Streaming for Edge Inferencing; Empowering Real-Time AI Applications
Tue, 06 Feb 2024 10:17:30 -0000
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In the era of rapid technological advancements, artificial intelligence (AI) has made its way from research labs to real-world applications. Edge inferencing, in particular, has emerged as a game-changing technology, enabling AI models to make real-time decisions at the edge. To harness the full potential of edge inferencing, streaming technology plays a pivotal role, facilitating the seamless flow of data and predictions. In this blog, we will explore the significance of streaming for edge inferencing and how it empowers real-time AI applications.
The Role of Streaming
Streaming technology is a core component of edge inferencing, as it allows for the continuous and real-time transfer of data between devices and edge servers. Streaming can take various forms, such as video streaming, sensor data streaming, and audio streaming, or depending on the specific application's requirements. This real-time data flow enables AI models to make predictions and decisions on the fly, enhancing the overall user experience and system efficiency.
Typical Use Cases
Streaming for edge inferencing is already transforming various industries. Here are some examples:
- Smart cities—Edge inferencing powered by streaming technology can be used for real-time traffic management, crowd monitoring, and environmental sensing. This enables cities to become more efficient and responsive.
- Healthcare—Wearable devices and IoT sensors can continuously monitor patients, providing early warnings for health issues and facilitating remote diagnosis and treatment.
- Retail—Real-time data analysis at the edge can enhance customer experiences, optimize inventory management, and provide personalized recommendations to shoppers.
- Manufacturing—Predictive maintenance using edge inferencing can help factories avoid costly downtime by identifying equipment issues before they lead to failures.
Dell Streaming Data Platform
The Dell Streaming Data Platform (SDP) is a comprehensive solution for ingesting, storing, and analyzing continuously streaming data in real-time. It provides one single platform to consolidate real-time, batch, and historical analytics for improved storage and operational efficiency.
The following figure shows a high-level overview of the Streaming Data Platform architecture streaming data from the edge to the core.
Figure 1. SDP high-level architecture
Using SDP for Edge Video Ingestion Inferencing
The key advantage of SDP, in the context of edge, is the low footprint and the ability to deal with long-term data storage. Because the platform is built on Kubernetes, storing all that data is a matter of adding nodes. Now that you have all that data, using it becomes a practice in innovation. By autotiering storage upon ingestion, SDP allows for unlimited historical data retrieval for analysis alongside real-time streaming data. This enables endless business insights at your fingertips, far into the future.
One key advantage of SDP at edge deployment is its capability of supporting real-time inferencing for edge AI/ML applications as live data is ingested into SDP. Data insights can be obtained without delay while such data is also persistently stored using SDP’s innovative tiered-storage design that provides long-term storage and data protection.
For computer vision use cases, SDP provides plugins for the popular open-source multimedia processing framework GStreamer that enables easy integration with GStreamer video analytics pipelines. See the GStreamer and GStreamer Plugins.
Figure 2. Edge Inferencing with SDP
Optimized for Deep Learning at Edge
Figure 3. Using SDP for visual embedding of computer vision
SDP was also optimized to process video streaming data and process it at the edge by adding frame detection. Saving video frames combined with an integrated vector database enables to handle video processing at the edge.
SDP is optimized for deep learning by providing semantic embedding of ingested data, especially for unstructured data such as images and videos. As unstructured data is ingested into SDP, they are processed by an embedding pipeline that leverages pretrained models to extract semantic embeddings from raw data and persist such embeddings in a vector database. Such semantic embedding of raw data enables advanced data management capabilities as well as support for GenAI type of applications. For example, these embeddings can provide domain-specific context for GenAI type of query using Retrieval Augmented Generation (RAG).
Optimized for Edge AI
As AI/ML applications are becoming more widely adopted at the edge, SDP is ideally suited to support these applications by enabling real-time inference at the edge. Compared to traditional edge AI applications where data is transported to the cloud or core for inferencing, SDP can provide real-time inference right at the edge when live data is ingested so that inference latency can be greatly reduced.
In addition, SDP embraces rapidly emerging deep learning and GenAI applications by providing advanced data semantic embedding extraction and embedding vector storage, especially for unstructured multimedia data such as audio, image, and video data.
Figure 4. Unstructured data embedding vector generation
Long-Term Storage
SDP is designed with an innovative tiered-storage architecture. Tier-1 provides high performance local storage while tier-2 provides long-term storage. Specifically, tier-1 data storage is provided by replicated Apache Bookkeeper backed by a local storage to guarantee data durability once data is ingested into SDP. Asynchronously, data is flushed into tier-2 long-term storage such as Dell’s PowerScale to provide unlimited data storage and data protection. Alternatively, on NativeEdge, replicated Longhorn storage can also be used as long-term storage for SDP. With long term data storage, analytics applications can consume unbounded data to gain insights over a long period of time.
The following figure illustrates the long-term storage architecture in SDP.
Figure 5. SDP long-term storage
Cloud Native
SDP is fully cloud-native in its design with distributed architecture and autoscaling. SDP can be readily deployed on any Kubernetes environment in the cloud or on-premises. On NativeEdge, SDP is deployed on a K3s cluster by the NativeEdge Orchestrator. In addition, SDP can be easily scaled up and down as the data ingestion rate and application workload vary. This makes SDP flexible and elastic in different NativeEdge deployment scenarios. For example, SDP can leverage Kubernetes and autoscale its stream segment stores to adapt to changing data ingestion rates.
Automating the Deployment of SDP on Dell NativeEdge
Dell NativeEdge is an edge operations software platform that helps customers securely scale their edge operations to power any use case. It streamlines edge operations at scale through centralized management, secure device onboarding, zero-touch deployment, and automated management of infrastructure and applications. With automation, open design, zero-trust security principles, and multicloud connectivity, NativeEdge empowers businesses to attain their wanted outcomes at the edge.
Dell NativeEdge provides several features that make it ideal for deploying SDP on the edge, including:
- Centralized management—Remotely manage your entire edge estate from a central location without requiring local, skilled support.
- Secure device onboarding with zero-touch provisioning—Automate the deployment and configuration of the edge infrastructure managed by NativeEdge, while ensuring a zero-trust chain of custody.
- Zero-trust security enabling technologies—From secure component verification (SCV) to secure operating environment with advanced security control to tamper-resistant edge hardware and software integrity, NativeEdge secures your entire edge ecosystem throughout the lifecycle of devices.
- Lifecycle management—NativeEdge allows complete lifecycle management of your fleet of edge devices as well as applications.
- Multicloud app orchestration—NativeEdge provides templatized application orchestration using blueprints. It also provides the flexibility to choose the ISV application and cloud environments for your edge application workloads.
Deploying SDP as a Cloud Native Service on top of NativeEdge-Enabled Kubernetes Cluster
In a previous blog, we provided insight into how we can turn our Edge devices into a Kubernetes cluster using NativeEdge Orchestrator. This step creates the foundation that allows us to deploy any edge service through a standard Kubernetes Helm package.
Figure 6. Deploying SDP solution on NativeEdge-enabled Kubernetes
The Deployment Process
SDP is built as a cloud native service. The deployment of SDP includes a set of microservices as well as Ansible playbooks to automate the configuration management of those services.
The main blueprint that deploys the SDP app is shown in the following figure. It is a TOSCA node definition of an Application Module type, and it invokes an Ansible playbook to configure and start the deployment process.
Figure 7. Deploying SDP App
For the deployment, SDP is one of the available services we can choose from the NativeEdge Catalog under the Solutions tab. In the following figure, an HA SDP service is deployed on top of a Kubernetes cluster.
Figure 8. Select the SDP service from the NativeEdge Catalog
In the following figure, as part of the deployment process, we can provide input parameters. In this case, we provide configuration parameters that can vary from one edge location to another. We use the same blueprint definition with different deployment parameters to adjust various edge requirements, like different location requirements, different configurations, and so on.
Figure 9. Deploy the SDP blueprint and enter the inputs
SDP service is deployed as can be seen in the following figure.
Figure 10. Create an SDP instance by performing the install workflow
Benefits of Streaming Services
Streaming services is a critical part of any edge inferencing solution and comes with the following benefits:
- Reduced latency—Streaming ensures that data is processed when it is generated. This minimal delay is crucial for applications where even a few milliseconds can make a significant difference, such as when autonomous vehicles need to react quickly to changing road conditions.
- Enhanced privacy—By processing data at the edge, streaming minimizes the need to send sensitive information to the cloud for processing. This enhances user privacy and security, which is a critical consideration in applications like healthcare and smart homes.
- Improved scalability—Streaming can efficiently handle large volumes of data generated by edge devices, making it a scalable solution for applications that involve multiple devices or sensors.
- Real-time decision making—Streaming enables AI models to make decisions in real time, which is vital for applications like predictive maintenance in industrial settings or emergency response systems.
- Cost efficiency—By performing inferencing at the edge, streaming reduces the need for continuous cloud processing, which can be costly. This approach optimizes resource utilization and cost savings,
- Adaptability—Streaming is flexible and adaptable, making it suitable for a wide range of applications. Whether it is processing visual data from cameras or analyzing sensor data, streaming can be customized to meet specific needs.
NativeEdge Support for Edge AI-Enabled Streaming Through Integrated SDP Integration
NativeEdge comes with built-in support for SDP which comes with an edge-optimized streaming solution geared specifically to fit edge use cases such as video inferencing.
NativeEdge is a great choice for edge AI-enabled streaming because:
- It is optimized for edge AI data processing
- It has a long-term storage
- It is cloud native
- It has a low footprint
Optimized for NativeEdge
SDP lifecycle management is fully automated on NativeEdge Orchestrator. SDP is available as Solutions in the NativeEdge Catalog. To deploy an instance of SDP on NativeEdge, a customer simply selects SDP from the NativeEdge Catalog under the Solutions tab and triggers the SDP deployment. The SDP blueprint deploys an SDP cluster on NativeEdge Endpoints. Once SDP is deployed, its ongoing day two operations are also managed by NativeEdge Orchestrator, providing a seamless experience for customers.
NativeEdge also comes with a fully optimized stack for handling AI workload through the support integrated accelerators through GPU pass through, SRIOV, and so on.
Support for Custom Streaming Platform
NativeEdge provides an open platform that easily plugs into your custom streaming platform through the support of Kubernetes and Blueprints, which is an integrated part of NativeEdge Orchestrator.
References
For more information, see the following:
Related Blog Posts
Litmus and Dell NativeEdge - A Powerful Duo for Improving Industrial IoT Operations
Wed, 08 May 2024 15:18:51 -0000
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Edge AI plays a significant role in the digital transformation of the industrial Internet of things (IIoT). It improves efficiency, productivity, and decision-making processes in the following areas:
- Predictive maintenance—AI algorithms can analyze data from sensors and other connected devices to predict equipment failures before they happen.
- Anomaly detection—AI can identify abnormal patterns or anomalies in data collected from various sensors.
- Operations optimization—AI algorithms can optimize industrial processes by analyzing data and adjusting parameters in real time.
- Supply chain optimization—AI can optimize supply chain processes by analyzing data from inventory levels, demand forecasting, and logistics.
- Quality control—AI-powered vision systems and machine learning algorithms can be implemented in manufacturing quality control. These systems can identify defects or deviations from quality standards, ensuring that only high-quality products reach the market.
- Energy management—AI can analyze energy consumption patterns and optimize energy usage in industrial settings.
- Continuous improvement—AI facilitates continuous improvement by learning from data over time.
Figure 1. Industrial IoT 4.0
This blog demonstrates the benefits of the edge solutions integration on top of NativeEdge with Litmus, one of the integrated solutions.
Industrial IoT Edge AI with NativeEdge and Litmus
Dell NativeEdge serves as a platform for deploying and managing edge devices and applications at the edge. One notable addition to NativeEdge’s latest version is the ability to deliver an end-to-end solution on top of the platform that includes PTC, Litmus, Telit Cinterion, Centerity, and others. This capability allows users to get a consistent and simple management from bare-metal provisioning to a full-blown solution that is fully automated.
Figure 2. Introducing Edge Solutions on top of NativeEdge
Introduction to Litmus
Litmus is an industrial IoT platform that helps businesses collect, analyze, and manage data from IIoT devices. Dell NativeEdge is a cloud-based and on-premise software solution that helps businesses improve their email security and delivery.
Litmus includes two main parts:
- Litmus Edge Manager
- Litmus Edge
Litmus Edge Manager
Litmus Edge Manager serves as a central management console or interface for configuring, monitoring, and managing the Litmus Edge deployments and Litmus Edge.
Figure 3. Litmus Edge Manager
Litmus Edge
Litmus Edge is an industrial edge computing platform designed for edge inferencing locally in real time. It facilitates edge and IoT device management, supports various industrial protocols, enables analytics and machine learning at the edge, and emphasizes security measures.
Figure 4. Litmus Edge platform
Litmus Edge provides a flexible solution for organizations to optimize data processing, enhance device connectivity, and derive insights directly at the edge of their industrial IoT deployments through a simple no-code user experience.
Figure 5. No-Code Editor for Edge Inferencing
Deploying the Litmus Solution on NativeEdge
First, deploy the Litmus Edge. Multiple Litmus Edge instances can be deployed on multiple NativeEdge Endpoints. Each Litmus Edge is connected to sensors like robotic arms and CNCs. The following image shows the blueprint that provisions the Litmus Edge VM from a Litmus Edge image.
The following figure shows the Litmus Edge topology on NativeEdge. We can see the NativeEdge Litmus VM provisioned as well as the binary Litmus image and their dependencies.
We can also see that there is an SDP node, where data is streamed to and persisted.
Figure 6. Litmus Edge blueprint topology
The second blueprint provisions the Litmus Edge Manager VM that can connect to multiple Litmus Edges on multiple NativeEdge Endpoints.
The following figure shows the Litmus Edge Manager topology on NativeEdge. The Litmus Edge Manager can also be provisioned on vSphere. We can see the NativeEdge Litmus Manager VM provisioned as well as the binary Litmus manager image and their dependencies.
Figure 7. Litmus Edge Manager blueprint topology
Let us look at how a NativeEdge user interacts with Litmus Edge. From the NativeEdge App Catalog, choose the deploy Litmus Edge Manager or Litmus Edge (or both) and go to the deployment inputs customization.
Figure 8. NativeEdge App Catalog
On the deployment inputs, you can customize the IP address and hostname to access the Litmus Edge Manager. This includes the number of vCPUs to allocate for the Litmus Manager VM.
Figure 9. Litmus Edge deployment inputs
After deployment execution, we can see in the following figure that we provisioned multiple Litmus Edges. We can provision a fleet of Litmus Edges that are connected and managed by one Litmus Edge Manager.
Figure 10. Litmus Edge deployment
Conclusion
Dell NativeEdge provides fully automated, secure device onboarding from bare metal to cloud. As a DevEdgeOps platform, NativeEdge also gives the ability to validate and continuously manage the provisioning and configuration of those device endpoints in a secured manner. This reduces the risk of failure or security breaches due to misconfiguration or human error by detecting those potential vulnerabilities earlier in the pre-deployment development process.
The introduction of NativeEdge Orchestrator enables customers to have consistent and simple management of integrated solutions across their entire fleet of new and existing devices, supporting external services, VxRail, and soon other cloud infrastructures. The separation between the device management and solution is the key to enabling consistent operational management between different solution vendors and cloud infrastructures.
The specific integration between NativeEdge and Litmus provides a full-blown IIoT management platform from bare metal to cloud. It also simplifies the ability to process data at the edge by introducing edge AI inferencing through a simple no-code interface.
The solution framework allows vendors to use Dell NativeEdge as a generic edge infrastructure framework, addressing fundamental aspects of device fleet management. Vendors can then focus on delivering the unique value of their solution, be it predictive maintenance or real-time monitoring, as demonstrated by the Litmus use case.
References
- Litmus Edge | Dell Technologies Validated Design for Manufacturing Edge with Litmus - TechBook | Dell Technologies Info Hub
- Litmus Live Demo
- DevEdgeOps Defined | Dell Technologies Info Hub
- Simplify Edge Operations - Flipbook | Multimedia for the NativeEdge Platform | Dell Technologies Info Hub
Will AI Replace Software Developers?
Thu, 02 May 2024 09:38:01 -0000
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Over the past year, I have been actively involved in generative artificial intelligence (Gen AI) projects aimed at assisting developers in generating high-quality code. Our team has also adopted Copilot as part of our development environment. These tools offer a wide range of capabilities that can significantly reduce development time. From automatically generating commit comments and code descriptions to suggesting the next logical code block, they have become indispensable in our workflow.
According to a recent study by McKinsey, quantify the level of productivity gain in the following areas:
Figure 1. Software engineering: speeding developer work as a coding assistant (McKinsey)
This study shows that “The direct impact of AI on the productivity of software engineering could range from 20 to 45 percent of current annual spending on the function. This value would arise primarily from reducing time spent on certain activities, such as generating initial code drafts, code correction and refactoring, root-cause analysis, and generating new system designs. By accelerating the coding process, Generative AI could push the skill sets and capabilities needed in software engineering toward code and architecture design. One study found that software developers using Microsoft’s GitHub Copilot completed tasks 56 percent faster than those not using the tool. An internal McKinsey empirical study of software engineering teams found those who were trained to use generative AI tools rapidly reduced the time needed to generate and refactor code and engineers also reported a better work experience, citing improvements in happiness, flow, and fulfilment.”
What Makes the Code Assistant (Copilot) the Killer App for Gen AI?
The remarkable progress of AI-based code generation owes its success to the unique characteristics of programming languages. Unlike natural language text, code adheres to a structured syntax with well-defined rules. This structure enables AI models to excel in analyzing and generating code.
Several factors contribute to the swift evolution of AI-driven code generation:
- Structured nature of code–Code follows a strict format, making it amenable to automated analysis. The consistent structure allows AI algorithms to learn patterns and generate syntactically correct code.
- Validation tools–Compilers and other development tools play a crucial role. They validate code for correctness, ensuring that generated code adheres to language specifications. This continuous feedback loop enables AI systems to improve without human intervention.
- Repeatable work identification–AI excels at identifying repetitive tasks. In software development, there are numerous areas where routine work occurs, such as boilerplate code, data transformations, and error handling. AI can efficiently recognize and automate these repetitive patterns.
From Coding Assistant to Fully-Autonomous AI Software Engineer
The Cognition & Development Lab at Washington University in St. Louis investigates how infants and young children think, reason, and learn about the world around them. Their research focuses on the development of early social-cognitive capacities. They are the makers of Devin, the world’s first AI software engineer.
Devin possesses remarkable capabilities in software development in the following areas:
- Complex engineering tasks–With advances in long-term reasoning and planning, Devin can plan and execute complex engineering tasks that involve thousands of decisions. Devin recalls relevant context at every step, learns over time, and even corrects mistakes.
- Coding and debugging–Devin can write code, debug, and address bugs in codebases. It autonomously finds and fixes issues, making it a valuable teammate for developers.
- End-to-end app development–Devin builds and deploys apps from scratch. For example, it can create an interactive website, incrementally adding features requested by the user and deploying the app.
- AI model training and fine-tuning–Devin sets up fine-tuning for large language models, demonstrating its ability to train and improve its own AI models.
- Collaboration and communication–Devin actively collaborates with users. It reports progress in real-time, accepts feedback, and engages in design choices as needed.
- Real-world challenges–Devin tackles real-world GitHub issues found in open-source projects. It can also contribute to mature production repositories and address feature requests. Devin even takes on real jobs on platforms like Upwork, writing and debugging code for computer vision models.
The Devin project is a clear indication of how fast we move from simple coding assistants to more complete engineering capabilities.
Will AI Replace Software Developers?
When I asked this question recently during a Copilot training session that our team took, the answer was “No”, or to be more precise “Not yet”. The common thinking is that it provides a productivity enhancement tool that will save developers from spending time on tedious tasks such as documentation, testing, and so on. This could have been true yesterday, but as seen with project Devin, it already goes beyond simple assistance to full development engineering. We can rely on the experience from past transformations to learn a bit more about where this is all heading.
Learning from Cloud Transformation: Parallels with Gen AI Transformation
The advent of cloud computing, pioneered by AWS approximately 15 years ago, revolutionized the entire IT landscape. It introduced the concept of fully automated, API-driven data centers, significantly reducing the need for traditional system administrators and IT operations personnel. However, beyond the mere shrinking of the IT job market, the following parallel events unfolded:
- Traditional IT jobs shrank significantly–Small to medium-sized companies can now operate their IT infrastructure without dedicated IT operators. The cloud’s self-service capabilities have made routine maintenance and management more accessible.
- Emergence of new job titles: DevOps, SRO, and more–As organizations embrace cloud technologies, new roles emerge. DevOps engineers, site reliability operators (SROs), and other specialized positions became essential for optimizing cloud-based systems.
- The rise of SaaS startups–Cloud computing lowered the barriers of entry for delivering enterprise-grade solutions. Startups capitalized on this by becoming more agile and growing faster than established incumbents.
- Big tech companies’ accelerated growth–Tech giants like Google, Facebook, and Microsoft swiftly adopted cloud infrastructure. The self-service nature of APIs and SaaS offerings allowed them to scale rapidly, resulting in record growth rates.
Impact on Jobs and Budgets
While traditional IT jobs declined, the transformation also yielded positive outcomes:
- Increased efficiency and quality–Companies produced more products of higher quality at a fraction of the cost. The cloud’s scalability and automation played a pivotal role in achieving this.
- Budget shift from traditional IT to cloud–Gartner’s IT spending reports reveal a clear shift in budget allocation. Cloud investments have grown steadily, even amidst the disruption caused by the introduction of cloud infrastructure, see the following figure:
Figure 2. Cloud transformation’s impact on IT budget allocation
Looking Ahead: AI Transformation
As we transition to the era of AI, we can anticipate similar trends:
- Decline in traditional jobs–Just as cloud computing transformed the job landscape, AI adoption may lead to the decline of certain traditional roles.
- Creation of new jobs–Simultaneously, AI will create novel opportunities. Roles related to AI development, machine learning, and data science will flourish.
Short Term Opportunity
Organizations will allocate more resources to AI initiatives. The transition to AI is not merely an evolutionary step; it is a strategic imperative.
According to a research conducted by ISG on behalf of Glean, Generative AI projects consumed an average of 1.5 percent of IT budgets in 2023. These budgets are expected to rise to 2.7 percent in 2024 and further increase to 4.3 percent in 2025. Organizations recognize the potential of AI to enhance operational efficiency and bridge IT talent gaps. Gartner predicts that Generative AI impacts will be more pronounced in 2025. Despite this, worldwide IT spending is projected to grow by 8 percent in 2024. Organizations continue to invest in AI and automation to drive efficiency. The White House budget proposes allocating $75 billion for IT spending at civilian agencies in 2025. This substantial investment aims to deliver simple, seamless, and secure government services through technology.
The impact of AI extends far beyond the confines of the IT job market. It permeates nearly every facet of our professional landscape. As with any significant transformation, AI presents both risks and opportunities. Those who swiftly embrace it are more likely to seize the advantages.
So, what steps can software developers take to capitalize on this opportunity?
Tips for Software Developers in the Age of AI
In the immediate term, developers can enhance their effectiveness when working with AI assistants by acquiring a combination of the following technical skills:
- Learn AI basics–I would recommend starting the learning with AI Terms 101. I also recommend following the leading AI podcasts. I found this useful to keep myself up to date in this space and learn some useful tips and updates from industry experts.
- Use coding assistant tools (Copilot)–Coding assistant tools are definitely the low-hanging fruit and probably the simplest step to get into the AI development world. There is a growing list of tools that are available and can be integrated seamlessly into your existing development IDE. The following provides a useful reference to The Top 11 AI Coding Assistants to Use in 2024.
- Learn machine learning (ML) and deep learning concepts–Understanding the fundamentals of ML and deep learning is crucial. Familiarize yourself with neural networks, training models, and optimization techniques.
- Data science and analytics–Developers should grasp data preprocessing, feature engineering, and model evaluation. Proficiency in tools like Pandas, NumPy, and scikit-learn is beneficial.
- Frameworks and tools–Learn about popular AI frameworks such as TensorFlow, and PyTorch. These tools facilitate model building and deployment.
More skilled developers will need to learn how to create their own “AI engineers” which they will train and fine tune to assist them with user interface (UI), backend, and testing development tasks. They could even run a team of “AI engineers” to write an entire project.
Will AI Reduce the Demand for Software Engineers?
Not necessarily. In the case of cloud transformation, developers with AI expertise will likely be in high demand. Those who will not be able to adapt to this new world are likely to stay behind and face the risk of losing their job.
It would be fair to assume that the scope of work, post-AI transformation, will grow and will not stay stagnant. As an example, we will likely see products adding more “self-driving” capabilities, where they could run more complete tasks without the need for human feedback or enable close to human interaction with the product.
Under this assumption, the scope of new AI projects and products is going to grow, and that growth should balance the declining demand for traditional software engineering jobs.
Conclusion
As a history enthusiast, I often find parallels in the past that can serve as a guide to our future. The industrial era witnessed disruptive technological advancements that reshaped job markets. Some professions became obsolete, while new ones emerged. As a society, we adapted quickly, discovering new growth avenues. However, the emergence of AI presents unique challenges. Unlike previous disruptions, AI simultaneously impacts a wide range of job markets and progresses at an unparalleled pace. The implications are indeed profound.
Recent research by Nexford University on How Will Artificial Intelligence Affect Jobs 2024-2030 reveals some startling predictions. According to a report by the investment bank Goldman Sachs, AI could potentially replace the equivalent of 300 million full-time jobs. It could automate a quarter of the work tasks in the US and Europe, leading to new job creation and a productivity surge. The report also suggests that AI could increase the total annual value of goods and services produced globally by 7 percent. It predicts that two-thirds of jobs in the US and Europe are susceptible to some degree of AI automation, and around a quarter of all jobs could be entirely performed by AI.
The concerns raised by Yuval Noa Harari, a historian and professor at the Department of History of the Hebrew University of Jerusalem, resonate with many. The rapid evolution of AI may indeed lead to significant unemployment.
However, when it comes to software engineers, we can assert with confidence that regardless of how automated our processes become, there will always be a fundamental need for human expertise. These skilled professionals perform critical tasks such as maintenance, updates, improvements, error corrections, and the setup of complex software and hardware systems. These systems often require coordination among multiple specialists for optimal functionality.
In addition to these responsibilities, computer system analysts play a pivotal role. They review system capabilities, manage workflows, schedule improvements, and drive automation. This profession has seen a surge in demand in recent years and is likely to remain in high demand.
In conclusion, AI represents both risk and opportunity. While it automates routine tasks, it also paves the way for innovation. Our response will ultimately determine its impact.
References
- Economic potential of generative AI | McKinsey
- Introducing Devin, the first AI software engineer (cognition-labs.com)
- IT Spending & Budgets: Trends & Forecasts 2024
- Organizations continue to invest in AI and automation to drive efficiency
- This substantial investment aims to deliver simple, seamless, and secure government services through technology
- AI Terms 101: An A to Z AI Terminology Guide for Beginners
- 11 AI Podcasts That Will Shape Your Perspective (geekflare.com)\
- How Will Artificial Intelligence Affect Jobs 2024-2030 | Nexford University
- The Top 11 AI Coding Assistants to Use in 2024 | DataCamp
- Yuval Harari On The Future of Jobs & Technology, Intelligence vs Consciousness, & Future Threats to Humanity - Jacob Morgan (thefutureorganization.com)