Tag Archives: data

Adopting Impermanence as a COVID Response

“All conditioned things are impermanent – when one sees this with wisdom, one turns away from suffering.”

-Gautama Buddha

In times of chaos and tribulation, it seems wise to refer to the teachings of those who sought to understand suffering. Impermanence is the word that comes to mind, yet humanity finds comfort in permanence. 

At the August 14th Ethnic Media Services briefing on the science behind COVID-19, doctors on the frontlines reaffirmed the motif I had been seeing – a contradictory society seeks change, yet is resistant to it.

This moment of truth in American history requires quick and consistent change. I wonder, can we rise up to the challenge?

Dr. Ashish Jha, Professor of Global Health at the Harvard T.H. Chan School of Public Health and the Director of the Harvard Global Health Institute remarked “America may have the worst response of any country in the world, to this pandemic” and added that we were in the same position, if not worse condition than Brazil, Russia, and Turkey. Further, he stresses that success with outbreak control has nothing to do with imposing government structures, the culture of the country, or the wealth of a nation. 

Government: Russia’s authoritarian government is struggling with containment.

Culture: East Asian and European countries are dissimilar in their cultural practices but both have managed to lower their COVID rates. 

Wealth: Vietnam, a developing nation, until recently, had avoided COVID-related deaths.

“It’s tempting to look for explanations for why other countries are doing better”, cautions Dr. Jha. He logically builds to the conclusion that where we have failed is in deploying ONE action effectively across all states. That is all that is required. With one-third of the U.S. population on the brink of succumbing to the pandemic, one third already fully at risk, and one-third managing to keep the pandemic at bay, mismatched messaging is wreaking havoc. Without a coordinated response from strong federal leadership, the COVID death numbers will not plateau. 

The onus of information dissemination and access to resources lies heavily on those in positions of power but behavioral change can come from the top-down and the bottom-up. 

Impermanence. The ability to adopt thought that lasts for an undetermined period of time. 

No one wants to be in lockdown. No one wants to wear a mask outside. No one wants to continuously get tested.

Just one of these, fully implemented and enforced, could be the key to end suffering. 

Dr. Nirav Shah, Senior Scholar at Stanford University’s Clinical Excellence Research Center and an elected member of the National Academy of Medicine, informs his research from the positive COVID control he has seen in Asian countries where schools remain open. He notes, “Right now there is a false choice between lives and livelihood.” That choice drives contention and spreads misinformation.

What is needed to re-open safely?

Early warning systems, broad & efficient testing, effective quarantine/isolation, adequate treatment capacity, actionable data collection, and vaccines. 

He brings forth antigen testing as the cheaper, faster method to detect COVID. Cost-effective and almost instantaneous results, I am feeling more optimistic as he continues to speak.

Source: U-T reporter Jonathan Wosen

Early warning systems and actionable data collection rely on the immediate transfer of information to an online database to make it accessible. Temperature monitoring using a thermometer linked to the internet would increase the efficiency of detecting COVID hotspots and roll out timely mandates required to limit spread. Dr. Shah’s blend of technology and the pandemic is the obvious way to move forward. Daily reporting is the necessary next step.

Source: Covid Act Now

So why haven’t we already been using this technology?

“We really need to start to think about a fundamentally different approach that protects privacy and lets public health [professionals] do their job”, Dr. Shah frustratedly shakes his head.

He is moving fast and hits a wall with effective quarantine/isolation and vaccines. The U.S. has expended no energy to strategize or provided resources for isolation and most vaccines are a year out still. 

“We are not anywhere close to doing well”, ends Dr. Shah. 

It seems Dr. Shah and Dr. Jha come to similar conclusions – the United States has the resources and the intelligence to rewrite the course we have taken with regards to the pandemic.

A grim message but I leave with positive outcomes. Testing is changing and so is data collection. Mitigation and prevention of COVID is plausible.

Can we adapt? Can we change? Can we make space for impermanence in our lives to end suffering?


Srishti Prabha is the Assistant Editor at India Currents and has worked in low income/affordable housing as an advocate for children, women, and people of color. She is passionate about diversifying spaces, preserving culture, and removing barriers to equity.

Count the Ticking TikToks

The summer has been eventful for ByteDance, the owner of the rapidly growing social network TikTok. First, the government of India banned the application from distribution in the country due to concerns that the Chinese government is accessing user data. Then, a number of US companies warned employees to remove TikTok from their work phones. Most recently, US President Donald J. Trump threatened to ban TikTok in the US.

Into this maelstrom has stepped Microsoft CEO Satya Nadella with an offer to purchase the US business of TikTok. Nadella has earned a reputation as a savvy operator. He has restored Microsoft’s growth with smart bets on various types of business software, and a strong push to move the users of various applications, including the company’s lucrative Office products on to the online Office 365 version. Nadella has also remade the image of the swaggering giant as a kinder, gentler, more thoughtful company.

Image of Satya Nadella by Brian Smale

Microsoft’s purchase of TikTok would be Nadella’s riskiest bet to date. If Beijing, in fact, views TikTok as a crucial asset for influencing US political and social discourse, it could attempt to put backdoors into the software and service. Microsoft would need to work hard to extricate them, and they could result in TikTok’s being shut down anyway.

Also, with TikTok, Microsoft would enter the politically fraught world of social-content moderation. Microsoft has assiduously avoided political controversy, but TikTok would inevitably force Nadella to enter that arena in one way or another. For example, critics have loudly complained that TikTok censored videos of recent Hong Kong protests, citing that as evidence of Chinese government control. One can imagine similar discontent, due to slights — real or perceived — arising among any number of causes, particularly at either extreme of the US political spectrum.

TikTok’s present valuation $5 billion has critics warning that Microsoft is about to overpay. That is one of many things that could halt the deal altogether — valuation, government intervention, and fresh revelations of spying on users being just a few.

Yet the logic of the acquisition is clear. TikTok is under threat of closure by the US federal government. It’s hard to imagine that Microsoft will pay its full valuation price. For ByteDance, this may offer a graceful exit from a business that it realizes will only create more problems. So, Nadella may be making a smart bet — one with less to lose and more to gain than others realize.

Microsoft would increase its market presence by simultaneously acquiring both a social medium and an application popular with the younger crowd. It has long pined for more of the under-25 group, and TikTok may fulfill that aspiration most clearly and cleanly. Also, TikTok, a kinder, gentler social network than Facebook and Twitter, aligns culturally with Microsoft’s carefully groomed image.

The platform is designed to encourage discovery and consumption, but not to fan the flames of extremism. That does entail algorithmically controlling content more carefully and spreading new content more slowly than Facebook and Twitter care to. To date, however, moderation has been a lesser problem on TikTok than on other platforms and, due to its design and mechanism, is likely to remain so.

With TikTok would come a large and growing pool of user-generated video data for training Microsoft’s artificial intelligence (AI) engines. In theory, if Microsoft can continue to grow TikTok’s user base, its advertising benefits to Microsoft may be enormous. Microsoft’s cash flow would benefit from the added diversity of the advertising revenue and potentially of another rapidly growing source: social advertising. To put this into perspective, Amazon’s fastest-growing revenue stream, of late, has been advertising sales on its powerful eCommerce platform.

The purchase’s major benefit to Microsoft and the US public may be the ability of US consumers to continue to use an innovative platform for free expression and creativity after rescuing it from the quicksand of politics. Yes, we must remain vigilant in limiting government spying (which, let’s be honest, both sides engage in) and restrictive business practices (in which China is clearly the worst offender). But ultimately the potential of such technology as TikTok is to soar above partisanship and divisiveness to let people connect and create.

Certainly, social networks have created their fair share of problems for society, and TikTok is not a perfect vessel. People will find ways to abuse its potential. For now, however, Microsoft’s purchase of TikTok would, in a rare win-win, benefit Microsoft, TikTok’s users, and society.

And just as the US learned from India’s ban, India now needs to learn from it. China’s National Intelligence Law of 2017 requires all of its companies and citizens to ‘support, assist and cooperate with the state intelligence work’. If China decided to launch more aggressive moves against India, it could have its companies intercept private communications, shut down key services, or even sabotage infrastructure. This is why the US State Department launched the Clean Network program: to purge Chinese companies from US infrastructure. This applies to telecoms carriers, cloud services, undersea cables, apps, and app stores.

Removing Chinese-developed infrastructure will take time. But India can surely take a page out of the US State Department’s book and require companies such as Xiaomi, Haier, Oppo, Vivo, Oneplus, Huawei, and Motorola to sell their Indian products to local players. Companies such as Reliance, Mahindra, and Tata have the capability and funding and could win in the same way as Microsoft.


Vivek Wadhwa is a distinguished fellow, Labour and Worklife Program, Harvard Law School, US, and co-author of the forthcoming book, From Incremental to Exponential: How Large Companies Can See the Future and Rethink Innovation.

This piece was first published here.

License for embedded image can be found here.

A Parallel Pandemic in the Shadows: Women Affected

Coronavirus brings the simmering issue of gender inequity to a violent boil. 

A barrage of data can leave you with less information than the data dictates. For some, it has become a hobby to get instant updates on Coronavirus infection rates, death rates, and trends. 

“You may not control all the events that happen to you, but you can decide not to be reduced by them”, Maya Angelou advises. Yet, the reductive nature of statistics are difficult to escape. One data point can blind us to the barriers of entry, the treacherous path, the years of turmoil, the fallen and left behind, and the unseen. 

Numbers indicate that men are being affected by COVID-19 at higher rates. But where does that leave our women?

In the US, prior to the pandemic, the workforce was 51% women, revealed Dr. C. Nicole Mason, President and CEO of the Institute for Women’s Policy Research, at the May 22, 2020 EMS Briefing. A staggeringly high statistic, one that has taken many years to reach. From an inaccessible job market to wage gaps, having a workforce that was representative of women was an achievement.

However, from the time the pandemic began, that number has dropped to 47%. The last time such a distribution existed was in 2000 –  a complete loss of the gains made in the last 20 years, in a short 3 months. 

Global trends indicate that women are – on and off the frontlines – being affected by what is now being called the Shadow Pandemic. Dr. Estela Rivero,  Research Associate within the Pulte Institute for Global Development’s Evidence and Learning Division, shares that women are being burdened with the unpaid work that accompanies shelter in place orders. 

Unpaid work is defined by labor that has no direct remuneration; taking care of the house, your children, your children’s education, caregiving for the disabled and elderly all fall under this category. Imagine, if you were to hire someone to do said work, you would be paying them 24 hours a day. Women take on these extra tasks in conjunction with a part-time or full-time job. 

“Who is bearing the brunt of taking care of the children? Who is bearing the brunt of the online schooling?”, asks Dr. Beatrice Duncan, Rule of Law Advisor for UN Women, when she speaks about the increase in unpaid work by women. 99.9% of women, globally, are experiencing a spike in unpaid work and Duncan implores the collective to rationalize the impact of this gender disparity.  

Women are disproportionately impacted by unpaid work and caregiving during the pandemic, Dr. Estela Rivera informs. A quick look at the two tables above indicates that the burden of unpaid work has fallen on women prior to the pandemic. 

Coronavirus brings the simmering issue of gender inequity to a violent boil. Women, all around the world, with or without the pandemic, have been doing more unpaid work AND on average, work more hours (unpaid and paid) than men.

(Dr. C Nicole Mason, left; Dr. Estela Rivera, top-right; Dr. Beatrice Duncan, bottom-right)

“COVID-19 has, really, exposed some of the fragility of our economic, social and political systems”, Dr. Mason articulates. “We knew that there was something underneath the numbers. Even though women were in the workforce in record numbers, many women and families were still struggling to make ends meet. Measuring the economy by low levels of unemployment… didn’t capture the day to day realities of women and their families.”

Women are overrepresented in the health, education, and hospitality sectors, all of which have taken a hit during the pandemic and historically have lower pay. With unemployment for women jumping from 3% to 15% in the US, during the shelter in place, they are facing the loss of jobs, inadequate savings to survive the pandemic and potentially, having to make the difficult choice to choose work over their children. 

If women are to re-enter the workforce with equal footing, creation of new jobs, equal wages, increased basic pay, childcare provided by employers, flexibility with schedules, and social support systems for women, need to become part of the government’s structural dialogue. 

The economy and its jobs have changed and recovery requires adaptation. Otherwise, the violent boil will overflow, destroying everything in its wake. 

The path forward begs the question: What policies do we need long term for women and their families to succeed? 

Srishti Prabha is the Assistant Editor at India Currents and has worked in low income/affordable housing as an advocate for children, women, and people of color. She is passionate about diversifying spaces, preserving culture, and removing barriers to equity.

This American Snapshot Costs $1.5 Trillion Dollars a Year

Census Day, when the United States takes its once-every-decade collective selfie, is April 1.

Those who don’t include themselves in the decennial snapshot will cost themselves and their communities thousands of dollars’ worth of government tax spending — $1.5 trillion annually nationwide (https://tinyurl.com/Census-drivenSpending) for the next 10 years, and other benefits too, with no chance to get added to the picture until 2030.

But Census Day isn’t the actual deadline for being included. It’s just the day listed on the census questionnaires (https://tinyurl.com/2020censusquestionnaire): “How many people were living or staying in this house, apartment or mobile home on April 1, 2020?”

For this question, include yourself, all the kids, all the relatives or friends who live there, and roommates.  Information given to the census will never be shared with landlords.

Until the corona virus hit, the actual deadline for filling out the census was July 31.  Now the Census Bureau has extended the deadline to August 15.

The nine-question questionnaires themselves are already available for people to answer online,

At the website https://my2020census.gov, and will remain available in a dozen different languages until the Aug. 15 deadline.  Many people have already received “invitations” in the mail to answer the census online, with an ID number customized for their address.

Whether you have an invitation or not, you can still go to that https://my2020census.gov website and fill out the questionnaire. 

The Census Bureau has also begun sending out print copies of  the questionnaire through the mail. 

People can also be counted by making a telephone call, to (844) 330-2020 if they speak English, or to one of 13 numbers, listed below, for other languages.  The call centers, however, are not fully staffed due to stay-at-home orders for the corona virus, so this method could involve longer wait times on the phone.

You can also wait for an “enumerator,” a Census  Bureau employee who will be dispatched starting in May to visit addresses that have not yet responded online, or by mail, or by phone.

Although the Census Bureau says it has offered jobs to 600,000 people – 100,000 more than it anticipated hiring – it is also delaying the “onboarding” process, which includes fingerprinting and background checks, for at least a couple of weeks due to concerns surrounding COVID-19.

The census requirement is included in the U.S. Constitution, and a national census has been conducted every 10 years since 1790. Participation is required. 

From 1790 to 1820, Census Day was the first Monday of August. Then it was moved to early June until 1910, when it was moved to April 15.  In 1920, in an effort to avoid interfering with farm work, Census Day was Jan. 1.  But when that census showed how the country was becoming increasingly urbanized, Census Day was shifted to April 1, where it has remained ever since.

Census data is used to try to evenly distribute political representation in Congress.  Currently, every member of the 435-seat House of Representatives has about 750,000 constituents.

The data also helps businesses decide where to invest, helps state and local governments determine where new schools and roads are needed, and directs the federal government to where kids are living who qualify for Head Start, or need any of more than 100 other federally funded programs providing child care and development, education, nutrition, health care and much more.

The personal information the census collects – your name, address, age, race, the household phone number – is kept strictly confidential for 72 years.  The Census Bureau is forbidden to share that information with other government agencies, including police, the FBI, ICE, everybody.

California has invested more money than any other state in census outreach in an effort to ensure that all its people are counted this year.  The website CaliforniaCensus.gov can

Direct you to Questionnaire Assistance Centers and kiosks where you will be able to get some help filling out the forms if you need it.   

By May, if you haven’t filled out the census form, a census enumerator will come to your address.  There are several ways to make sure it’s really a census worker.  You can ask to see their official U.S. Census Bureau I.D. badge, which will have their name and photograph, along with an expiration date and a Department of Commerce watermark.

They will also be using a hand-held computer device and carrying a census bag. You can verify that they’re who they say they are by calling (800) 923-8282 to speak to a local representative.

Also, no census worker will ask about your citizenship status, or your social security number, or any banking information.  Nor will they ask for a payment or donation of any type. 

If you want help completing your census form, the Census Bureau has phone lines in 14 languages to provide that:

English (844) 330-2020

Spanish (844) 468-2020

Chinese (Mandarin) (844) 391-2020

Chinese (Cantonese)  (844) 398-2020

Vietnamese (844) 461-2020

Korean (844) 392-2020

Russian (844) 417-2020

Arabic (844) 416-2020

Tagalog (844) 478-2020

Polish (844) 479-2020

French (844) 494-2020

Haitian Creole (844) 477-2020

Portuguese (844) 474-2020

Japanese (844) 460-2020

The state of California is providing online assistance in the following languages: 

CQ Arabic: https://californiacensus.org/ar/

Armenian:  https://californiacensus.org/hy/

Khmer: https://californiacensusorg/km

Persian: https://californiacensus.org/fa/

Korean: https://californiacensus.org/ko/

Japanese: https://californiacensus.org/ja/

Punjabi: https://californiacensus.org/pa/

Russian: https://californiacensus.org/ru/

Chinese (simplified): https://californiacensus,org/zh-hans/

Chinese (traditional): https://californiacensus.org/zh-hant/

Tagalog: https://californiacensus.org/tg/

Vietnamese: https://californiacensus.org/vi/


Coverage for Census 2020 has been facilitated through a grant from the United Way Bay Area.

Ethical Challenges of Artificial Intelligence

AI is widely misunderstood and still too rudimentary for us to be worrying. But it’s not too soon to contemplate the ethical implications of intelligent machines and systems. An AI system is only as good as the data it receives. It is able to interpret them only within the narrow confines of the supplied context. It can’t distinguish causation from correlation.

AI has the potential to be as transformative to the world as electricity, by helping us understand the patterns of information around us. But it is not close to living up to the hype. The super-intelligent machines and runaway AI that we fear are far from reality; what we have today is a rudimentary technology that requires lots of training. What’s more, the phrase artificial intelligence might be a misnomer — because human intelligence and spirit amount to much more than what bits and bytes can encapsulate.

I encourage readers to go back to the ancient wisdoms of their faith to understand the role of the soul and the deeper self. This is what shapes our consciousness and makes us human, what we are always striving to evolve and perfect. Can this be uploaded to the cloud or duplicated with computer algorithms? I don’t think so.

What about the predictions that AI will enable machines to have human-like feeling and emotions? This, too, is hype. Love, hate and compassion aren’t things that can be codified. Not to say that a machine interaction can’t seem human — we humans are gullible, after all. According to Amazon, more than 1 million people had asked their Alexa-powered devices to marry them in 2017 alone. I doubt those marriages, should Alexa agree, would last very long!

Today’s AI systems do their best to replicate the functioning of the human brain’s neural networks, but their emulations are very limited. They use a technique called Deep Learning. After you tell a machine exactly what you want it to learn and provide it with clearly labelled examples, it analyses the patterns in those data and stores them for future application. The accuracy of its patterns depends on completeness of data. So the more examples you give it, the more useful it becomes.

Herein lies a problem, though — an AI system is only as good as the data it receives. It is able to interpret them only within the narrow confines of the supplied context. It doesn’t “understand” what it has analysed — so it is unable to apply its analysis to other scenarios. And it can’t distinguish causation from correlation.

AI shines in performing tasks that match patterns in order to obtain objective outcomes. Examples of what it does well include playing chess, driving a car on a street and identifying a cancer lesion in a mammogram. These systems can be incredibly helpful extensions of how humans work, and with more data, the systems will keep improving. Although an AI machine may best a human radiologist in spotting cancer, it will not, for many years to come, replicate the wisdom and perspective of the best human radiologists. And it won’t be able to empathise with a patient in the way that a doctor does. This is where AI presents its greatest risk and what we really need to worry about — use of AI in tasks that may have objective outcomes but incorporate what we would normally call judgement. Some such tasks exercise much influence over people’s lives. Granting a loan, admitting a student to a university, or deciding whether children should be separated from their birth parents due to suspicions of abuse falls into this category. Such judgements are highly susceptible to human biases — but they are biases that only humans themselves have the ability to detect.

And AI throws up many ethical dilemmas around how we use technology. It is being used to create killing machines for the battlefield with drones which can recognise faces and attack people. China is using AI for mass surveillance, and wielding its analytical capabilities to assign each citizen a social credit based on their behaviour. In America, AI is mostly being built by white people and Asians. So, it amplifies their inbuilt biases and misreads African Americans. It can lead to outcomes that prefer males over females for jobs and give men higher loan amount than women. One of the biggest problems we are facing with Facebook and YouTube is that you are shown more and more of the same thing based on your past views, which creates filter bubbles and a hotbed of misinformation. That’s all thanks to AI.

Rather than worrying about super-intelligence, we need to focus on the ethical issues about how we should be using this technology. Should it be used to recognise the faces of students who are protesting against the Citizenship (Amendment) Act? Should India install cameras and systems like China has? These are the types of questions the country needs to be asking.

Vivek Wadhwa is a distinguished fellow and professor, Carnegie Mellon University’s College of Engineering, Silicon Valley.

This article was republished with permission from the author.

https://economictimes.indiatimes.com/tech/ites/why-we-need-to-focus-on-the-ethical-challenges-of-artificial-intelligence/articleshow/73010169.cms

Don’t Believe the Hype About AI

To borrow a punch line from Duke professor Dan Ariely, artificial intelligence is like teenage sex: “Everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.” Even though AI systems can now learn a game and beat champions within hours, they are hard to apply to business applications.

M.I.T. Sloan Management Review and Boston Consulting Group surveyed 3,000 business executives and found that while 85 percent of them believed AI would provide their companies with a competitive advantage, only one in 20 had “extensively” incorporated it into their offerings or processes. The challenge is that implementing AI isn’t as easy as installing software. It requires expertise, vision, and information that isn’t easily accessible.

When you look at well known applications of AI like Google’s AlphaGo Zero, you get the impression it’s like magic: AI learned the world’s most difficult board game in just three days and beat champions. Meanwhile, Nvidia’s AI can generate photorealistic images of people who look like celebrities just by looking at pictures of real ones.

AlphaGo and Nvidia used a technology called generative adversarial networks, which pits two AI systems against each another to allow them to learn from each other. The trick was that before the networks battled each other, they received a lot of coaching. And, more importantly, their problems and outcomes were well defined.

Most business problems can’t be turned into a game, however; you have more than two players and no clear rules. The outcomes of business decisions are rarely a clear win or loss, and there are far too many variables. So it’s a lot more difficult for businesses to implement AI than it seems.

Today’s AI systems do their best to emulate the functioning of the human brain’s neural networks, but they do this in a very limited way.  They use a technique called deep learning, which adjusts the relationships of computer instructions designed to behave like neurons. To put it simply, you tell an AI exactly what you want it to learn and provide it with clearly labelled examples, and it analyzes the patterns in those data and stores them for future application. The accuracy of its patterns depends on data, so the more examples you give it, the more useful it becomes.

Herein lies a problem: An AI is only as good as the data it receives. And it is able to interpret that data only within the narrow confines of the supplied context. It doesn’t “understand” what it has analyzed, so it is unable to apply its analysis to scenarios in other contexts. And it can’t distinguish causation from correlation. AI is more like an Excel spreadsheet on steroids than a thinker.

The bigger difficulty in working with this form of AI is that what it has learned remains a mystery — a set of indefinable responses to data.  Once a neural network is trained, not even its designer knows exactly how it is doing what it does. As New York University professor Gary Marcus explains, deep learning systems have millions or even billions of parameters, identifiable to their developers only in terms of their geography within a complex neural network. They are a “black box,” researchers say.

Speaking about the new developments in AlphaGo, Google/DeepMind CEO Demis Hassabis reportedly said, “It doesn’t play like a human, and it doesn’t play like a program. It plays in a third, almost alien, way.”

Businesses can’t afford to have their systems making alien decisions. They face regulatory requirements and reputational concerns and must be able to understand, explain, and demonstrate the logic behind every decision they make.

For AI to be more valuable, it needs to be able to look at the big picture and include many more sources of information than the computer systems it is replacing. Amazon is one of the few companies that has already understood and implemented AI effectively to optimize practically every part of its operations from inventory management and warehouse operation to running data centers.

In inventory management, for example, purchasing decisions are traditionally made by experienced individuals, called buyers, department by department. Their systems show them inventory levels by store, and they use their experience and instincts to place orders. Amazon’s AI consolidates data from all departments to see the larger trends — and relate them to socioeconomic data, customer-service inquiries, satellite images of competitors’ parking lots, predictions from The Weather Company, and other factors. Other retailers are doing some of these things, but none as effectively as Amazon.

This type of approach is also the basis of Echo and Alexa, Amazon’s voice-based home appliances. According to Wired, by bringing all of its development teams together and making machine learning a corporate focus, Amazon is solving a problem many companies have: disconnected islands of data. Corporate data are usually stored in disjointed datasets in different computer systems. Even when a company has all the data needed for machine learning, they usually aren’t labelled, up-to-date, or organized in a usable manner. The challenge is to create a grand vision for how to put these datasets together and use them in new ways, as Amazon has done.

AI is advancing rapidly and will surely make it easier to clean up and integrate data. But business leaders will still need to understand what it really does and create a vision for its use. That is when they will see the big benefits.

The article has been posted here with the express permission of the author.