Machine
Learning (ML) and Artificial Intelligence (AI): Big Data and 3 D Printing – Part four
by
Dr. RGS Asthana
Senior
Member IEEE
Figure 1: A structure from plastic
pavilion created through AI-powered 3D printing - the five-meter high structure was
constructed by a robot-armed printer guided by AI-powered computer vision.
Summary
We discuss here some
details of ML, AI and Big Data tech apps in
areas including Data Security, Personal Security, Financial Trading,
Healthcare, Marketing Personalization, Fraud Detection, Making Recommendations,
Online Search, Natural Language Processing, Smart Cars and Self- driving
Vehicle.
Similarly, we do describe
some Apps using 3D printing, AI & ML as well as big data tech in Industries
such as Medical, Aerospace and Aviation, Automotive, Industrial printing,
architecture, Shoe and Food.
The way forward
section gives brief on Apps developed using ML, AI, Big Data and 3D printing and
also some insight on where this multi-disciplinary approach will take us.
Keywords
Machine Learning (ML) Tools, Artificial Intelligence (AI), Neural Networks, Internet of Things (IoT),
Prelude
Before we read this article it is recommended that one also
sees references from [1-7]. As knowledge is considered key for learning, early
computers had severe limitation and that was lack of data. Today, Computer, LapTops,
Tabs and mobiles have Operating System software which takes care of inputs
coming from either keyboard, Internet, Wi-Fi, Bluetooth or cloud connectivity. The data from these sources always comes in a
format which OS can understand and display correctly if so desired. This has
led to vast amount of data availability on the cloud, social media as well as
on the internet also thus overwhelming the earlier limitation. With advances in
Computer Vision technology, it has become possible to tag objects in any
picture with minimum and necessary human intervention only.
"Big Data" concept/technology [8] which came
in existence only a few years ago; is no more a common buzzword and the recent advances
in ML and AI enable labeling and synthesis of huge amounts of training data
through big data analytics. We have self-service big data
tools on the web for data pre-processing before it may be useable by ML and AI software
and become suitable for unsupervised learning. One can appreciate, ML systems can only be as
good as the data they train on and the secret is transforming raw operational
data into learnable features.
Ai Build – a London firm - has created [9] a robot-armed printer,
capable of making complex patterns at amazing speeds. 3D printers as usual produce designs
layer by layer while following a digital blueprint. But because even minimal
mistakes can be a disaster to the whole structure, they have to plot data
slowly as compared to traditional industrial machines.
Foundry app from MIT is like Photoshop for 3-D
materials, which lets you design objects
made of new composite materials that have the optimal mechanical, thermal, and
conductive properties required for a task, the only limitation creativity in a
person using this software is. In brief,
with 3D printing one can manufacture things which are not possible to
manufacture with traditional manufacturing process (see figure 1).
Today Artificial Neural
Network (ANN) [2] based systems are becoming popular as they function more
similarly to a human brain. ANN are formed from connected “neurons”, all
capable of carrying out a data-related task – such as recognizing something,
failing to recognize it, matching a piece of information to another piece and
answering a question about the relationship between them.
A
neuron passes on the results of its work to an adjacent neuron, which then
processes it further. The network changes
and adapts based on the data that passes through it, so as a more efficiently transaction
with the next bit of data it comes across, it can be thought of as “learning”,
in much the same way as our brains do. Big
Data has played a major role in training and thus improving learning.
The term ‘Deep learning’ is derived from “deep” neural nets which are
built by layering many networks on top of each other. Due to the increasing power and falling price
of computer servers and advent of cloud computing, machines with enough processing
power are now available as well as viable to run these networks. Now you don’t need to own infra but democratization
of data or pay for actual use by minute in server- less environment is becoming
common way of processing data today.
Big Data
Apps [3-5]
It may be noted that Big
Data is a technology which is used
within a process where as 3D printing
is at the output stage of a process. As
per IBM, 90% of data
available today in the whole world,
is mainly generated from sources such as social media, IoT [6], transactions, pictures and videos. ML and AI are dependent on large amounts of data. But big data is hard
to organize and analyze. As more and more data is available the
ML, AI and Big Data tools including Self-service big data tools available on the web are
most helpful in analyzing and handling
this data and draw meaningful conclusions based on the intelligent
algorithm invented while doing research on ML, AI and Big Data, thus, making the learning process accurate and robust. ML algorithms can process more
information not only speedily but also spot more patterns than their human
counterparts. There are thousands of cases which are better handled with the
help of automated procedures based on ML, AI, Big Data procedures but we site only a few. Areas where Big Data is playing
a role with ML and AI include:
Data Security
In
situations, where ML algorithms can look for patterns in how data in the cloud
is accessed, and report abnormalities that could predict security breaches. Well,
very often it is personal data which you volunteer to offer while say filling
car insurance forms online and you don’t want it to get in wrong hands.
Personal Security
ML helps
to eliminate false alarms and spot things human screeners might miss in security
screenings at airports, stadiums, concerts, and other venues. Use of
technology, not only ensures safety but also speeds up the process
significantly.
Financial Trading
To
predict what the stock markets trend on any day is an area of interest to many
people. But ML algorithms are improving their performance almost every day.
Many trading firms use proprietary systems to predict and execute trades at
high speeds and high volume. And humans can’t possibly compete with machines
when it comes to consuming vast quantities of data or the speed with which they
can execute a trade.
Healthcare
Visualize
that you enter a hospital/clinic to meet your doctor with an ache. The doctor enters your symptoms in his/her
laptop/computer which tells your doctor how to diagnose and treat your problem
based on latest research taking into account your family history and EPR.
Similarly, an MRI or an X-ray can be better examined by the help of a
computer as radiologist cannot detect problems that could be too small for a human
to see.
One
study used computer assisted diagnosis (CAD) when to
review the early mammography scans of women [14] who later developed breast
cancer, and the computer spotted 52% of the cancers as much as a year before
the women were officially diagnosed.
In medicine, ML is being used to process genomic data to help
doctors understand, and predict, how cancer spreads, leading to
development of more effective treatments for the disease. IBM’s Watson [14] helps oncologist to make
the best care decisions for their cancer patients.
The
company Medecision [15] developed
an algorithm to identify eight variables for predicting avoidable
hospitalizations in diabetes patients.
As more
EPRs (meaning Electronic Patient Record data) a ML and AI based system sees
that reflect dangerous sepsis events in hospitals, the better a system can
predict them before they really happen.
Marketing Personalization
Marketing
personalization is all about how to know more about your customers so you can
sell them exactly what they want. Now in retail, ML enables
to match shoppers with products they would like to buy online whereas in the
bricks ‘n’ mortar world shop assistants use to personalize the service they
offered to their customers.
Google
was first to give selective ads only to those who wish to buy a particular
item. At online store if you look at a product
but don’t buy it — you will soon see digital ads across the web for that exact product for days afterward.
Fraud Detection
ML today
is able to spot cases of fraud across many fields. PayPal, for example, is
using ML to fight money laundering by precisely distinguishing between
legitimate and fraudulent transactions between buyers and sellers.
Making Recommendations
Amazon
or Netflix services use such Apps. ML algorithms analyze your activity on the
net while you do online shopping or write a mail to your friend and compare the
info with millions of other customers in the database to determine what you
might like to buy next and then recommend that product to you. An interesting
area of successful use besides many of ML and AI algorithms is collecting
fitness data for users through smart sensors and tracking similar users based
on the fitness data captured in the database and analyzing and then
recommending fitness products to the users [17].
Online Search
ML algorithms are exploited by Google and
its competitors in improving the Search operation i.e. what the search engine
comprehends [18] by your search input? RankBrain [4], the new AI/ML algorithm, is
now used by Google for SEO. RankBrain algorithm decides what mixture of core
algorithms are to be used to get best search results. For instance, in certain
search results, RankBrain might learn that the most important signal is the
META Title. Adding more significance to the META Title matching algorithm might
lead to a better searcher experience. But in another search result, this very
same signal might have a horrible correlation with a good searcher experience.
So in that other vertical, another algorithm, maybe PageRank is used by Google.
This implies that, in each search result, Google has a completely different mix
of algorithms. RankBrain, in fact, is only a computer program used to sort
through the billions of pages and select the ones it finds most relevant for a
particular query. The overall search
algorithm of Google is called ‘Hummingbird’. As per Google [16], gradual
rollout of RankBrain began in early 2015.
This approach of Google in using ML for page ranking makes life of SEO
industry difficult and industry is trying to catch up with Google and modify
websites to get better page ranking. Thus use of ML/AI tool by Google - called
RankBrain - has really changed the future of SEO Industry. RankBrain and other
forms of AI will keep on improving with time and at some point surpass the
human brain. And at this point, nobody knows where this technology will lead
us.
Natural Language Processing (NLP)
As
per Wikipedia, Natural
language processing (NLP) is a
field of computer science, AI, and computational
linguistics concerned with the interactions between computers and human (natural) languages and, in particular, concerned with
programming computers to fruitfully process large natural language document(s).
NLP is being
used in all sorts of exciting applications across disciplines. ML algorithms
with natural language can stand in for customer service agents like SIRI, and
Cortana from Apple Inc. and Microsoft corp. respectively.
Smart Cars
The smart cars use combo of technologies including ML. AI and Big
Data and may not only
incorporate Internet of Things [6] in automotive Technology but also utilities
like Vacuum cleaners and Smart thermostat solutions.
In smart cars, AI based systems learn about its owner and its environment,
i.e., it may adjust the internal settings like temperature, audio, seat
position, etc. — automatically based on the driver, report and even fix
problems and also drive itself
We are already seeing
trials of driverless cars from large companies such as Audi, Tesla and Google
to name a few, with a number of other enterprises viz. GM, Fiat Chrysler and
Ford are developing new solutions and want to put their cars for show in
less than 5 years. Apple seems to have some thoughts on the project. SDCs are likely to be more efficient
and safer than conventional cars with people driving the car. Moreover, SDCs
are likely to reduce congestion as well emissions.
In 2018 the first SDCs will appear
for the public. Why should one own a car today especially in urban
areas? This question comes to one’s mind, particularly, when you can
call a car with your phone and a car will show up at your location and
drive you to your destination. You don’t have to worry to park it, you
only pay for the driven distance and can be free to do anything you like.
Self-driving Cars
(SDCs) [20, 21]
We are already seeing trials of
driverless cars from large companies such as Audi, Tesla and Google to name a
few, with a number of other enterprises, viz., GM, Fiat Chrysler and Ford are developing new solutions and want to put
their cars for show in less than 5 years. Apple seems to have some thoughts on
the project.
SDCs are likely to be more efficient and
safer than conventional cars with people driving the car. Moreover, SDCs are
likely to reduce congestion as well emissions.
In 2018 the first SDCs will appear
for the public. Why should one own a car today especially in urban areas? This question comes to one’s mind,
particularly, when you can call a car with your phone and a car will show up at
your location and drive you to your destination. You don’t have to worry to park it, you only
pay for the driven distance and can be free to do anything you like.
If one extrapolates this scenario, one will
soon realize that children in future will not need a driver's license as they
would not like to own a car. Further, it
will change the cities, because we will need very few cars to meet the
requirement. Parking space allocated in the cities is likely to get free and
can be converted into parks and thus change the landscape.
Worldwide about 1.2 million people die
each year in car accidents. We at
present have one accident every 100,000 km, but with SDCs on road, this rate is
likely to fall to one accident in 10 million km. That will save about
million lives each year.
Uber has already launched its driverless
taxi fleet in Pittsburgh, USA [20]. The
cars have lasers, cameras and number of sensors but no driver. The cars are to
be operated by the web based application. To begin with the riders will be
accompanied by two technicians well trained to take over driver’s role if the
situation arises. This shows that driver less cars will be perfect much earlier
than 2030.
With the
driverless cars becoming popular all driving Jobs in other areas will also disappear.
Along with driving vehicles, the transportation industry has a huge
number of supporting roles that will also vanish. Virtually every vehicle that
requires a human operator today will find itself competing with an autonomous
version sometime in the future. Road construction and repair is a huge
industry that will eventually be taken over by unmanned bots and drones.
As we move
from owned to shared vehicles, much of the transportation economy will also
disappear. Here are a few more of our
soon-to-be-forgotten professions. There
are a number of businesses that keep our cars operational and looking good.
These too will dwindle over time.
Because of all the things that can go wrong in today’s congested
traffic, many other issues will also disappear. With cars today only being
used 4% of the average day, we build a massive parking infrastructure to
accommodate both the long-term and short-term storage of unused vehicles. These
will all lose their importance over time. In an autonomous vehicle era,
most police departments will shrink to a fraction of their current size.
Future highways will not require near as many safety features. While we will still need to repair roads in
the future, repair activities will no longer be a major impediment to the flow
of traffic. Traffic law has grown to
become a significant portion of the justice system penal code. In our
autonomous future, every car will be driven exactly the same way, so ageist,
sexist, racist and regional driver prejudices will cease to exist [22].
3 D Printing [3]
The
price of the cheapest 3D printer today is about $400 which used to be $18,000
say, 10 years before. It not only became
cheaper but also 100 times faster during this period. The following areas where 3D printing is
showing smart signs of advantage include:
Personalized
Printing
3D printing
holds immense potential to translate imagination into reality. Given the boom
in digital art and design, we can now 3D print almost anything we imagine after
drawing it up virtually. Hobbyists and enthusiasts can use this technology to
add multiple dimensions to their idea and perceptions.
Medical
Industry
One day, 3D printers may create not only
many things, but also human organs [3] – which may look unlikely preposition
today. The material used to print an organ would obviously be different from
what is used to print a bike, and one needs to find out what kinds of materials
will work, such as, titanium powder for making bones. Printing a complete organ
is a very slow process, this permits an organic object to be built
up in a great many very thin layers. Layers of living cells are deposited
onto a gel medium and slowly built up to form three dimensional structures by
using inkjet techniques. In addition to outputting cells, most bio-printers, therefore, need to also
output a dissolvable gel to support and protect cells during printing. 3D printing has great potential to service custom design needs. It is
true that there is nothing more custom than a human body.
Can a supercomputer
with artificial intelligence take better care of you better than your doctor
can [11]? IBM is applying its
cognitive computing AI technology to recommend cancer
treatment in rural areas in
the U.S., India, and China; where there is a shortage of oncologists, e.g., IBM
Watson could read electronic patient record (EPR), analyze imagery of the
cancer, and even look at gene sequencing of the tumor and recommend optimal
treatment plan for a particular person. The
idea is not to replace doctor but provide help to people who don’t have access
to a specialist doctor and offer a system which has really augmented and essential intelligence to provide proper
diagnosis as well as treatment of the ailment.
With the advancement of this technology due to its bonding with
AI, patients get better quality of 3D printed implants and prosthetics
irrespective of where they are located in the world.
Aerospace & aviation industries
The aerospace
and aviation industry has benefitted a lot with the developments in the metal
additive manufacturing sector with the introduction of 3D printing technology. NASA for example
prints combustion chamber liners using selective laser melting and the FAA
cleared GE Aviation’s first 3D printed jet engine part to fly. Spare airplane parts are already 3D printed in remote
airports. The space station now has a printer that eliminates the need for
the large number of spare parts they used to have in the past.
Automotive
industry
Though, the
automotive industry was amongst the first to use 3D printing tech, but it used
this tech to low volume prototyping applications only.
These days,
the use of 3D printing in automotive is evolving from relatively simple concept
models for fit and finish checks and design verification, to functional parts
that are used in test vehicles, engines, and platforms. It is expected that 3D
printing in the automotive industry will generate over $1.1 billion dollars by 2019.
Industrial
printing
This
technology is used for rapid Prototyping in the manufacturing sector. Thus, it allows
ideas to develop faster than ever. This
reduces time required from concept to the development phase. Using prototyping
injection mold tools is an expansive process but with 3D printing the creation
of parts and/or tools through additive manufacturing works out much cheap and
thus affordable.
Companies also
use 3D printers for short run custom manufacturing; in fact, the printed
objects are not prototypes but the actual end user product.
Architecture
[12]
A plastic pavilion is created
by a London based firm – Ai Build - through AI-powered 3D printing. The combination of robotic arm and artificial
eyes let the printer produce this intricate pattern without sacrificing speed.
Its 48 pieces were printed (figure 1) in just over two weeks, rather than the
months it would have taken a typical 3D printer.
In China, they already
3D printed a complete 6-storey office building.
Shoe
Industry
All
major shoe companies started 3D-printing shoes. 3D
printed shoes are attractive for customers because the shoes are custom-made
for each person’s unique feet. Few people have feet that are identical. In
other words, your left foot might be slightly wider or smaller than your right
foot. Because of this, finding shoes that fit both feet perfectly is rare.
Food Industry
3D food printing offers
a range of potential benefits. It can be healthy and good for the environment
because it can help to convert alternative ingredients such as proteins from
algae, beet leaves, or insects into tasty products. In just 15
years over 90% of all restaurants will use some form of a 3D food printer in
their meal preparations, a number of roads designated for driverless-vehicle
only and 20% of all new buildings will be 3D printed.
Way
forward
In summary, AI tools can be used to
get the true value of big data; and machine learning is one of the technical
mechanisms that really facilitates AI to exploit Big Data. We refer AI, ML and
Big data combo to as ‘big data analytics’.
Given the huge bank of opportunities
in 3D printing it is sure that 3D printing will take over many more industries
in the near future. Considering the prediction, startups and entrepreneurs in
India are seeing immense potential in 3D printing technologies. To name a few,
following is a list of Indian startups which are playing in this avenue: think3D,
Brahma3, Global 3D Labs, Fractal Works, ShaperJet and 3Dify, among many others.
The technology becomes more powerful
when you use multi-disciplinary approach. In Part 3 of this article [7], we
described Rotimaker Robot which is combo of AI, Robotics, and 3D Printing
Techs. Similarly, in military tech best example in Iraq US war was killing of
Scud missiles by US. First move satellites over the complete war zone. Then use
infra-red cameras on satellite to detect missile launch as intense heat is
generated when missile is launched. Satellite
computes the launch location by using GPS tech.
Pass on launch location to the US frigate which not only kills the
missile but also hit the launch location to damage the missile launching site.
In the whole process reaction time is key factor. To minimize this one will
soon perfect the Laser weapon which will hit both missile and launch location.
This will of course need multi-disciplinary tech including AI and will happen
soon. These are secret techs and are not disclosed. Figure 1 shows one
structure of a combo of 3 D printing, robotics and AI used to create a full plastic
pavilion.
At the end 2017, new smart phones will have 3D
scanning possibilities. By 2030, 10% of everything that's being
produced will be 3D printed.
References
[1] Progress and
Perils of Artificial Intelligence (AI)
[2] Invited Chapter 6 - Evolutionary Algorithms and Neural Networks, Pages 111-136, R.G.S. Asthana
in book, Soft Computing and Intelligent Systems (Theory and Applications),
Academic Press Series in Engineering, Edited by:Naresh K. Sinha, Madan M. Gupta
and Lotfi A. Zadeh ISBN: 978-0-12-646490-0
http://www.sciencedirect.com/science/book/9780126464900
[3] Future 2030
by Dr. RGS Asthana, Senior Member IEEE
[4] Machine
Learning (ML) and Artificial Intelligence (AI) – Part 1, by Dr. RGS Asthana, Senior Member IEEE
[5] Machine Learning
(ML) and Artificial Intelligence (AI) – Part Two, by Dr. RGS Asthana, Senior Member
IEEE
[6] Internet of Things (IoT)
[7] Machine
Learning (ML) and Artificial Intelligence (AI): Cognitive Services and Robotics
– Part Three by Dr. RGS Asthana, Senior Member IEEE
https://plus.google.com/113904112609362672954/posts/EA9hZcY6AwL
[8] 5 big data trends that will
shape AI in 2017
[9] A vision for 3D precision: this
robot arm 'prints' giant structures using AI
[10]
Designing for 3-D printing
[11] How 3D
Printing and IBM Watson Could Replace Doctors
[12] A vision for 3D precision: this robot
arm 'prints' giant structures using AI
[13] How machine learning (ML) &
artificial intelligence (AI) is changing our world
[14] How ML, Big Data and AI are
changing Healthcare forever
[15] Medecision website
[16] FAQ: All about
the Google RankBrain algorithm
[17] Big Data & ML: Case study
of a Fitness Product Recommender application
[18] How
Google uses machine learning in its search algorithms
[19] 5 big
data trends that will shape AI in 2017
[20] Uber launches its
first driverless taxi fleet in US, Times of India, New Delhi edition, Sep. 15, 2016.
[21] Netflix, SPOTIFY – Autonomous
Driving?
[22] 128
Things that will disappear in the driverless car era
http://www.futuristspeaker.com/job-opportunities/128-things-that-will-disappear-in-the-driverless-car-era/
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