Big data is here and unavoidable
For years, we’ve written about big data and showcased the progression of business intelligence available now to brands of every size, in fact, most businesses have a feel for this type of data – open a spreadsheet of your sales data and you already know it’s just a bunch of numbers unless they are analyzed and filtered. Today, I want to review what big data is, how it is currently being used, what this means for the future, and most importantly, how it can be cherry picked and why it can upset entire industries.
[ba-pullquote align=”right”]”Big data” is typically consisting of at least dozens of terabytes in a single data set.[/ba-pullquote]“Big data” is defined as large data sets which cannot be managed with simple, common software that captures and processes the data, and is typically consisting of at least dozens of terabytes in a single data set. The challenges of big data are really big. It is described by Gartner analyst, Doug Laney as being three-dimensional, i.e. increasing volume (amount of data), velocity (speed of data in/out), and variety (range of data types, sources).
Let’s talk about how BIG this really is
Let me illustrate. The University of Nebraska physics department has 1.6 petabytes of data – that’s 1.6 million gigabytes in one department at one school. Boeing jet engines can produce 10 terabytes of operational information for every 30 minutes they turn. As of 2012, the average smartphone user has 736 pieces of personal data collected every day, stored for one to five years by service providers.
[ba-pullquote align=”right”]By 2020, there will be 5,200 gigabytes of data for every human on Earth.[/ba-pullquote]IBM’s chief executive, Virginia Rometty said, “By one estimate there will be 5,200 gigabytes of data for every human on the planet by 2020. And powerful new computing systems can store and make sense of it nearly instantaneously.” It has also been predicted that in the coming years, over 200,000 big data specialists will be required to make sense of the barrage of data being collected.
Big data is already being used today in a big way
Big data is a big deal and it’s not just because there’s a lot of it. In fact, today alone, SumAll raised $4 million and DataSift raised a whopping $42 million to help businesses make sense of their data as it relates to social media.
[ba-pullquote align=”right”]Retailers are analyzing your facial expressions on camera to tell if you’re a happy shopper, and tracking your gender, age, and size as you walk in the door.[/ba-pullquote]Big data is already used in amazing ways by the retail industry by analyzing shopper height and size as they walk in the door to determine age, gender, and more, and even have cameras analyzing facial expressions while you’re shopping to gauge your experience. If that doesn’t impress you, there’s already a seasoned company that is tracking “visual mentions” online so if you share a picture of your Starbucks cup on Instagram, even if you don’t say Starbucks or use GPS, Starbucks can see that their logo, even if curved, was used online on a social network.
Predicting the future with big data
But it’s not just that data is having a tremendous impact on life today, it is still a young sector with many startups yet to pop up to solve the data conundrums. SiftScience fights fraud using machine-learning that learns from data to recognize patterns of fraudulent behavior based on past examples, and Hadoop helps companies analyze massive amounts of generating about user behavior and their own operations while Recorded Future uses algorithms that unlock predictive signals based on web chatter to determine a brand anticipate risks and capitalize opportunities.
[ba-pullquote align=”right”]Intel is working on technology using big data to allow you to see three cars ahead, behind, and beside you.[/ba-pullquote]There are already projects in the works that allows forecasters to predict weather up to 42 days in advance, potentially saving lives and billions of dollars a year.Intel is working on a big data project that allows cars to communicate so drivers will be able to see three cars in front of, behind, and to the left and right – simultaneously. Ford is developing vehicle-to-vehicle and vehicle-to-infrastructure systems to warn drivers of potentially hazardous traffic events, like cars going through red lights.
But big data has some really big problems
First, and least upsetting, is that there are big problems with demographics, leaving brands with a lot of data that doesn’t yet mean much. Why? Incomplete self reporting is a huge issue because brands are still focused on using social networking profile data to gather intelligence on their site users, fans, and the like, but when they rely on this data, people may not be completely truthful (they may say they are 32, but they’re 12, and so forth). Additionally, privacy does protect users to a certain extent, blocking intelligence gathering by brands. Lastly, data is still largely inconsistent and unconnected – you may have a Twitter account and Facebook account, but a third party doesn’t know that unless (a) you use the same username consistently or (b) you grant access to both accounts through that third party.
While other problems exist (like how will we ever store all of this data, disseminate it, and make sense of it, and does it all really matter?), the biggest one we see is the potential for cherry picking, because when you look at a data set, it still takes a human to actually determine what is important to garner from that data set.
[ba-pullquote align=”right”]Big data may mean more information, but it also means more false information.[/ba-pullquote]Industry expert Nassim Taleb opined in February, “With big data, researchers have brought cherry-picking to an industrial level. Modernity provides too many variables, but too little data per variable. So the spurious relationships grow much, much faster than real information. In other words: Big data may mean more information, but it also means more false information.”
Taleb addresses something that could lead one to think that big data is faulty and bad, but perhaps Taleb is really pointing out the human nature that is still required in some instances of analyzing big data – and most people would not typically question a researcher or their methods, leaving analysis in its youngest phase subjective.
Chris Treadaway, CEO and Founder of Polygraph Media which is famous for data-driven analytics said, “To analyze big data, you have to know when you have enough data, know that you’re looking at the right data, and know how and when to draw conclusions from the data using methods developed from statistics theory and data science. That’s the great irony of “big data” – it’s as much of an art as a science, which is why the best efforts are multidisciplinary.”
“Big data can find tremendous hidden relationships,” Treadaway continued, “but you have to make sure your bias isn’t to find conclusions that don’t exist. Bias can cause the situation Taleb describes, and will cause disinformation as he says. If you’re cautious, discerning, and careful, you can make the most of big data. But there are pitfalls for the careless.”
And the coup de gras
[ba-pullquote align=”right”]Your performance data, finances, company info and more are already being repackaged for public consumption and monetization.[/ba-pullquote]The coup de gras is that professionals are being threatened by new ways big data is being used, but they are not recognizing it as a big data issue.
Several industries are seeing data about them individually, their performance, their company, their finances, all analyzed and repackaged for public consumption or monetization.
Imagine a site launches tomorrow based on publicly available data and you’re a social media consultant. Let’s say that this new site looks at who has recommended you on LinkedIn, Yelp, Angie’s List and so on, and has determined that the people recommending you are clients of yours, based on the assumption that it is the only reason they’d recommend you or review you. The new site also analyzes words and pictures used in your online bios to determine characteristics about you.
Then, they take those reviews and characteristics and quantify you into a score, giving you more points if someone from Coca Cola reviewed you than if the local dentist reviewed you, implying that you’re a higher quality consultant if you’ve worked with a major brand like Coca Cola than if you worked with a local dentist (God forbid you specialize in social media for independent medical professionals).
Then, Google gets interested in this new site and they invest, and later, they want to use that data to populate your Google+ profile, so now you, the social media consultant, has a score next to their face to determine how good you are at your job.
What’s wrong with that?
[ba-pullquote align=”right”]You must understand that data requires a human to determine what is relevant, which doesn’t always allow for the full context of the data points.[/ba-pullquote]Data is subjective, even when raw – it takes humans to determine what data points in the sea of data are relevant, and it doesn’t always take into account the context surrounding that data. You, the social media consultant, could have taken a two year sabbatical to execute social media strategies pro bono for three tiny charities, four local restaurants, two African orphanages, and a spa, earning a reputation for your high quality of work and compassion that can’t possibly quantified by a computer.
This scenario is fake. For now. But with every human generating billions of data points every year, evaluations are just the first of many steps in what is to come with big data – the data is now generated, and it is a race to see what can be displayed about you and your business so that companies can sell to you or repackage your data and sell it to someone else. Even your brand will be using big data to gain insights into your customers so you can better serve them.
[ba-pullquote align=”right”]The race is on to see what can be displayed online about you and your business, which is being repackaged and resold.[/ba-pullquote]There are pros and cons to big data, but the reality is that it is unavoidable, even if you ignore it or misunderstand it. Consumers need to begin to recognize when they see big data, and understand that it may not be the true context of that data, as it is ripe with humans’ decisions regarding what is important about a data set. This is just the beginning.
China no longer dependent on U.S. for smartphone components
(TECH NEWS) Trump’s trade war, more specifically, the ban on shipping phone components, to China has begun to take a toll on chip manufacturing.
Once upon a time, the U.S. and China were buddies, exporting and importing from each other with ease. However, President Trump’s recent actions regarding trade with China is certainly putting a damper on things.
It seems that Chinese companies have moved past the need to import certain products, like smartphone chips, from the U.S. – something they previously relied heavily on by working with American companies like Qorvo, Inc. in North Carolina, Skyworks, Inc. in Massachusetts, Broadcom, Inc. in California, and Cirrus Logic in Texas.
Since the ban in May, Trump specifically barred shipments from the U.S. from companies like Qualcomm and Intel Corp to companies like Chinese tech conglomerate, Huawei Technologies Co. But much like the bans that came before the Trump administration, it didn’t last long. With tensions high, the U.S. actually recently started rolling back some aspects of the ban and started making exceptions that allow American tech companies to continue to work with Chinese companies like Huawei.
Of course, China’s lack of U.S. parts hasn’t stopped them from rolling out new and improved products. As a matter of fact, in September, Huawei unveiled its newest phone, the Mate 30, which boasts highly-desired features, such as a curved screen and a wide angle camera. This makes the phone a pretty solid competitor of Apple’s newest iPhone, the iPhone 11, of which China was sent 10 million of in September and October.
After Huawei’s announcement, investment and banking firm UBS, and Japanese technology lab Fomalhaut Techno Solutions, partnered up and took to their labs to analyze the phone’s components. Their analysis was simple and straightforward. They found that there were absolutely zero American components in the phone. In fact, the chips in the Mate 30 are actually from Huawei’s in-house chip design agency, HiSilicon. They also provided Huawei with WiFi and Bluetooth chips. With HiSilicon’s 20 + years experience in the industry, 200+ chipsets, and 8000+ patents, it’s no wonder U.S. chip companies are getting nervous. Qualcomm, for example, announced a 31-40% decrease in estimated chip shipments over the next year.
Although the chip ban has made a big impact on larger U.S. companies who make and supply chips to China, there are still many other businesses that have been affected in Trump’s trade war. As it happens, U.S. Commerce Secretary Wilbur Ross recently confessed that, since May, when the ban was put in place, the U.S. has received at least 260 requests, asking that they excuse them from the ban and be allowed to work with China as they previously had.
But really, at the end of the day, with so many American companies relying on China for both import and export, it’s probable that the ban will be short-lived and that exceptions won’t need to be made. As Americans, we can be hopeful that the end-result of this trade war will be a positive one, but only time will tell.
AI cameras could cut down traffic deaths, but there may be flaws
(TECH NEWS) Traffic accidents have plagued humanity since motor vehicles were created, can AI help cut down on text and drive incidents?
What if we told you Australian officials believe they have found a way to reduce driving deaths by almost 30% in just two years? It’s a pretty appealing concept. After all, Australia alone faces an average of over 3 deaths a day due to driving accidents. And Australia’s average death rate clocks in at just half of what we face in the United States.
There’s just one problem with Australia’s proposed solution: it’s basically Big Brother.
Basically, Australia plans to use AI cameras to catch people texting and driving. There are plenty of places that have outlawed texting and driving, but that rule is very hard to enforce – it basically means catching someone in the act. With AI cameras, hands free driving can be monitored and fined.
Australia has already started rolling out some of these systems in South Wales. Because this is a new initiative, first time offenses will be let off with a warning. The following offenses can add up quickly, though, with fines anywhere from $233 to $309 USD. After a six month trial period, this program is projected to expand significantly.
But there are real concerns with this project.
Surprisingly, privacy isn’t one of these worries. Sure, “AI cameras built to monitor individuals” sounds like a plot point from 1984, but it’s not quite as dire as it seems. First, many places already have traffic cameras in order to catch things like people running red lights. More importantly, though, is the fact these machines aren’t being trained to identify faces. Instead, the machine learning for the cameras will focus on aspects of distracted driving, like hands off the wheel.
The bigger concern is what will come from placing the burden of proof on drivers. Because machine learning isn’t perfect, it will be paired with humans who will review the tagged photographs in order to eliminate false positives. The problem is, humans aren’t perfect either. There’s bound to be false positives to fall through the cracks.
Some worry that the imperfect system will slow down the judicial system as more people go to court over traffic violations they believe are unfair. Others are concerned that some indicators for texting while driving (such as hands off the wheel) might not simply apply texting. What if, for instance, someone was passing a phone to the back seat? Changing the music? There are subtleties that might not be able to be captured in a photograph or identified by an AI.
No matter what you think of the system, however, only time can tell if the project will be effective.
DeepComposer: AWS’ piano keyboard turns AI up to 11
(TECH NEWS) Amazon has been busy with machine learning, which includes a camera, a car, and now DeepComposer that’s able to add to classics on the fly
Musicians, listen up, there’s a new kid in town, its name is DeepComposer and it’s coming to take your creativity and turn it up to 11.
Artificial Intelligence has taken a leap into what has long been considered the “pinnacle of human creativity”, as Amazon revealed what is said to be the world’s first machine learning-enabled keyboard capable of creating music.
Amazon unveiled its AWS DeepComposer keyboard Monday during AWS re:Invent, a learning conference Amazon Web Services hosted for the global cloud computing community in Las Vegas.
Demonstrating DeepComposer’s abilities, Dr. Matt Wood, Amazon’s VP of Artificial Intelligence, played a snippet of Beethoven’s “Ode to Joy” and then let the keyboard riff on it with drums, synthesizer, guitar, and bass, sharing a more rockin’ version of the masterpiece.
Generative AI, is considered by scientists at MIT to be one of the most promising advances in AI in the past decade, Wood told the crowd. Generative AI allows for a machine not only to learn from example, as a human would but to take it next level and connect the dots, making the next creative step to composing something completely new.
“It [Generative AI] opens the door to an entire world of possibilities for human and computer creativity, with practical applications emerging across industries, from turning sketches into images for accelerated product development, to improving computer-aided design of complex objects, Amazon said on its AWS re:Invent website.
How does it work? The Generative AI technique pits two different neural networks against each other to produce new and original digital works based on sample inputs, according to Amazon. The generator creates, the discriminator provides feedback for tweaks and together they create “exquisite music”, Wood explained.
A user inputs a melody on the keyboard, then using the console they choose the genre, rock, classical, pop, jazz or create your own and voila, you have a new piece of music. Then, if so desired users can share their creations with the world through SoundCloud.
This is the third machine learning teaching device Amazon has made available, according to TechCrunch. It introduced the DeepLens camera in 2017 and in 2018 the DeepRacer racing cars. DeepComposer isn’t available just yet, but AWS account holders can sign up for a preview once it is.
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