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Jony Ive, Apple’s chief design officer, is returning to his management role within Apple’s design group after handing off managerial duties in 2023. 9to5Mac noted that Ive’s design deputies Dye and Howarth were no longer listed on Apple’s leadership page earlier today, and news of Ive’s return broke from Bloomberg.
From our story this morning:
Also of note, Apple’s Leadership webpage no longer lists Alan Dye, VP of user interface design, while VP of industrial design Richard Howarth is also no longer listed. We assume there has been no change of roles here, as Apple has not announced anything, but we’ve reached out to the company for clarity and will update when we hear back.
Bloomberg got confirmation on the record about Jony’s new role:
“With the completion of Apple Park, Apple’s design leaders and teams are again reporting directly to Jony Ive, who remains focused purely on design,” Amy Bessette, a company spokeswoman, said Friday in a statement.
Ive famously led Apple’s legendary design team on a day-to-day basis prior to July 1, 2023, but shifted his role two years ago amid Apple Park’s development. This was the original memo announcing the shift two years ago:
I have exciting news to share with you today. I am happy to announce that Jony Ive is being promoted to the newly created position of Chief Design Officer at Apple.
Jony is one of the most talented and accomplished designers of his generation, with an astonishing 5000 design and utility patents to his name. His new role is a reflection of the scope of work he has been doing at Apple for some time. Jony’s design responsibilities have expanded from hardware and, more recently, software UI to the look and feel of Apple retail stores, our new campus in Cupertino, product packaging and many other parts of our company.
Design is one of the most important ways we communicate with our customers, and our reputation for world-class design differentiates Apple from every other company in the world. As Chief Design Officer, Jony will remain responsible for all of our design, focusing entirely on current design projects, new ideas and future initiatives. On July 1, he will hand off his day-to-day managerial responsibilities of ID and UI to Richard Howarth, our new vice president of Industrial Design, and Alan Dye, our new vice president of User Interface Design.
Richard, Alan and Jony have been working together as colleagues and friends for many years. Richard has been a member of the Design team for two decades, and in that time he has been a key contributor to the design of each generation of iPhone, Mac, and practically every other Apple product. Alan started at Apple nine years ago on the Marcom team, and helped Jony build the UI team which collaborated with ID, Software Engineering and countless other groups on groundbreaking projects like iOS 7, iOS 8 and Apple Watch.
Please join me in congratulating these three exceptionally talented designers on their new roles at Apple.
At the time, it seemed certain that Apple was carefully preparing for Ive’s eventual retirement from the company. Dye and Howarth, who served as VPs of User Interface Design and Industrial Design, respectively, were presumably being groomed to be the face of Apple design post-Jony, but the duo received little public exposure following the initial announcement. No keynote slots and limited interviews.
Meanwhile, Jony Ive has remained in the spotlight at Apple, publicly discussing Apple’s new campus and appearing publicly to talk about the state of Apple design. 9to5Mac reported on a recent appearance at The New Yorker’s TechFest in October where Ive spoke about his history at Apple and the future of design.
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Following friction between top Apple Human Interface Vice President Greg Christie and Senior Vice President Jony Ive, Apple’s hardware and software design is being dramatically shaken up, according to sources familiar with the matter. After adding human interface design direction to his responsibilities in 2012, Ive will soon completely subsume Apple’s software design group, wresting control away from long-time human interface design chief Christie, according to sources briefed on the matter. Previous to this shakeup, all Apple software design has been led by Christie, who has reported to Craig Federighi, and Ive has been attending interface design meetings and providing instruction…
The design shakeup at Apple will result in Christie soon leaving the company, with all software designers now working directly under Ive with the rest of his industrial design team instead of within Federighi’s engineering group. Sources say that Christie’s upcoming departure is significant and stems from a falling out with Ive.
When Ive tasked Apple’s Human Interface team with redesigning iOS 7 to include an entirely new look, Christie and Ive reportedly clashed over design direction, after which Ive is said to have circumvented Christie’s leadership of the team during the new operating system’s development.
Christie’s impending departure comes in the months ahead of significant updates to both iOS and OS X. While iOS 8 will retain the same overall design as iOS 7, OS X 10.10 will be revamped with a flatter look that loses the textures that Christie utilized to make the iPhone the most popular gadget on the planet.
Christie is also known to App Store software creators as the herald of Apple’s design aesthetic as he frequently held application design sessions and user-interface review meetings with developers at Apple’s WWDC conferences.
Christie’s exit from Apple also comes amidst the encore for Apple and Samsung’s high profile trial in San Jose, California. While Christie has long worked under the shadows of Apple’s most senior executives, the original iPhone designer was given stage time last week as a key witness for Apple in court and Christie was made available for interviews with the Wall Street Journal and NPR. The interviews position Christie as critical to the iPhone’s success, making his departure a significant loss for the Cupertino-based company.
Christie also has hundreds of Apple patents in his name such as the iconic “Slide to Unlock” patent that is crucial to Apple’s patent infringement claims against Samsung. It is unclear at this time if Christie plans to join another company or retire from the industry completely.
Christie’s upcoming leave from Apple will mark one of the most significant design exits in several years and represents a complete changing of the guard for Apple’s software design style. Over the past two years, Scott Forstall and Christie will have both exited Apple, and Ive will have secured his position at Apple as the central product design decision maker for the foreseeable future in the post-Steve Jobs era.
While Apple’s executive shakeup in 2012 unlocked the silos separating hardware and software, this latest change will completely remove any internal boundaries between Apple’s hardware and software design and will likely result in even more well-integrated products. (Top image by Michael Steeber).
Update: Apple has confirmed to the Financial Times that Christie is leaving Apple.
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Technology is playing a key role in supply chain management for digital transformation Where can you see the impact of technology implementation in the supply chain?
In today’s era, technology assistance is needed in each and every step and function of the supply chain process. Supply chains generate big data and analytics is needed to streamline this data to information & gain intelligent insights. Let us have a look at the individual stages of the supply chain & understand how technology helps in bringing improved efficiencies. Starting from the Planning stage. It is undoubtedly the most data-consuming stage of the supply chain process as it needs even the minutest of the details to predict, plan and align the operations. Planning as an operation is interlinked with all major supply chain stages like manufacturing, logistics, inventory, warehousing, packaging, etc. Thus, data needs to be continuously pulled in from all the software that individually manages these processes. For example, Analysis of data from WMS is helpful to plan the inventory and accordingly run the manufacturing. Similarly, data from ERP gives an overview of orders helping the system understand market demand and accordingly work with manufacturing & transportation to give the requisite supply. Nowadays, technology has revolutionized the production/manufacturing stage to another level. Especially, if we see the chemical or paint industry, the machinery now is capable of interpreting the product composition and properties and automatically defining the right method of production of the product as per the customer requirement. For example, BASF, a German producer of paints recently adapted machine learning technology to interpret customer requirements & give inputs to manufacturing units to prepare the paints in the composition required by the clients. Quality assessment is another area where technology has helped various industries. Agricultural seed processors use a variety of cameras in real-time to get the quality assessments of seeds. This kind of data is collected, compiled, and analyzed to get compliance reports that can further help in strengthening the manufacturing processes. Then comes the most important stage of supply chain management – Logistics & Transportation management. This stage alone takes up 70-80% of supply chain costs hence, we need to make sure it functions at its 100% efficiency. This stage consumes a lot of data starting from details of the goods, consignor/consignee information, vehicle & vendor details, route to be followed, driver details, driver performance, etc. Thus, this phase needs a lot of coordination internally within the department and with external devices & agencies. Fuel consumption, Speed compliance, On-time deliveries, and Delivery plan are a few of the key analytics that directly helps in performance evaluation & logistics cost savings. We, at Elixia, have completed almost 12Million trips & helped various companies save 5-10% of their logistics costs. Apart from these stages, analytics also plays a very crucial role in Vendor or 3rd party management. A successful supply chain works in close collaboration with vendor partners who come into the picture at all stages of the supply chain. Data, technology, and ultimately analytics make it possible to consolidate all data and keep a track of their performance, which finally comes in handy at the time of contract renewals. For example, if a company deals with multiple vendors for transportation then it’s imperative to have constant tracking of parameters like trips completed, rates provided (in comparison to market rates), their order acceptance rates, and OTIF (on-time, in-full) delivery score, driver performance, etc. A similar case can be taken for companies who outsource packaging, raw material supply, temperature monitoring, etc. Analytics can help them capture the cost structure and define cost-effective models for partner collaborations.Key technologies backing the required Analytics
Machine learning & Artificial intelligence are the backbones of supply chain analytics. These technologies continuously study the data, develop algorithms and give human-like responses to manage the supply chains. IoT constantly works in the background to collect & provide the raw data that is needed for the AI to work effectively. Digital twin & Control tower then together come into the picture for end-to-end management of supply chains. Control towers closely monitor all the functions of supply chain processes, identify the gap areas & suggest necessary corrective actions. Digital twin, which is nothing but a virtual representation of the original process, shows how the recommended actions can bring overall benefit to the supply chain. Analytics here works in tandem with all of these technologies to give immediate, practical, and to-the-point solutions to ensure supply chains work smoothly and efficiently. So, in a gist, this is how technology & analytics go hand-in-hand in providing a resilient & cost-effective supply chain management solution.Author:
It’s been five years since the LoveLetter worm hit the Internet, becoming
one of the first global malware outbreaks and one of the costliest.
The question is, though, are we any smarter today than we were back then?
And are we any safer?
Some security experts and industry watchers say we simply are not.
”For the average user, the answer is that they are not any smarter or
any safer,” says Steve Sundermeier, a vice president at Central Command,
an anti-virus company based in Medina, Ohio. ”May’s Sober and Mytob
variants are all mass-mailing attacks that appeal to people’s curiosity.
Five years ago we learned about this social engineering tactic, so today
you’d think lessons would be learned, but obviously they haven’t been.”
According to some estimates, the LoveLetter worm, also widely known as the I Love You worm, caused about $8.8
billion in damages and lost productivity. The malware used what was then
a new form of online trickery — social engineering. Arriving in users’
inboxes appearing to be from family and friends, it used the enticing
subject line ‘I Love You’. Eager to receive such sweet tidings, millions
of users were duped into opening the dangerous email, infecting their
After LoveLetter came many more viruses and worms that used social
engineering to trick people into opening executables and downloading
malicious code. Teaching employees to beware of these schemes became a
key part of IT’s job.
But the tricks are still coming, and we’re all still falling for them,
according to Sundermeier.
”I would say it’s five years later and home users are just as dumb,” he
adds. ”For corporate users, it’s better, but we still need more user
education. There are always different vectors for infections. IM software
is becoming a problem… People are downloading file sharing programs and
worms are entering there… When employees go home, they go online with
their work laptops and they bring viruses back to the office.”
Andrew Jaquith, a senior analyst at the Yankee Group, an industry analyst
firm based in Boston, says corporate IT managers are using a lot more
tools and technologies to safeguard their companies, but that doesn’t
mean they’re a whole lot safer.
”When car makers put anti-lock brakes on cars, people started driving
faster,” says Jaquith. ”It’s called the security compensation theory. I
think a lot of the controls we put in place are helping, but they’re not
necessarily making us more secure because the threats are spreading
elsewhere or we’re changing our behaviors — for the worse — because we
feel more secure.”
And Jaquith says the tried-and-true social engineering tricks are working
just as well today as they were at the beginning.
”If you send a clever enough subject line, like ‘the memo you requested’
or ‘Britney Spears naked’, some people are still going to open that
email,” he notes. ”We’re largely a little smarter than that now. People
generally know that promises of Britney Spears unveiled aren’t what they
appear to be. We tell our kids, ‘Don’t accept rides from strangers’. We
should tell our workers, ‘Don’t accept emails from strangers’.”
But Ken Dunham, director of malicious code at iDefense, Inc., a security
and anti-virus company, says it’s unfair to compare today’s security to
what we had back in the year 2000. As security levels increase, so does
the maturity of the attackers. IT and security administrators may be
trying harder and using more and better technology, but they’re also up
against a bigger and more aggressive foe today.
training, he says. ”People are people and you can train all you want.
There’s no magic bullet.”
Jaquith says company users are the big problem when it comes to security
a network, adding that most users would probably give up their password
to a stranger for a piece of chocolate.
”P.T. Barnum had it all wrong,” he says. ”There are dozens of suckers
born every minute.”
Prior to getting initiated to Data Science, I was working in data intensive Data-warehousing for more than 6 years. In 2013, I had an opportunity to work in a problem wherein we were required to build a model to predict the probability of a customer buying a product as part of transformation initiative. This has widened my horizon.
Until that time, I was under the impression that data can be used only for presenting or analyzing what had happened. I did some research on Analytics, Predictive Modelling and I realized that even though it is an extension of BI it requires different skill sets Ii.e. of Statistics, Machine Learning, SQL, and Business acumen in industry one is working.Initial Days
I had good knowledge of SQL and bit of business acumen. I was determined to learn Statistics and Machine Learning. In 2014, I enrolled in online course by Edvancer. Edvancer has provided me solid foundation in Analytics using SAS and R.
I had plans to do MBA but could not pursue it full time as I could not leave my job. I did not want to do MBA through distance learning. Based on my search pattern google had recommended few Analytics program by reputed business schools. I had applied for IIM Bangalore and Great Lakes Institute of Management Chennai and was shortlisted from both of them.
I had opted for Great Lakes as IIM Bangalore conducts most of the classes on weekends and was not in a position to attend classes on Saturday. Moreover, recording of classes were not available in IIMB. I was not in Bangalore at that point of time hence attending classes over the web would mean missing on peer learnings.Great Lakes and Analytics Edge
I underwent Great Lakes PGPBABI program during period 2014-2023. As I am not a MBA graduate, I was able to fill certain gaps in my career and got good exposure and solid foundation on Analytics.
After completion of PGPBABI program at Great Lakes, I did “Analytics Edge” program on edx (This tip was shared by Kunal. Thanks Kunal!). As part of Analytics Edge we were supposed to take part in Kaggle competition which is part of grading. I was able to finish in top 90 among 2932 contestants (Top 3%) which gave me great boost to my confidence.Application of Whatever I learnt
Now the time has come to apply whatever I learned in my workplace. I have mentioned my interest to my boss who were happy to support me. Initial days were challenging as “In theory there is no difference between theory and practice, but in practice there is”.
To overcome this challenge, Analytics Vidhya (AV) and its community were lot of help. I used to get my queries clarified over the discuss portal. What was even more helpful was the hackathon conducted by AV regularly. I made it a point to not to miss any hackathon even when I was travelling.
Hackathon allows you to benchmark yourselves with others and get introduced to wider community and make friends. Like many others, I started at the bottom but slowly improved to finish within top 10 many times and within top 10% in a Kaggle competition.
For every hackathon, I try something new. Sometimes it works and sometimes I get my fingers burned by overfitting the public leaderboard. Each of my experience in hackathon is worth weight in Gold. Thomas Edison once remarked “I did not fail 1000 times but found out 1000 ways how not to make a bulb”. My experience is similar to this.
I am happy with my current ranking of being in top 10 in AV (at time of writing this article) but I am trying my best to improve my ranking. Still lot of distance to be traveled before reaching heights of @SRK or @Vopani or others in top chúng tôi will remain my inspiration to excel. By the time I reach that level I am sure they would be have doubled their level.
These are some of FAQ for people in their mid careers and would like to make a transition to Data Science. I have answered to the extent I know. Please feel free to correct me if wrong.How difficult/easy is to transition?
There is no single answer to this question. In fact, if one has enough data, one can run classification model to predict the probability of transition with the following variables (Some I can think of)
Chance of Internal transition (Y/N)
Previous experience in BI
Expertise in R
Expertise in Python
Expertise in SAS
Expertise in R and Python
Strong Background in Marketing , Risk, Supply Chain, Finance etc…
Expertise in SQL
In depth understanding of statistical concepts
Passion in Data Science
Comfortable with numbers
Does one need to have all the above criteria to make a transition to Analytics? No, it depends on number of years of total experience (Relevant Analytics experience is assumed to be nil) as below (For MBA candidates strong background in Marketing, Risk, Supply Chain or Finance is assumed):
0 -1 years or Freshers: Passion in Data Science, Curiosity and Comfortable with numbers. Good to have expertise in SQL and in-depth understanding of statistical concepts.
1-2 years: Freshers + Expertise in either R or Python or SAS
2-5 years: 1-2 years+ In depth understanding of statistical concepts. Chances of transition is more if either chance of internal transition or previous experience in BI is applicable. Good to have knowledge of both R and Python or experience in SAS (Non data science).
5-10 years: 2-5 years + domain knowledge are applicable + knowledge of both R and Python or experience in SAS (Non data science). Chances of transition is more if either chance of internal transition or previous experience in BI is applicable. If one is Non MBA candidate it is good to have strong background in Marketing, Risk, Supply Chain or Finance.What works and what does not?
Internal transition is much better way of making transition. If one have more than 5 years of experience, having learn only data science course will not help. One need to substantiate his / her knowledge and learn to apply the knowledge acquired in his industry.
For those people who are having more than 10 years of experience, they should be capable of providing end to end solution i.e. Starting from finding a use case to executing it and presenting the ROI to the stakeholders.What kind of Challenges can come during the transition?
Losing patience, aversion to coding, lack of domain knowledge in the industry the candidate has worked etc…
One has to be patient and wait for opportunities and grab them when they present itself. I have seen persons who after few months, get demotivated and do not have the will to pursue data science careers. Obviously it depends on the passion component of the candidate also.
There are few people who get trained in GUI driven softwares like SAS Enterprise Miner or SPSS and not willing to train themselves in R or Python as they are averse to writing codes. They should overcome this aversion to stand better chance.
I have seen certain people especially people coming from software industry lack the domain knowledge required to provide end to end solution. They are comfortable with coding but do not have business acumen. This may be big handicap especially when your number of experience is more and relevant experience is less.
It is true that as number of non relevant experience increases chances of transition to data science diminishes but probability never becomes zero. Remember!
Being at the right place at the right time having right skills matter.
“Think like a CEO”. Do not restrict oneself to just model building. Get involved in all phases of Data Science right from problem definition to ROI justification.How much time, effort and resources are required to make transition?
During the first year or initial period, imagine yourselves training for Olympics. If one is working he has to put 2 hours of study in the morning and another 2 hours in the evening. Work with lot of data and be positive.
Accept failures as stepping stones for success. Time taken to transition would range anywhere from 6 months to 2 years or sometime more than 2 years.
How does industry view transitions?
Industry view favorably transitions of candidates having less than 5 years of total non data science experience. For others with more than five years of total experience but less relevant experience in data science, they consider data science skills as only as an add on.Summary
Data Science is not about new tools or technology. Some of the algorithms used in Data Science were conceived 30-40 years back. It is all about “Data to Decisions”. To be a good data scientist you need to have mixture of Coding skills, data management skills, modeling skills, business skills to succeed.
Do not confine yourselves to a tool or algorithm. There is nothing like good or bad tools or algorithm. Whatever works for the business to solve their problems and improve ROI are welcome. Transition for mid career people takes time and one should have patience and passion to make a successful transition.
Disclaimer: Our stories are published as narrated by the community members. They do not represent Analytics Vidhya’s view on any product / services / curriculum.
Bloomberg published a story this weekend on how an Apple-Sony merger makes a lot of sense. It doesn’t. Let’s see if we can’t break down each argument point by point:
…adding insult to injury, Apple Inc. continues to hold the spotlight. It really is one of the great business stories of the past 50 years. Sony, the inventor of the Walkman, ceded its leadership in portable music players to Apple’s iPod and continues to lose ground.
It really makes you wonder why Apple Chief Executive Officer Steve Jobs doesn’t just buy Sony.
It can’t. Sony is worth over $45 billion – about the same as Yahoo! which also isn’t possible for Apple to purchase (and might make more sense). A merger? Yeah, I can see Steve Jobs ceding some control of Apple to outside forces.
Plus, what would Apple get? Vaios that run Windows Vista? A semi successful TV hardware business? Some screen manufacturing? The only valuable thing to Apple would be the Playstation line and perhaps the media-entertainment divisions which would, of course, alienate the other media companies that compete on iTunes.
If Sony is smart, they admit they messed up trying to become a media company and spin off that division. They focus on hardware and better software and stop being crippled by the MPAA/RIAA-like division of their company. Of course Stringer is NOT the guy to do this. When Sony gives up on him, this will be the natural tendency.
Such rumors have circulated before, knocking joysticks out of the hands of regulars of Tokyo’s Akihabara electronics district. It’s techie blasphemy to suggest such a thing. It’s almost unthinkable that a name that rose from the ashes of World War II to become a cornerstone of Japan Inc. would be sold to a Silicon Valley guy who doesn’t wear a tie.
The rumors that have circulated in the (distant) past were that Apple was dying and needed someone to rescue. It was always Sony buying Apple. Since Apple is worth over twice what Sony is, that obviously isn’t an option. Also, Sony would fire Stinger in a heartbeat to be led by Steve Jobs – not that he would ever do it.
Apple’s owning Sony makes sense on many levels, though. Imagine the merger of two companies that boast many of the world’s top design experts. Imagine how our living rooms might look, how we might communicate, how we might work, and how we might view what’s possible in 10 years if Sony, which is strong on hardware, and Apple, which excels in software, got together.
The argument here is that Sony’s hardware is better than Apple’s. I think there would be a lot of people who’d disagree with that. Sony does have a much broader line of hardware – which Apple might want to expand into at some point – but not all at once. I think one of Apple’s strengths is its simplicity.
Few words roll eyes like “synergies,” and yet Apple and Sony would enjoy a bull market in them. Sony needs to restore the “cool factor” it once had and Apple now owns. Apple needs Sony’s content — movies, music — to sell to its iPod and Apple TV enthusiasts. Why negotiate deals with record labels and film studios when you can own them?
Because if you buy Sony, you lose all of the other content. It is kinda like AMD buying ATI. Intel isn’t playing much ball with ATI anymore and the AMD-ATI merger is looking like a disaster. Imagine that times a million.
Apple probably wouldn’t mind controlling the Blu-Ray technology that analysts say offers lucrative revenue streams. Sony would give Apple the game franchise it lacks. Its camera line-up also could serve Apple well — an iCamera, anyone? Or an iPhone equipped with one of Sony’s high-definition camcorders? And don’t forget Sony’s impressive stable of patents.
Optical is dead to Apple. And Apple isn’t in the camera business. It made the first digital camera (Quicktake 100) and currently includes cameras in all of its portables but is unlikely to want to jump into a fiercely competitive marketplace where it doesn’t have much expertise.
Japan also is a market that Apple has yet to dominate. It’s not just Japan’s flagging economy. It’s also about the large number of domestic rivals with a high level of name recognition in a fiercely competitive market.
You won’t find a more mobile-phone-obsessed population anywhere than Japan. Adding some Sony designs to the iPhone alone could mean huge profits. And Apple might find Sony’s distribution channels helpful in boosting its Asian business.
Apple does plenty good in Japan. Some would say better than Sony in its own hometown. Picking up a falling star isn’t going to help Apple.
Sony is having trouble getting its groove back. Even after falling amid subprime-loan turmoil in markets, Apple’s market value is $107 billion to Sony’s $44 billion. Operating margins really tell the story. Sony’s was 0.86 percent at the end of March 2007 versus 2.48 percent in 2003. Apple’s was 18.37 percent at the end of September versus 0.40 percent in 2003.
One could argue Sony is looking a bit Microsoft-esque. Microsoft was slow to realize the extent to which the Googles of the world made it seem more Old Economy than New Economy. It has taken Sony some time to understand Apple’s threat.
So why would Apple want to be associated with Sony again?
Of course, Jobs may have little interest in owning a company with so many fleas. Sony’s bureaucracy, scale and ingrained corporate culture could be a huge distraction for Apple, which has been successful offering a small number of products it makes very, very well. Also, Apple already is looking beyond today to tomorrow. Sony is more about today.
Some Sony product lines could be sold to help Apple pay for its acquisition. Yet how much debt would Apple be willing to take on to finance it? And given Sony’s baggage, an argument can be made that Nintendo Co. would be a better fit for Apple.
Apple doesn’t buy companies too often. Usually when it does it is to acquire a specific software or technology for products it wants to build. A Sony merger isn’t in its DNA. Apple also doesn’t do debt anymore.
Sony has an illustrious history of innovation and coming back from the brink. Counting Sony out could be a dangerous bet for competitors and investors alike. That doesn’t mean Jobs shouldn’t be thinking bigger. As bold acquisitions go, Sony may be Apple’s one and only.
Their illustrious history is coming back from World War II and making the frst transistor radio in 1954 under founder Akio Morita. Since he left in 1994, it has been a steady decline. Things haven’t really been that good since 1989 when they picked up Columbia from Coca Cola. Or since the betamax philosophy took root (kit).
(William Pesek is a Bloomberg News columnist. The opinions expressed are his own.)
Nice work Bloomberg.
For someone who follows Apple and to a lesser extent, Sony very carefully, this type of speculation seems a bit “out there”.
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