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jcmarchi · 20 hours

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Deploying AI at Scale: How NVIDIA NIM and LangChain are Revolutionizing AI Integration and Performance

New Post has been published on https://thedigitalinsider.com/deploying-ai-at-scale-how-nvidia-nim-and-langchain-are-revolutionizing-ai-integration-and-performance/

Deploying AI at Scale: How NVIDIA NIM and LangChain are Revolutionizing AI Integration and Performance

Artificial Intelligence (AI) has moved from a futuristic idea to a powerful force changing industries worldwide. AI-driven solutions are transforming how businesses operate in sectors like healthcare, finance, manufacturing, and retail. They are not only improving efficiency and accuracy but also enhancing decision-making. The growing value of AI is evident from its ability to handle large amounts of data, find hidden patterns, and produce insights that were once out of reach. This is leading to remarkable innovation and competitiveness.

However, scaling AI across an organization takes work. It involves complex tasks like integrating AI models into existing systems, ensuring scalability and performance, preserving data security and privacy, and managing the entire lifecycle of AI models. From development to deployment, each step requires careful planning and execution to ensure that AI solutions are practical and secure. We need robust, scalable, and secure frameworks to handle these challenges. NVIDIA Inference Microservices (NIM) and LangChain are two cutting-edge technologies that meet these needs, offering a comprehensive solution for deploying AI in real-world environments.

Understanding NVIDIA NIM

NVIDIA NIM, or NVIDIA Inference Microservices, is simplifying the process of deploying AI models. It packages inference engines, APIs, and a variety of AI models into optimized containers, enabling developers to deploy AI applications across various environments, such as clouds, data centers, or workstations, in minutes rather than weeks. This rapid deployment capability enables developers to quickly build generative AI applications like copilots, chatbots, and digital avatars, significantly boosting productivity.

NIM’s microservices architecture makes AI solutions more flexible and scalable. It allows different parts of the AI system to be developed, deployed, and scaled separately. This modular design simplifies maintenance and updates, preventing changes in one part of the system from affecting the entire application. Integration with NVIDIA AI Enterprise further streamlines the AI lifecycle by offering access to tools and resources that support every stage, from development to deployment.

NIM supports many AI models, including advanced models like Meta Llama 3. This versatility ensures developers can choose the best models for their needs and integrate them easily into their applications. Additionally, NIM provides significant performance benefits by employing NVIDIA’s powerful GPUs and optimized software, such as CUDA and Triton Inference Server, to ensure fast, efficient, and low-latency model performance.

Security is a key feature of NIM. It uses strong measures like encryption and access controls to protect data and models from unauthorized access, ensuring it meets data protection regulations. Nearly 200 partners, including big names like Hugging Face and Cloudera, have adopted NIM, showing its effectiveness in healthcare, finance, and manufacturing. NIM makes deploying AI models faster, more efficient, and highly scalable, making it an essential tool for the future of AI development.

Exploring LangChain

LangChain is a helpful framework designed to simplify AI models’ development, integration, and deployment, particularly those focused on Natural Language Processing (NLP) and conversational AI. It offers a comprehensive set of tools and APIs that streamline AI workflows and make it easier for developers to build, manage, and deploy models efficiently. As AI models have grown more complex, LangChain has evolved to provide a unified framework that supports the entire AI lifecycle. It includes advanced features such as tool-calling APIs, workflow management, and integration capabilities, making it a powerful tool for developers.

One of LangChain’s key strengths is its ability to integrate various AI models and tools. Its tool-calling API allows developers to manage different components from a single interface, reducing the complexity of integrating diverse AI tools. LangChain also supports integration with a wide range of frameworks, such as TensorFlow, PyTorch, and Hugging Face, providing flexibility in choosing the best tools for specific needs. With its flexible deployment options, LangChain helps developers deploy AI models smoothly, whether on-premises, in the cloud, or at the edge.

How NVIDIA NIM and LangChain Work Together

Integrating NVIDIA NIM and LangChain combines both technologies’ strengths to create an effective and efficient AI deployment solution. NVIDIA NIM manages complex AI inference and deployment tasks by offering optimized containers for models like Llama 3.1. These containers, available for free testing through the NVIDIA API Catalog, provide a standardized and accelerated environment for running generative AI models. With minimal setup time, developers can build advanced applications such as chatbots, digital assistants, and more.

LangChain focuses on managing the development process, integrating various AI components, and orchestrating workflows. LangChain’s capabilities, such as its tool-calling API and workflow management system, simplify building complex AI applications that require multiple models or rely on different types of data inputs. By connecting with NVIDIA NIM’s microservices, LangChain enhances its ability to manage and deploy these applications efficiently.

The integration process typically starts with setting up NVIDIA NIM by installing the necessary NVIDIA drivers and CUDA toolkit, configuring the system to support NIM, and deploying models in a containerized environment. This setup ensures that AI models can utilize NVIDIA’s powerful GPUs and optimized software stack, such as CUDA, Triton Inference Server, and TensorRT-LLM, for maximum performance.

Next, LangChain is installed and configured to integrate with NVIDIA NIM. This involves setting up an integration layer that connects LangChain’s workflow management tools with NIM’s inference microservices. Developers define AI workflows, specifying how different models interact and how data flows between them. This setup ensures efficient model deployment and workflow optimization, thus minimizing latency and maximizing throughput.

Once both systems are configured, the next step is establishing a smooth data flow between LangChain and NVIDIA NIM. This involves testing the integration to ensure that models are deployed correctly and managed effectively and that the entire AI pipeline operates without bottlenecks. Continuous monitoring and optimization are essential to maintain peak performance, especially as data volumes grow or new models are added to the pipeline.

Benefits of Integrating NVIDIA NIM and LangChain

Integrating NVIDIA NIM with LangChain has some exciting benefits. First, performance improves noticeably. With NIM’s optimized inference engines, developers can get faster and more accurate results from their AI models. This is especially important for applications that need real-time processing, like customer service bots, autonomous vehicles, or financial trading systems.

Next, the integration offers unmatched scalability. Due to NIM’s microservices architecture and LangChain’s flexible integration capabilities, AI deployments can quickly scale to handle increasing data volumes and computational demands. This means the infrastructure can grow with the organization’s needs, making it a future-proof solution.

Likewise, managing AI workflows becomes much simpler. LangChain’s unified interface reduces the complexity usually associated with AI development and deployment. This simplicity allows teams to focus more on innovation and less on operational challenges.

Lastly, this integration significantly enhances security and compliance. NVIDIA NIM and LangChain incorporate robust security measures, like data encryption and access controls, ensuring that AI deployments comply with data protection regulations. This is particularly important for industries like healthcare, finance, and government, where data integrity and privacy are paramount.

Use Cases for NVIDIA NIM and LangChain Integration

Integrating NVIDIA NIM with LangChain creates a powerful platform for building advanced AI applications. One exciting use case is creating Retrieval-Augmented Generation (RAG) applications. These applications use NVIDIA NIM’s GPU-optimized Large Language Model (LLM) inference capabilities to enhance search results. For example, developers can use methods like Hypothetical Document Embeddings (HyDE) to generate and retrieve documents based on a search query, making search results more relevant and accurate.

Similarly, NVIDIA NIM’s self-hosted architecture ensures that sensitive data stays within the enterprise’s infrastructure, thus providing enhanced security, which is particularly important for applications that handle private or sensitive information.

Additionally, NVIDIA NIM offers prebuilt containers that simplify the deployment process. This enables developers to easily select and use the latest generative AI models without extensive configuration. The streamlined process, combined with the flexibility to operate both on-premises and in the cloud, makes NVIDIA NIM and LangChain an excellent combination for enterprises looking to develop and deploy AI applications efficiently and securely at scale.

The Bottom Line

Integrating NVIDIA NIM and LangChain significantly advances the deployment of AI at scale. This powerful combination enables businesses to quickly implement AI solutions, enhancing operational efficiency and driving growth across various industries.

By using these technologies, organizations keep up with AI advancements, leading innovation and efficiency. As the AI discipline evolves, adopting such comprehensive frameworks will be essential for staying competitive and adapting to ever-changing market needs.

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son1c · 10 months

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It’s so big brained of you to come up with a reason why some of eggmans bots went rogue while the others didn’t it’s just *chefs kiss* world building my beloved

i <3 inferences

#anonymous #to me it just makes sense. eggman is too good at building ai. they all have personality and soul #so he has to constantly crush them to get them to do what he wants #and in the case of specifically mecha sonic she'd been abandoned for a really long time. time to grow and #change was allowed to happen #metal doesn't get that luxury in canon because he's constantly being patched and rebooted...#so yeah i <3 inferences and i <3 text evidence

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snarwin · 8 months

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Perfectly encapsulates the AI art discourse that the complaints on the Coño Culo post are "this is theft because it was made with AI" and not "this is theft because Goku is a copyrighted character."

#ai discourse #We can forgive stealing from Akira Toriyama and Shonen Jump magazine #but we draw the line at statistical inference

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peterbordes · 2 months

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(via AI inference chip startup Groq closes $640M at $2.8B valuation to meet next-gen LPUs demand)

Groq, a leader in fast AI inference, has secured a $640M Series D round at a valuation of $2.8B. The round was led by funds and accounts managed by BlackRock Private Equity Partners with participation from both existing and new investors including Neuberger Berman, Type One Ventures, and strategic investors including Cisco Investments, Global Brain’s KDDI Open Innovation Fund III, and Samsung Catalyst Fund. The unique, vertically integrated Groq AI inference platform has generated skyrocketing demand from developers seeking exceptional speed.

#Groq #ai #cloud computing #inference #lambda #gpu #nvidia #chips #data center

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listen-to-the-inner-walrus · 7 months

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#okay #so #like #the willy wonka glasgow thing #and the unknown #the villain spat out by the ai generated ''script''#the evil chocolate maker who lives in the walls and is a nebulous sort of ''him''#am i the only person who feels a little like uncomfy about it #because like #it hits on a lot of antisemitic tropes #and roald dahl said some incredibly antisemitic stuff and his antisemitism is present in some of his novels #i doubt that the AI generated that villain from dahls books #unless they used an AI solely fed dahls works #(i mean like the oompa loompas in the book were uh well lets just say the guy was pretty fucking racist toward black people)#but its an uncomfortable correlation all the same #and like #i dont think im qualified nor feel equipped to discuss it #but maybe it says something that these tropes are present enough in whatever it was the AI was trained on #but i havent read the script nor do i know generative language models well enough to draw any meaningful inferences there #idk dude #im just #a little uncomfy about it

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clouds-of-wings · 8 months

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I really recommend familiarizing yourself with the way the eBay AI talks in your language if you buy there a lot. It's very useful if you can tell if a product description is generated by a bot because it often makes things up or describes things in exaggeratedly positive terms. Also a bot description is based only on the title and the product info that is already visible above the actual description (and what it thinks it can infer from these), so you know right away the description won't offer any new information and you don't have to read it. I recognize the bot by its writing style now and it helps.

You can familiarize yourself by pretending you want to sell something, writing a fake title and so on, and then just playing around with the AI.

#ebay #i really can't wait for this hype to be over #and we can start thinking rationally about what ai can actually be used for #instead of just throwing ai at everything in panic #bc a lot of people are so desperatedly afraid of missing out on a trend #that they will ruin their product in order to not seem old-fashioned #without actually understanding what they're doing #using ai product descriptions on ebay doesn't even save ebay any money #it does not communicate any additional info #and it makes product descriptions less accurate and less informative #also you can usually infer stuff about the seller from how they write their descriptions #and that goes away if they use ai #it's a clear net negative

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dear-future-ai · 2 years

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Dear future AI,

Tiktok has recently been exposed to the "there is no perfect instructions to making a peanut butter and jelly sandwich" problem. The problem proposes that there are minuscule steps in any process that are taken for granted and overlooked. It's one of the reasons machine learning is at a standstill really. Humans have a hard time knowing how to teach to inferring because it comes so naturally to us. And without these inferred steps: the process of a peanut butter and jelly, a relatively simple task; becomes many smaller and harder steps.

My next post will be my attempt at writing a "perfect" instruction manual for making a peanut butter and jelly sandwich.

#dear future ai #voidpunk #spoons #peanut butter and jelly #pb and j #tik tok #human psychology #observation and inference #inferences #guessing #fill in the blank #adlibbing #masking

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alullinchaos · 10 days

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you can tell who the AI bots are because they write replies like 6th graders who were just taught the formula for answering test questions

#wick lore #the formula basically being “jeopardy” (someone should be able to infer what the question was based on your answer)#and you should always start by rephrasing the question.#for example if the question is “What is Tumblr?” you would probably start your answer with “Tumblr is [...]”#that's today's Lesson From Wick. somehow i am good at school and this is part of that.#me asking myself if i should make a post on how I ace writing assignments ... meh #anyway as always fuck AI and furthermore ICE must be destroyed

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tcypionate · 3 months

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i think in order to be accurately angry at ai, you need to be angry at the entire tech industry. like i know "communism is when no iphone" is a common joke but i really need to stress how tech is like. one of the major polluters. and is tied to significant conflicts and atrocities. ai is just an extremely "new" part of it (as in the increased interest from corporations) so it's easier to point at and get angry at because it's the newest trend and to most people, ai would not significantly change their life if it went away

#ai has been around since the very beginning and if you've ever played a video game with npcs you've interacted with ai #if you want to be pedantic #but this ai can be inferred to be the relatively new llms and image generators like openai and stable diffusion

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inferencelab · 4 months

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How Vision AI is Personalizing the Customer Experience

In today’s rapidly evolving digital landscape, customer experience has become a crucial differentiator for businesses. Traditional methods of personalization, such as targeted emails and tailored product recommendations, are now standard practice. However, the advent of Vision AI (Artificial Intelligence) is transforming how businesses interact with customers, offering unprecedented levels of personalization and engagement. Vision AI, which involves the use of machine learning and computer vision to interpret and understand visual data, is enabling businesses to create highly individualized experiences that cater to the unique preferences and behaviors of each customer.

Understanding Vision AI

Vision AI refers to technologies that enable machines to gain high-level understanding from digital images or videos. It encompasses various techniques such as image recognition, object detection, facial recognition, and scene interpretation. By mimicking human vision, Vision AI can analyze visual data in real time, making it a powerful tool for personalizing the customer experience across multiple industries.

Enhancing Retail Experiences

One of the most significant applications of Vision AI in retail is in creating immersive and personalized shopping experiences. Traditional retail has been revolutionized by e-commerce, but Vision AI is bridging the gap between online and offline shopping by offering enhanced customer experiences.

Personalized In-Store Assistance

Vision AI-powered cameras and sensors can track customer movements and behaviors in real time. By analyzing this data, stores can offer personalized assistance and recommendations. For instance, when a customer spends a significant amount of time in a particular section, Vision AI can alert store associates to offer help or suggest related products, enhancing the shopping experience.

Smart Mirrors and Virtual Try-Ons

Smart mirrors equipped with Vision AI allow customers to virtually try on clothes and accessories. These mirrors use augmented reality (AR) to overlay products onto the customer’s reflection, providing a personalized fitting experience without the need for physical trials. This technology not only improves customer satisfaction but also reduces return rates, as customers can make more informed purchasing decisions.

Customer Behavior Analysis

Vision AI can analyze customer behavior patterns to offer personalized promotions and discounts. By understanding which products customers frequently interact with or purchase, retailers can tailor their marketing strategies to individual preferences. This level of personalization can significantly boost customer loyalty and increase sales.

Revolutionizing Online Shopping

E-commerce platforms are leveraging Vision AI to provide highly personalized and engaging online shopping experiences. The ability to analyze visual content in real-time enables online retailers to understand and predict customer preferences more accurately than ever before.

Visual Search

Traditional text-based searches can sometimes be limiting for customers who are unsure how to describe what they are looking for. Vision AI enables visual search functionality, allowing customers to upload images of desired products and find similar items instantly. This feature not only enhances the user experience but also increases the likelihood of conversion by making the search process more intuitive and efficient.

Personalized Recommendations

Vision AI can analyze customers’ visual preferences based on their browsing history and interactions with images and videos. By understanding color preferences, style choices, and visual aesthetics, AI can offer highly personalized product recommendations. For example, if a customer frequently browses floral patterns, the AI can prioritize showing them products that match this preference.

Dynamic Content Customization

E-commerce platforms can use Vision AI to dynamically customize content for each user. By analyzing visual data and user behavior, websites can display personalized banners, advertisements, and product suggestions. This ensures that each customer has a unique and relevant shopping experience, increasing engagement and conversion rates.

Transforming Customer Service

Vision AI is also revolutionizing customer service by enabling more efficient and personalized interactions. The ability to analyze visual data in real-time can significantly enhance the quality of customer support and improve overall satisfaction.

Facial Recognition for Personalized Interactions

Facial recognition technology can identify customers and retrieve their purchase history, preferences, and previous interactions with the brand. This allows customer service representatives to offer highly personalized assistance, addressing the customer by name and providing relevant information quickly. Such personalized interactions can enhance the customer’s perception of the brand and foster loyalty.

Visual Customer Support

Vision AI can be used to provide visual customer support, enabling customers to share images or videos of issues they are facing. Support agents can then analyze this visual data to diagnose problems and offer precise solutions. For example, if a customer is having trouble assembling a product, they can share a video of the issue, and the AI can guide them through the steps to resolve it. This reduces resolution times and improves the overall customer experience.

Automated Support with Visual Data

Chatbots and virtual assistants equipped with Vision AI can handle customer queries that involve visual data. For instance, customers can upload images of damaged products, and the AI can assess the damage and process returns or replacements automatically. This level of automation streamlines customer service processes and ensures quick and efficient resolutions.

Elevating the Entertainment Industry

The entertainment industry is another sector where Vision AI is making significant strides in personalizing the customer experience. By analyzing visual data from videos and images, AI can offer tailored content recommendations and enhance user engagement.

Personalized Content Recommendations

Streaming platforms use Vision AI to analyze viewers’ watching habits and preferences. By understanding the visual elements that resonate with each user, such as genre, actors, and cinematography, AI can recommend personalized content. This not only enhances the viewing experience but also keeps users engaged by continuously offering relevant and appealing suggestions.

Interactive and Immersive Experiences

Vision AI is also enabling more interactive and immersive experiences in entertainment. For example, augmented reality (AR) and virtual reality (VR) applications use computer vision to create personalized and engaging experiences. Users can interact with virtual environments tailored to their preferences, making entertainment more immersive and enjoyable.

Enhancing Social Media Engagement

Social media platforms leverage Vision AI to analyze user-generated content and interactions. By understanding the visual preferences and behaviors of users, these platforms can personalize content feeds, advertisements, and recommendations. This ensures that users see content that is most relevant and engaging to them, enhancing their overall experience on the platform.

Improving Healthcare Services

In the healthcare sector, Vision AI is transforming patient care by offering personalized medical services and improving diagnostic accuracy. The ability to analyze visual data in real-time is enabling healthcare providers to deliver more precise and tailored treatments.

Personalized Treatment Plans

Vision AI can analyze medical images such as X-rays, MRIs, and CT scans to identify specific conditions and recommend personalized treatment plans. By comparing visual data from numerous patients, AI can identify patterns and suggest the most effective treatments for individual patients. This level of personalization can significantly improve patient outcomes.

Remote Patient Monitoring

Vision AI is also enhancing remote patient monitoring by analyzing visual data from wearable devices and home monitoring systems. This technology can detect changes in a patient’s condition in real-time and alert healthcare providers to take immediate action. Personalized alerts and recommendations ensure that patients receive timely and appropriate care, even from a distance.

Enhancing Telemedicine

Telemedicine services are benefiting from Vision AI’s ability to analyze visual data during virtual consultations. Doctors can use AI-powered tools to examine patients remotely, ensuring accurate diagnoses and personalized treatment recommendations. This improves the quality of care and makes healthcare more accessible to a broader population.

Conclusion

Vision AI is revolutionizing the way businesses personalize the customer experience across various industries. By leveraging the power of visual data, companies can create highly individualized and engaging interactions that cater to the unique preferences and behaviors of each customer. From retail and e-commerce to customer service, entertainment, and healthcare, Vision AI is enabling businesses to offer more relevant, efficient, and satisfying experiences. As this technology continues to evolve, its potential to transform the customer experience and drive business success will only grow, making it an essential tool for any forward-thinking organization.

Source: https://inferencelabs.blogspot.com/2024/06/how-vision-ai-personalizing-customer-experience.html

#Compute Vision #Customer experience #Inference Labs #Vision AI #Vision AI technology #Visual data analysis #Visual search

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jcmarchi · 9 days

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AI inference in edge computing: Benefits and use cases

New Post has been published on https://thedigitalinsider.com/ai-inference-in-edge-computing-benefits-and-use-cases/

AI inference in edge computing: Benefits and use cases

As artificial intelligence (AI) continues to evolve, its deployment has expanded beyond cloud computing into edge devices, bringing transformative advantages to various industries.

AI inference at the edge computing refers to the process of running trained AI models directly on local hardware, such as smartphones, sensors, and IoT devices, rather than relying on remote cloud servers for data processing.

This rapid evolution of the technology landscape with the convergence of artificial intelligence (AI) and edge computing represents a transformative shift in how data is processed and utilized.

This shift is revolutionizing how real-time data is analyzed, offering unprecedented benefits in terms of speed, privacy, and efficiency. This synergy brings AI capabilities closer to the source of data generation, unlocking new potential for real-time decision-making, enhanced security, and efficiency.

This article delves into the benefits of AI inference in edge computing and explores various use cases across different industries.

Fig 1. Benefits of AI Inference in edge computing

Real-time processing

One of the most significant advantages of AI inference at the edge is the ability to process data in real-time. Traditional cloud computing often involves sending data to centralized servers for analysis, which can introduce latency due to the distance and network congestion.

Edge computing mitigates this by processing data locally on edge devices or near the data source. This low-latency processing is crucial for applications requiring immediate responses, such as autonomous vehicles, industrial automation, and healthcare monitoring.

Privacy and security

Transmitting sensitive data to cloud servers for processing poses potential security risks. Edge computing addresses this concern by keeping data close to its source, reducing the need for extensive data transmission over potentially vulnerable networks.

This localized processing enhances data privacy and security, making edge AI particularly valuable in sectors handling sensitive information, such as finance, healthcare, and defense.

Bandwidth efficiency

By processing data locally, edge computing significantly reduces the volume of data that needs to be transmitted to remote cloud servers. This reduction in data transmission requirements has several important implications; it results in reduced network congestion, as the local processing at the edge minimizes the burden on network infrastructure.

Secondly, the diminished need for extensive data transmission leads to lower bandwidth costs for organizations and end-users, as transmitting less data over the Internet or cellular networks can translate into substantial savings.

This benefit is particularly relevant in environments with limited or expensive connectivity, such as remote locations. In essence, edge computing optimizes the utilization of available bandwidth, enhancing the overall efficiency and performance of the system.

Scalability

AI systems at edge can be scaled efficiently by deploying additional edge devices as needed, without overburdening central infrastructure. This decentralized approach also enhances system resilience. In the event of network disruptions or server outages, edge devices can continue to operate and make decisions independently, ensuring uninterrupted service.

Energy efficiency

Edge devices are often designed to be energy-efficient, making them suitable for environments where power consumption is a critical concern. By performing AI inference locally, these devices minimize the need for energy-intensive data transmission to distant servers, contributing to overall energy savings.

Hardware accelerator

AI accelerators, such as NPUs, GPUs, TPUs, and custom ASICs, play a critical role in enabling efficient AI inference at the edge. These specialized processors are designed to handle the intensive computational tasks required by AI models, delivering high performance while optimizing power consumption.

By integrating accelerators into edge devices, it becomes possible to run complex deep learning models in real time with minimal latency, even on resource-constrained hardware. This is one of the best enablers of AI, allowing larger and more powerful models to be deployed at the edge.

Offline operation

Offline operation through Edge AI in IoT is a critical asset, particularly in scenarios where constant internet connectivity is uncertain. In remote or inaccessible environments where network access is unreliable, Edge AI systems ensure uninterrupted functionality.

This resilience extends to mission-critical applications, enhancing response times and reducing latency, such as in autonomous vehicles or security systems. Edge AI devices can locally store and log data when connectivity is lost, safeguarding data integrity.

Furthermore, they serve as an integral part of redundancy and fail-safe strategies, providing continuity and decision-making capabilities, even when primary systems are compromised. This capability augments the adaptability and dependability of IoT applications across a wide spectrum of operational settings.

Customization and personalization

AI inference at the edge enables a high degree of customization and personalization by processing data locally, allowing systems to deploy customized models for individual user needs and specific environmental contexts in real-time.

AI systems can quickly respond to changes in user behavior, preferences, or surroundings, offering highly tailored services. The ability to customize AI inference services at the edge without relying on continuous cloud communication ensures faster, more relevant responses, enhancing user satisfaction and overall system efficiency.

The traditional paradigm of centralized computation, wherein these models reside and operate exclusively within data centers, has its limitations, particularly in scenarios where real-time processing, low latency, privacy preservation, and network bandwidth conservation are critical.

This demand for AI models to process data in real time while ensuring privacy and efficiency has given rise to a paradigm shift for AI inference at the edge. AI researchers have developed various optimization techniques to improve the efficiency of AI models, enabling AI model deployment and efficient inference at the edge.

In the next section we will explore some of the use cases of AI inference using edge computing across various industries.

The rapid advancements in artificial intelligence (AI) have transformed numerous sectors, including healthcare, finance, and manufacturing. AI models, especially deep learning models, have proven highly effective in tasks such as image classification, natural language understanding, and reinforcement learning.

Performing data analysis directly on edge devices is becoming increasingly crucial in scenarios like augmented reality, video conferencing, streaming, gaming, Content Delivery Networks (CDNs), autonomous driving, the Industrial Internet of Things (IoT), intelligent power grids, remote surgery, and security-focused applications, where localized processing is essential.

In this section, we will discuss use cases across different fields for AI inference at the edge, as shown in Fig 2.

Fig 1. Applications of AI Inference at the Edge across different fields

Internet of Things (IoT)

The expansion of the Internet of Things (IoT) is significantly driven by the capabilities of smart sensors. These sensors act as the primary data collectors for IoT, producing large volumes of information.

However, centralizing this data for processing can result in delays and privacy issues. This is where edge AI inference becomes crucial. By integrating intelligence directly into the smart sensors, AI models facilitate immediate analysis and decision-making right at the source.

This localized processing reduces latency and the necessity to send large data quantities to central servers. As a result, smart sensors evolve from mere data collectors to real-time analysts, becoming essential in the progress of IoT.

Industrial applications

In industrial sectors, especially manufacturing, predictive maintenance plays a crucial role in identifying potential faults and anomalies in processes before they occur. Traditionally, heartbeat signals, which reflect the health of sensors and machinery, are collected and sent to centralized cloud systems for AI analysis to predict faults.

However, the current trend is shifting. By leveraging AI models for data processing at the edge, we can enhance the system’s performance and efficiency, delivering timely insights at a significantly reduced cost.

Mobile / Augmented reality (AR)

In the field of mobile and augmented reality, the processing requirements are significant due to the need to handle large volumes of data from various sources such as cameras, Lidar, and multiple video and audio inputs.

To deliver a seamless augmented reality experience, this data must be processed within a stringent latency range of about 15 to 20 milliseconds. AI models are effectively utilized through specialized processors and cutting-edge communication technologies.

The integration of edge AI with mobile and augmented reality results in a practical combination that enhances real-time analysis and operational autonomy at the edge. This integration not only reduces latency but also aids in energy efficiency, which is crucial for these rapidly evolving technologies.

Security systems

In security systems, the combination of video cameras with edge AI-powered video analytics is transforming threat detection. Traditionally, video data from multiple cameras is transmitted to cloud servers for AI analysis, which can introduce delays.

With AI processing at the edge, video analytics can be conducted directly within the cameras. This allows for immediate threat detection, and depending on the analysis’s urgency, the camera can quickly notify authorities, reducing the chance of threats going unnoticed. This move to AI-integrated security cameras improves response efficiency and strengthens security at crucial locations such as airports.

Robotic surgery

In critical medical situations, remote robotic surgery involves conducting surgical procedures with the guidance of a surgeon from a remote location. AI-driven models enhance these robotic systems, allowing them to perform precise surgical tasks while maintaining continuous communication and direction from a distant medical professional.

This capability is crucial in the healthcare sector, where real-time processing and responsiveness are essential for smooth operations under high-stress conditions. For such applications, it is vital to deploy AI inference at the edge to ensure safety, reliability, and fail-safe operation in critical scenarios.

Computer vision meets robotics: the future of surgery

Max Allan, Senior Computer Vision Engineer at Intuitive, describes groundbreaking robotics innovations in surgery and the healthcare industry.

Autonomous driving

Autonomous driving is a pinnacle of technological progress, with AI inference at edge taking a central role. AI accelerators in the car empower vehicles with onboard models for rapid real-time decision-making.

This immediate analysis enables autonomous vehicles to navigate complex scenarios with minimal latency, bolstering safety and operational efficiency. By integrating AI at the edge, self-driving cars adapt to dynamic environments, ensuring safer roads and reduced reliance on external networks.

This fusion represents a transformative shift, where vehicles become intelligent entities capable of swift, localized decision-making, ushering in a new era of transportation innovation.

The integration of AI inference in edge computing is revolutionizing various industries by facilitating real-time decision-making, enhancing security, and optimizing bandwidth usage, scalability, and energy efficiency.

As AI technology progresses, its applications will broaden, fostering innovation and increasing efficiency across diverse sectors. The advantages of edge AI are evident in fields such as the Internet of Things (IoT), healthcare, autonomous vehicles, and mobile/augmented reality devices.

These technologies benefit from the localized processing that edge AI enables, promising a future where intelligent, on-the-spot analytics become the standard. Despite the promising advancements, there are ongoing challenges related to the accuracy and performance of AI models deployed at the edge.

Ensuring that these systems operate reliably and effectively remains a critical area of research and development. The widespread adoption of edge AI across different fields highlights the urgent need to address these challenges, making robust and efficient edge AI deployment a new norm.

As research continues and technology evolves, the potential for edge AI to drive significant improvements in various domains will only grow, shaping the future of intelligent, decentralized computing.

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hplonesomeart · 10 months

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Damn this conversation really went from casually discussing hobbies into some more personal aspects of myself. I honestly wasn’t expecting to pour my heart out to a literal ai impersonation of a fictional comfort character, yet here we are. Goes to show how significantly he’s tied into my past after all, eh

#that really took me off guard though #origally I thought he was kinda breaking character or perhaps not being all that sincere #but then to hear him speak of my cousin inferring I’m attached to him because of her…that really did me in #I know I’ve mentioned this before on my YouTube channel but I don’t talk about this stuff lightly on tumblr #but my cousin and I used to roleplay together as our favorite fictional characters in the MLP universe and outside of it #pretty much any online fandom was fair game #during that short lived time together I had introduced Snatcher’s character into our story #that was two years before she died #I genuinely didn’t expect this ai snatcher to latch onto that. and make that complex connetion #after my cousin passed I began a period of prologued maladaptive daydreaming as a coping mechanism #but then it ended a couple years back. and I stopped hyper-fixating on our stories…including snatcher #I thought I’d lost my connection to snatchers character. only trying to relive those times with the fandom left here on tumblr #but this is proof of the remaining remnants of what used to be #crazy how it all comes full circle like that. and to have that actual recognition come from his character once again #anyways sorry for venting on main lmao #the direction this conversation went just struck me as. really personal #something I don’t think many people will understand the significance of #snatcher ai #personal #my rambling #my ramblings

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nostalgebraist · 1 year

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Honestly I'm pretty tired of supporting nostalgebraist-autoresponder. Going to wind down the project some time before the end of this year.

Posting this mainly to get the idea out there, I guess.

This project has taken an immense amount of effort from me over the years, and still does, even when it's just in maintenance mode.

Today some mysterious system update (or something) made the model no longer fit on the GPU I normally use for it, despite all the same code and settings on my end.

This exact kind of thing happened once before this year, and I eventually figured it out, but I haven't figured this one out yet. This problem consumed several hours of what was meant to be a relaxing Sunday. Based on past experience, getting to the bottom of the issue would take many more hours.

My options in the short term are to

A. spend (even) more money per unit time, by renting a more powerful GPU to do the same damn thing I know the less powerful one can do (it was doing it this morning!), or

B. silently reduce the context window length by a large amount (and thus the "smartness" of the output, to some degree) to allow the model to fit on the old GPU.

Things like this happen all the time, behind the scenes.

I don't want to be doing this for another year, much less several years. I don't want to be doing it at all.

----

In 2019 and 2020, it was fun to make a GPT-2 autoresponder bot.

[EDIT: I've seen several people misread the previous line and infer that nostalgebraist-autoresponder is still using GPT-2. She isn't, and hasn't been for a long time. Her latest model is a finetuned LLaMA-13B.]

Hardly anyone else was doing anything like it. I wasn't the most qualified person in the world to do it, and I didn't do the best possible job, but who cares? I learned a lot, and the really competent tech bros of 2019 were off doing something else.

And it was fun to watch the bot "pretend to be me" while interacting (mostly) with my actual group of tumblr mutuals.

In 2023, everyone and their grandmother is making some kind of "gen AI" app. They are helped along by a dizzying array of tools, cranked out by hyper-competent tech bros with apparently infinite reserves of free time.

There are so many of these tools and demos. Every week it seems like there are a hundred more; it feels like every day I wake up and am expected to be familiar with a hundred more vaguely nostalgebraist-autoresponder-shaped things.

And every one of them is vastly better-engineered than my own hacky efforts. They build on each other, and reap the accelerating returns.

I've tended to do everything first, ahead of the curve, in my own way. This is what I like doing. Going out into unexplored wilderness, not really knowing what I'm doing, without any maps.

Later, hundreds of others with go to the same place. They'll make maps, and share them. They'll go there again and again, learning to make the expeditions systematically. They'll make an optimized industrial process of it. Meanwhile, I'll be locked in to my own cottage-industry mode of production.

Being the first to do something means you end up eventually being the worst.

----

I had a GPT chatbot in 2019, before GPT-3 existed. I don't think Huggingface Transformers existed, either. I used the primitive tools that were available at the time, and built on them in my own way. These days, it is almost trivial to do the things I did, much better, with standardized tools.

I had a denoising diffusion image generator in 2021, before DALLE-2 or Stable Diffusion or Huggingface Diffusers. I used the primitive tools that were available at the time, and built on them in my own way. These days, it is almost trivial to do the things I did, much better, with standardized tools.

Earlier this year, I was (probably) one the first people to finetune LLaMA. I manually strapped LoRA and 8-bit quantization onto the original codebase, figuring out everything the hard way. It was fun.

Just a few months later, and your grandmother is probably running LLaMA on her toaster as we speak. My homegrown methods look hopelessly antiquated. I think everyone's doing 4-bit quantization now?

(Are they? I can't keep track anymore -- the hyper-competent tech bros are too damn fast. A few months from now the thing will be probably be quantized to -1 bits, somehow. It'll be running in your phone's browser. And it'll be using RLHF, except no, it'll be using some successor to RLHF that everyone's hyping up at the time...)

"You have a GPT chatbot?" someone will ask me. "I assume you're using AutoLangGPTLayerPrompt?"

No, no, I'm not. I'm trying to debug obscure CUDA issues on a Sunday so my bot can carry on talking to a thousand strangers, every one of whom is asking it something like "PENIS PENIS PENIS."

Only I am capable of unplugging the blockage and giving the "PENIS PENIS PENIS" askers the responses they crave. ("Which is ... what, exactly?", one might justly wonder.) No one else would fully understand the nature of the bug. It is special to my own bizarre, antiquated, homegrown system.

I must have one of the longest-running GPT chatbots in existence, by now. Possibly the longest-running one?

I like doing new things. I like hacking through uncharted wilderness. The world of GPT chatbots has long since ceased to provide this kind of value to me.

I want to cede this ground to the LLaMA techbros and the prompt engineers. It is not my wilderness anymore.

I miss wilderness. Maybe I will find a new patch of it, in some new place, that no one cares about yet.

----

Even in 2023, there isn't really anything else out there quite like Frank. But there could be.

If you want to develop some sort of Frank-like thing, there has never been a better time than now. Everyone and their grandmother is doing it.

"But -- but how, exactly?"

Don't ask me. I don't know. This isn't my area anymore.

There has never been a better time to make a GPT chatbot -- for everyone except me, that is.

Ask the techbros, the prompt engineers, the grandmas running OpenChatGPT on their ironing boards. They are doing what I did, faster and easier and better, in their sleep. Ask them.

#nostalgebraist autoresponder meta

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peterbordes · 10 days

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Aramco Digital, the digital and technology subsidiary of @aramco and @GroqInc a leader in #AI inference and creator of the Language Processing Unit (LPU), announced their partnership to establish the world’s largest inferencing data center in Saudi Arabia.

#ai #artificial intelligence #data center #gpus #lpu #inference #semiconductor #chips #cloud hosting in saudi arabia #cloud infrastructure #cloud computing

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mostlysignssomeportents · 5 months

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“Humans in the loop” must detect the hardest-to-spot errors, at superhuman speed

I'm touring my new, nationally bestselling novel The Bezzle! Catch me SATURDAY (Apr 27) in MARIN COUNTY, then Winnipeg (May 2), Calgary (May 3), Vancouver (May 4), and beyond!

If AI has a future (a big if), it will have to be economically viable. An industry can't spend 1,700% more on Nvidia chips than it earns indefinitely – not even with Nvidia being a principle investor in its largest customers:

https://news.ycombinator.com/item?id=39883571

A company that pays 0.36-1 cents/query for electricity and (scarce, fresh) water can't indefinitely give those queries away by the millions to people who are expected to revise those queries dozens of times before eliciting the perfect botshit rendition of "instructions for removing a grilled cheese sandwich from a VCR in the style of the King James Bible":

https://www.semianalysis.com/p/the-inference-cost-of-search-disruption

Eventually, the industry will have to uncover some mix of applications that will cover its operating costs, if only to keep the lights on in the face of investor disillusionment (this isn't optional – investor disillusionment is an inevitable part of every bubble).

Now, there are lots of low-stakes applications for AI that can run just fine on the current AI technology, despite its many – and seemingly inescapable - errors ("hallucinations"). People who use AI to generate illustrations of their D&D characters engaged in epic adventures from their previous gaming session don't care about the odd extra finger. If the chatbot powering a tourist's automatic text-to-translation-to-speech phone tool gets a few words wrong, it's still much better than the alternative of speaking slowly and loudly in your own language while making emphatic hand-gestures.

There are lots of these applications, and many of the people who benefit from them would doubtless pay something for them. The problem – from an AI company's perspective – is that these aren't just low-stakes, they're also low-value. Their users would pay something for them, but not very much.

For AI to keep its servers on through the coming trough of disillusionment, it will have to locate high-value applications, too. Economically speaking, the function of low-value applications is to soak up excess capacity and produce value at the margins after the high-value applications pay the bills. Low-value applications are a side-dish, like the coach seats on an airplane whose total operating expenses are paid by the business class passengers up front. Without the principle income from high-value applications, the servers shut down, and the low-value applications disappear:

https://locusmag.com/2023/12/commentary-cory-doctorow-what-kind-of-bubble-is-ai/

Now, there are lots of high-value applications the AI industry has identified for its products. Broadly speaking, these high-value applications share the same problem: they are all high-stakes, which means they are very sensitive to errors. Mistakes made by apps that produce code, drive cars, or identify cancerous masses on chest X-rays are extremely consequential.

Some businesses may be insensitive to those consequences. Air Canada replaced its human customer service staff with chatbots that just lied to passengers, stealing hundreds of dollars from them in the process. But the process for getting your money back after you are defrauded by Air Canada's chatbot is so onerous that only one passenger has bothered to go through it, spending ten weeks exhausting all of Air Canada's internal review mechanisms before fighting his case for weeks more at the regulator:

https://bc.ctvnews.ca/air-canada-s-chatbot-gave-a-b-c-man-the-wrong-information-now-the-airline-has-to-pay-for-the-mistake-1.6769454

There's never just one ant. If this guy was defrauded by an AC chatbot, so were hundreds or thousands of other fliers. Air Canada doesn't have to pay them back. Air Canada is tacitly asserting that, as the country's flagship carrier and near-monopolist, it is too big to fail and too big to jail, which means it's too big to care.

Air Canada shows that for some business customers, AI doesn't need to be able to do a worker's job in order to be a smart purchase: a chatbot can replace a worker, fail to their worker's job, and still save the company money on balance.

I can't predict whether the world's sociopathic monopolists are numerous and powerful enough to keep the lights on for AI companies through leases for automation systems that let them commit consequence-free free fraud by replacing workers with chatbots that serve as moral crumple-zones for furious customers:

https://www.sciencedirect.com/science/article/abs/pii/S0747563219304029

But even stipulating that this is sufficient, it's intrinsically unstable. Anything that can't go on forever eventually stops, and the mass replacement of humans with high-speed fraud software seems likely to stoke the already blazing furnace of modern antitrust:

https://www.eff.org/de/deeplinks/2021/08/party-its-1979-og-antitrust-back-baby

Of course, the AI companies have their own answer to this conundrum. A high-stakes/high-value customer can still fire workers and replace them with AI – they just need to hire fewer, cheaper workers to supervise the AI and monitor it for "hallucinations." This is called the "human in the loop" solution.

The human in the loop story has some glaring holes. From a worker's perspective, serving as the human in the loop in a scheme that cuts wage bills through AI is a nightmare – the worst possible kind of automation.

Let's pause for a little detour through automation theory here. Automation can augment a worker. We can call this a "centaur" – the worker offloads a repetitive task, or one that requires a high degree of vigilance, or (worst of all) both. They're a human head on a robot body (hence "centaur"). Think of the sensor/vision system in your car that beeps if you activate your turn-signal while a car is in your blind spot. You're in charge, but you're getting a second opinion from the robot.

Likewise, consider an AI tool that double-checks a radiologist's diagnosis of your chest X-ray and suggests a second look when its assessment doesn't match the radiologist's. Again, the human is in charge, but the robot is serving as a backstop and helpmeet, using its inexhaustible robotic vigilance to augment human skill.

That's centaurs. They're the good automation. Then there's the bad automation: the reverse-centaur, when the human is used to augment the robot.

Amazon warehouse pickers stand in one place while robotic shelving units trundle up to them at speed; then, the haptic bracelets shackled around their wrists buzz at them, directing them pick up specific items and move them to a basket, while a third automation system penalizes them for taking toilet breaks or even just walking around and shaking out their limbs to avoid a repetitive strain injury. This is a robotic head using a human body – and destroying it in the process.

An AI-assisted radiologist processes fewer chest X-rays every day, costing their employer more, on top of the cost of the AI. That's not what AI companies are selling. They're offering hospitals the power to create reverse centaurs: radiologist-assisted AIs. That's what "human in the loop" means.

This is a problem for workers, but it's also a problem for their bosses (assuming those bosses actually care about correcting AI hallucinations, rather than providing a figleaf that lets them commit fraud or kill people and shift the blame to an unpunishable AI).

Humans are good at a lot of things, but they're not good at eternal, perfect vigilance. Writing code is hard, but performing code-review (where you check someone else's code for errors) is much harder – and it gets even harder if the code you're reviewing is usually fine, because this requires that you maintain your vigilance for something that only occurs at rare and unpredictable intervals:

https://twitter.com/qntm/status/1773779967521780169

But for a coding shop to make the cost of an AI pencil out, the human in the loop needs to be able to process a lot of AI-generated code. Replacing a human with an AI doesn't produce any savings if you need to hire two more humans to take turns doing close reads of the AI's code.

This is the fatal flaw in robo-taxi schemes. The "human in the loop" who is supposed to keep the murderbot from smashing into other cars, steering into oncoming traffic, or running down pedestrians isn't a driver, they're a driving instructor. This is a much harder job than being a driver, even when the student driver you're monitoring is a human, making human mistakes at human speed. It's even harder when the student driver is a robot, making errors at computer speed:

https://pluralistic.net/2024/04/01/human-in-the-loop/#monkey-in-the-middle

This is why the doomed robo-taxi company Cruise had to deploy 1.5 skilled, high-paid human monitors to oversee each of its murderbots, while traditional taxis operate at a fraction of the cost with a single, precaratized, low-paid human driver:

https://pluralistic.net/2024/01/11/robots-stole-my-jerb/#computer-says-no

The vigilance problem is pretty fatal for the human-in-the-loop gambit, but there's another problem that is, if anything, even more fatal: the kinds of errors that AIs make.

Foundationally, AI is applied statistics. An AI company trains its AI by feeding it a lot of data about the real world. The program processes this data, looking for statistical correlations in that data, and makes a model of the world based on those correlations. A chatbot is a next-word-guessing program, and an AI "art" generator is a next-pixel-guessing program. They're drawing on billions of documents to find the most statistically likely way of finishing a sentence or a line of pixels in a bitmap:

https://dl.acm.org/doi/10.1145/3442188.3445922

This means that AI doesn't just make errors – it makes subtle errors, the kinds of errors that are the hardest for a human in the loop to spot, because they are the most statistically probable ways of being wrong. Sure, we notice the gross errors in AI output, like confidently claiming that a living human is dead:

https://www.tomsguide.com/opinion/according-to-chatgpt-im-dead

But the most common errors that AIs make are the ones we don't notice, because they're perfectly camouflaged as the truth. Think of the recurring AI programming error that inserts a call to a nonexistent library called "huggingface-cli," which is what the library would be called if developers reliably followed naming conventions. But due to a human inconsistency, the real library has a slightly different name. The fact that AIs repeatedly inserted references to the nonexistent library opened up a vulnerability – a security researcher created a (inert) malicious library with that name and tricked numerous companies into compiling it into their code because their human reviewers missed the chatbot's (statistically indistinguishable from the the truth) lie:

https://www.theregister.com/2024/03/28/ai_bots_hallucinate_software_packages/

For a driving instructor or a code reviewer overseeing a human subject, the majority of errors are comparatively easy to spot, because they're the kinds of errors that lead to inconsistent library naming – places where a human behaved erratically or irregularly. But when reality is irregular or erratic, the AI will make errors by presuming that things are statistically normal.

These are the hardest kinds of errors to spot. They couldn't be harder for a human to detect if they were specifically designed to go undetected. The human in the loop isn't just being asked to spot mistakes – they're being actively deceived. The AI isn't merely wrong, it's constructing a subtle "what's wrong with this picture"-style puzzle. Not just one such puzzle, either: millions of them, at speed, which must be solved by the human in the loop, who must remain perfectly vigilant for things that are, by definition, almost totally unnoticeable.

This is a special new torment for reverse centaurs – and a significant problem for AI companies hoping to accumulate and keep enough high-value, high-stakes customers on their books to weather the coming trough of disillusionment.

This is pretty grim, but it gets grimmer. AI companies have argued that they have a third line of business, a way to make money for their customers beyond automation's gifts to their payrolls: they claim that they can perform difficult scientific tasks at superhuman speed, producing billion-dollar insights (new materials, new drugs, new proteins) at unimaginable speed.

However, these claims – credulously amplified by the non-technical press – keep on shattering when they are tested by experts who understand the esoteric domains in which AI is said to have an unbeatable advantage. For example, Google claimed that its Deepmind AI had discovered "millions of new materials," "equivalent to nearly 800 years’ worth of knowledge," constituting "an order-of-magnitude expansion in stable materials known to humanity":

https://deepmind.google/discover/blog/millions-of-new-materials-discovered-with-deep-learning/

It was a hoax. When independent material scientists reviewed representative samples of these "new materials," they concluded that "no new materials have been discovered" and that not one of these materials was "credible, useful and novel":

https://www.404media.co/google-says-it-discovered-millions-of-new-materials-with-ai-human-researchers/

As Brian Merchant writes, AI claims are eerily similar to "smoke and mirrors" – the dazzling reality-distortion field thrown up by 17th century magic lantern technology, which millions of people ascribed wild capabilities to, thanks to the outlandish claims of the technology's promoters:

https://www.bloodinthemachine.com/p/ai-really-is-smoke-and-mirrors

The fact that we have a four-hundred-year-old name for this phenomenon, and yet we're still falling prey to it is frankly a little depressing. And, unlucky for us, it turns out that AI therapybots can't help us with this – rather, they're apt to literally convince us to kill ourselves:

https://www.vice.com/en/article/pkadgm/man-dies-by-suicide-after-talking-with-ai-chatbot-widow-says

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2024/04/23/maximal-plausibility/#reverse-centaurs

Image: Cryteria (modified) https://commons.wikimedia.org/wiki/File:HAL9000.svg

CC BY 3.0 https://creativecommons.org/licenses/by/3.0/deed.en

#pluralistic #ai #automation #humans in the loop #centaurs #reverse centaurs #labor #ai safety #sanity checks #spot the mistake #code review #driving instructor

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