No matter where you look, AI seems to be taking over everything. While many are content with services provided by companies such as OpenAI and Nvidia, there are those in the tech industry who detest this reliance. Now, Apple is looking to develop its own AI servers and software solutions so that it can provide new capabilities to its products while being independent of mainstream services. What challenges does AI present with regard to computing power and hardware, what exactly is Apple doing, and are we seeing a new trend of engineers and companies going their own way on such services?
- Hardware struggling to keep up with increasing AI demand and developers forced to make difficult choices if they don’t quickly catch up are likely to be held back.
- Apple’s development of in-house AI servers will transform the market by optimising hardware and software for performance benefits and enhanced data privacy.
- Companies developing their own I solutions can lead to increased independence, customisation, and protection of user data, potentially marking a new trend in the tech industry.
Challenges in AI: Computing Power and Hardware
The past few decades have seen the computing industry undergo a transformation on an unimaginable scale. In the 90s, computers were primarily used for processing and storing user data, and the term “hardware” generally referred to the physical components of a computer system. However, when the 21st century emerged, the rapid growth of internet technologies and data collection combined with the shrinking size of transistors saw computers become an integral part of everyday life. Computers no longer existed solely in the office, butinsteadin every home, in every car, and even on some individuals’ persons.
As the demand for computing power increased exponentially, researchers and engineers developed new hardware technologies, such as multi-core CPUs and GPUs, so that computers could be made smaller while providing greater processing capabilities. For example, the development of the first multi-core CPUs allowed for each the core to be designed to handle specific tasks efficiently while the use of GPUs allowed the CPU to focus more on background tasks.
However, hardware development is slowly becoming a bottleneck in AI development. While new hardware technologies are still being developed, such as quantum computers, which could potentially solve some of AI’s most complex tasks, they are still in their infancy and far from being practical for everyday use.
The Struggle of Current AI Hardware Solutions
Current solutions being offered by hardware manufacturers are struggling to keep up with the ever-increasing demand for computing resources from AI developers. For example, fields such as medical science are increasingly turning to AI for diagnostic medicine due to the ability of AI to read countless amounts of research while finding relationships between data sets that would otherwise be too complex for a physician to read. However, the hardware needed to run such algorithms efficiently doesn’t currently exist, and as such, AI is often being held back by the hardware on which it is being run.
As a result of this, AI developers are often forced to make difficult choices when developing new systems. One such choice is to reduce the number of weights in neural networks used in the predictive algorithm to reduce memory usage, with another option being to reduce the number of cores used to run the neural network to reduce energy usage.
In conclusion, hardware is struggling to keep up with the ever-increasing demand from AI developers. Hardware needed to run AI efficiently doesn’t currently exist (or at least for large practical AI), and AI developers are often forced to make difficult choices when developing new systems. If the hardware doesn’t quickly catch up to the ever-changing AI industry, it is likely that AI will continue to be held back by the hardware it is being run on.
Apple’s Strategic Business Moves
As the demand for AI continues to increase exponentially, it comes as no surprise that Apple is reportedly developing its own range of AI servers. While the technology has come a long way since its inception, current off-the-shelf solutions are still far from ideal for creating large AI data centres capable of servicing a wide range of clientele. Of course, there are many who will be quick to point out that Apple’s development of its own AI servers will create a conflict of interest, with the company simultaneously producing both hardware and software that will be optimised for each other.
Recent reports indicate that Apple is leveraging TSMC’s 3nm process to develop its custom AI server processors. This advancement is projected to enhance the efficiency and performance of AI operations within Apple’s ecosystem, targeting mass production by the second half of 2025. By controlling the hardware development, Apple aims to optimise its AI capabilities, ensuring superior integration with its software solutions.
But when considering Apple’s long history of vertical integration, this conflict of interest may actually be a blessing in disguise. For example, the company has already demonstrated its ability to create processors that outperform its competition, having been the first to introduce a fully custom SoC to the mobile market. While others have followed suit, only the Apple M1, M2, and M3 have proven to be the biggest game changers, thanks to their ability to fully utilise the parent company’s operating system, software, and architecture.
Apple’s Pioneering Approach to AI Hardware Development
Apple’s development of proprietary AI server technology exemplifies the shift towards more tailored AI solutions. By creating bespoke hardware, Apple can ensure that its AI systems are finely tuned to meet the specific needs of its applications, from enhancing user experiences on personal devices to driving innovation in enterprise solutions.
With regard to AI, the development of an in-house server will allow Apple to fine-tune its hardware and software. This will see significant performance benefits, thus allowing Apple to expand its AI offerings to both the consumer and business markets. Furthermore, the ability to create its own servers will also allow Apple to address the growing concerns surrounding data privacy. For example, personal data is only ever stored on Apple servers when absolutely necessary, and the development of private AI servers will further help to strengthen the position.
If the latest server rumours turn out to be true, it may not be long before Apple takes the AI market by storm, thanks to its ability to create hardware and software optimised for each other. While we will have to wait for more details to be unveiled, one thing is for sure, the introduction of Apple AI servers will shake up the market in a big way.
This development follows the broader industry trend of tech giants investing heavily in AI research and infrastructure. Apple’s focus on in-house AI server solutions not only reflects its strategic priorities but also positions it to better compete with other major players who are similarly pursuing customised AI hardware to enhance their service offerings.
Exploring the Shift: Engineers and Companies Taking Independent Paths
From character recognition to face detection, AI continues to play a crucial role in many consumer products. While Apple may have been one of the first to dabble in AI, it certainly won’t be the last, with companies left, right, and centre all developing their own AIs.
For example, Amazon has also been working on its own AI solution, called Amazon SageMaker, to help small businesses develop their own AI applications. Even Tesla has developed its own in-house AI hardware solution called Dojo, said to be one of the fastest supercomputers in the world. So why are these companies choosing to develop their own AI solutions? The answer simply put is independence.
One of the most striking examples of the need for independence from mainstream AI services is the numerous arguments against companies such as Google and Facebook (now Meta). Simply put, such companies have been suspected (and in some cases proven), to be scraping information from emails, profiles, and files for the purpose of creating user clusters and improving their AI. While this may be quick and easy for the sake of improving AI, it is undoubtedly a violation of the privacy of those who have placed their trust in such businesses. As a result, companies that host their own AI solutions have the option to keep the data local to their servers, thus protecting customer data.
Another major advantage of having one’s own AI solution is its ability to customise the AI to the specific application. While off-the-shelf AI solutions certainly have a wide range of capabilities, they are unable to understand the specific requirements of a project. For example, the automotive industry has specific regulations and requirements that do not apply to the general public. Thus, a generic AI solution may not be able to recognise and apply these regulations. Companies that host their own AI solutions can view the requirements of their application and develop AI to be used only for that purpose.
Looking forward, it is likely that more and more companies will turn towards developing their own AI solutions. With the computing power available to large tech companies, developing advanced AI solutions is easier than ever, and the numerous libraries and tools available make the transition from no AI to advanced AI in a matter of years. The removal of dependence on mainstream AI services not only helps companies improve their own services but also helps protect user data. Thus, we may be witnessing a new trend of engineers and companies going their own way on such services, and it could not have come too soon.
