Artificial intelligence provides many benefits to companies and people. Helping students with their homework and using it in security systems and in-car voice commands in the automotive industry can be shown among the simplest examples of this.
It can also be used in areas such as quality monitoring in production and facilitating payment transactions in the retail industry. But for a while, AI development seems to be confined to certain areas. AMD executive Matt Foley also made a clear call for AI to reach its true potential.
AMD revealed the realities of artificial intelligence: It is not used for really useful work!
Data flow is of great importance for artificial intelligence applications to be effective. Organizations often develop customized AI-based applications using their own data. Of course, approaches based on SaaS solutions can also be used.
For the training and development process of artificial intelligence models, it is critical that the hardware and software work like an ecosystem, that is, its integration. Because a large amount of data and optimized algorithms are used for model training.
Giant partnership for artificial intelligence technology! What awaits us next?
NVIDIA and Microsoft announced that they will work together to help organizations accelerate their AI initiatives.
CIOs, on the other hand, need to consider factors such as process efficiency, flexibility and measuring benefits when investing in AI applications. But at the same time, they must pay attention to issues such as compliance with privacy governance regulations and data security.
The following is the statement made by Matt Foley, Director of Field Application Engineering, AMD EMEA:
“Artificial intelligence has become a popular topic after years of waiting to come to light, and the possibilities offered by services like ChatGPT and DALL-E have made it even more popular. But AI has to offer much more than helping with student assignments and producing uncanny works of art.
It can have a significant impact on the industry by empowering companies with applications such as computer vision, natural language processing and product recommendation systems. CIOs must now decide which approach to take to effectively leverage this technology when developing in-house AI-based applications.
Potential applications for artificial intelligence began to spread widely. Computer vision in the automotive industry could enable automated safety systems that recognize pedestrians on the road. Natural language processing can facilitate in-car voice commands. In production, computerized control and vision can monitor quality and recommend proactive maintenance.
Payment processes can be facilitated through automatic product and customer recognition in the retail industry. Financial services can detect transaction anomalies to prevent fraud. Medical companies can improve the speed and accuracy of diagnoses. Companies of all kinds can increase the richness and quality of their corporate searches, making valuable data easier to find.
The lifecycle of machine learning has two main phases, training and inference. The training phase takes large amounts of data and applies artificial intelligence to recognize patterns and build models. For this, high-performance computing data center servers equipped with best-of-breed processors such as 4th Gen AMD EPYC processors and data center accelerators such as AMD Instinct are used.
The inference phase involves applying the model to real-world data to produce an actionable output. At this stage, similar hardware to the training stage can be used, as well as embedded devices such as AMD Versal and Zynq System-on-Chip (SoC).
AI applications need comprehensive models derived from rich data to be effective. However, prebuilt tools have advanced significantly in terms of providing models trained on external data, either publicly available or commercially available. However, the most valuable data for an organization is contained within its own body. This will require some in-house enhancements and the use of the most cost-effective hardware and software ecosystem.
An AI-based application can provide clearer results when customized with the organization’s own data. However, as AI becomes more prevalent, a mix of approaches based on in-house applications as well as publicly available Software as a Service (SaaS) solutions will likely yield the strongest results.
The key here is to have consistent access across stacks used for training and inference. AMD Unified Inference Frontend (UIF) by itself provides a key enabler to industry-standard artificial frameworks such as Tensor Flow, PyTorch, WinML, and Open Neural Network Exchange (ONNX).
These too can integrate seamlessly with the Vitis AI Platform for an AMD EPYC CPU stack, ROCm software-driven AMD Instinct GPU stack, or an embedded stack with AMD Versal or Zynq adaptive SoCs.
When developing a strategy for investing in AI applications, CIOs must ensure that they spend the company’s money effectively to generate adequate returns. Process efficiency improvements, productivity, and the flexibility of IT infrastructure are at the heart of measuring benefits. CIOs must choose the best architecture for their solutions that can be implemented quickly. That’s why it’s so important to have a wide variety of AI stacks to choose from.
CIOs will also need to conduct an impact assessment throughout the application lifecycle to ensure it is securely managed for responsible innovation and also complies with privacy governance regulations and frameworks such as NIST.
This is because AI models will likely be derived from sensitive or proprietary data, or both, and its privacy must be protected as valuable Intellectual Property. The cost implications of all these factors mean that CIOs must invest in areas where the benefits are clear, long-lasting, and deliver key productivity improvements.
However, building the algorithms underlying AI models is both time-consuming and costly. They are increasingly dependent on the availability of larger datasets and AI architects who can work on a wide variety of use cases.
This is another reason why a consistent unified platform is necessary. The size of the parameters for the models has grown from thousands to hundreds of billions in just ten years, which is a staggering increase in quantity. Training a model with so many parameters requires a sufficiently large amount of data and careful data management.
It is also very important to guard against data bias, which can skew the results. This has become a regular criticism of general AI practices trained on publicly available datasets. Correcting this bias requires significant improvement. All this means that model training is time consuming and costly.
Key decision makers in determining an AI-based enterprise application investment strategy will be executive teams. They must consider where AI is needed, take inventory of use cases, categorize risk levels, and then assess how mature the necessity of existing AI solutions is.
Either way, leveraging a flexible existing platform like AMD UIF and optimized libraries like AMD ZenDNN and the ROCm HIP compiler provides agility. So the benefits of tried-and-tested code can be combined with business-specific customization on the right platform to suit investment levels.
AI is still in the early adoption stage and has already experienced many generations of ups and downs. But today’s AI promises to be the start of a cycle fueled by extremely large models and large datasets. This “big data” phase for AI-based solutions, whether in-house developed applications or industry-provided application frameworks, is now in a clear growth phase.
As greater adoption of AI in business leads to better practices, this means better frameworks will be developed and the efficiency of business processes will increase. These improvements and benefits will further accelerate application development, increase benefits and reduce the cost of adoption. AI-powered applications should be on every CIO’s agenda as soon as the benefits outweigh the costs for an organization.“