Type: Process Essays
Sample donated: Marguerite Newton
Last updated: July 20, 2019
1.0 INTRODUCTION 1.
1 Objective of the study A. To investigate the differencebetween machine learning and artificial intelligence.B. To investigate the use ofmachine learning in changing the lives of the people.
C. To investigate how machinelearning is being incorporated in the smartphone industry.D. To investigate about the machinelearning library i.
e. Tensor flow 1.2 Artificial intelligence andmachine learningMachinelearning is a branch in computer science that allow the computer the ability tolearn without being programmed explicitly. Artificial Intelligence is the broader conceptof machines being able to carry out tasks in a way that we would consider”smart”. Machine Learning is a current application of AI based around the ideathat we should really just be able to give machines access to data and let themlearn for themselves. 1.3 Uses ofmachine learningOne of the most popular use of AI in machine learning, wherecomputers, software and others perform through cognition (like human brain).
Example of area where machine learning is heading include the following: – 1.4 Virtual personal assistantsSiri, google Now, Alexa and many more are some of the many popularexamples of virtual assistants. As the name says they try to assist in findinginformation when asked to find something over the voice. All you have to do isask “what is the weather today?”,” when is the Manchester united playing” or”set an alarm for 3pm”.Machine learning is a very important part of personal assistantfor they gather and refine the information on the basis of previous encounterwith them. Later this set of data is used to make results that are specific toyour preferences.
Virtual assistants are used in a variety of platforms likesmart speaker like google home and amazon echo, smartphones like Samsung Bixbyand google pixel as google assistant and mobile apps like google allo. 1.5 Video surveillanceVideo surveillance is a very hard task and a boring but withmachine learning it can be an automated process since training the computersthey can handle this task .Computers can detect crime just by tracking unusualbehaviour using machine learning. 1.6 Social mediaSocial media like Facebook, twitter, Instagram and many others usemachine learning to personalize news feed, adds and many more.On application like camera the use of face recognition in order toidentify people in a specific scene and also identify their faces so as to addeffects like smoothing on their face. Machine learning is alsobeing used in apps like Facebook so as to identify people we may know andsuggest that we add them as our friend.
Also apps like Pinterest uses computervision to find objects in images and suggest similar pins accordingly.1.7 Email spam and malwarefilteringApps like Gmail use machine learning to classify email intoprimary, social, important and spams.
This is with the help of filtering beingdone under the hood using machine learning. Over 325,000 malwares are foundevery day and each piece of code is 90-98%similar to its previous versions. Thesecurity program that are powered by ML understands the pattern for coding.
1.8 Online customer supportMicrosoft bots are being used to provide chatrooms where peoplecan report about the services they get and this is due to machine learning.similarly, when one opens a browser the search is customized for that specificperson. For example, YouTube is highly customized for each and every personaccording to what they like watching thanks to machine learning. 1.9 Product recommendationsWhen you buy something online you start receiving email relatingto that product in other stores also when you browse online you see some sitessuggesting things to buy which are close to your taste. This is due to machinelearning which compiles your likes and taste as you browse the internetcombined with an algorithm working under the hood.
1.10 Fraud detectionOnline fraud detection is among the frontier that machine learningis taking head on by trying to analyse illegal online transaction andpreventing money laundering e.g. PayPal. This is done by using a set of toolsthat can help compare millions of transactions happening and distinguishbetween legit and illegal transactions. 1.11 Introduction ofmachine learning to smartphonesFirst phone was made by alexander graham in 1876 and it became arevolutionary gadget as the 1900 approached.
The phone was used for basicservices like calling and texting at the end of 20th century. As theyears progressed the phone morphed from basic phone to feature phone and laterto smartphone which was introduced in 2000 i.e.
Sony Ericson R380. This was a revolutionaryidea in its time since it featured a capacitive touchscreen something that hasnever been seen before on a phone.As the smartphone momentum started and many companies joined innamely Apple, Android and many more. Due to demand of new features thesmartphone industry has tried to out do one another and in the process, sell more.This has made the companies making the phone to invest huge in research anddevelopment so as to come up with new features. Artificial intelligence hasalways been a new frontier for the phone but the computing power has alwaysbeen a constraint. The smartphone computing power cannot be able to be enoughto train models which are necessary for the learning process of a artificialintelligence. Training a model entails providing a lot of data to that modeluntil it can recognize a certain data.
This is taxing on a smartphone which ishas low computing power and so training is done on a computer workstation andonce that is done it is then ported back to the device via tensorflowframework. 1.12 TensorflowTensorflow an open source software library for dataflowprogramming in a wide range of tasks. The main tasks is its application inneural network which for the base for training data models.
Its mainly utilizedby google in machine learning with which it provides through its range ofapplication e.g. Google keyboard which has predictive typing.
Tensorflow is alightweight library which is perfect for smartphones. In may 2017 google releasedtensorflowlite which main aim is to provide light weight machine learning inandroid powered smartphones (especially android 8.0 Oreo). The core oftensorflow is programmed in c++ 2 Research methodologyResearch data was collected using the methods like directobservation, interview and online data collection. It outlines and specify theway in which the research is to be carried on.
2.1 Research methods and designA research design is a blueprint of methods and procedures used incollecting and analyzing variable when conducting a research study. A specific suitablequestion for study in a research project should be considered and then choose asuitable method of conducting the research.
This is important forsuccessful coverage of the highlighted objectives and completion of theresearch. Research data was gathered through participants observation e.g. useof senses like eyes by examining people in a targeted population.
There wasalso the case of examining earlier records on artificial intelligence fromwhere we have valuable information pertaining to inception of this technology 2.2 Target population and studyareaTarget population involves the people I want to gather informationfrom and in my case, involves any person who owns a smartphone. The featureslike predictive typing which involves use of google keyboard will be an easytask.2.
3 Direct observationIt is also called observational study and it is a method ofgetting evaluative information which entails an evaluator watching his/hersubject in his/her place of living and not changing the environment. This typeof data collection is used together with other data collection procedures e.g.survey, questionnaires etc. The main aim of this is to evaluate a happening behaviour process,event or when results can be seen. When observing the subject one should notmake them aware of your purpose since this can alter the observation and forthat reason the subject should not be aware.
There are two types of directobservation i.e. structured direct observation and unstructured directobservation. Structured direct observation are used when we want to getstandardized information and result in quantitative data while unstructureddirect observation involves looking at natural happenings and get qualitativedata.
This involves observing the functions offered by some smartphonesthat range from facial detection in photos to predictive typing in keyboards.When one uses the google keyboard it learns a person typing patterns and makesout the words a person types this intern stores those words in a database andlater reproduces them when one is writing a text of a Facebook post.2.4 Online data collectionOnline data collection was among the main research methodology usedto come up with this information. Sending online interviews can be a trickysince then evaluator and the participant have never met and this makes sharingprivate information very hard. Therefore, one must first come with ways for theparticipant to trust you and accept to share that information 3 Literature review3.1 Global perspectiveIt is estimated that more than 80% ofall households in United States now have computers in their home, and of those,almost 92% have internet access. As computers became more prevalent in Americansociety, the next natural advancement in communication was through theinternet.
This is rising trend is due to invention of the smartphones.Smartphone features are the main selling point today i.e. the one who outsmarts the other in terms ofproviding better features that customer is willing and able for such asmartphone then he/she takes the day.Apple a tech company is a leadingcompetitor in this field ,innovating every year to come with gadgets thatimpresses everyone and this has created a group of apple royalists. They are willing to spend more than a1000$ for a smartphone. The company latest innovation ispowered by machine learning to provide security to its flashy iPhone x. Theinnovation could not have happened without machine learning ,no matter how muchyou code i.
e. control statement, methods, api it couldn’t be done . Machinelearning is about training the computer to think like a person withoutexplicitly coding it. This would then involve training a model by showing it alot of data and later it would be able to make guesses about that specific datae.g. imagine how can you code a program that distinguishes between apple andoranges ,one would argue that you can by saying that apple an apple is betweenred and maroon and orange is yellow and so you can develop a program thatanalyses the pixel in those pictures and if the colour coincides with thecolour of yellow .
This can be true but wat about if the program is fed of black and white photos then theprogram cannot determine .This is where machine learning comes in and bytraining a model it can be able to make pretty close guess of a black and whitepicture of apple and orange since it wastrained with a wide range of data e.g.
black and white pictures of apple andoranges ,shape of apples and many more.Apple use in machine learning wasinform of face id in which the phone would make a mesh of your face when youwant to unlock it and compare it to the stored mesh in the phone and if itcoincides it would unlock the phone. The problem is with face id the person couldjust hold a picture of your face and the phone would unlock. So they came witha system that would project dots on your face and the camera using this dots itwould be able to make a 3d mesh of your face and using machine learning a lotof your facial characteristics would be taken for comparison and the modelwould be able to guess it was that actual person close to probability of amillion to one. This made it possible even to unlock your phone wearingglasses, wearing a lot of make up or changing ones look by maybe growing abeard. 4 Finding and observationsThepossibilities with machine learning is endless as long as the computing powerof the smartphone keeps up. Apple had to develop A11 fusion chip (the mostpowerful chip in smartphone to this date) to handle such computing requirementsneeded by the smartphone to train models of one’s face.
5 RecommendationsThe bottom line is that everything related tomachine learning and artificial intelligence is tasking to smartphones as ofthis date, but as companies continue innovating and coming up with better CPUand GPU architecture then this task will run buttery smooth and even open newfrontiers in this specific field. References1. “Build and train machinelearning models on our new Google Cloud TPUs”. Google.
May 17, 2017.Retrieved May 18, 2017. Google’s new machine learning framework is going to putmore AI on your phone.2. Drury, C. G.
(1995). Methods fordirect observation of performance. In J. R. Wilson & E. N.
Corlett (Eds.),Evolution of human work; a practical ergonomics methodology. Philadelphia:Taylor and Francis.
3. Alpaydin, Ethem (2010). Introduction to Machine Learning. London: The MITPress. ISBN 978-0-262-01243-0. Retrieved 4 February 2017.4. “MachineLearning: What it is and why it matters”.
www.sas.com. Retrieved 2016-03-29.
5. Mohri, Mehryar; Rostamizadeh,Afshin; Talwalkar, Ameet (2012). Foundations of Machine Learning. USA,Massachusetts: MIT Press. ISBN 9780262018258.
TensorFlow.org. Retrieved November 10, 2015.”TensorFlow Release”. Retrieved Dec 22, 20177. Metz, Cade (November 9, 2015). “Google Just OpenSourced TensorFlow, Its Artificial Intelligence Engine”. Wired.
Retrieved November 10, 2015.8. Perez, Sarah (November 9, 2015). “GoogleOpen-Sources The Machine Learning Tech Behind Google Photos Search, Smart ReplyAnd More”.
TechCrunch. Retrieved November 11, 2015.9. Savov, Vlad (September 12, 2017). “iPhone X announced withedge-to-edge screen, Face ID, and no home button”. The Verge.
Vox Media. Retrieved December 13, 2017.10. Brandom, Russell(September 12, 2017). “The five biggest questionsabout Apple’s new facial recognition system”. The Verge. Vox Media.
Archived from theoriginal on November 15, 2017. Retrieved November 9, 2017.11. Ng, Alfred(September 27, 2017).
“Is Face IDsecure? Apple takes on lingering questions”. CNET. CBS Interactive.
Archived from theoriginal on October 2, 2017. Retrieved November 9, 2017.12. Daniel B.
Kline(2017-01-30). “Alexa, How BigIs Amazon’s Echo?”. The Motley Fool.
13. “Watson – Stories of how AI and Watson aretransforming business and our world”. Ibm.com. Retrieved 2017-12-10.14. Lynn La(2017-02-27). “EverythingGoogle Assistant can do on the Pixel”.
CNET. Retrieved 2017-12-10.15. “MachineLearning: What it is and why it matters”.
www.sas.com. Retrieved 2016-03-29.16. Goldberg, David E.
;Holland, John H. (1988). “Genetic algorithms and machine learning”. Machine Learning. 3 (2):95–99.17.
Dickson,Ben. “Exploitingmachine learning in cybersecurity”. TechCrunch.
Retrieved 2017-05-23.18. Urbanowicz, Ryan J.;Moore, Jason H. (2009-09-22).
“Learning Classifier Systems: A Complete Introduction,Review, and Roadmap”. Journal of Artificial Evolution andApplications. 2009: 1–25.
doi:10.1155/2009/736398. ISSN 1687-6229. Tan, Steinbach, and Kumar, Introduction to Data Mining, Addison-Wesley, 2005.20.
Bishop, C. M. (2006), PatternRecognition and Machine Learning, Springer, ISBN 0-387-31073-8