Alternatives to Stanhope AI

Audiense identifies and understands any audience that matters to you, no matter how specific/unique it is. You can create reports effortlessly using filter options such as demographics, user profiles, affinities and job roles, obtaining highly personalised audiences and segments. Then you'll be able to make better marketing decisions, adapt your targeting strategies, improve relevancy and drive high-performance campaigns at scale. What are the problems we trying to help with? - Traditional consumer research is time-consuming, static and expensive - We make it possible to get social data to become a part of the strategy - Personalisation and brand strategy are now very tactical at the expense of performance marketing What makes Audiense unique Our Audiense Insights platform create the segments within the audiences based on interconnections between people (“Who knows who”). Audiense can infer the shared interests and affinities between consumers thanks to how those connections are created between them (“How they know each other”). Audiense unique social consumer segmentation provides a great data foundation for Persona development, understanding the audiences that matter most or finding new audience opportunities.

Easy integration of 3D vision into your app Roux SDK is a stable, reliable, and simple API that allows developers to interact with a variety of depth-sensors. Roux is easy to use even if you are not a computer vision expert. Roux does the heavy lifting so that you can concentrate on creating programs that make use of 3D data in new ways. Roux can fix flying pixels and auto-patch over meshes. From scanning to SLAM, Roux can do it all. Roux does much more than create 3D scans. Our proprietary technology has the features that other teams would need to create from scratch. Live, on-device collaboration tracking, scanning and meshing are all possible. Scandy's 3D software is available for iOS, MacOS, and Linux. The SDK is easy to initialize - just drop the framework into your project, and you're ready to go. To make your development easier, we've done the heavy lifting in computer vision.

Zyphra, an artificial intelligence company with offices in Palo Alto and Montreal, is growing in London. We're developing MaiaOS, an agent system that combines advanced research in next-gen neuronal network architectures (SSM-hybrids), long-term memories & reinforcement learning. We believe that the future of AGI is a combination of cloud-based and on-device strategies, with an increasing shift towards local inference. MaiaOS was built around a deployment platform that maximizes the efficiency of inference for real-time Intelligence. Our AI and product teams are drawn from top organizations and institutions, including Google DeepMind and Anthropic. They also come from Qualcomm, Neuralink and Apple. We have deep expertise across AI models, learning algorithms, and systems/infrastructure with a focus on inference efficiency and AI silicon performance. The Zyphra team is dedicated to democratizing advanced artificial intelligence systems.

Explore the mind for everyone. Explorer is a useful and inexpensive tool for researchers in Neuroscience and Engineering. We want to make the world aware of the possibilities for brain research. Our platform integrates machine learning tools and automatic patterns analysis so that anyone can explore the brain, whether they are a professional or a novice researcher. This opens up the possibility of brain research.

Kubernetes is a highly scalable platform for model inference that uses standards-based models. Trusted AI. KServe, a Kubernetes standard model inference platform, is designed for highly scalable applications. Provides a standardized, performant inference protocol that works across all ML frameworks. Modern serverless inference workloads supported by autoscaling, including a scale up to zero on GPU. High scalability, density packing, intelligent routing with ModelMesh. Production ML serving is simple and pluggable. Pre/post-processing, monitoring and explainability are all possible. Advanced deployments using the canary rollout, experiments and ensembles as well as transformers. ModelMesh was designed for high-scale, high density, and often-changing model use cases. ModelMesh intelligently loads, unloads and transfers AI models to and fro memory. This allows for a smart trade-off between user responsiveness and computational footprint.

Enjoy a new era in science-backed music that will help you unlock your best self. Brain.fm's focus music blends into the background to help you focus better. It also stimulates the brain with rhythmic beats that support sustained attention. While other music may seem to grab your attention, it can make it difficult to think and do work, even when you don't know it. Brain.fm's function music is designed to optimize your brain and improve your performance. Brain.fm has patents on key methods for creating functional music. This includes technology to elicit strong neurophase locking, which allows neurons to engage in coordinated activity. Brain.fm draws on neuroscience and psychology to create hypotheses about how to make best music. This is to help us study, push us in a workout, or get to sleep. These sounds are then created and tested on a large scale to determine what works.

AI is changing everything. AI is becoming ubiquitous. More intelligence is moving to the end devices today, and mobile is quickly becoming the dominant AI platform. Building on the smartphone foundation and the scale of mobile, Qualcomm envisions making AI ubiquitous--expanding beyond mobile and powering other end devices, machines, vehicles, and things. To make this a reality, we are developing, commercializing, and marketing power-efficient on-device AI and edge cloud AI. AI allows devices and things to perceive, reason and act intuitively. AI, which draws inspiration from the human brain will enhance our human abilities by being a natural extension to our senses. Through seamless interactions in everyday life, AI will personalize our lives and enhance our experience. Gartner predicts that AI augmentation will bring $3.3 trillion in business value by 2021. These benefits can be achieved across industries by combining cloud inference and on-device intelligence.

Software is what transforms a vehicle into a smart machine. Open source software stack NVIDIA DRIVE™, enables developers to quickly build and deploy a variety state-of the-art AV applications. This includes perception, localization, mapping, driver monitoring, planning and control, driver monitoring and natural language processing. DRIVE OS, the foundation of the DRIVE SoftwareStack, is the first secure operating system for accelerated computation. It includes NvMedia to process sensor input, NVIDIACUDA®, libraries for parallel computing implementations that are efficient, NVIDIA TensorRT™ for real time AI inference, as well as other tools and modules for accessing hardware engines. NVIDIA DriveWorks®, a SDK that provides middleware functions over DRIVE OS, is essential for autonomous vehicle development. These include the sensor abstraction layer (SAL), sensor plugins, data recorder and vehicle I/O support.

NVIDIA Picasso, a cloud service that allows you to build generative AI-powered visual apps, is available. Software creators, service providers, and enterprises can run inference on models, train NVIDIA Edify foundation model models on proprietary data, and start from pre-trained models to create image, video, or 3D content from text prompts. The Picasso service is optimized for GPUs. It streamlines optimization, training, and inference on NVIDIA DGX Cloud. Developers and organizations can train NVIDIA Edify models using their own data, or use models pre-trained by our premier partners. Expert denoising network to create photorealistic 4K images The novel video denoiser and temporal layers generate high-fidelity videos with consistent temporality. A novel optimization framework to generate 3D objects and meshes of high-quality geometry. Cloud service to build and deploy generative AI-powered image and video applications.

A global, infinitely scalable, AI powered, collaborative cloud platform that is globally accessible and meets the highest standards for security and compliance. A leading, easy-to-use platform for neuroscience professionals. It was designed by data scientists and neuroimaging experts to meet the unique and challenging needs of the community. Optimized for your specific needs, whether you are conducting clinical trials, designing new algorithms, or leveraging brain-related data.

We are a biotechnology company in clinical stage. We decode biology by integrating technological innovations across biology and chemistry to industrialize drug discovery. CRISPR genome editing and synthetic Biology allow for greater control over biology. Advanced robotics allows for reliable automation of complex laboratory research on an unprecedented scale. Neural network architectures allow for iterative analysis and inference from large, complex, in-house data sets. Cloud solutions increase the flexibility of high-performance computation. To build a next-generation biopharmaceutical business, we are using new technology to create virtuous learning cycles around datasets. A synchronized combination hardware, software, and data that is used to industrialize drug discovery. Redefining the traditional drug discovery process. One of the most extensive, broadest, and deepest pipelines in any technology-enabled drug company.

Groq's mission is to set the standard in GenAI inference speeds, enabling real-time AI applications to be developed today. LPU, or Language Processing Unit, inference engines are a new end-to-end system that can provide the fastest inference possible for computationally intensive applications, including AI language applications. The LPU was designed to overcome two bottlenecks in LLMs: compute density and memory bandwidth. In terms of LLMs, an LPU has a greater computing capacity than both a GPU and a CPU. This reduces the time it takes to calculate each word, allowing text sequences to be generated faster. LPU's inference engine can also deliver orders of magnitude higher performance on LLMs than GPUs by eliminating external memory bottlenecks. Groq supports machine learning frameworks like PyTorch TensorFlow and ONNX.

AgentBench is a framework for evaluating the performance and capabilities of autonomous AI agents. It provides a set of benchmarks to test different aspects of an agent’s behavior such as task-solving, decision-making and adaptability. AgentBench evaluates agents on tasks in different domains to identify strengths and weakness. For example, the ability of agents to plan, reason and learn from feedback. The framework provides insights into how an agent can handle real-world scenarios that are complex. It is useful for both research as well as practical development. AgentBench is a tool that helps improve autonomous agents iteratively, ensuring that they meet standards of reliability and efficiency before being used in larger applications.

Amazon SageMaker makes it easy for you to deploy ML models to make predictions (also called inference) at the best price and performance for your use case. It offers a wide range of ML infrastructure options and model deployment options to meet your ML inference requirements. It integrates with MLOps tools to allow you to scale your model deployment, reduce costs, manage models more efficiently in production, and reduce operational load. Amazon SageMaker can handle all your inference requirements, including low latency (a few seconds) and high throughput (hundreds upon thousands of requests per hour).

Open-Source Programming Frameworks for Agentic AI. AutoGen provides a multi-agent dialogue framework as a high level abstraction. This framework allows you to easily build LLM workflows. AutoGen is a collection of systems that cover a wide range and complexity of applications. AutoGen supports enhanced LLM Inference APIs that can be used to improve performance and reduce costs.

NetMind.AI, a decentralized AI ecosystem and computing platform, is designed to accelerate global AI innovations. It offers AI computing power that is affordable and accessible to individuals, companies, and organizations of any size by leveraging idle GPU resources around the world. The platform offers a variety of services including GPU rental, serverless Inference, as well as an AI ecosystem that includes data processing, model development, inference and agent development. Users can rent GPUs for competitive prices, deploy models easily with serverless inference on-demand, and access a variety of open-source AI APIs with low-latency, high-throughput performance. NetMind.AI allows contributors to add their idle graphics cards to the network and earn NetMind Tokens. These tokens are used to facilitate transactions on the platform. Users can pay for services like training, fine-tuning and inference as well as GPU rentals.

Improvement in CAT, SATs and GCSEs. English, maths and science for primary and high schools. Combining learning science, AI and neuroscience. Intelligent personalization improves engagement and understanding of students. Teachers can save hours of time by reducing the amount of time they spend on marking, analysis and creating resources. Data insights that support targeted, timely interventions. CENTURY helps teachers make effective interventions while saving them time with marking and data analyses. Our vision is that every teacher and student has access to intelligent tools to help them succeed. Our in-house teachers develop all our content, which is aligned with the national curriculum. Identify quickly where learning is secure, and which areas require revisiting. Use the data from these assessments as a basis for your planning and intervention. Use data insights to track student performance and make decisions about what to teach next.

Amazon Elastic Inference allows for low-cost GPU-powered acceleration to Amazon EC2 instances and Sagemaker instances, or Amazon ECS tasks. This can reduce the cost of deep learning inference by up 75%. Amazon Elastic Inference supports TensorFlow and Apache MXNet models. Inference is the process by which a trained model makes predictions. Inference can account for as much as 90% of total operational expenses in deep learning applications for two reasons. First, standalone GPU instances are usually used for model training and not inference. Inference jobs typically process one input at a time and use a smaller amount of GPU compute. Training jobs can process hundreds of data samples simultaneously, but inference jobs only process one input in real-time. This makes standalone GPU-based inference expensive. However, standalone CPU instances aren't specialized for matrix operations and are therefore often too slow to perform deep learning inference.

Amazon EC2 Inf1 instances were designed to deliver high-performance, cost-effective machine-learning inference. Amazon EC2 Inf1 instances offer up to 2.3x higher throughput, and up to 70% less cost per inference compared with other Amazon EC2 instance. Inf1 instances are powered by up to 16 AWS inference accelerators, designed by AWS. They also feature Intel Xeon Scalable 2nd generation processors, and up to 100 Gbps of networking bandwidth, to support large-scale ML apps. These instances are perfect for deploying applications like search engines, recommendation system, computer vision and speech recognition, natural-language processing, personalization and fraud detection. Developers can deploy ML models to Inf1 instances by using the AWS Neuron SDK. This SDK integrates with popular ML Frameworks such as TensorFlow PyTorch and Apache MXNet.

NASA was able to automate research and analysis using the Analyst Toolbox platform powered by BrainDocs. NASA simply needed to train data mining agents to do the work. These data agents analyzed and scored unstructured research against NASA technology roadmaps for relevancy. The Advanced Concepts Office was able to score proposals using cognitive agents, allowing it to perform statistical analysis within its information-based decision framework for strategic investments. Our solution, which was trained for NASA's domain taxonomy and combined interactive search and discovery provided the research methodology to support NASA's future in space, aerospace, and robotics.

AI advances will eliminate the need for trial and error in medical practice. Our digital twins allow for confident and rapid clinical trials. We work in neurology, immunology and metabolic disease. TwinRCTs achieve full enrollment faster because they need fewer patients in order to achieve the same power of traditional trial designs. Enrollment time in late-stage trials can be shortened. TwinRCTs increase the power of early-stage studies without adding additional patients, allowing them to observe treatment effects. Early-stage studies can be used to make confident decisions and attract participants. TwinRCTs increase the chances of participants receiving experimental treatments because they use smaller controls groups. Position clinical trials using digital twins to achieve regulatory success. Unlearn engineers the future of medicine using artificial intelligence. We create and deploy new types generative models based on patient-level data.

Ambient.ai's computer vision intelligence transforms security tools, operations, and outcomes, allowing physical security teams to move from reactive to proactive operations. Computer vision is changing how humans and machines work together in the real world. Computer vision increases human productivity by automating repetitive tasks. We are a team consisting of computer vision and machine perception experts who apply cutting-edge research to the physical security needs of organizations. The trade-off between privacy and security is a false dichotomy. You can respect privacy while increasing group security. We don't and won't use facial recognition.

Letta lets you create, deploy and manage agents at scale. REST APIs allow you to build production applications that are backed by microservices. Letta gives your LLM services advanced reasoning capabilities, transparent long-term memories (powered by MemGPT), and transparent long term memory. We believe that programming agents begin with programming memory. Self-managed memory is introduced for LLMs by the researchers who developed MemGPT. Letta's Agent Development Environment allows you to see the entire sequence of tool call, reasoning and decision that explains agent outputs. Most systems are built using frameworks that only allow prototyping. Letta' was built by systems engineers to be used at scale, so that the agents you create will become more useful over time. You can debug your agents and interrogate the system without having to rely on closed AI megacorps' black box services.

Robotic automation is a solution to warehouse operations that have changed in scale and complexity. It was developed by some of the most respected AI researchers. The Covariant Brain, which was trained on millions of picks from Covariant robots across the globe, allows robots to autonomously select virtually any SKU or item within minutes. Covariant's AI-powered Robotic Putwall sorts items from mixed-SKU tees, closing labor gaps and increasing throughput. Each deployment is carefully tailored to fit your existing floorplan, systems, upstream/downstream processes, and floorplan. Each robot learns from millions upon millions of picks made by connected robots in warehouses all over the globe using fleet learning. Covariant robots are able to handle almost any SKU or item right out of the box.

Microlearning can transform your learning culture. You will find new, relevant content and a world-class learning experience. Your people can access ongoing virtual soft skills training wherever they are. Flexible integration options make it possible to transform your intranet, intranet and social channels into dynamic learning destinations. Blue Ocean Brain, a microlearning company that is grounded in neuroscience and modern learning methods, offers scalable design solutions and collaborative consultation to assist companies of all sizes and industries to create a culture of continuous learning that supports and aligns with their strategic priorities. Blue Ocean Brain's interactive content is produced fresh every day of the year and offers a flexible user experience that allows for the delivery of mission-critical material in a way that fits into their daily workday.

The Free Motion Capture Project (FreeMoCap), aims to offer everyone free, research-grade markerless motion capture software. We are creating a user-friendly framework to connect a variety of open-source tools from computer vision and machine-learning communities to accurately record full body 3D movement of animals, humans, and other objects. We want to make the mind-blowing, future-shaping technologies that underpin FreeMoCap's core functionality available to all those who can benefit. We use a "Universal Design" development approach to create a system that meets the needs of professional researchers while remaining intuitive for a 13-year old with no technical training.

Deep reinforcement learning (DRL), combines the benefits of deep learning with the power of reinforcement learning by combining the power of sequential decision making with reinforcement learning. Learnable's DRL AI can self-produce extensive simulation data, which allows for continuous self-upgrades and optimal predictive output. Learnable has created three types of AI models, each with different capabilities, using DRL technologies. Interactive AI learns by interacting with humans. Interactive AI's sophisticated ability to understand different types of human feedback allows it to build a cognitive system that can interpret human intent through interactions. eXplainableAI (XAI), is able to understand the deep logic behind events, actions, and rewards, much like the human brain. XAI is able to explain its decisions, which is different from other forms AI.

Our platform can help you understand your employees' thoughts, feelings, and behaviors. Our neuroscience model connects the employee experience to the key happiness and engagement factors in your organization. With 10M+ global data points, you can drive your people strategy. Our Neuroscience-based Prebuilt Surveys can measure the employee experience. Neuroscience-based machine learning and sentiment analyses are used to analyze real-time data. Online reports can be shared with your teams via slides, spreadsheets, and PDFs.

Bittensor, an open-source protocol, powers a blockchain-based decentralized machine-learning network. Machine learning models are trained collaboratively, and rewarded by TAO based on the informational value that they provide to the collective. TAO also allows external access to the network, allowing users extract information while tuning its activities according to their needs. Our vision is to create an artificial intelligence market, a transparent, open and trustless environment where consumers and producers can interact. A novel, optimized approach to the development and distribution artificial intelligence technology that leverages the capabilities of a distributed ledger. Its facilitation of open ownership and access, decentralized governance and the ability of global computing power and innovation to be harnessed within an incentive framework.

Your software can see objects in video and images. A few dozen images can be used to train a computer vision model. This takes less than 24 hours. We support innovators just like you in applying computer vision. Upload files via API or manually, including images, annotations, videos, and audio. There are many annotation formats that we support and it is easy to add training data as you gather it. Roboflow Annotate was designed to make labeling quick and easy. Your team can quickly annotate hundreds upon images in a matter of minutes. You can assess the quality of your data and prepare them for training. Use transformation tools to create new training data. See what configurations result in better model performance. All your experiments can be managed from one central location. You can quickly annotate images right from your browser. Your model can be deployed to the cloud, the edge or the browser. Predict where you need them, in half the time.

The world's fastest knowledge graph and reasoning engine. Oxford Semantic Technologies was founded by three professors from the University of Oxford. They were inspired by extensive research in Knowledge Representation and Reasoning. Out of this research, RDFox, the most powerful knowledge graph and reasoning engine, emerged. RDFox is an AI reasoning engine that mirrors the principles of human reasoning. Its unrivaled reasoning abilities, which rely on accuracy, truth and explainability, empower the next generation AI applications. RDFox's ability to infer new knowledge from only factual data ensures that results are grounded in reality. RDFox’s incremental reasoning capabilities allow the AI rules to be applied in real-time to the database as data is changed, added, or removed. This happens without the need to restart. Only the relevant data is updated, without the need to re-analyze all the data.

NVIDIA NeMo megatron is an end to-end framework that can be used to train and deploy LLMs with billions or trillions of parameters. NVIDIA NeMo Megatron is part of the NVIDIAAI platform and offers an efficient, cost-effective, and cost-effective containerized approach to building and deploying LLMs. It is designed for enterprise application development and builds upon the most advanced technologies of NVIDIA research. It provides an end-to–end workflow for automated distributed processing, training large-scale customized GPT-3 and T5 models, and deploying models to infer at scale. The validation of converged recipes that allow for training and inference is a key to unlocking the power and potential of LLMs. The hyperparameter tool makes it easy to customize models. It automatically searches for optimal hyperparameter configurations, performance, and training/inference for any given distributed GPU cluster configuration.

Qualcomm® Snapdragon Ride™, Platform is one the most advanced, flexible and customizable automated driving platforms in the automotive industry. It will allow automotive suppliers and manufacturers to easily deploy the safety, convenience, and autonomous driving features they need today. With the potential to scale in future. Auto-ready, reliable performance at low power, with greater simplicity and better automotive safety. The Snapdragon Ride Platform is passively or air-cooled, unlike other autonomous driving solutions that need liquid cooling. Multi-ECU aggregation allows our customizable platform to scale from active safety to convenience to full Self-Driving across more vehicles. The new Snapdragon Ride Autonomous Stack, which is high-performance and energy-efficient, combines with the hardware to create one of the most robust vehicle perceptions and driving brains.

We conduct cutting-edge research in artificial intelligence and implement it to give financial investors an advantage. Transforming the financial market through groundbreaking neuro-prediction. Our algorithms combine graph-based learning and deep reinforcement learning algorithms to model and predict time series. Neuri aims to generate synthetic data that mimics the global financial markets and test it with complex simulations. Quantum optimization is the future of supercomputing. Our simulations will be able to exceed the limits of classical supercomputing. Financial markets are dynamic and change over time. We develop AI algorithms that learn and adapt continuously to discover the connections between different financial assets, classes, and markets. The application of neuroscience-inspired models, quantum algorithms and machine learning to systematic trading at this point is underexplored.

Exafunction optimizes deep learning inference workloads, up to a 10% improvement in resource utilization and cost. Instead of worrying about cluster management and fine-tuning performance, focus on building your deep-learning application. Poor utilization of GPU hardware is a common problem in deep learning applications. Exafunction allows any GPU code to be moved to remote resources. This includes spot instances. Your core logic is still an inexpensive CPU instance. Exafunction has been proven to be effective in large-scale autonomous vehicle simulation. These workloads require complex custom models, high numerical reproducibility, and thousands of GPUs simultaneously. Exafunction supports models of major deep learning frameworks. Versioning models and dependencies, such as custom operators, allows you to be certain you are getting the correct results.

Sportlogiq uses machine learning and computer vision to generate data and insights. Sportlogiq's cutting edge analytics products are helping elite football, soccer, and hockey teams make better decisions. Sportlogiq uses cutting-edge machine learning and computer vision technology to collect and analyze sports data. This data is used to support recruitment and analytics in elite sports worldwide. We enable teams across hockey, soccer, football to gain unprecedented insights into their sport. This allows for faster, more informed decision-making within entire organizations. Unique Sportlogiq metrics can be used to perform analysis, opposition scouting and recruitment, as well as player development. Broadcast tracking data makes it possible to add critical context and make your data more useful. Access to all football (soccer), data from any league in the world.

We license AI software across the L2-L4 autonomous stack, including perception, intent modeling and path planning. Perception and intent prediction with the highest accuracy, leading to safer autonomous systems. Unsupervised learning allows for learning from large datasets. Our technologies are capital-efficient by several orders of magnitude, enabling a much lower cost of developing. Helm.ai's full scene vision-based segmentation combined with Ouster's Lidar SLAM output. Helm.ai L2+ autonomous driving across highways 280, 92, 101 and lane-keeping with ACC lane changes. Helm.ai pedestrian segmentation with key-point predictions. Pedestrian segmentation with keypoint detection. Corner cases for rain lane detection and Lidar-vision Fusion. Full scene semantic segmentation. Botts dots and faded lane markings.

The Intel Distribution of OpenVINO makes it easy to adopt and maintain your code. Open Model Zoo offers optimized, pre-trained models. Model Optimizer API parameters make conversions easier and prepare them for inferencing. The runtime (inference engines) allows you tune for performance by compiling an optimized network and managing inference operations across specific devices. It auto-optimizes by device discovery, load balancencing, inferencing parallelism across CPU and GPU, and many other functions. You can deploy the same application to multiple host processors and accelerators (CPUs. GPUs. VPUs.) and environments (on-premise or in the browser).

A data science platform for life scientists. Transform your microbiome research into useful applications Get new, safe and effective products on the market faster. Combine the right combination of data science and collaboration tools to make rapid progress in cancer therapy. Effective digital technology solutions can improve crop productivity and accelerate research. Import both small and large data. You can either use our templates or create your own schema. Our format inference algorithm synthesizes the parsing functions and allows you to override if necessary, without any coding. For bulk imports, you can use our interactive import screens and CLI. Your data is more than just bits. Solvuu automatically calculates summary statistics and generates rich interactive visualizations. You can explore and gain insight into your data instantly; you can even slice and dice it as needed.

Create and manage AI applications without the need to move data to complex vector databases and pipelines. Integrate AI, vector search and real-time inference directly with your database. Python is all you need. All your AI models can be deployed in a single, scalable deployment. The AI models and APIs are automatically updated as new data is processed. You don't need to duplicate your data or create an additional database to use vector searching and build on it. SuperDuperDB allows vector search within your existing database. Integrate and combine models such as those from Sklearn PyTorch HuggingFace, with AI APIs like OpenAI, to build even the most complicated AI applications and workflows. With simple Python commands, deploy all your AI models in one environment to automatically compute outputs in your datastore (inference).

Our SDKs are easy to use and reach millions of users every day who use Passio to transform their homes, businesses, health, and lives. We help businesses transform their apps with real-time computer vision on-device and AI-driven experiences. Bring your home improvement and paint store into your customers' homes and let them visualize and buy your paints and home remodeling products. Your customers can make better purchasing decisions by seeing your products in augmented reality in their home and by using computer vision for identifying their remodel scenarios, surfaces types and surface conditions. Remodel AI includes a flexible painter that takes advantage of AR technology to offer you multiple options for room scanning and paint visualisation. It only takes seconds to transform a room, and your users are delighted to see the new environment in real-time.

Arena empowers businesses from all industries to take high-frequency, critical path decision fully autonomously. Autopilot for high-frequency business decision making. Autonomy OS, which is similar to a physical robot's sensor, brain and arm, is made up of three components: the sensor, the head, and the arm. The sensor measures, while the brain makes decisions and the arm takes actions. The entire system works in real-time and automatically. Autonomy OS encodes heterogeneous data using different latency profiles. This includes streaming real-time and structured data series, as well as unstructured data such images and text. These data are used to train machine learning models. Autonomy OS also adds contextual data from Arena's demand graph, a daily updated index of factors that influence consumer demand and supply. This includes product prices, availability by location, and demand proxies from various social media platforms. Customers' preferences and behavior change, supply routes are unexpectedly disrupted and competitors alter their strategy.

Artificial intelligence (AI) and computer vision help train equipment to control quality of products in manufacturing, train robots for autonomous movement and safety, train cameras and controllers to control traffic on retail. They can also recognize the types and colors of cars and food in the fridge and create a map or 3D model from video. Algorithms can help you predict sales, identify the relationship between publications and metrics, and create personal offers. They also interpret and visualize data and extract the most important information from text and video. Data Mining, regression and classification, correlation, and cluster analysis. Decision trees, prediction models. Graphs. Neural networks. Text classification, understanding and summarization, auto-tagging, named entity recognition, compare to text similarity and sentiment analysis, dialog, and QA systems. Detection, segmentation and recognition. Recovery and image/video generation.

Nokia's OSS portfolio is a valuable resource for CSPs. It comes with 5G future X network architecture and digital value platforms. CSPs are in a unique position to show digital-native behavior, and make more autonomous decisions. The new OSS allows for the transition from autonomous processes to dynamic lifecycle management with closed loop automated capabilities. It is built around intelligence, automation, and closed-loop management of the service lifecycle to provide an exceptional OSS experience. The transformational journey towards new OSS involves the consolidation of standalone capabilities with assurance, moving from network and service-focused to customer and business paradigm, to zero-touch automation. The new OSS unifies operations in order to support next-generation digital and value chains and partner ecosystems.

Lamini allows enterprises to transform proprietary data into next-generation LLM capabilities by offering a platform that allows in-house software teams the opportunity to upgrade to OpenAI level AI teams, and build within the security provided by their existing infrastructure. Optimised JSON decoding guarantees a structured output. Fine-tuning retrieval-augmented retrieval to improve photographic memory. Improve accuracy and reduce hallucinations. Inferences for large batches can be highly parallelized. Parameter-efficient finetuning for millions of production adapters. Lamini is the sole company that allows enterprise companies to develop and control LLMs safely and quickly from anywhere. It uses the latest research and technologies to create ChatGPT, which was developed from GPT-3. These include, for example, fine-tuning and RLHF.

CentML speeds up Machine Learning workloads by optimising models to use hardware accelerators like GPUs and TPUs more efficiently without affecting model accuracy. Our technology increases training and inference speed, lowers computation costs, increases product margins using AI-powered products, and boosts the productivity of your engineering team. Software is only as good as the team that built it. Our team includes world-class machine learning, system researchers, and engineers. Our technology will ensure that your AI products are optimized for performance and cost-effectiveness.

DadePay was originally created as a SaaS app that works with all browsers and doesn't require any specific operating system. The open-source technology stack used to create the advanced accounts receivable automation software for DadePay is robust, secure, and proven. This allows us to operate our technology infrastructure at a significant cost advantage over our competitors. The DadePay product line shares a common code base. It also features a patented inference engine, which achieves the highest industry-standard invoice matching rates. DadePay employs the most recent automated AR technology, including responsive design and Google-like search of any element in data, to provide innovative payment processing systems accessibility and usability. DadePay's payment automation software was built using open-source technologies, such as the Java-based Ruby on Rails framework.

Compute with Hivenet is a powerful, cost-effective cloud computing platform offering on-demand access to RTX 4090 GPUs. Designed for AI model training and compute-intensive tasks, Compute provides secure, scalable, and reliable GPU resources at a fraction of the cost of traditional providers. With real-time usage tracking, a user-friendly interface, and direct SSH access, Compute makes it easy to launch and manage AI workloads, enabling developers and businesses to accelerate their projects with high-performance computing. Compute is part of the Hivenet ecosystem, a comprehensive suite of distributed cloud solutions that prioritizes sustainability, security, and affordability. Through Hivenet, users can leverage their underutilized hardware to contribute to a powerful, distributed cloud infrastructure.

PRODRIVER is Embotech’s solution to the problem motion planning for highly-automated or autonomous vehicles. It is an integral part of the autonomous driving software stack. It is located in the so-called "decision making" layer. PRODRIVER, as a motion planner is responsible for generating drivable trajectories and directly actuator commands like steering, accelerating, and braking. These are calculated based on the environment information. PRODRIVER solves optimization problems in real-time by continuously making predictions. Its most important inputs include information about the drivable area, its obstacles and a goal. This could be a position or an object such as progress along a route. Its outputs can be used to direct control the vehicle, or to set-points for low-level controllers to track the vehicle. This diagram shows how PRODRIVER integrates into a typical autonomous vehicle software platform.

Service assurance is a key goal for digital-first businesses. It has become the lifeblood of their business applications. These applications are becoming more complex due to the advent of 5G, edge, and containerized cloud-native infrastructures. RDAF consolidates disparate data sources and converges on the root cause using dynamic AI/ML pipelines. Then, intelligent automation is used to remediate. Data-driven companies should evaluate, assess, and implement RDAF to speed innovation, reduce time to value, meet SLAs, and provide exceptional customer experiences.