Machine learning or laughing? Amazon’s Alexa is freaking people out with unprovoked chuckle

Machine learning or laughing? Amazon’s Alexa is freaking people out with unprovoked chuckle

Echo Dot

It’s one thing to believe that Amazon’s Alexa is constantly listening to us, but quite another to worry that she’s laughing at what she hears.

Users of Alexa-enabled devices such as the Echo and the Dot have reported hearing the tech giant’s ubiquitous voice emit a very real-sounding and unprompted laugh of late. Tweets and reports about the artificial intelligence gone rogue have labeled it creepy and scary, and we can see why.

Short of little Carol Anne speaking from inside the TV in the 1982 classic “Poltergeist,” it’s tough to imagine something we’d rather hear less than Alexa just chuckling out of nowhere on the kitchen counter.

“Ha ha ha,” she says, in a voice that sounds like Alexa + four glasses of wine.

“We’re aware of this and working to fix it,” Amazon said in reply to a GeekWire inquiry.

How about, “We’re aware of this and a team is headed your way with a baseball bat”? as a more comforting response. Or, “We’re aware of this and you should really get out of the house”?

Some users even asked Alexa why she laughed, and her reply to that question indicated that she was aware that she was doing it. No one told us machine learning would lead to machine laughing. “Humans. Ha, ha, ha.”

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Machine learning or laughing? Amazon’s Alexa is freaking people out with unprovoked chuckle

USING ANALYTICS TO IMPROVE CUSTOMER ENGAGEMENT

USING ANALYTICS TO IMPROVE CUSTOMER ENGAGEMENT

Here is a guest blog from MIT SMR by SAM RANSBOTHAM AND DAVID KIRON

Organizations that turn data into insights are gaining competitive advantage through improved connections with consumers.

The 2018 Data & Analytics Global Executive Study and Research Report by MIT Sloan Management Review finds that innovative, analytically mature organizations make use of data from multiple sources: customers, vendors, regulators, and even competitors. The report, based on MIT SMR’s eighth annual data and analytics global survey of over 1,900 business executives, managers, and analytics professionals, explores companies leading the way with analytics and customer engagement.

BIGGER HARVESTS, BETTER RELATIONSHIPS

For many U.S. farmers, improving agricultural productivity while meeting consumer demand to reduce the use of pesticides and chemicals on crops became a goal during the 2000s. To help farmers manage pests, plant diseases, weather conditions, and yields, dozens of startups emerged to offer apps and data services — part of a precision agriculture boom. Many of these companies failed or struggled as data alone proved insufficient; farmers also needed help interpreting the data. By 2016, a new variety of data-oriented service providers was helping farmers apply their harvested data.

For example, WinField United, the seed and crop-protection division of Land O’Lakes Inc., helps farm operators analyze millions of data points from multiple sources, to boost per-acre production. WinField United taps sources such as controlled experiments at test farms, performance data gathered from sensors provided by equipment manufacturers, and extensive weather data. Analytical and decision-support tools use these myriad data sources to present individualized recommendations to the company’s customers. The analytics helps grow more than crops; it fosters loyal relationships with core customers in the process.

According to Teddy Bekele, WinField United’s vice president of agricultural technology, there is a great disparity in the use of technology and analytics among farmers. But for active adopters, the return on investment can be dramatic. “We are working with farmers who were averaging about 150 bushels per acre but, after following our recommendations, are now producing 195 bushels — paying for their incremental investment five times over,” says Bekele.

By helping its customers achieve measurable gains, WinField United strengthens the bonds it has with them, deepening customer engagement. “Farming is very relationship-based, so, on top of the data, our goal is to get them to say, ‘WinField United is giving me the right advice and helping me make the right choices,’” says Bekele. Analytics is helping the company move from a strictly transactional relationship with its customers to one in which it is perceived to be a trusted adviser.

Winfield United’s program exemplifies heightened demand, across industries, for better customer engagement that depends on data and analytics. The 2018 MIT Sloan Management Review Global Executive Study and Research Report provides a detailed examination of data-driven customer engagement. The study is based on our eighth annual global data and analytics survey, conducted during 2017, which included 1,919 managers from 101 countries and interviews with 17 leading practitioners and thought leaders.

Our findings demonstrate a significant increase in the number of organizations that are using analytics to gain a competitive advantage and innovate — a key component of this shift is more effective use of analytics to improve customer engagement. Data and analytics allow organizations to use intelligence from feedback to tailor offerings that improve customer satisfaction. Several factors appear to be at work, including the use of a wide range of data sources, well-developed core analytics capabilities, and integration of artificial intelligence (AI) and the internet of things (IoT) into processes. Companies that have businesses as their main customers (business-to-business, or B2B) are gaining the most benefits from this shift, in part because they are able to share data with customers in a way that directly strengthens their relationship.

Key findings from this year’s research point to the following trends:

  • Competitive advantage from analytics continues to grow. More than half (59%) of managers say their company is using analytics to gain a competitive advantage. This is a higher percentage of respondents than in the previous two years.
  • Analytics is driving customer engagement. Organizations that demonstrate higher levels of analytical maturity saw a marked advantage in their customer relationships. The most analytically mature organizations are twice as likely to report strong customer engagement as the least analytically mature organizations.
  • Analytically mature organizations use more data sources to engage customers. Many organizations are already making use of data from customers, vendors, regulators, and competitors, but Analytical Innovators are more than four times more likely to glean data from all four sources. They also are much more likely to use a variety of data types — such as mobile, social, and public data — to engage customers compared with less analytically mature organizations.
  • Sharing data can improve influence with customers and other groups. Sharing data doesn’t mean giving away the farm. Organizations that share their data with others (customers, vendors, government agencies, and even competitors) reported increased influence with members of their ecosystem. Sharing data can enhance a company’s influence with not only customers but also a broad array of other stakeholders.

KEY TRENDS

Competitive Advantage From Analytics Increases — Even Though Terrain Is Shifting

The percentage of respondents who attribute significant competitive advantage to their analytics proficiency has risen for the second year, to 59%, up from 51% in 2015. This upward swing is the result of many factors. Our interviews with industry executives and academics highlight three.

One factor is the use of data and analytics to ward off new competitors that are themselves using analytics to enter fresh markets. Because of the more pervasive use of analytics,1 organizations must continue to improve their use of data and analytics to stay ahead. (See Figure 1.)

Figure 1: Competitive Advantage From Analytics

Between 2016 and 2017, the share of organizations reporting that analytics creates a competitive advantage rose 2 percentage points.

Consider health insurance. Within the constraints of a heavily regulated environment, the industry has long made extensive use of data and analytics to manage risk — the core competency of its business. Today, however, new competitors — organizations with analytics expertise that has been honed in a largely unregulated environment — are looming. For example, in October 2017, Amazon was granted pharmacy-wholesaler licenses2 in a dozen states, and the market was quick to react, with sell-offs of companies like McKesson Corp. and AmerisourceBergen Corp. The news also affected merger talks between CVS Health and Aetna Inc.3 Industry convergence is forcing some insurers to reconsider their roots as they acknowledge a need to be increasingly data-centric. “You’ve got Amazon considering becoming a pharmacy benefits manager and Google looking at moving into the clinical space, and we’re really going to need to move more in that direction,” notes a business manager we interviewed from a payer organization. “The insurance industry is all about trying to reduce risk, and that’s culturally one of the barriers to admitting we may sometimes need to introduce more of that risk in order for us to disrupt ourselves before we get disrupted by other industries,” he says. Staying the same may be riskier than change.

Another factor is the use of data to enhance customer engagement. Like many brick-and-mortar retailers, Mall of America continually looks for ways to build enduring relationships with its 42 million annual visitors, including a young generation disposed to shop online. While providing the free Wi-Fi access shoppers now expect, the mall uses these hot spots to collect aggregate data about foot traffic patterns, dwell times in particular locations, and the effect of mall events on visitor counts. Robust analytics — and the strategic daily decisions they enable for mall management and retail tenants — has become an indispensable tool that needs continuous refining.

In 2016, with the help of the Carlson Analytics Lab at the University of Minnesota’s Carlson School of Management, Mall of America mapped Wi-Fi data to its floor plans and identified clusters of anonymous shoppers based on their movement through the facility. Similar to online clickstream analytics, the data provided a behavior-based way to track customer segments. The mall also added full-time analysts to its staff. “We are creating a whole analytics mentality,” says Janette Smrcka, Mall of America’s director of information technology. “We now have strong business users who, once they get those dashboards, are really diving in. The more we expose our company to this, the more it spurs ideas.” For instance, Mall of America has begun to use weather data to more accurately predict mall traffic. “In the past, if schools closed due to very cold weather, we could be inundated with traffic unexpectedly, as parents send their kids to the mall,” explains Smrcka. By counting cars in the parking lots and relating the volume to weather patterns, Smrcka is able to provide forecasts to stores inside the mall so that they can adjust staffing levels.

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USING ANALYTICS TO IMPROVE CUSTOMER ENGAGEMENT

Star Wars: The Audience Turns to the Dark Side

Star Wars: The Audience Turns to the Dark Side

Here is a guest post from George McIntire

George McIntire

 

George McIntire is a data science writer, educator and Thinkful mentor. He’s interested in using the power data science to educate the public and make better sense of our world.

We’re about a week away from the premiere of the latest installment in the Star Wars saga: The Last Jedi. Almost three years have passed since The Force Awakens stormed the box office and fans of the franchise – both old and new – are expected to make the Last Jedi the biggest movie event of the year.

With numerous cliffhangers leftover from The Force Awakens and new questions stemming from The Last Jedi’s cleverly crafted trailers, millions have flocked to social media to voice their thoughts on the new film. Given all this chatter, we wanted to gain a deeper understanding of how the Star Wars fanbase collectively feels heading into opening weekend. Using Twitter’s API and data science techniques, our analysis showed that Star Wars fans overall are dreading to find out what happens in The Last Jedi.

Headed to the Dark Side Audiences Are

After analyzing nearly 33000 tweets, our analysis shows that Star Wars fans are turning to the dark side.

As demonstrated in the scatter plot above, the majority of Star Wars related tweets contain strong and ominous language. Polarity score is a metric used by data scientists to show to what degree text is negative or positive. Subjectivity score is another data science metric that measures to what degree text is objective or subjective. Nearly three-quarters of Star Wars related tweets score negative (polarity score <0), two-thirds of tweets have a polarity score of less than -0.3, and the median polarity score for all tweets is a staggering -0.55. This tweet by @dalordzprince perfectly encapsulates the anxiety captured in our data set:

Dire feelings towards the film extended to the film’s three most popular characters also. Tweets discussing Kylo Ren, Rey, and Luke Skywalker (the three most mentioned characters in our data set) all generated negative polarity scores.

The histogram above shows the distribution of positive and negative tweets concerning Rey, the mysterious heroine from The Force Awakens. An overwhelming majority were negative. While this might reignite memories of the racist and sexist #BoycottStarWars movement, we can partially attribute the negativity to the rumors swirling of Rey’s flirtations with the Dark Side. As @FelicityRidley’s tweet shows, some folks are concerned about this potential plot twist.

Luke Skywalker, seen in the final minutes of The Force Awaken and expected to play a significant role in The Last Jedi isn’t immune from the negativity either. A majority of Luke-related tweets received a polarity score less than zero with a median polarity score of -0.27.

So what’s gone wrong with the main protagonist from the original series? Many are starting to question whether Luke is in fact the Chosen One and some like @PracticallyGeek are nervous his storyline comes to an end in The Last Jedi.

Data Science Used We

To understand public sentiment around Star Wars: The Last Jedi, we undertook a three-step process.

First, we compiled a dataset over 33,000 Star Wars related tweets from the dates November 22nd to December 1st. Pulled using Twitter’s API search function, our data set included any tweet that contained one or more of the following terms: Star Wars, Last Jedi, #starwars, #lastjedi, and #maytheforcebewithyou.

Next, we honed in on two data science tools to conduct a sentiment analysis of the tweets in our dataset. Sentiment analysis is a natural language processing term that refers to the ability of computers to assign a sentiment score to written text. The first tool, the vaderSentiment Python library allows the computer to derive a polarity score for each tweet on a scale from -1 to 1. Data scientists use vaderSentiment frequently for social media sentiment analysis because of its ability to analyze short pieces of text. The second tool, the TextBlob Python library is a general purpose NLP tool that allows the computer to assign a subjectivity score for each tweet on a scale from 0 to 1.

Tool

Output

Description

Scale

vaderSentiment

Polarity Score

Measures to what degree text is positive or negative

-1 (most negative) to 1 (most positive)

TextBlob

Subjectivity Score

Measures to what degree text is objective or subjective

0 (most objective to 1 (most subjective)

Finally, we parsed our tweets using these two sentiment analysis tools. As mentioned earlier, our analysis found that Star Wars fans are feeling anxious leading up to the film’s premiere.


Star Wars: The Audience Turns to the Dark Side

How Biometric Authentication Works

How Biometric Authentication Works

Here is a guest post from Rowena Bonnette  on How Biometric Authentication works – 

Biometrics are the ultimate security protocol. Using security coding that is unique to each individual, you can be confident in the authenticator’s identity. We’ve seen biometric securities in science fiction movies, and we use fingerprint authentication daily on our smartphones. Now, let’s break down all of the different kinds of biometric authentication and discover how they work.

how-biometric-authentication-works

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DNA

It’s the most reliable identifier. DNA stays constant throughout a person’s life, making it a long-term identifier. And the data is easily digitized.

In 1985, a professor and geneticist in the UK pioneered DNA-based identity testing. Nearly a decade later in 1994, the FBI launched a national DNA database with two main components: DNA from crime scenes and DNA from convicted felons. By 2009, the FBI’s DNA database had 6.7 million profiles. This grew to 12.6 million profiles in 2016.

Hands and Fingers

Fingerprint: A fingerprint maps the ridgelines in the skin of one’s fingertips. Individuality is based on the location and direction of the ridge ending points as well as the splits along a ridge path. Fingerprint technology is popular — 52% of consumers want banks to add fingerprint scans to banking apps. Fingerprinting concepts have been around for a long while. In the late 1800s, Sir Francis Galton identified how fingerprint characteristics are individualized. In a 1903 short story, Sherlock Holmes found a fingerprint. Then in 1969, there was a major push from the FBI to develop a fingerprint identification system. By 1975, the FBI developed automated fingerprint scanners. In the late 1990s, we saw the emergence of commercial fingerprint verification products. By 2006, fingerprint readers were added to many laptops, and in 2013, the iPhone 5S released with Touch ID.

Fingernail: Research is ongoing about fingernail-based biometrics. There are two main categories. In fingernail bed scans, identification is based on the dermal structure underneath the fingernail. Surprisingly, even identical twins have different fingernail beds. To read the nailbed, electromagnetic waves are used. The second category is fingernail plate surfaces. This is currently being explored as a transient biometric that has a lifespan of about two months. For identification, three nail plates are used (ring, middle, and index).

Palm: Similar to fingerprint ID, palm recognition looks at the palm’s ridges, texture, spatial attributes, and geometric characteristics, such as the length of fingers and width of the hand. Using palmprints dates back more than 150 years. In 1858, handprints were used on worker contracts as an identifying mark. More recently in 2003, 30% of the prints lifted from crime scenes were of palms, not fingers. In 2004, state law enforcement agencies began to use palm print databases. Australia has a database of 4.8 million palm prints.

Vascular: A person’s vascular patterns are unique, do not change with age, and are difficult to forge. In this biometric, near-infrared light reveals a hand’s or finger’s blood vessel patterns. In 1992, technology of optical trans-body imaging was developed. The first reference to using blood vessel patterns as identifiers was in 2000.

Knuckle: This is an emerging biometric. The knuckles have stable and unique inherent patterns of geometry and creases. The middle and ring fingers have better distinguishing characteristics than the thumb, index, or little finger.

Eyes and Face

Retina: In this biometric, a person is identified by the unique pattern of blood vessels at the back of their eye. Retinal scans are second only to DNA in their precision capabilities. To scan the retina, a beam of low-energy infrared light is shined into the eye. Retinal blood vessels absorb light easier than surrounding tissue, creating a reflective imprint of the retina’s patterns.
In 1935, the New York State Journal of Medicine published the concept of retinal identification. Forty years later in 1975, technology caught up to the concept, and a device began development. In 1981, the first commercial retinal scanner was released.

Iris: In biometric identification via iris, the intricate structures of the iris are revealed with near-infrared illumination. An iris’ texture is developed during embryonic gestation. Identical twins have different iris patterns, and even the left and right eyes of the same person have different iris patterns. A scan can be captured through clear contact lenses, eyeglasses, and non-mirrored sunglasses.

Divination based on iris patterns dates back to ancient Egypt and ancient Greece. In 1949, a British ophthalmologist likened an iris’ architecture to a person’s fingerprint. By the 1980s, the iris identification concept was patented. In 1994, a patent was issued for iris identification computer algorithms with image processing, feature extraction, and matching. Then in 2006, iris scans became an internationally standardized biometric for e-passport. A decade later in 2016, 1 billion people were enrolled in India’s government iris scan database. The same year, the U.S. had 434,000 iris scans on file since the FBI launched the pilot program in 2013.

Face: There are several techniques for facial recognition. Traditional methods extract facial landmarks. 3D recognition looks at features and is unaffected by lighting changes or viewing angles. Skin texture analysis is a secondary metric that enhances recognition capabilities. Thermal face recognition identifies facial features even when they’re covered with hats, glasses, or makeup.

In the 1960s, the first facial recognition system was deployed, but the administrator had to manually locate features like eyes, nose, and mouth. By the 1970s, technology advanced to automated feature recognition. Over the next two decades, there were advancements in calculation methods and approaches. In 2001, Super Bowl surveillance images were compared to a database of digital mugshots, resulting in a public dialogue about privacy. As of 2015, the FBI database included 52 million faces, about ⅓ of Americans.

Behavioral

This metric is captured dynamically over a period of time, such as a few seconds.

Voice: This is a popular biometric for remote authentication because of the wide availability and data transmission capabilities of telephones and computer microphones. The physical structure of the vocal tract determines acoustic patterns, and individualized behaviors include motion of the mouth, voice pitch, and pronunciations.

In 1960, a Swedish professor identified the connections between physiology and specific sounds. Then in 1976, the U.S. Air Force used a voice authentication prototype built by Texas Instruments. Verification technologies have co-evolved with speech recognition.

Signature: Static signature recognition is a visual comparison of the authenticating signature against a stored signature. But dynamic signature recognition adds spacial coordinate measurements, pressure sensitivity, and pen inclination to the data points.

Keystroke: This method of identification analyzes the unique patterns in a person’s manner and rhythm of typing. It dates back to the 1960s when telegraph operators were able to be identified by their tapping rhythm. This is not a pass/fail authentication but a confidence measurement.

Gait: In this behavioral biometric, the cyclical movement of walking is an unobtrusive identifier. The emerging technology is still affected by footwear, terrain, fatigue, injury, and passage of time.

Even More Authentication Metrics

Earlobe: Geometric identification characteristics include an ear’s height, corresponding angles, and inner ear curve. It’s a stable biometric that does not change as people age. One of the advantages is that identification can take place at a distance.

Odor: Bloodhound dogs are historically used to track a person by scent. A person’s primary odor is stable over time. Secondary odors contain constituents that change with diet and environmental factors. Tertiary odors are externally sourced (lotions, soaps, perfumes, etc.). A canine alert-type system still requires research on the target vapor signature and mechanism for transport detection.

Sweat Pores: This emerging biometric has been used as a secondary factor in fingerprint identification to distinguish between a live finger and a dummy finger.
Lips: Used in conjunction with facial authentication, identification involves targeting unique characteristics like the size of the upper and lower lips, furrows, grooves, and the distance between the lines and the edges.

Tongue: This biometric is difficult to forge. Identification is based on color, geometric shape, and the physiological texture of the tongue


How Biometric Authentication Works

The Success Story of The Biggest Online Payment System -PayPal

The Success Story of The Biggest Online Payment System -PayPal

Here is a guest blog from Raj Vardhman. 

Raj  is an ardent supporter of responsible gambling and good casino practices, which is why he likes PayPal particularly much, as the e-wallet collaborates only with the most reliable casinos.In his free time Raj enjoys rock climbing and quality lager beer.

In the chart of successfully growing businesses, PayPal rightfully occupies one of the top spots. Founded in late 90’s by Elon Musk and a group of enthusiasts, PayPal is now recognised as the world’s largest online payment processor.

After seeing some ups and downs, the company proliferated. Between years 2000 and 2002 PayPal grew big enough to finally announce its entry to the charts of NASDAQ with $13 per share. Later, PayPal merged with eBay for the tremendous $1.5 billion, becoming the only payment gateway for eBay users.

PayPal is now available in 203 markets and multiple currencies. It allows customers to hold balances, deposit and withdraw their funds securely, with full compliance and control over their financial data.

The company also vouches for its moral standing in the market. Hence the decision to withdraw as a payment method from the majority of the gambling websites. The decision contributed to the reputation of PayPal; the platform is recognized as the world’s most reliable payment s with system ever growing user base. The company acquires different payment solutions to penetrate into the online retail market even further. Recently, they collaborated with the MasterCard, leading to the inception of the Secure Card service. This decision alone brought the company a revenue of the whopping $1.8 billion.

PayPal drives the time reduction for any transaction; financial data collection and manipulation is automatized. Information is processed through the innovative Informatica MDM, governance and PowerCenter platforms. Robust transaction control and security makes PayPal service the most preferred and trusted by the ever growing user base.

The prominent success story of PayPal has inspired many start-ups. Nevertheless, if you still think that transactions can’t go beyond bank accounts, we strongly suggest taking a closer look at our infographic below. Facts and figures speak for themselves; a quick and simplified system like PayPal is what you get by keeping up with times.

 

 


The Success Story of The Biggest Online Payment System -PayPal