Apple Iphone Research Paper

 

3

1.

Introduction

In today‟s world

technologies take a significant part of our lives. Large investments intoInformation Technology sector and big interest in technologies from

“Generation Y” society

determine fast development of products that quickly become widely used in all over the world.Due to the fast growth in number of consumer electronic devices and technologies that areimplemented into it, the process of Technology Convergence became present. This means thatinstead of carrying multiple devices to perform different tasks

 – 

such as making telephone calls,sending email messages, listening to music or photographing

 – 

today‟s society is able to do all

that with one small portable device

 – 

smartphone.More and more smartphones are sold each day and smartphones are taking over the market of mobile phones

 – 

against low-end feature phones. Few companies compete to grab attention of smartphone lovers

 – 

to gain bigger share of the smartphone market

 – 

Research in Motion (RIM)with its BlackBerry product line, HTC, Samsung, Motorola, Sony Ericsson, Palm, Apple and, of course, Nokia. However, there is only one manufacturer whose products grab the largestattention of tech-world, media and customers

 – 

that one manufacturer is Apple. In 2007 Appleentered mobile phone industry with a revolutionary mobile device

 – 

iPhone. After that, each year,around the same time

 – 

in late June or early July

 – 

Apple released a new generation of theiPhone. Now, there has already been four generations of iPhones.

Many Apple products‟ fans and people who are interested in smartphone industry expected and

waited for the tradition to keep on going

 – 

they expected to see a new generation of iPhone inthis year Worldwide Developers Conference (WWDC) event in June, 2011. However, there was

no big buzz from the Apple‟s side about the new iPhone, and just a few days ago – 

on 31 May2011

 – 

Apple officially announced the purpose of this year WWDC event, and it did not sayanything about the new hardware devices. This raised questions why the new generation of iPhone will not be introduced in this June-July period, and when will it be introduced.The purpose of this paper is to

implement product life cycle concept on the product‟s salesfigures in order to validate company‟s marketing decisions

considering the introduction of newproduct. To achieve this purpose, number of objectives has to be reached first:1.

Analyze the

 product‟s

sales pattern;

 

Apple's first AI research paper wins prestigious machine learning award

By Mikey Campbell
Friday, August 25, 2017, 06:23 pm PT (09:23 pm ET)

Apple's first publicly issued academic paper, research focusing on computer vision systems published in December, recently won a Best Paper Award at the 2017 Conference on Computer Vision & Pattern Recognition, one of the most sought after prizes in the field.




Considered one of the most influential conferences in the field according to the h-index, a metric for scholarly works, CVPR in July selected Apple's paper as one of two Best Paper Awards.

According to AppleInsider reader Tom, who holds a PhD in machine learning and computer vision, the CVPR award is one of the most sought after in the field.

This year, the conference received a record 2,680 valid submissions, of which 2,620 were reviewed. Delegates whittled down that number to 783 papers, granting long oral presentations to 71 entrants. Apple's submission ultimately made its way to the top of the pile, an impressive feat considering it was the company's inaugural showing.

CVPR's second Best Paper Award went to Gao Huang, Zhuang Liu, Laurens van der Maaten and Kilian Q. Weinberger for their research on "Densely Connected Convolutional Networks." Research for the paper was conducted by Cornell University in collaboration with Tsinghua University and Facebook AI Research.

Titled "Learning from Simulated and Unsupervised Images through Adversarial Training," Apple's paper was penned by computer vision expert Ashish Shrivastava and a team of engineers including Tomas Pfister, Oncel Tuzel, Wenda Wang, Russ Webb and Apple Director of Artificial Intelligence Research Josh Susskind. Shrivastava presented the research to CVPR attendees on July 23.



As detailed when it saw publication in December, Apple's public research paper describes techniques of training computer vision algorithms to recognize objects using synthetic images.

According to Apple, training models based solely on real-world images are often less efficient than those leveraging synthetic data cause because computer generated images are usually labeled. For example, a synthetic image of an eye or hand is annotated as such, while real-world images depicting similar objects are unknown to the algorithm and thus need to be described by a human operator.

As noted by Apple, however, relying completely on simulated images might yield unsatisfactory results, as computer generated content is sometimes not realistic enough to provide an accurate learning set. To help bridge the gap, Apple proposes a system of refining a simulator's output through SimGAN, a take on "Simulated+Unsupervised learning." The technique combines unlabeled real image data with annotated synthetic images using Generative Adversarial Networks (GANs), or competing neural networks.

In its study, Apple applied SimGAN to the evaluation of gaze and hand pose estimation in static images. The company says it hopes to one day move S+U learning beyond to support video input.

Like other Silicon Valley tech companies, Apple is sinking significant capital into machine learning and computer vision technologies. Information gleaned from such endeavors will likely enhance consumer facing products like Siri and augmented reality apps built using ARKit. The company is also working on a variety of autonomous solutions, including self-driving car applications, that could make their way to market in the coming months or years.

"We're focusing on autonomous systems," Cook said in a June interview. "It's a core technology that we view as very important. We sort of see it as the mother of all AI projects."

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