TECHNOLOGY

Why is Apple So Successful? Apple’s Machine Learning Strategy & Self Driving Car Project

Published

2 years ago

January 26, 2022

Why is Apple So Successful? Apple’s Machine Learning Strategy & Self Driving Car Project

Apple is successful due to its high quality products, unique culture, loyal fan base, excellent customer service and highly skilled workforce.

The success of Apple goes beyond its simple products as it’s using the latest technologies including artificial intelligence, machine learning and deep learning.

Machine Learning (ML) is increasingly dominating the keynotes where Apple executives take the stage to introduce new features for iPhones, iPads, Macbooks and the Apple Watch. Machine learning enables systems to learn without being explicitly programmed. Based on algorithms and huge datasets for training, systems learn to recognize patterns that had not previously been defined. The acquired knowledge can then also be applied to new data.

Apple’s machine learning strategy centres around its devices. The company has positioned itself as a pioneer of in-device machine learning technology, with its superior security and potential for creating unique, user-engaging experiences.

Apple is targeting 2024 to produce a passenger vehicle that could include its own breakthrough battery technology.

What’s The History of Apple?

Source: Office Timeline

Apple Inc., formerly known as Apple Computer Inc., is a technology multinational company that has its headquarters in Cupertino, California.

The company specializes in designing, developing, and selling consumer electronics, computer software, and online services. Apple Inc. was founded by two young tech enthusiasts, Steve Jobs and Steve Wozniak.

The Macintosh, released in 1984, introduced the Graphical User Interface (GUI) to the mainstream. Apple’s second product, the Apple II was a huge success. It was the first personal computer to earn such mass-market success.

The company started struggling after it ousted Steve Jobs back in 1985. It was even on the verge of bankruptcy when Steve Jobs returned to the company in 1997.

Steve brought in some brilliant ideas and lifted the spirit of the company. He led the company to recovery by introducing the iPod in 2001, the iPhone in 2007, and the iPad in 2010. Apple earned a profit of almost $40 million in the 2014 fiscal year.

Steve Jobs died of cardiac arrest in 2011. Tim Cook, his longtime deputy, took over as the CEO and has presently been leading the company to success.

How is Apple Implementing Machine Learning?

Source: Statista

Over the course of the last several years, Apple has bought startups such as Emotient, Turi, Glimpse, RealFace, Shazam, SensoMotoric, Silk Labs, Drive.ai, Laserlike, SpectralEdge, Voysis, XNOR.ai, and more, all with the aim of improving the artificial intelligence and machine learning capabilities of its products and services.

Apple’s machine learning approach addresses two major pain points: training of models on the cloud is expensive, and getting them to work on mobile devices is difficult at best.

The company has always stood for that intersection of creativity and technology.

Machine learning is a key component of Apple’s broader initiatives.

Apple’s machine learning strategy continues to be focused on running workloads locally on devices, rather than relying heavily on cloud-based resources, as competitors Google, Amazon, and Microsoft do.

Machine learning is very much a part of Apple’s overall strategy, and it is building it into the very fabric of its devices and services.

Uses Cases of Machine Learning in Apple Devices

Source: Log Point

By running on machine learning models on-device, Apple guarantees that the data never leaves the device of the users.

If Apple can provide tools that make it feasible to train models locally at reasonable speeds, it could further increase its hardware footprint because for individual developers training models locally on a single machine is much more cost effective than doing so on the cloud.

Here’s a quick recap of the more prominent machine learning features developed by Apple:

Facial Recognition: Apple uses biometrics to map facial features from a photograph or video. Smart cameras can help identify your friends or family members in your photos.
Native Sleep Tracking: This uses machine learning to classify your movements and detect when you’re sleeping. The same mechanism also allows the Apple Watch to track new activities.
Handwashing: It not only detects the motion but also the sound of handwashing, starting a countdown timer to make sure you’re washing for as long as needed.
Library Suggestions: A folder in the new App Library layout will use “on-device intelligence” to show apps you’re “likely to need next.” It’s small but potentially useful.
Translation: This works completely offline, thanks to on-device machine learning. It detects the languages being spoken and can even do live translations of conversations.
Handwriting Recognition: Apple is receiving and interpreting intelligible handwritten input from sources such as paper documents, photographs, touch-screens and other devices. Apple is doing a great job at image recognition tasks, and identifying both Chinese and English characters is a fitting challenge.

What Are The Key Drivers Behind Apple’s Success?

Apple’s success lies in a strategic vision that transcended simple desktop computing to include mobile devices and wearables. Both performance and design are key drivers of the Apple brand and its ongoing success.

A significant part of the credit for the iPhone’s success has to go to the App Store because the app ecosystem keeps customers coming back to Apple year after year when their mobile contracts are up for renewal, Apple has pushed developers to integrate AI into their third-party apps.

When Apple launched Siri, it became the first widely used natural language processing (NLP)-powered assistant.

Apple is also relying on a talented workforce. It is hiring the very best and most creative designers and engineers to build widely successful easy to use products.

Why is The Apple Car Project Still Secretive?

Source: Roadzen

Hundreds of Apple employees are working on developing a self-driving Apple-branded car aimed at consumers.

Apple’s car project is still in the works. Deep integration with iOS is also expected. Apple is still waiting for autonomous testing permits received from DMV. A self-driving software is also being tested.

The company is also developing a new battery design that has the potential to radically reduce the cost of batteries and increase the vehicle’s range.

Apple is working on a monocell design that will bulk up the individual battery cells and free up space inside the battery pack by removing pouches and modules that hold battery materials. This will allow for more active material in a smaller package. The battery technology has been described as “next level” and similar to “the first time you saw the iPhone.”

Conclusion

Apple’s ultimate dream is to dominate every sector by leveraging machine learning. Developing a safe self-driving car represents a supply chain challenge for Apple, a company with deep pockets that makes hundreds of millions of electronics products each year with parts from around the world, but has never made a car. It’s important to bear in mind that it took Elon Musk’s Tesla 17 years before it finally turned into a sustained profit making car.

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TECHNOLOGY

Next-gen chips, Amazon Q, and speedy S3

Published

5 months ago

December 1, 2023

Max

AWS re:Invent, which has been taking place from November 27 and runs to December 1, has had its usual plethora of announcements: a total of 21 at time of print.

Perhaps not surprisingly, given the huge potential impact of generative AI – ChatGPT officially turns one year old today – a lot of focus has been on the AI side for AWS’ announcements, including a major partnership inked with NVIDIA across infrastructure, software, and services.

Yet there has been plenty more announced at the Las Vegas jamboree besides. Here, CloudTech rounds up the best of the rest:

Next-generation chips

This was the other major AI-focused announcement at re:Invent: the launch of two new chips, AWS Graviton4 and AWS Trainium2, for training and running AI and machine learning (ML) models, among other customer workloads. Graviton4 shapes up against its predecessor with 30% better compute performance, 50% more cores and 75% more memory bandwidth, while Trainium2 delivers up to four times faster training than before and will be able to be deployed in EC2 UltraClusters of up to 100,000 chips.

The EC2 UltraClusters are designed to ‘deliver the highest performance, most energy efficient AI model training infrastructure in the cloud’, as AWS puts it. With it, customers will be able to train large language models in ‘a fraction of the time’, as well as double energy efficiency.

As ever, AWS offers customers who are already utilising these tools. Databricks, Epic and SAP are among the companies cited as using the new AWS-designed chips.

Zero-ETL integrations

AWS announced new Amazon Aurora PostgreSQL, Amazon DynamoDB, and Amazon Relational Database Services (Amazon RDS) for MySQL integrations with Amazon Redshift, AWS’ cloud data warehouse. The zero-ETL integrations – eliminating the need to build ETL (extract, transform, load) data pipelines – make it easier to connect and analyse transactional data across various relational and non-relational databases in Amazon Redshift.

A simple example of how zero-ETL functions can be seen is in a hypothetical company which stores transactional data – time of transaction, items bought, where the transaction occurred – in a relational database, but use another analytics tool to analyse data in a non-relational database. To connect it all up, companies would previously have to construct ETL data pipelines which are a time and money sink.

The latest integrations “build on AWS’s zero-ETL foundation… so customers can quickly and easily connect all of their data, no matter where it lives,” the company said.

Amazon S3 Express One Zone

AWS announced the general availability of Amazon S3 Express One Zone, a new storage class purpose-built for customers’ most frequently-accessed data. Data access speed is up to 10 times faster and request costs up to 50% lower than standard S3. Companies can also opt to collocate their Amazon S3 Express One Zone data in the same availability zone as their compute resources.

Companies and partners who are using Amazon S3 Express One Zone include ChaosSearch, Cloudera, and Pinterest.

Amazon Q

A new product, and an interesting pivot, again with generative AI at its core. Amazon Q was announced as a ‘new type of generative AI-powered assistant’ which can be tailored to a customer’s business. “Customers can get fast, relevant answers to pressing questions, generate content, and take actions – all informed by a customer’s information repositories, code, and enterprise systems,” AWS added. The service also can assist companies building on AWS, as well as companies using AWS applications for business intelligence, contact centres, and supply chain management.

Customers cited as early adopters include Accenture, BMW and Wunderkind.

Want to learn more about cybersecurity and the cloud from industry leaders? Check out Cyber Security & Cloud Expo taking place in Amsterdam, California, and London. Explore other upcoming enterprise technology events and webinars powered by TechForge here.

TECHNOLOGY

HCLTech and Cisco create collaborative hybrid workplaces

Published

5 months ago

November 29, 2023

Max

Digital comms specialist Cisco and global tech firm HCLTech have teamed up to launch Meeting-Rooms-as-a-Service (MRaaS).

Available on a subscription model, this solution modernises legacy meeting rooms and enables users to join meetings from any meeting solution provider using Webex devices.

The MRaaS solution helps enterprises simplify the design, implementation and maintenance of integrated meeting rooms, enabling seamless collaboration for their globally distributed hybrid workforces.

Rakshit Ghura, senior VP and Global head of digital workplace services, HCLTech, said: “MRaaS combines our consulting and managed services expertise with Cisco’s proficiency in Webex devices to change the way employees conceptualise, organise and interact in a collaborative environment for a modern hybrid work model.

“The common vision of our partnership is to elevate the collaboration experience at work and drive productivity through modern meeting rooms.”

Alexandra Zagury, VP of partner managed and as-a-Service Sales at Cisco, said: “Our partnership with HCLTech helps our clients transform their offices through cost-effective managed services that support the ongoing evolution of workspaces.

“As we reimagine the modern office, we are making it easier to support collaboration and productivity among workers, whether they are in the office or elsewhere.”

Cisco’s Webex collaboration devices harness the power of artificial intelligence to offer intuitive, seamless collaboration experiences, enabling meeting rooms with smart features such as meeting zones, intelligent people framing, optimised attendee audio and background noise removal, among others.

Tags: Cisco, collaboration, HCLTech, Hybrid, meetings

TECHNOLOGY

Canonical releases low-touch private cloud MicroCloud

Published

5 months ago

November 28, 2023

Max

Canonical has announced the general availability of MicroCloud, a low-touch, open source cloud solution. MicroCloud is part of Canonical’s growing cloud infrastructure portfolio.

It is purpose-built for scalable clusters and edge deployments for all types of enterprises. It is designed with simplicity, security and automation in mind, minimising the time and effort to both deploy and maintain it. Conveniently, enterprise support for MicroCloud is offered as part of Canonical’s Ubuntu Pro subscription, with several support tiers available, and priced per node.

MicroClouds are optimised for repeatable and reliable remote deployments. A single command initiates the orchestration and clustering of various components with minimal involvement by the user, resulting in a fully functional cloud within minutes. This simplified deployment process significantly reduces the barrier to entry, putting a production-grade cloud at everyone’s fingertips.

Juan Manuel Ventura, head of architectures & technologies at Spindox, said: “Cloud computing is not only about technology, it’s the beating heart of any modern industrial transformation, driving agility and innovation. Our mission is to provide our customers with the most effective ways to innovate and bring value; having a complexity-free cloud infrastructure is one important piece of that puzzle. With MicroCloud, the focus shifts away from struggling with cloud operations to solving real business challenges” says

In addition to seamless deployment, MicroCloud prioritises security and ease of maintenance. All MicroCloud components are built with strict confinement for increased security, with over-the-air transactional updates that preserve data and roll back on errors automatically. Upgrades to newer versions are handled automatically and without downtime, with the mechanisms to hold or schedule them as needed.

With this approach, MicroCloud caters to both on-premise clouds but also edge deployments at remote locations, allowing organisations to use the same infrastructure primitives and services wherever they are needed. It is suitable for business-in-branch office locations or industrial use inside a factory, as well as distributed locations where the focus is on replicability and unattended operations.

Cedric Gegout, VP of product at Canonical, said: “As data becomes more distributed, the infrastructure has to follow. Cloud computing is now distributed, spanning across data centres, far and near edge computing appliances. MicroCloud is our answer to that.

“By packaging known infrastructure primitives in a portable and unattended way, we are delivering a simpler, more prescriptive cloud experience that makes zero-ops a reality for many Industries.“

MicroCloud’s lightweight architecture makes it usable on both commodity and high-end hardware, with several ways to further reduce its footprint depending on your workload needs. In addition to the standard Ubuntu Server or Desktop, MicroClouds can be run on Ubuntu Core – a lightweight OS optimised for the edge. With Ubuntu Core, MicroClouds are a perfect solution for far-edge locations with limited computing capabilities. Users can choose to run their workloads using Kubernetes or via system containers. System containers based on LXD behave similarly to traditional VMs but consume fewer resources while providing bare-metal performance.

Coupled with Canonical’s Ubuntu Pro + Support subscription, MicroCloud users can benefit from an enterprise-grade open source cloud solution that is fully supported and with better economics. An Ubuntu Pro subscription offers security maintenance for the broadest collection of open-source software available from a single vendor today. It covers over 30k packages with a consistent security maintenance commitment, and additional features such as kernel livepatch, systems management at scale, certified compliance and hardening profiles enabling easy adoption for enterprises. With per-node pricing and no hidden fees, customers can rest assured that their environment is secure and supported without the expensive price tag typically associated with cloud solutions.

Tags: automation, Canonical, MicroCloud, private cloud