The main competitors:

• Intel
• AMD

The last-minute addition:

• Apple

But before we crown the winner, we need to go back to the starting line.

Intel: The Favorite to Win the Race

Intel created the first commercial microprocessor (the 4004) back in ’71 and introduced an 8-bit microprocessor (the 8008) a year later [1]. By the mid-’70s, most printers, cash registers, and terminals used Intel microprocessors like the 8080, which became the blueprint for subsequent designs. The company has pretty much dominated the CPU market ever since.

Intel had little competition in the ’70s. Panafacom — a company formed by Fujitsu, Panasonic, and Fuji Electric — introduced a commercial 16-bit microprocessor called the MN1610 in ’75 [2]. But there was little else that broke into the mainstream.

Intel continued its reign into the ’80s and ’90s. Sure, other companies joined the CPU race — Motorola in ’85 with the 32-bit 68020+68851; ARM in ’85; and Hitachi in ’92 — but Intel became synonymous with processors. It introduced the 80286 in ’82; the 32-bit 80386 in ’85; and the original Pentium microprocessor in ’93. (This was the first processor with the now-famous x86 “superscalar” micro-architecture.)

Intel had few competitors. But this was about to change.

AMD(American Micro Devices) Joins the CPU Race

AMD introduced x86-64 extension to the popular x86 micro-architecture in 2000. Just a year later, AMD released the Athlon (with 1 GHz!) Two years after this, AMD introduced the Athlon 64 — the world’s first 64-bit consumer CPU [3].

AMD was coming for Intel’s throne. AMD technologies proved popular and established the company as a genuine rival to Intel, which hit back with the Pentium 4, one of its most iconic models.

Intel released subsequent models under the Pentium brand name — the Pentium M in 2000 and the Pentium D in 2006 — then vPro in 2007 and the Core i-Series and Atom in 2008. The company moved to the new Platform Controller Hub (PCH) design in 2010, eliminating its famous Northbridge chip.

Intel vs. AMD 

Over the last decade, Intel and AMD have raced neck and neck. The biggest developments? Intel released its Core i3, i5, and i7 processors in 2010, while AMD released the first 8-core CPU for desktops and Ryzen processors based on Zen architecture in 2011 and 2017, respectively.

For the last 10 years or so, Intel chips tend to rank better when it comes to performance per core [4], but AMD offers more “cores” for money and advanced onboard graphics. Value-for-money is around the same. We think both companies make great laptop processors. However, AMD scores better on desktop CPUs. Our honest opinion: Intel’s products have lost their edge over the last few years, while AMD continues to innovate.

Could Apple Win the CPU Race?

Earlier this year, Apple shocked the tech sector by entering the CPU race [5]. It will create its own processors for MacBooks (based on ARM architecture). Welcome to Apple Silicon. Of course, Apple has used Intel products for years, so this development came as a shock to many of us in the industry.

We welcome this news. Apple has created strong CPUs for iPhones and iPads for a while now, and market diversity is always good.

The Winner

Intel has long been the CPU market leader, but things are changing. AMD continues to create strong products, and Apple has joined the race. But which product should you choose? It depends on various factors like device (desktop or laptop?), function (gaming or business?), and price.

There’s a lot of choices out there nowadays and, as Apple proves, there’s much more to CPUs than just Intel!

_Sources:

_{[1] http://www.vintagecalculators.com/html/busicom_141-pf_and_intel_4004.html}

_{[2] http://www.cpu-museum.com/161x_e.htm}

_{[3] http://www.cpu-world.com/CPUs/K8/TYPE-Athlon%2064.html}

_{[4] https://www.digitaltrends.com/computing/amd-vs-intel/#:~:text=Overall%2C%20both%20companies%20produce%20processors,price%20and%20better%20onboard%20graphics.}

_{[5] https://www.trustedreviews.com/news/four-reasons-why-apple-is-right-to-snub-intel-and-amd-4038431}

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But there’s another significant concern that might quickly outweigh the others. A.I. tools create data — lots of data. NVIDIA plans on shipping 1.5 million A.I. server units per year by 2027, which would consume 85.4 terawatt-hours of electricity annually with current technologies. That’s significantly more than the total electrical consumption of countries like Austria, Switzerland, and Peru.

And those data centers will need to store that data somewhere. If A.I. data is accessed regularly, it will end up on hard drives — and there may not be enough hard drives to meet the demand.

Hard drive manufacturers will need to scale operations to address A.I. data.

An analysis from Everypixel Journal puts the size of the A.I. data footprint into perspective:

• In 2023, A.I. tools created about 15.47 billion images — more than photographers have taken in the last 150 years.
• People create about 34 million images per day.
• A.I. data usage isn’t limited to asset creation. ChatGPT’s website receives nearly 1.5 billion visitors per month, with each user spending about 7 minutes and 36 seconds on the website per visit.

Unfortunately, the data storage industry isn’t positioned to handle the sudden (and potentially exponential) increase. In 2022, researchers at Aston University predicted a “data storage crunch” within years — which could lead to reduced internet speeds for global users, with potentially dire economic consequences. 

New technologies could help to address A.I.’s storage consumption.

There’s some good news: The vast majority of A.I.-generated data will be cold — after its creation, it won’t be accessed regularly (and we’d expect that most data will never need to be accessed again). 

That makes data tapes a viable solution. LTO (Linear Tape Open) 9 can store 45 terabytes per cartridge (compressed), and the LTO project’s LTFS file system allows for fast read access when necessary. 

Of course, data centers will probably have issues separating A.I.-generated data from human-generated data — so HDD-based storage will still be necessary at the enterprise level for the foreseeable future. While modern tape formats are fast, they’re not comparable to HDD-based RAID arrays. 

Two major hard drive technologies can extend areal densities without sacrificing reliability: 

• Heat-Assisted Magnetic Recording (HAMR), which uses heat to complement magnetization.
• Shingled Magnetic Recording (SMR), which overlaps hard drive tracks slightly resulting in narrower tracks (and greater storage capacities).

Apart from technology improvements, data centers can address limited storage media with more efficient data-handling methods. Hard drive manufacturers can shore up their supply chains to improve logistics (and limit the environmental effects of suddenly massive media production).

At Datarecovery.com, we’ve seen the storage industry find novel ways to adapt to new challenges. We’ve also invested in our own research and development to provide reliable services for enterprises — including RAID data recovery, ransomware recovery, and penetration (PEN) testing.

To learn more, call 1-800-237-4200 to speak with a member of our team or submit a request online.

The post Artificial Intelligence Could Strain Global Data Storage Capacities appeared first on Datarecovery.com.

Implementing Automated Marketing Campaigns

One of the hardest parts of advertising — especially online advertising, where the audience is a whole lot larger than those who drive by a single billboard or walk past a single sign in a business’s hometown — is determining who exactly the target audience is and how you can reach them best. Thanks to machine learning, companies can implement automated marketing campaigns to better determine the target audience, more efficiently communicate with them, and more effectively bring them in as new customers.

Through this implementation of machine learning, marketers can spend their time and efforts on creating campaigns catered directly to their target audience instead of having to spend that time and effort on determining the target audience in the first place. Not only is it a big time-saver, it’s also an essential tool for businesses and marketers alike.

Simplifying the Recruiting and Hiring Process

If you thought the job hunting process was hard, wait until you end up on the other side of it — the recruiting and hiring process is incredibly strenuous on businesses, not only because they have to conduct days and weeks of interviews, but also because they have to sort through countless resumes in order to identify who they actually want to interview in the first place. As of late, machine learning has been used to simplify this recruiting and hiring process by automatically sorting through resumes and cover letters to weed out candidates that don’t fit the bill.

By utilizing machine learning for this process, recruiters and hirers don’t have to exhaust themselves diving into a pile of resumes and cover letters and can save all their efforts for the actual interviews themselves. It’s a big time-saver, and it also helps prevent any accidental oversights.

Improving Customer Relations

Those who work in the customer service industry have to exercise extreme patience and an expert-level of know-how in order to both deal with unhappy customers and help them to resolve whatever issue it is that they’re facing. This is especially true when dealing with customers who are asking questions that can be easily answered via the business’s website or FAQ. Because of this, businesses have begun using machine learning to improve their customer relations.

Customer service boosted with machine learning uses tools like artificially intelligent chat bots as the first line of defense, so to speak: Customers will speak with the AI bot first, and if their question can’t be answered, then they’ll be directed to a representative. This is an enormous resource for those who spend most of the day answering easy questions that clog up the customer service hotline, making it harder for those with more complex calls to get through.

Enhancing Security Measures

No matter the size of the business, cybersecurity and cybersecurity threats are a very real concern. As long as financial information and other personal data is on the internet, there will be cybercriminals with the intent to take it and exploit it for their own personal gain. With that being said, it’s not always easy to keep these attacks from happening — no matter if you’re a small business or a big corporation. Thankfully, machine learning is working to enhance online security measures like never before.

By knowing how to self-adjust and interpret key patterns, machine learning and artificially intelligent security software can work together to identify and altogether prevent fraud from occurring on a business’s website. It’s become a must-have innovation for any business that conducts business online.

Finding (and Correcting) Mistakes In Real Time

Even the smallest error can have an enormous impact on a business — from a misplaced decimal on an important financial document to a typo on a legal contract to a simple mistake on the operating hours, these little slips are just a part of being human, but they can also have a very real and very serious effect. With the help of machine learning and artificial intelligence, these mistakes can actually be found and corrected in real time.

Machine learning is designed to identify patterns and recognize algorithms, which means that it can be implemented into your business’s finances or your business’s website and alert you to those mistakes in your dataset as they occur. This can be a real life-saver for businesses, to say the least.

The post 5 Ways Machine Learning Is Impacting Businesses appeared first on Datarecovery.com.

What Is a Data Lake?

In simple terms, a data lake is an enormous digital reservoir that can store large amounts of raw data in its native and respective format while also providing some sort of audit of said data. In fact, an image of a lake is actually a pretty accurate visualization of what a data lake acts like: data types of all shapes and sizes are simply tossed into the data lake and stored without the need for any sort of file conversion (just like how you could toss a rock, a stick, and a fish all into a lake and it’d all end up in the same place, sinking or floating in the water).

Following that same thinking, a data lake acts similarly: No matter if it’s structured data in the form of a spreadsheet or from a database, semi-structured data like XML or JSON, unstructured data like emails and documents, or binary data like audio or videos, a data lake will accept and store it and analyze all regardless of format.

This differs from a data warehouse in a few key aspects: For one, data warehouses operate using groups of smaller datasets to maintain top speeds. Data lakes do away with this, which sacrifices speed somewhat but allows for all data to be presented in one space instead of various subsets. A data warehouse will require less storage for a given breadth it covers (number of widgets, customers, etc.) due to its structure, narrower focus, and higher level of curation. A data lake requires more storage for that same breadth that is covered to allow for all data to be presented holistically in one space and in original files including various formats with less overall structure. It could be the difference between a few terabytes versus a few petabytes. Users could use both storage patterns if they felt like it.

What Are the Benefits of a Data Lake?

Data lakes are hugely beneficial because they allow for companies with large amounts of data to centralize everything in one place, even if it doesn’t all fit nicely together in one uniform format. All too often, a company has to split up their structured data, their semi-structured data, their unstructured data, and their binary data across multiple storage spaces and hop from platform to platform in order to do reports, analytics, and visualizations.

With a data lake, companies can view all their data in one place and make those comprehensive and hugely insightful observations much more accurately without running the risk of missing anything important stored somewhere else. When running a business, especially a large one with a truly substantial amount of data across various formats, this insight is an invaluable thing to have: It informs future decisions about the company’s path forward by displaying trends that let leaders know what’s working best and what could use improvement.

Are Data Lakes Safe?

Because data lakes are such a new, up-and-coming technology in the world of data storage, there are some valid concerns about whether or not data lakes are as safe as some of the aforementioned storage solutions. After all, safety is just as important as ease, right? You certainly wouldn’t want some pesky virus or dastardly malware coming along and doing a virtual cannonball into your data lake.

The short answer is that your data is always facing the threat of a safety risk, and companies must always make sure they’re taking proper safety precautions with their data because of this. This includes always encrypting your data and only downloading data from safe, reputable places that you can trust. This way, you can ensure your company’s most precious information is protected from both the outside and the inside.

The Bottom Line: Don’t End up with a Data Swamp

While data lakes are clearly a useful and unique business tool for their ability to centralize data across multiple formats and display that data in an insightful way, it’s important to make sure that you keep your data lake safe and — perhaps just as importantly — organized in a concise manner. The last thing you want is your data lake turning into a data swamp, where data is disorganized and messy, impossible to parse through, and as result much less useful.

While research continues to be done on data lakes and the emerging technology continues to become more well-known in the world of data, one thing remains true: the opportunity to centralize data of all different types in their original formats in one convenient location that can also provide key insight is definitely something worth looking into.

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