribution of potentially harmful AI technology, and a framework called SECure that attempts to quantify the environmental and social impact of AI.

“Fairness, safety, reliability, explainability, robustness, accountability — we all agree that they are critical,” Aleksandra Mojsilovic, head of AI foundations at IBM Research and codirector of the AI Science for Social Good program, wrote in a 2018 blog post. “Yet, to achieve trust in AI, making progress on these issues will not be enough; it must be accompanied with the ability to measure and communicate the performance levels of a system on each of these dimensions.”

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Big Data – VentureBeat

Intel researchers create AI system that rates similarity of 2 pieces of code

July 29, 2020 Big Data

In partnership with researchers at MIT and the Georgia Institute of Technology, Intel scientists say they’ve developed an automated engine — Machine Inferred Code Similarity (MISIM) — that can determine when two pieces of code perform similar tasks, even when they use different structures and algorithms. MISIM ostensibly outperforms current state-of-the-art systems by up to 40 times, showing promise for applications from code recommendation to automated bug fixing.

With the rise of heterogeneous computing — i.e., systems that use more than one kind of processor — software platforms are becoming increasingly complex. Machine programming (a term coined by Intel Labs and MIT) aims to tackle this with automated, AI-driven tools. A key technology is code similarity, or systems that attempt to determine whether two code snippets show similar characteristics or achieve similar goals. Yet building accurate code similarity systems is a relatively unsolved problem.

MISIM works because of its novel context-aware semantic structure (CASS), which susses out the purpose of a given bit of source code using AI and machine learning algorithms. Once the structure of the code is integrated with CASS, algorithms assign similarity scores based on the jobs the code is designed to perform. If two pieces of code look different but perform the same function, the models rate them as similar — and vice versa.

CASS can be configured to a specific context, enabling it to capture information that describes the code at a higher level. And it can rate code without using a compiler, a program that translates human-readable source code into computer-executable machine code. This confers the usability advantage of allowing developers to execute on incomplete snippets of code, according to Intel.

Intel says it’s expanding MISIM’s feature set and moving it from the research to the demonstration phase, with the goal of creating a code recommendation engine to assist internal and external researchers programming across its architectures. The proposed system would be able to recognize the intent behind an algorithm and offer candidate codes that are semantically similar but with improved performance.

That could save employers a few headaches — not to mention helping developers themselves. According to a study published by the University of Cambridge’s Judge Business School, programmers spend 50.1% of their work time not programming and half of their programming time debugging. And the total estimated cost of debugging is $ 312 billion per year. AI-powered code suggestion and review tools like MISIM promise to cut development costs substantially while enabling coders to focus on more creative, less repetitive tasks.

“If we’re successful with machine programming, one of the end goals is to enable the global population to be able to create software,” Justin Gottschlich, Intel Labs principal scientist and director of machine programming research, told VentureBeat in a previous interview. “One of the key things you want to do is enable people to simply specify the intention of what they’re trying to express or trying to construct. Once the intention is understood, with machine programming, the machine will handle the creation of the software — the actual programming.”

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Big Data – VentureBeat

Variable order in InterpolatingFunction

July 29, 2020 BI News and Info

I’m attempting to deal with a interpolating a function f[x,t] on a variable grid. My x-variable is continuous, so there’s no difficulty in fitting a cubic spline to it with InterpolationOrder->3 for f[x,1], but because I can’t violate causality and fit using future data, I need to ensure that InterpolationOrder->0 for f[1,t]. Is it possible to do this in any way using the Interpolate command? Or would the best way be to set up N different x-Interpolations at each of my measured t=t_i coordinates and connect them with some messy If[] statement?

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Category Archives: Business Intelligence

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Variable order in InterpolatingFunction

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