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IDTechEx Reports on The Age of Artificial Intelligence: AI Chips to 2034
The Age of Artificial Intelligence: AI Chips to 2034
Artificial Intelligence is transforming the world as we know it; from the success of DeepMind over Go world champion Lee Sedol in 2016 to the robust predictive abilities of OpenAI’s ChatGPT, the complexity of AI training algorithms is growing at a startlingly fast pace, where the amount of compute necessary to run newly-developed training algorithms appears to be doubling roughly every four months. In order to keep pace with this growth, hardware for AI applications is needed that is not just scalable – allowing for longevity as new algorithms are introduced while keeping operational overheads low – but is also able to handle increasingly complex models at a point close to the end-user.
Drawing from the “AI Chips: 2023–2033” and “AI Chips for Edge Applications 2024–2034: Artificial Intelligence at the Edge” reports, IDTechEx predicts that the growth of AI, both for training and inference within the cloud and inference at the edge, is due to continue unabated over the next ten years, as our world and the devices that inhabit them become increasingly automated and interconnected.
The why and what of AI chips
The notion of designing hardware to fulfill a certain function, particularly if that function is to accelerate certain types of computations by taking control of them away from the main (host) processor, is not a new one; the early days of computing saw CPUs (Central Processing Units) paired with mathematical coprocessors, known as Floating-Point Units (FPUs). The purpose was to offload complex floating point mathematical operations from the CPU to this special-purpose chip, as the latter could handle computations more efficiently, thereby freeing the CPU up to focus on other things.
As markets and technology developed, so too did workloads, and so new pieces of hardware were needed to handle these workloads. A particularly noteworthy example of one of these specialized workloads is the production of computer graphics, where the accelerator in question has become something of a household name: the Graphics Processing Unit (GPU).
Just as computer graphics required the need for a different type of chip architecture, the emergence of machine learning has brought about a demand for another type of accelerator, one that is capable of efficiently handling machine learning workloads. Machine learning is the process by which computer programs utilize data to make predictions based on a model and then optimize the model to better fit with the data provided, by adjusting the weightings used. Computation, therefore, involves two steps: Training and Inference.
The first stage of implementing an AI algorithm is the training stage, where data is fed into the model, and the model adjusts its weights until it fits appropriately with the provided data. The second stage is the inference stage, where the trained AI algorithm is executed, and new data (not provided in the training stage) is classified in a manner consistent with the acquired data.
Of the two stages, the training stage is more computationally intense, given that this stage involves performing the same computation millions of times (the training for some leading AI algorithms can take days to complete). As such, training takes place within cloud computing environments (i.e. data centers), where a large number of chips are used that can perform the type of parallel processing required for efficient algorithm training (CPUs process tasks in a serialized manner, where one execution thread starts once the previous execution thread has finished. In order to minimize latency, large and numerous memory caches are utilized so that most of the execution thread’s running time is dedicated to processing. By comparison, parallel processing involves multiple calculations occurring simultaneously, where lightweight execution threads are overlapped such that latency is effectively masked. Being able to compartmentalize and carry out multiple calculations simultaneously is a major benefit for training AI algorithms, as well as in many instances of inference). By contrast, the inference stage can take place within both cloud and edge computing environments. The aforementioned reports detail the differences between CPU, GPU, Field Programmable Gate Array (FPGA) and Application-Specific Integrated Circuit (ASIC) architectures, and their relative effectiveness in handling machine learning workloads.
Within the cloud computing environment, GPUs currently dominate and are predicted to continue to do so over the next ten-year period, given Nvidia’s dominance in the AI training space. For AI at the edge, ASICs are preferred, given that chips are more commonly designed with specific problems in mind (such as for object detection within security camera systems, for example). As the below graph shows, Digital Signal Processors (DSPs) also account for a significant share of AI coprocessing at the edge, though it should be noted that this large figure is primarily due to Qualcomm’s Hexagon Tensor Processor (which is found in their modern Snapdragon products) being a DSP. Should Qualcomm redesign the HTP such that it strays from being a DSP, then the forecast would heavily skew in favour of ASICs.
AI as a driver for semiconductor manufacture
Chips for AI training are typically manufactured at the most leading-edge nodes (where nodes refer to the transistor technology used in semiconductor chip manufacture), given how computationally intensive the training stage of implementing an AI algorithm is. Intel, Samsung, and TSMC are the only companies that can produce 5 nm node chips. Out of these, TSMC is the furthest along with securing orders for 3 nm chips. TSMC has a global market share for semiconductor production that is currently hovering at around 60%. For the more advanced nodes, this is closer to 90%. Of TSMC’s six 12-inch fabs and six 8-inch fabs, only two are in China, and one is in the USA. The rest are in Taiwan. The semiconductor manufacture part of the global supply chain is therefore heavily concentrated in the APAC region, principally Taiwan.
Such a concentration comes with a great deal of risk should this part of the supply chain be threatened in some way. This is precisely what occurred in 2020 when a number of complementing factors (discussed further in the “AI Chips: 2023 – 2033” report) led to a global chip shortage. Since then, the largest stakeholders (excluding Taiwan) in the semiconductor value chain (the US, the EU, South Korea, Japan, and China) have sought to reduce their exposure to a manufacturing deficit, should another set of circumstances arise that results in an even more exacerbated chip shortage. This is shown by the government funding announced by these major stakeholders in the wake of the global chip shortage, represented below.
These government initiatives aim to spur additional private investment through the lure of tax breaks and part-funding in the way of grants and loans. While many of the private investments displayed pictorially below were made prior to the announcement of such government initiatives, other additional and/or new private investments have been announced in the wake of them, spurred on as they are by the incentives offered through these initiatives.
A major reason for these government initiatives and additional private spending is the potential of realizing advanced technology, of which AI can be considered. The manufacture of advanced semiconductor chips fuels national/regional AI capabilities, where the possibility for autonomous detection and analysis of objects, images, and speech are so significant to the efficacy of certain products (such as autonomous vehicles and industrial robots) and to models of national governance and security, that the development of AI hardware and software has now become a primary concern for government bodies that wish to be at the forefront of technological innovation and deployment.
Growth of AI chips over the next decade
Revenue generated from the sale of AI chips (including the sale of physical chips and the rental of chips via cloud services) is expected to rise to just shy of USD$300 billion by 2034, at a compound annual growth rate of 22% from 2024 to 2034. This revenue figure incorporates the use of chips for the acceleration of machine learning workloads at the edge of the network, for telecom edge, and within data centers in the cloud. As of 2024, chips for inference purposes (both at the edge and within the cloud) comprise 63% of revenue generated, with this share growing to more than two-thirds of the total revenue by 2034.
This is in large part due to significant growth at the edge and telecom edge, as AI capabilities are harnessed closer to the end-user. In terms of industry vertical, IT & Telecoms is expected to lead the way for AI chip usage over the next decade, with Banking, Financial Services & Insurance (BFSI) close behind, and Consumer Electronics behind that. Of these, the Consumer Electronics industry vertical is to generate the most revenue at the edge, given the further rollout of AI into consumer products for the home. More information regarding industry vertical breakout can be found in the relevant AI reports.
The “AI Chips for Edge Applications 2024–2034: Artificial Intelligence at the Edge” report gives analysis pertaining to the key drivers for revenue growth in edge AI chips over the forecast period, with deployment within the key industry verticals – consumer electronics, industrial automation, and automotive – reviewed. More generally, the report covers the global edge AI Chips market across six industry verticals, with 10-year granular forecasts in six different categories (such as by geography, by chip architecture, and by application). IDTechEx also offers expert-led data and analysis on these and other related topics through a market intelligence subscription.
This article is from “Technology Innovations Outlook 2024-2034”, a complimentary magazine of analyst-written articles by IDTechEx providing insights into a number of areas of technology innovation, assessing the landscape now and giving you the outlook for the next decade.
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The European Union (EU) has finalized the AI Act, a comprehensive regulatory framework designed to address the ethical, legal, and societal implications of artificial intelligence (AI). This landmark legislation has significant implications for employment law and compliance, affecting how organizations develop, deploy, and manage AI technologies. This article explores the key provisions of the EU AI Act and its impact on employment law and compliance.
Overview of the EU AI Act
The EU AI Act aims to ensure that AI technologies are developed and used in a manner that respects fundamental rights, promotes transparency, and mitigates risks. The legislation categorizes AI systems into different risk levels, imposing varying requirements based on the potential impact on individuals and society.
Key Provisions:
Risk-Based Approach: AI systems are categorized into four risk levels: unacceptable risk, high risk, limited risk, and minimal risk. High-risk AI systems are subject to stricter regulatory requirements.
Transparency and Accountability: Organizations must ensure transparency and accountability in the development and deployment of AI systems, including providing clear information about the functioning and decision-making processes.
Human Oversight: High-risk AI systems must incorporate human oversight to ensure that AI decisions can be reviewed and contested.
Data Governance: The Act imposes strict data governance requirements to ensure the quality, accuracy, and fairness of data used in AI systems.
Implications for Employment Law
The EU AI Act has significant implications for employment law, affecting how organizations use AI technologies in hiring, performance evaluation, and workplace monitoring.
Key Implications:
Fair Hiring Practices: AI systems used in hiring and recruitment must ensure fairness and non-discrimination, avoiding biases that could adversely impact candidates.
Performance Evaluation: AI-driven performance evaluation systems must be transparent and provide employees with the opportunity to contest decisions.
Workplace Monitoring: AI technologies used for workplace monitoring must respect employees’ privacy and comply with data protection regulations.
Compliance Requirements
Organizations must comply with the EU AI Act’s requirements to ensure the ethical and legal use of AI technologies. Compliance involves several key steps and considerations.
Compliance Steps:
Risk Assessment: Conducting a thorough risk assessment to categorize AI systems and determine the applicable regulatory requirements.
Transparency Measures: Implementing measures to ensure transparency in AI decision-making processes, including clear documentation and communication with affected individuals.
Human Oversight: Establishing mechanisms for human oversight and intervention in AI decision-making processes, particularly for high-risk AI systems.
Data Management: Ensuring robust data governance practices to maintain the quality, accuracy, and fairness of data used in AI systems.
Challenges in Compliance
Complying with the EU AI Act presents several challenges for organizations, requiring careful planning and execution.
Key Challenges:
Complexity: The complexity of the regulatory requirements can be challenging to navigate, particularly for organizations with multiple AI systems.
Data Management: Ensuring data quality and fairness requires robust data management practices and continuous monitoring.
Resource Allocation: Implementing compliance measures can be resource-intensive, requiring investment in technology, personnel, and training.
The Role of HR and Compliance Teams
Human resources (HR) and compliance teams play a critical role in ensuring that organizations comply with the EU AI Act and address its implications for employment law.
Key Responsibilities:
Policy Development: Developing and implementing policies that align with the EU AI Act’s requirements and promote ethical AI use.
Training and Education: Providing training and education for employees on the ethical and legal implications of AI technologies.
Monitoring and Auditing: Continuously monitoring and auditing AI systems to ensure compliance and address any issues that arise.
Future Prospects
The EU AI Act represents a significant step towards regulating AI technologies and ensuring their ethical and legal use. As organizations adapt to the new regulatory landscape, the focus will be on developing AI systems that are transparent, fair, and accountable.
Future Trends:
Innovation in Compliance: The development of innovative compliance solutions, such as AI-driven compliance tools, will help organizations navigate the regulatory landscape.
Global Impact: The EU AI Act is expected to influence AI regulation globally, setting a benchmark for other jurisdictions to follow.
Continuous Improvement: Organizations will continue to improve their AI systems and compliance practices, fostering a culture of ethical AI use.
Conclusion
The finalization of the EU AI Act has significant implications for employment law and compliance, requiring organizations to ensure the ethical and legal use of AI technologies. By adhering to the Act’s provisions and implementing robust compliance measures, organizations can navigate the complex regulatory landscape and promote transparency, fairness, and accountability in their AI systems. As the AI regulatory landscape continues to evolve, organizations must remain vigilant and proactive in addressing the ethical and legal challenges associated with AI.
Source of the news: SHRM
The post The EU AI Act Finalized: Implications for Employment Law and Compliance appeared first on HIPTHER Alerts.
The medical technology (Medtech) industry is at the forefront of healthcare innovation, developing cutting-edge solutions that improve patient outcomes and streamline healthcare delivery. However, compliance remains a critical concern, with regulatory requirements often viewed as a barrier to innovation. This article explores how compliance, rather than stifling innovation, can drive it by fostering a culture of quality, safety, and continuous improvement in the Medtech industry.
The Compliance Challenge in Medtech
The Medtech industry is subject to stringent regulatory requirements to ensure the safety and efficacy of medical devices. Compliance with these regulations is essential but can be resource-intensive and complex.
Key Compliance Requirements:
FDA Regulations: In the United States, the Food and Drug Administration (FDA) regulates medical devices, requiring rigorous testing and documentation to ensure safety and effectiveness.
EU MDR: The European Union’s Medical Device Regulation (EU MDR) sets stringent requirements for the marketing and distribution of medical devices in the EU.
ISO Standards: International standards, such as ISO 13485, provide a framework for quality management systems in the Medtech industry.
Compliance as a Driver of Innovation
While compliance is often seen as a barrier to innovation, it can also drive innovation by promoting a culture of quality and continuous improvement.
How Compliance Drives Innovation:
Quality Assurance: Compliance with regulatory requirements ensures that medical devices meet high standards of quality and safety, fostering trust and confidence among healthcare providers and patients.
Risk Management: Effective compliance programs help identify and mitigate risks, reducing the likelihood of product recalls and adverse events.
Continuous Improvement: Regulatory requirements encourage Medtech companies to continuously improve their processes and products, leading to innovative solutions that address unmet medical needs.
Market Access: Compliance with global regulatory standards enables Medtech companies to access international markets, expanding their reach and driving growth.
Balancing Compliance and Innovation
Medtech companies must strike a balance between compliance and innovation, ensuring that regulatory requirements do not stifle creativity and progress.
Strategies for Balancing Compliance and Innovation:
Integrated Compliance Programs: Developing integrated compliance programs that align with the company’s innovation goals and support a culture of quality and safety.
Collaborative Approach: Collaborating with regulatory agencies, industry partners, and stakeholders to develop innovative solutions that meet regulatory requirements.
Leveraging Technology: Utilizing advanced technologies, such as artificial intelligence (AI) and machine learning, to streamline compliance processes and enhance product development.
Continuous Training: Providing continuous training and education for employees to ensure they understand and adhere to regulatory requirements while fostering a culture of innovation.
Case Studies of Compliance-Driven Innovation
Several Medtech companies have successfully leveraged compliance as a driver of innovation, demonstrating that regulatory requirements can enhance, rather than hinder, progress.
Case Study Examples:
Innovative Product Development: A Medtech company developed a new medical device that met stringent regulatory requirements, resulting in a product that was safer and more effective than existing solutions.
Streamlined Approval Process: By developing a robust compliance program, a Medtech company streamlined the regulatory approval process, bringing their innovative product to market faster.
Global Market Access: Compliance with international standards enabled a Medtech company to expand into global markets, driving growth and innovation.
The Role of Regulatory Agencies
Regulatory agencies play a crucial role in fostering a compliance-driven culture of innovation in the Medtech industry.
Regulatory Agency Initiatives:
Innovation Pathways: Developing innovation pathways and expedited approval processes for breakthrough medical devices that address unmet medical needs.
Collaborative Partnerships: Forming collaborative partnerships with industry stakeholders to develop regulatory frameworks that support innovation.
Guidance and Support: Providing guidance and support to Medtech companies to help them navigate the regulatory landscape and develop compliant, innovative solutions.
Conclusion
Compliance in the Medtech industry is not a barrier to innovation but a driver of quality, safety, and continuous improvement. By fostering a culture of compliance and leveraging regulatory requirements as a catalyst for innovation, Medtech companies can develop cutting-edge solutions that improve patient outcomes and advance healthcare delivery. As the industry continues to evolve, balancing compliance and innovation will be essential for driving progress and ensuring the safety and efficacy of medical devices.
Source of the news: STAT News
The post Medtech Compliance: Not Regulation, but Innovation appeared first on HIPTHER Alerts.
Artificial intelligence (AI) is rapidly transforming the banking industry, providing innovative solutions to enhance operational efficiency, customer experience, and regulatory compliance. This article examines how AI can help banks navigate the complex regulatory compliance landscape, highlighting the benefits, challenges, and potential applications of AI in compliance.
The Regulatory Compliance Challenge
Banks operate in a highly regulated environment, with stringent requirements to ensure the integrity and stability of the financial system. Compliance with regulations such as anti-money laundering (AML), counter-terrorist financing (CTF), and data protection is crucial but can be resource-intensive and complex.
Key Compliance Requirements:
AML and CTF: Banks must monitor transactions to detect and prevent money laundering and terrorist financing activities.
Data Protection: Ensuring the privacy and security of customer data is paramount, particularly in light of regulations such as the General Data Protection Regulation (GDPR).
Reporting: Banks are required to submit detailed reports to regulators, demonstrating compliance with various regulatory requirements.
AI Applications in Regulatory Compliance
AI technologies offer a range of applications that can help banks streamline compliance processes, improve accuracy, and reduce costs.
Key AI Applications:
Transaction Monitoring: AI algorithms can analyze transaction data in real-time to identify suspicious activities and flag potential AML and CTF violations.
Customer Due Diligence: AI can automate the process of customer due diligence (CDD), verifying customer identities, and assessing risk profiles.
Regulatory Reporting: AI-powered tools can automate the collection, analysis, and submission of regulatory reports, ensuring timely and accurate compliance.
Risk Management: AI can analyze large datasets to identify and assess risks, enabling proactive risk management and mitigation.
Benefits of AI in Compliance
The integration of AI in regulatory compliance offers several benefits for banks, helping them navigate the complex regulatory landscape more effectively.
Key Benefits:
Efficiency: AI-driven automation reduces the time and effort required for compliance tasks, allowing banks to allocate resources more efficiently.
Accuracy: AI algorithms can process and analyze data with high precision, minimizing the risk of human error and ensuring accurate compliance.
Cost Savings: By automating compliance processes, AI can significantly reduce operational costs associated with regulatory compliance.
Scalability: AI solutions can scale to handle large volumes of data and transactions, making them suitable for banks of all sizes.
Challenges in Implementing AI for Compliance
Despite the benefits, implementing AI-driven compliance solutions also presents several challenges that banks must address.
Key Challenges:
Data Quality: The effectiveness of AI in compliance depends on the quality and completeness of the data. Banks must ensure that their data is accurate and up-to-date.
Regulatory Uncertainty: The regulatory landscape for AI is still evolving, and banks must stay abreast of new regulations and guidelines to ensure compliance.
Integration: Integrating AI solutions with existing systems and processes can be complex and requires careful planning and execution.
Ethical Considerations: Banks must consider the ethical implications of using AI, including issues related to transparency, fairness, and accountability.
Future Prospects of AI in Compliance
The future of AI-driven compliance in banking looks promising, with ongoing advancements in technology and increasing regulatory acceptance. As AI continues to evolve, it is expected to play an even more significant role in enhancing regulatory compliance and risk management.
Future Trends:
Advanced Analytics: The use of advanced analytics and machine learning algorithms will enable more sophisticated risk detection and management.
RegTech Solutions: Regulatory technology (RegTech) solutions that leverage AI will become more prevalent, providing banks with innovative tools to streamline compliance.
Collaboration with Regulators: Increased collaboration between banks and regulators will drive the development of AI standards and best practices for compliance.
Personalized Compliance: AI will enable personalized compliance solutions tailored to the specific needs and risk profiles of individual banks.
Conclusion
AI has the potential to revolutionize regulatory compliance in banking, offering significant benefits in terms of efficiency, accuracy, and scalability. While there are challenges to overcome, the future prospects of AI-driven compliance are bright, with ongoing advancements and increased regulatory acceptance paving the way for more innovative and effective solutions. As banks continue to embrace AI, they will be better equipped to navigate the complex regulatory landscape and ensure compliance in the digital age.
Source of the news: BizTech Magazine
The post Can AI Help Banks Navigate Regulatory Compliance? appeared first on HIPTHER Alerts.
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