Amazn go

“Seminar Report” Just Walk-out Technology
Institute of Engineering and Technology,
Bundelkhand University,Jhansi. Page 1
1.INTRODUCTION
Tech companies are constantly exploring new ways to sell us their goods, and Amazon's
latest example has plenty of people scratching their heads.The online retailer has announced
that it is opening a brick-and-mortar physical store in Seattle, Washington, so that you can
start buying your goods from Amazon in person rather than through Amazon.com. But the
most unique thing about this store, which is called Amazon Go, is that it doesn't have any
registers. You simply walk in, pick out what you want, and walk out. Amazon’s move to take
the grocery checkout counter completely out of the loop is the latest disappearing act for the
brick-and-mortar retail experience. We all know about Amazon as one of the world's leading
e-commerce hub. It has unveiled its first convenience store, a high-tech retail location called
“Amazon Go,” currently in a private beta testing in Seattle and scheduled to open to the
public early this very year. Amazon is calling this a "Just Walk Out" shopping experience.
1.1 A BRIEF IDEA
First there were supermarket shelves. Then came barcode scanners, then self-checkout lines,
and then online shopping. And now “No check-outs!”
Amazon has described Just Walk-out Technology as "a new kind of store with no checkout
required". That means, when you shop at Amazon Go, you'll never have to wait in line. The
store works with the new Amazon Go app. With that app, you can enter Amazon Go, take the
products you want, and go. The first Amazon Go store is basically a grocery store with
roughly 1,800 square feet of retail space.
Amazon said it began working on the store concept four years ago, with the idea that it
wanted to "push the boundaries of computer vision and machine learning to create a store
where customers could simply take what they want and go". Amazon Go therefore uses the
same types of technologies found in self-driving cars, such as computer vision, sensor fusion,
and deep learning.
The Just Walk-out Technology concept takes advantage of trends that already have been
changing retail – including smartphone apps that grant you access to the store, smart carts and
smart shelves that keep track of what you buy, and smart real-time inventory management on
the back end of the operation.

Figure.1. Front elevation of a typical Amazon Go- Just walk-out technology retail store.
1.2 HOW DIFFERENT IS JUST WALK-OUT TECHNOLOGY?
This technology is using a combination of artificial intelligence, computer vision, and data
pulled from multiple sensors to allow customers to only be charged for the stuff they picked
up. The computer vision aspect seems to indicate that there are cameras being used to track
you in the store. It'll be interesting to see the way it will successfully prevent stopping theft
and fraud.
The patent described a store that would work using a system of cameras, sensors, or RFID
readers to identify shoppers and the items they’ve chosen.
So, according to this Amazon patent application, which is describing Amazon's new Just
Walk Out technology, when a person exits the Amazon Go store, the store's system triggers a
receipt that is sent to the shopper indicating the items sold and the purchase price. As to how
Amazon would be able to connect a product with a specific shopper, the application
described the use of cameras that would take photos.
They would take photos when people enter the store, when they removed items from a shelf,
and when they left with items in their hands. There is also a mention of “facial recognition"
and user information, which may include images of the user, details about the user like height
and weight, user biometrics, a username and password, even user purchase history, etc.
This technology can detect when products are taken or returned to the shelves and keeps track
of them in your virtual cart. When you leave the store with your goods, Amazon will charge
your Amazon account (presumably the default payment option tied to the account), and send
you a receipt.

It is a camera-tracking system that also uses AI in the form of facial recognition or user
biometrics, as well as sensors, such as something in the label of products.
It’s called "just walk out" technology and when you walk out, your purchase is complete with
a receipt in your app, charged to your Amazon account. This is achieved by an entryway that
is similar to the subway turnstiles that you see in major cities. Yes, this sounds like magic,
retail magic.
Figure.2 The subway turnstile entrance area for the Amazon Go retail store.

2.DESCRIPTION OF THE TECHNOLOGY
The moment you enter an Amazon Go store, you scan your ID QR code to gain access. Only
Prime members can shop at the store and must have the app on a smartphone. There are no
cash registers or payment card machines. The app uses a number of systems including Geo
Location to place you as the bonafide user of the app and thus the customer entering the store.
It is at this time that Just walk-out technology will connect your QR Code scan with facial
recognition and cross confirm the customer’s identity. The Machine Learning system will
easily track the customer through the store and the entire shopping visit.
It is using a large spectrum of Artificial Intelligence (AI), Machine Learning (ML) and deep
learning garnered from decades of being a retailer. It starts with the hardware that includes
image sensors using camera optics, LIDAR arrays using laser sensing and other technology to
correctly identify the item on a shelf, taken off the shelf, returned back to the shelf or taken
out of the store.
The hardware is assisted by the 2009 acquisition of SnapTell by Amazon. They developed
image recognition technology that could identify a huge number of popular products just by
their images. By 2014 Amazon integrated this technology in its app for what has become
known as “show rooming”. This allows consumers to visit a local store, take a picture of a
product and instantly get a price comparison. This technology has been actively scanning
items at Amazon’s distribution center for over 6 years. This has build a Machine Learning
system that has a high degree of accuracy.
Every item in Amazon Go store can be identified in seconds with just about 30% of the
product visible with the current technology. Some of the identification is assisted by the
absolute location of the item on the shelf and the position of the customer. There are other
sensors that may also be in use with some items.
All of these sensors confirm the accuracy of the item. Over time as more customers shop at
Amazon Go stores the accuracy will increase to over the 99 percentile. The system is an order
of magnitude more complex then the current self-checkout systems that use a very minimal
degree of AI.
Just like when you visit a website and you are logged in, the Amazon Go shopping
experience is tracking all of your shopping behaviors. Over time this will inform Amazon on
the exact placement of products and how consumers may interact with them. Machine
Learning about the amount of time spent in the store and the transverse path you make
through the store will assist Amazon in creating customized, on demand discounts related to
your current or prior buying behavior.

Figure.3. A snapshot from the advertisement.
The sum total of AI, ML and advance sensors is combined in a way that has never been seen
before. It is a fundamental shift in how retail sales and retail payments will take place in the
future. The short answer is that it is an amalgamation of:
 Deep Learning Algorithms
 RFID Tags
 Artificial Intelligence
 Amazon Rekognition – Image Detection and Recognition
 Computer Vision
 An array of “Fusion sensors”
 Decades of data on how humans shop
The technology also is expert in identifying products using image recognition. Combine this
with the Fusion Sensors that cross confirm the new virtual “shopping cart” you create not
only just by taking an item in your hand, but also by putting it back, there is actually even
less of a likelihood of an erroneous transaction.

3.DEEP LEARNING ALGORITHMS
Deep Learning is a subfield of Artificial Intelligence that has been influenced by the
workings of brains. While simple models imitating how neurons in our brain work have
existed since the mid-1900’s, it’s only relatively recently that we have started creating several
layers of interconnected artificial neurons (hence the ‘Deep’ in deep learning) to process data.
To make good use of deep learning, there are two prerequisites: firstly, huge datasets, such as
those generated by images (e.g. google image search), video or audio feeds (e.g. driverless
cars), or browsing behavior on internet sites (e.g. e-commerce website navigation data).
Secondly, they require intense, typically distributed (on different computers) calculating
power.
Figure.4. The advertisement snapshot emphasising Deep Learning Algorithms.
3.1. ARE DEEP LEARNING ALGORITHMS COMMON?
Outside of technology giants and academia, deep learning algorithms are still relatively rare,
firstly because of the required computing power, secondly because of the scarcity of large,
well-structured datasets, and finally because of the technical hurdles to implement relatively
complex calculations at scale.
However, a number of technologies (Tensorflow, Theano and Keras, to name a few) have
made deep learning significantly more stable and accessible outside of the realm of academia.
Today, we see a quickly growing number of commercial deep learning applications, showing
that the technology, while young and still growing, has become useable in
businesses.However, deep learning is not useful in all environments, and comes with
challenges: Firstly, it is very hard to understand how a deep learning algorithm is making
decisions. This means that they are not suited when the question of "How did we make this
decision?" matters as much as the decision itself. Secondly, because of this 'black-box' nature
of deep learning, it is difficult to understand if the algorithm isn't making spurious decisions

based on issues with the data that is used to teach the algorithm. Deep learning, as such, still
requires a lot of fine-tuning.
3.2 WHY IS JUST WALK-OUT TECHNOLOGY USING DEEP
LEARNING ALGORITHMS?
Independently of the maturity and challenges of Deep Learning, it has two compelling
reasons to use these techniques:
 Return on Data: Inside of the Amazon Go stores, the company is set to start
collecting huge sets of data: video feeds, movement sensors, RFID trackers, and much
more, in order to know which customer has taken which item. As we previously
stated, deep learning algorithms are particularly well suited to handle these kinds of
tasks.
 Reputation: The company is further focusing on Artificial Intelligence as driver of
value in their operations. It recently formed a partnership with IBM, Facebook and
Google to further develop Artificial Intelligence. Amazon Go is a perfect window of a
use case of the Amazon Artificial Intelligence capacity, and technology leadership in
the retail world. Investing in deep learning allows them to further attract talented
researchers to help them develop the key technologies of the future.
3.3 WHAT IS DEEP LEARNING ENABLING THE TECHNOLOGY TO
DO?
Undoubtedly, Deep Learning, along with other algorithms, will be used to do more than just
automating checkout. We list some of the main reasons we believe Amazon is set to continue
to invest in in-store AI:
Data-driven Products Display: It will be able to track the movements of the consumers in
the store. Artificial Intelligence will then be able to learn from customer flows how to display
products at which place to increase cart size, and maximize sales.
Better assortment renewal: It will gather data about when an item is taken by a customer.
Deep Learning Algorithms can be able to define what are the best times, volumes and items
for assortment renewal, in order to decrease costs and ensure permanent customer
satisfaction.
Extreme Personalization of Services and Offerings: With in-store tracking, It will be able
to master omnichannel retailer. By having data not only about their customers as web visitor,
but also as store visitor, it will be able to finetune at a very granular level how it targets its
customers.

4.RFID TAGS
These are an advanced form of simple RF tags in that they uniquely identify the article to
which they've been attached: the radio signal that zaps from the article to the receiver
contains a digitally encoded identifier. That's how self-checkout machines in libraries work:
they beam radio waves into the RFID tag in the back of the book, receive the radio signal
back from the book, and decode this to figure out a digital code that uniquely identifies which
book you want to check out. A computer attached to the scanner does the rest (so in a library,
the self-checkout machine communicates with the library's computer to update the main
database whenever you check out or return a book). Unlike RF tags, RFID tags tend to work
over much shorter distances. Some actually have to be held right next to a reader device,
while others operate at a distance of 10cm (4 inches) or less.
Simple RFID tags are described as passive. Instead of containing batteries, they work entirely
by responding to the incoming radio waves from the scanner or transmitter. There is just
enough energy in those radio waves to activate the RFID chip. Passive tags typically send and
receive signals only a few centimeters, but not much more. An alternative form of RFID
technology, known as active tags, contain more advanced chips and tiny batteries to power
them. They can send and receive signals over much greater distances.Passive RFID tags
contain just three components:
 The chip—generates a unique identifier code for the particular tag.
 The substrate—the backing material (typically paper or plastic) to which the antenna
and chip are fixed.
 The antenna—catches incoming radio waves and sends them back out again.
Figure.5. A Passive RFID.

As we can see from this photo, most of the space in an RFID tag is occupied by the antenna:
the oval-shaped tracks around the edge. The antenna needs to be this big both to pick up radio
waves from the transmitter and (because there are no batteries) to convert them into energy to
power the chip. The chip itself is tiny—sometimes as small as the point of a pencil. Anti-
shoplifting RF tags are often smaller and simpler than this: instead of needing a chip to
generate a unique identifier code, all they have to do is receive the incoming radio waves and
retransmit the same electromagnetic energy at a different frequency.
4.1 ANTI-THEFT DETECTORS
These nifty devices work on Radio Frequency to detect items and are used in many countries
worldwide to nab shoplifters.So when someone shops at the Amazon GO store and exits,
passing through one of these,
Figure.6. Anti-theft ports at a store.
Radio Frequency waves are used to trigger the RFID(radio-frequency identity card), which
can be built into the barcode of items, to automatically list the items in the shopper’s cart and
charge the customer the amount; all this with no human interaction and no effort from the
customers!
Figure.7. A better view of an anti-theft port which uses radio frequency waves.

If you walk through the doorway without paying for something, the radio waves from the
transmitter (hidden in on one of the door gates) are picked up by the coiled metal antenna in
the label. This generates a tiny electrical current that makes the label transmit a new radio
signal of its own at a very specific frequency. The receiver (hidden in the other door gate)
picks up the radio signal that the tag transmits and sounds the alarm. Why doesn't the alarm
sound when you pay for something? You may have noticed that the checkout assistant passes
your item over or through a deactivating device (sometimes it's incorporated into the ordinary
barcode scanning mechanism, and sometimes it's completely separate). This destroys or
deactivates the electronic components in the RF label so they no longer pick up or transmit a
signal when you walk through the gates—and the alarm does not sound.
4.2 DISTINCTIONS
With RFID tags installed in their pilot stores, they could gather that training set quickly.
The data would then be fed into a deep learning system offline. Once the system was able to
identify the people and items from the training set videos perfectly, it could start running
alongside the RFID as an increasingly accurate double check, until eventually the RFID,
weight sensors, and so on could be phased out. It would be essentially an eagle-eyed robotic
shopkeeper watching every customer and instantly and continuously totaling their items.
Welcome to the future!

5.ARTIFICIAL INTELLIGENCE
Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, the
field of AI research defines itself as the study of "intelligent agents": any device that
perceives its environment and takes actions that maximize its chance of success at some
goal. Colloquially, the term "artificial intelligence" is applied when a machine mimics
"cognitive" functions that humans associate with other human minds, such as "learning" and
"problem solving" (known as Machine Learning). As machines become increasingly capable,
mental facilities once thought to require intelligence are removed from the definition. For
example, optical character recognition is no longer perceived as an exemplar of "artificial
intelligence", having become a routine technology. Capabilities currently classified as AI
include successfully understanding human speech, competing at a high level in strategic
game systems (such as Chess and Go), self-driving cars, intelligent routing in content
delivery networks, and interpreting complex data.
AI research is divided into subfields that focus on specific problems or on
specific approaches or on the use of a particular tool or towards satisfying
particular applications.
Figure.8. Specimen: Of the automatic shopping cart.
The central problems (or goals) of AI research include reasoning, knowlede, planning,
learning, natura language processing (communication), perception and the ability to move
and manipulate objects. General intelligence is among the field's long-term goals.
Approaches include statistical methods, computational intelligence, and traditional symbolic
AI. Many tools are used in AI, including versions of search and mathematical optimization,
logic, methods based on probability and economics. The AI field draws upon computer
science, mathematics, psychology, linguistics, philosophy, neuroscience and artificial
psychology.

The field was founded on the claim that human intelligence "can be so precisely described
that a machine can be made to simulate it". This raises philosophical arguments about the
nature of the mind and the ethics of creating artificial beings endowed with human-like
intelligence, issues which have been explored by myth, fiction and philosophy since
antiquity. Some people also consider AI a danger to humanity if it progresses
unabatedly. Attempts to create artificial intelligence have experienced many setbacks,
including the ALPAC report of 1966, the abandonment of perceptrons in 1970, the Lighthill
Report of 1973, the second AI winter 1987–1993 and the collapse of the Lisp machine
market in 1987.
In the twenty-first century, AI techniques, both "hard" and "soft" have experienced a
resurgence following concurrent advances in computer power, sizes of training sets, and
theoretical understanding, and AI techniques have become an essential part of the technology
industry, helping to solve many challenging problems in computer science.

6.AMAZON REKOGNITION – IMAGE DETECTION AN
RECOGNITION
What do we see when you look at this picture?
Figure.9. An image of a dog.
You might simply see an animal. Maybe you see a pet, a dog, or a Golden Retriever. The
association between the image and these labels is not hard-wired in to your brain. Instead,
you learned the labels after seeing hundreds or thousands of examples. Operating on a
number of different levels, you learned to distinguish an animal from a plant, a dog from a
cat, and a Golden Retriever from other dog breeds.
6.1 IMAGE DETECTION
Giving computers the same level of comprehension has proven to be a very difficult task.
Over the course of decades, computer scientists have taken many different approaches to the
problem. Today, a broad consensus has emerged that the best way to tackle this problem is
via deep learning. Deep learning uses a combination of feature abstraction and neural
networks to produce results that can be (as Arthur C. Clarke once said) indistinguishable
from magic. However, it comes at a considerable cost. First, you need to put a lot of work
into the training phase. In essence, you present the learning network with a broad spectrum of
labeled examples (“this is a dog”, “this is a pet”, and so forth) so that it can correlate features
in the image with the labels. This phase is computationally expensive due to the size and the
multi-layered nature of the neural networks. After the training phase is complete, evaluating
new images against the trained network is far easier. The results are traditionally expressed in
confidence levels (0 to 100%) rather than as cold, hard facts. This allows you to decide just
how much precision is appropriate for your applications.

6.2 WHAT IS AMAZON REKOGNITION?
Amazon Rekognition is powered by deep learning and built by Computer Vision over the
course of many years, this fully-managed service already analyzes billions of images daily. It
has been trained on thousands of objects and scenes, and is now available for you to use in
your own applications. We can use the Rekognition Demos to put the service through its
paces before dive in and start writing code that uses the Rekognition API.
Rekognition was designed from the get-go to run at scale. It comprehends scenes, objects,
and faces. Given an image, it will return a list of labels. Given an image with one or more
faces, it will return bounding boxes for each face, along with attributes. Let’s see what it has
to say about the picture of my dog (her name is Luna, by the way):
Figure.10. This is how Rekognition works and displays all data about the image.
Rekognition labeled Luna as an animal, a dog, a pet, and as a golden retriever with a high
degree of confidence. It is important to note that these labels are independent, in the sense
that the deep learning model does not explicitly understand the relationship between, for
example, dogs and animals. It just so happens that both of these labels were simultaneously
present on the dog-centric training material presented to Rekognition.
You can also use Rekognition to compare faces and to see if a given image contains any one
of a number of faces that you have asked it to recognize.

6.2.1 AVAILABILITY
All of this power is accessible from a set of API functions (the console is great for quick
demos). For example, you can call DetectLabels to programmatically reproduce my first
example, or DetectFaces to reproduce my second one. You can make multiple calls
to IndexFaces to prepare Rekognition to recognize some faces. Each time you do
this, Rekognition extracts some features (known as face vectors) from the image, stores the
vectors, and discards the image. You can create one or more Rekognition collections and
store related groups of face vectors in each one.
6.3 APPLICATIONS
Rekognition can be used in several different authentication and security contexts. Itcan
compare a face on a webcam to a badge photo before allowing an employee to enter a secure
zone. It can perform visual surveillance, inspecting photos for objects or people of interest or
concern. This is how it it works in Just walk-out technology.

7.COMPUTER VISION
In simple terms, Computer vision tasks include methods for acquiring, processing, analyzing
and understanding digital images, and extraction of high-dimensional data from the real
world in order to produce numerical or symbolic information, e.g., in the forms of decisions.
Computer vision is an interdisciplinary field related to, e.g., artificial intelligence, machine
learning, robotics, signal processing and geometry. The purpose of computer vision is to
program a computer to "understand" a scene or features in an image.
Figure.11. Computer Vision.
7.1 IMPLEMENTATION IN JUST WALK-OUT TECHNOLOGY
1. Weight sensors: Similar to the weight sensors installed in self-checkout kiosk in
Walmart or Target.
2. Trigger Switches: All the items are arranged very well in a straight order. They have
an inventory tracking mechanism that would trigger a “item lifted” stage. Next stage
(stage 2) would be to identify the shopper. This is where they will use various
techniques such as: Face recognition (from the time of entry where one scanned his
QR Code to link his identity to his Amazon Account).

3. Motion Monitoring: Movement of every person who enters the store could be
tracked using sensors and cameras (cameras are sensors too!) until the user exits the
store. The movement data generated this way will then be used for shopper
identification in the stage 2. A good technique to implement motion monitoring could
be to use a shopper’s phone location tracking API and periodic sampling of motion
data. For example: CoreLocation API of iOS has been getting consistently better.
A coordinate system have been built by the Amazon Go team to track every shopper,
and then the data would help resolve the stage 2.
4. The development team at Just walk-out should have definitely thought of a
probabilistic model for shopper identification where the certainty of identity will
increase based on different sensors. This model will be put to test when a lot of people
will enter the store. An interesting case to consider would be when two twins enter the
store wearing same clothes. If the physical appearance of the individuals is considered
to be exactly the same (hypothetical), then the model will weigh in other factors
(more sensors, human supervision, etc.) to complete stage 2.
5. Once stage 2 ends, all the items in a virtual shopping cart will be billed onto the user’s
Amazon account. The billing will have to be credit based unless Just walk-out
requires a prepaid balance. I would personally not like to keep a prepaid balance.
There might be losses from users who fail to pay back their balances. User’s credit
score will be affected most likely only when Amazon transfers the debt to a collection
agency. Although, Amazon has years of experience in payment processing in all
domains and also several partnerships that can be leveraged.
6. Billing will trigger once the user exits the store and the Amazon Go system will send
a receipt to the user for that particular shopping visit.
7. The Just walk-out system just like most Computer Vision projects will become better
with time and pilot initiatives. Also, identification of recurring shoppers will become
easier for the system with time due to previous user behavior.
8. Lastly, the whole Just walk out store is a sophisticated system where users interact
with it. Amazon’s researchers could potentially use all the data footprint available to
them to improve shopper identification. The technology that would be built for this
application could in fact be used for many other purposes and the greater good. It
could be installed in Airports, licensed to brands and stores where shoplifting is a big
trouble, preventing terrorism, etc,. Nevertheless, this system will be an exemplary AI
application. I see a lot of patents getting filed by Amazon in near future unless they
want to keep their technology a secret (ofcourse!)
9. One must also note that related technology for facial recognition of large masses and
identity verification does already exist amongst powerful agencies and organizations
with lots of resources.

8.SENSOR FUSION
In addition, it uses a technology called sensor fusion, which brings together data from
different sensors to increase the reliability and accuracy of the results. Here’s how the patent
filing describes the confluence of sensor data.
Figure.12. Some of the sensor technology at the Just walk-out retail store.
A lot of cameras and possibly lasers (in the form of LIDAR sensors) tracking what people do
in the store, what items from where get picked up, and what the user is carrying with thIn
some implementations, data from other input devices may be used to assist in determining the
identity of items picked and/or placed in inventory locations. For example, if it is determined
that an item is placed into an inventory location, in addition to image analysis, a weight of the
item may be determined based on data received from a scale, pressure sensor, load cell, etc.,
located at the inventory location. The image analysis may be able to reduce the list of
potentially matching items down to a small list. The weight of the placed item may be
compared to a stored weight for each of the potentially matching items to identify the item
that was actually placed in the inventory location. By combining multiple inputs, a higher
confidence score can be generated increasing the probability that the identified item matches
the item actually picked from the inventory location and/or placed at the inventory location.

9.ADVANTAGES & LIMITATIONS
Honestly this idea is genius. Why? Because this technology takes away the line aspect and
can speed up grocery shopping for the busy bees.Moreover, the whole project has multiple
advantages for amazon.
 It gets user data i.e their purchase patterns. The technology can sell these data, process
your purchasing patterns in their data ware house and give you selective coupons on
the products you are likely to buy next time. Like if you are buying Cola regularly, it
might give you a personalized coupon discount option, to make you buy an other
bottle or Cola or pepsi, and have savings.
 The app will also, show the customers the super markets around, by tracking location
where the cutomer can purchase with discounts on specific products. Like, while you
are on ride or walk, if you pass by a selected store, which has a discount on the PEPSI
product you buy, you are notified with push notification, which will most likely make
user go for a purchase. Thus the continous user interaction with amazon is
established.
 By going cashless, Just walk-out technology looks to meet two consumer demands –
Speed and ease. A conventional set-up can be aggravating and time-consuming: wait
in line, upload your shopping basket, deal with coupons, and bagging your items up
There are no such limitations to this technology. But there are certain disadvantages for the
retailers. The three reasons retailers should fear Amazon Go – Just walk-out technology are
given below –
 Its introducing video shows the convincing store in which the company has
broken conventional supermarket wisdom and its foray into food is one traditional
retailers should fear.
 “Just walk-out” says it all! Its simple straight forward and easy to understand for
shoppers and clearly underscores the benfits. The grocery industry has a tendency
to name and describe complex technologies in a way that confuses. This
technology breaks through all that.
 The third reason is Merchandising. The video is shot in the actual store and its
display appears to be on target.

10.CONCLUSION
“Any sufficiently advanced technology is indistinguishable from magic”- C. Clarke.
When Amazon invented 1-Click buying in 1997, it was said by many observers “This is
crazy, it is too fast. There will be too many false transactions”. It turns out after decades of 1-
click by Amazon and Apple (a licensee) this is not even a rounding error of error cases.
Amazon perfected 1-click shopping at the dawn of web commerce. No company in the world
has more data about buying behavior related to this type of system. Similarly, this technology
welcomes us to the future!
This magic is all achieved through a number of very advanced technologies. It is clear
Amazon thought about this for over 4 years and perfected the use case inside of their own
warehouses. Quite unknown and unseen by many is how Amazon cross confirmed the ML
and AI based image recognition they pioneered.
“This is just the beginning.” As “Moore’s Law says that computing power doubles every 18
to 24 months, and if that law holds, automation will creep into more and more corners of our
life, including shopping, employment and more. Governments will need to start studying the
coming technological wave and take steps to ensure that their citizens’ needs will be
addressed as employment opportunities fall.”

REFERENCES
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business-applications.html
 https://www.wired.com/insights/2014/07/machine-learning-just-walk-out-systems-
next-evolution-enterprise-intelligence-part/
 https://en.wikipedia.org/wiki/Artificial_intelligence
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 http://www.research.amazon.com/software/AMAZONResearch/multimedia/Compute
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 Terdiman, Daniel (2014) .Amazon’s TrueNorth processor grab
technology.http://www.cet.com/news//
 http://fortune.com/2016/04/08/hpe-sensor_fusion/

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