#Automated Vehicles. Autonomous Vehicles

[jcmarchi]

Future AGI Secures $1.6M to Launch the World’s Most Accurate AI Evaluation Platform

New Post has been published on https://thedigitalinsider.com/future-agi-secures-1-6m-to-launch-the-worlds-most-accurate-ai-evaluation-platform/

Future AGI Secures $1.6M to Launch the World’s Most Accurate AI Evaluation Platform

AI adoption is booming, yet the lack of comprehensive evaluation tools leaves teams guessing about model failures, leading to inefficiencies and prolonged iteration cycles.

Future AGI is tackling this problem head-on with the launch of its AI lifecycle management platform, designed to help enterprises achieve 99% accuracy in AI applications. To accelerate its vision, the company has secured $1.6 million in pre-seed funding, co-led by Powerhouse Ventures and Snow Leopard Ventures, with participation from Angellist Quant Fund, Swadharma Source Ventures, Saka Ventures, and a network of 30+ industry veterans and angel investors.

Closing the AI Accuracy Gap

Current AI tools fall short when it comes to delivering precise, actionable insights. Most evaluations are manual and superficial, often relying on vague assessments rather than systematic experimentation. Future AGI is changing the game by introducing deep multimodal evaluations, real-time observability, and continuous optimization.

Future AGI’s proprietary technology includes advanced evaluation systems for text and images, agent optimizers, and auto-annotation tools that cut AI development time by up to 95%. Enterprises can complete evaluations in minutes, enabling AI systems to be optimized for production with minimal manual effort.

“AI is becoming the new software, but its widespread adoption faces a critical challenge – reliability and accuracy at scale,” said Nikhil Pareek, CEO of Future AGI. “Today’s AI systems are probabilistic and error-prone, with improvement cycles taking 6-8 months. We’re building the foundational layer that ensures AI systems are trustworthy and reliable in production. Our platform isn’t just about workflow automation – we’re creating the data layer that continuously monitors, evaluates, and improves AI systems across multimodal interactions.”

Transforming AI Performance Across Industries

Future AGI is already delivering impactful results across industries:

A Series E sales-tech company used Future AGI’s LLM Experimentation Hub to achieve 99% accuracy in its agentic pipeline, compressing weeks of work into just hours.

An AI image generation company leveraged the platform to cut costs by 90% while maintaining 99% accuracy in catalog and marketing images.

The platform’s capabilities extend to robotics and autonomous vehicles, enabling enterprises to simulate edge cases and validate AI models before deployment.

Future AGI is redefining AI accuracy by enabling enterprises to:

Generate and manage synthetic datasets for AI model training.

Experiment with agentic workflows without writing code.

Conduct deep evaluations to pinpoint root causes of AI failures.

Automate optimizations using built-in scoring mechanisms.

A Visionary Team Driving AI Innovation

Future AGI was founded by Nikhil Pareek and Charu Gupta, who experienced firsthand the challenges of data collection, annotation, and model evaluation. Their frustration led them to build a solution that could revolutionize AI development.

Nikhil Pareek, a former AI founder with multiple patents and research papers, has worked on autonomous drones and data science challenges for Fortune 50 companies. Charu Gupta, a revenue growth expert, has led multiple startups from inception to scaling revenues up to $100 million.

With a team of 30 AI researchers and ML engineers from Microsoft, Amazon, and top Ivy League institutions, Future AGI is at the forefront of AI innovation, bringing patented technologies and deep expertise to solve AI’s most pressing challenges.

“The AI landscape is evolving rapidly, and one of the biggest challenges enterprises face today is ensuring the accuracy and reliability of their AI applications,” said Sri Peddu, General Partner at Powerhouse Ventures. “Future AGI’s innovative approach to solving this critical problem through their comprehensive AI lifecycle management platform positions them uniquely in the market. We believe their solution will be instrumental in helping companies achieve the highest accuracy levels required for production-grade AI applications.”

“We believe great people build great companies, and we know from our data that Future AGI is one of the top early-stage startups for attracting the best job applicants on Wellfound (fka AngelList Talent),” said Abraham Othman, PhD, managing partner of the AngelList Early-Stage Quant Fund.

Scaling to Meet Growing Market Demand

The funding will enable Future AGI to expand its product development, scale its engineering and growth teams, and enhance its proprietary technology stack. With headquarters in the Bay Area and an R&D center in Bangalore, Future AGI is strategically positioned to serve enterprises globally as AI transitions from experimental projects to mission-critical applications.

As AI advances into multimodal interactions combining text, images, audio, and video, accuracy and reliability will define success. Future AGI is laying the foundation for AI systems that enterprises can trust, ensuring AI innovations are not just cutting-edge but consistently accurate and dependable.

[aanews69]

Entregamos histórias. Também fornecemos guias, dicas e truques sobre como criar o seu próprio.Este canal é dedicado a coisas aleatórias que surgem em nossas ...

[techdriveplay]

Understanding the Difference Between 4G and 5G Networks

As our reliance on mobile connectivity grows, so does the need for faster, more efficient networks. Understanding the difference between 4G and 5G networks is crucial as 5G technology becomes more widely available, promising to revolutionise how we interact with the digital world. From browsing the web to powering autonomous vehicles and smart cities, 5G is set to offer significant advancements…

[artisticdivasworld]

Top Trucking Industry Trends of 2024: Adapting to a New Era of Innovation and Challenges

The trucking industry is experiencing significant changes, driven by advancements in technology, economic pressures, and environmental concerns. One of the biggest trends is the adoption of electric trucks. Many companies are feeling the push to reduce carbon emissions and meet sustainability goals. Electric trucks, while expensive upfront, are being seen as long-term investments due to lower…

[krazytechnews]

Keeping the Skies Safe: The Role of Airplane Upkeep

The marvel of modern aviation allows us to traverse the globe with unprecedented speed and efficiency. Behind the scenes of every safe and successful flight lies a rigorous and meticulous process of airplane upkeep. Ensuring the safety and reliability of aircraft involves a complex interplay of maintenance procedures, inspections, and operational protocols. This article delves into the critical…

[techtoio]

How AI is Transforming Everyday Technology

Introduction

Welcome to TechtoIO! Today, we delve into one of the most exciting developments in modern technology: artificial intelligence (AI). AI has moved beyond the realm of science fiction and is now an integral part of our daily lives. From smart homes to personalized recommendations, AI is transforming everyday technology in ways we once only dreamed of. In this article, we explore how AI is reshaping various aspects of our lives and what we can expect in the near future. Read to continue

[reallytoosublime]

Self-driving cars, also known as autonomous vehicles or driverless cars, are a revolutionary technological advancement poised to transform the way we commute, travel, and interact with our urban and rural environments. In this video, we'll discuss the progress made by Tesla in their development of self-driving cars, and how close we are to achieving this technology.

Self-driving cars are equipped with a range of sensors such as LiDAR, cameras, radar, and ultrasonic sensors. These sensors provide the vehicle with a comprehensive view of its surroundings, allowing it to perceive other vehicles, pedestrians, road signs, traffic lights, and obstacles in real-time.

The heart of self-driving cars lies in their AI systems. These AI algorithms process the data from sensors to make complex decisions and control the vehicle's movements. Machine learning and deep learning techniques are used to teach the AI system to recognize patterns, predict the behaviors of other road users, and respond appropriately to various scenarios.

Self-driving cars rely on high-definition maps to understand their location and the environment. These maps provide information about lane markings, road geometries, traffic signs, and more. Simultaneous Localization and Mapping technology is used to continuously update the vehicle's position within the mapped environment.

Self-driving cars represent a technological frontier that holds the promise of safer, more efficient, and more accessible transportation. While there are hurdles to overcome, ongoing advancements in AI, sensor technology, and infrastructure development continue to push the boundaries of what's possible in the realm of autonomous vehicles.

Self-Driving Cars: How Close We Are?

[youtubemarketing1234]

Self-driving cars, also known as autonomous vehicles or driverless cars, are a revolutionary technological advancement poised to transform the way we commute, travel, and interact with our urban and rural environments. In this video, we'll discuss the progress made by Tesla in their development of self-driving cars, and how close we are to achieving this technology.

Self-driving cars are equipped with a range of sensors such as LiDAR, cameras, radar, and ultrasonic sensors. These sensors provide the vehicle with a comprehensive view of its surroundings, allowing it to perceive other vehicles, pedestrians, road signs, traffic lights, and obstacles in real-time.

The heart of self-driving cars lies in their AI systems. These AI algorithms process the data from sensors to make complex decisions and control the vehicle's movements. Machine learning and deep learning techniques are used to teach the AI system to recognize patterns, predict the behaviors of other road users, and respond appropriately to various scenarios.

The AI system of a self-driving car interfaces with the vehicle's control systems, including the steering, throttle, and brakes. It translates its decisions into precise actions to navigate the vehicle safely and efficiently.

Self-driving cars rely on high-definition maps to understand their location and the environment. These maps provide information about lane markings, road geometries, traffic signs, and more. Simultaneous Localization and Mapping technology is used to continuously update the vehicle's position within the mapped environment.

Communication technology plays a crucial role in the functioning of self-driving cars. These vehicles can exchange information with each other and with infrastructure elements like traffic lights and road sensors. This communication enhances safety and enables cooperative maneuvers.

Self-driving cars represent a technological frontier that holds the promise of safer, more efficient, and more accessible transportation. While there are hurdles to overcome, ongoing advancements in AI, sensor technology, and infrastructure development continue to push the boundaries of what's possible in the realm of autonomous vehicles.

Self-Driving Cars: How Close We Are?

[jcmarchi]

DeepSeek vs. OpenAI: The Battle of Open Reasoning Models

New Post has been published on https://thedigitalinsider.com/deepseek-vs-openai-the-battle-of-open-reasoning-models/

DeepSeek vs. OpenAI: The Battle of Open Reasoning Models

Artificial Intelligence (AI) transforms how we solve problems and make decisions. With the introduction of reasoning models, AI systems have progressed beyond merely executing instructions to thinking critically, adapting to new scenarios, and handling complex tasks. These advancements significantly impact industries such as healthcare, finance, and education. From improving diagnostic accuracy to detecting fraud and enhancing personalized learning, reasoning models are becoming essential tools for addressing real-world challenges.

DeepSeek and OpenAI have emerged as the two leading innovators in the field. DeepSeek has distinguished itself with its modular and transparent AI solutions developed to meet the specific needs of industries that demand precision and accountability. Its focus on adaptability has made it a preferred choice for businesses in healthcare and finance. Meanwhile, OpenAI continues to lead with versatile models like GPT-4, which are widely recognized for their ability to handle various tasks, including text generation, summarization, and coding.

As these two organizations advance AI reasoning capabilities, their competition results in significant progress in the field. Both DeepSeek and OpenAI are playing key roles in developing more innovative and more efficient technologies that have the potential to transform industries and change the way AI is utilized in everyday life.

The Rise of Open Reasoning Models in AI

AI has transformed industries by automating tasks and analyzing data. However, the rise of open reasoning models represents a new and exciting development. These models go beyond simple automation. They think logically, understand context, and solve problems dynamically. Unlike traditional AI systems that rely on pattern recognition, reasoning models analyze relationships and make decisions based on context, making them essential for managing complex challenges.

Reasoning models have already proven effective across industries. In healthcare, they analyze patient data to diagnose illnesses and recommend treatments. In autonomous vehicles, they process real-time sensor data to ensure safety. In finance, they detect fraud and predict trends by examining large datasets. Their flexibility and precision enable them to adapt to diverse needs and deliver reliable solutions.

This shift has increased competition among major AI companies, including DeepSeek, OpenAI, Google DeepMind, and Anthropic. Each brings unique benefits to the AI domain. DeepSeek focuses on modular and explainable AI, making it ideal for healthcare and finance industries where precision and transparency are vital. OpenAI, known for its general-purpose models like GPT-4 and Codex, excels in natural language processing and problem-solving across many applications.

DeepSeek’s model, R1, uses a modular framework, thus enabling businesses to tailor it to specific tasks. It excels in areas requiring deep reasoning, such as medical data analysis and financial pattern detection. OpenAI’s o1 model, based on its GPT architecture, is highly adaptable and performs exceptionally well in natural language processing and text generation.

Pricing also reflects their strategic priorities. DeepSeek offers flexible, cost-effective solutions for businesses of all sizes, while OpenAI provides powerful APIs and documentation, though its premium features may be more expensive for smaller organizations. Both companies are advancing rapidly. DeepSeek focuses on multi-modal reasoning and explainable AI, while OpenAI enhances contextual learning and explores quantum computing integration.

DeepSeek and OpenAI: A Detailed Comparison

 Below is a comprehensive comparison of DeepSeek R1 and OpenAI o1, focusing on their features, performance, pricing, applications, and future developments. Both models represent AI advancements but cater to different needs and industries.

Features and Performance

DeepSeek R1: Precision and Efficiency

DeepSeek R1 is an open-source reasoning model for tasks requiring advanced problem-solving, logical inference, and contextual understanding. Developed with a budget of just $5.58 million, it has achieved remarkable efficiency, showing how smaller investments can yield high-performing models.

One of its prominent features is the modular framework that helps businesses customize the model for specific industry needs. This flexibility is enhanced by the availability of distilled versions, such as Qwen and Llama variants, which optimize performance for targeted applications while reducing computational requirements.

DeepSeek R1 relies on a hybrid training approach, combining Reinforcement Learning (RL) with supervised fine-tuning. The RL component enables the model to improve autonomously while fine-tuning ensures accuracy and coherence. This approach has helped DeepSeek R1 achieve substantial results in reasoning-heavy benchmarks:

When benchmarked on AIME 2024, an advanced mathematics test, DeepSeek R1 scored 79.8%, slightly higher than OpenAI o1.

On MATH-500, a high-school-level math problem-solving benchmark, it achieved 97.3%, surpassing OpenAI o1’s 96.4%. In SWE-bench, which evaluates software engineering tasks, DeepSeek R1 scored 49.2%, compared to OpenAI o1’s 48.9%.

However, in general-purpose benchmarks like GPQA Diamond and multitask language understanding (MMLU), DeepSeek R1 scored 71.5% and 90.8%, respectively, slightly lower than OpenAI o1.

OpenAI o1: Versatility and Scale

OpenAI o1 is a general-purpose model built on GPT architecture. It is designed to excel in natural language processing, coding, summarization, and more. With a broader focus, OpenAI o1 caters to diverse use cases, supported by its robust developer ecosystem and scalable infrastructure.

The model performs excellently in coding tasks, scoring 96.6% on Codeforces, a popular platform for algorithmic reasoning. It also leads in general knowledge benchmarks like MMLU, achieving 91.8%, slightly ahead of DeepSeek R1.

While it slightly lags in mathematics and reasoning-specific tasks, OpenAI o1 compensates with its speed and adaptability in NLP applications. For example, it excels in text summarization, question-answering, and creative writing, making it suitable for businesses with varied AI requirements.

Pricing and Accessibility

DeepSeek R1: Affordable and Open

DeepSeek R1’s most significant advantages are its affordability and open-source nature. The model is freely accessible on DeepSeek’s platform, offering up to 50 daily messages at no cost. This accessibility extends to its API pricing, which is 96% cheaper than OpenAI’s rates. The price is $2.19 per million tokens for output, compared to OpenAI’s $60 for the same volume. This pricing model makes DeepSeek R1 especially appealing to startups and small businesses.

Moreover, open-source licensing under MIT terms allows developers to customize, modify, and deploy the model without restrictive licensing fees. This makes it an attractive option for enterprises looking to integrate AI capabilities while minimizing costs.

OpenAI o1: Premium Features

OpenAI o1 offers a premium AI experience focusing on reliability and scalability. However, its pricing is significantly higher. The API costs $60 per million tokens for output, and advanced features are only available through subscription plans. While this makes OpenAI a more expensive option, its extensive documentation and developer support justify the cost for larger organizations with complex needs.

Applications

DeepSeek R1 Applications

DeepSeek R1 is ideal for industries that require precision, transparency, and cost-effective AI solutions. Its focus on reasoning-heavy tasks makes it especially useful in scenarios where explainable AI is critical. Potential applications include:

Healthcare: DeepSeek R1 can analyze complex medical data, identify patterns in patient histories, and assist in diagnosing conditions, such as detecting early signs of diseases with high accuracy. These capabilities can be valuable in research hospitals, diagnostic labs, and telemedicine platforms.

Finance: The model’s ability to detect complex patterns makes it suitable for fraud detection and risk assessment. It can assist financial institutions in monitoring transactions and identifying irregularities to reduce financial crime rates.

Education: DeepSeek R1 can power adaptive learning systems by tailoring educational content to individual learners’ progress and needs. This can improve engagement and learning outcomes in online education platforms.

Legal and Compliance: With its modular design, DeepSeek R1 can help in legal contract analysis and compliance monitoring, making it valuable for law firms and regulatory industries.

Scientific Research: Its reasoning capabilities allow it to assist in hypothesis testing and data interpretation, supporting research institutions working on complex problems like genomics or material science.

OpenAI o1 Applications

OpenAI o1, with its general-purpose design, has already demonstrated its utility across a wide range of industries. Its versatility and adaptability make it well-suited for tasks involving natural language processing, creative output, and customer interaction. Common applications include:

Customer Service: OpenAI o1 has been widely deployed in creating chatbots that provide human-like interactions. These chatbots are used by e-commerce platforms, banking institutions, and tech support systems to handle customer inquiries and improve satisfaction.

Content Creation: Businesses frequently use OpenAI o1 to generate high-quality text, including marketing materials, product descriptions, and long-form reports. Its ability to produce coherent and creative content saves marketing teams time and effort.

Coding and Development: With its strong coding assistance capabilities, OpenAI o1 helps developers debug code, generate snippets, and improve software development efficiency.

Creative Industries: OpenAI o1 has been applied to generate storylines, scripts, and even lyrics for creative projects, making it a favourite in the media and entertainment industries.

Future Prospects and Trends

DeepSeek’s Roadmap

DeepSeek is investing in multi-modal reasoning, aiming to integrate visual and text-based reasoning for more comprehensive AI applications. Its emphasis on explainable AI ensures transparency and trust, making it a preferred choice in ethical and regulated industries like healthcare and finance. Additionally, DeepSeek plans to expand its distilled model lineup, offering even more efficient and specialized solutions.

OpenAI’s Vision

OpenAI continues to innovate with plans to enhance contextual learning and integrate its models with emerging technologies, such as quantum computing. CEO Sam Altman recently emphasized the importance of scaling computational resources to achieve breakthroughs in AI. OpenAI is also accelerating its release schedule to stay competitive, with a focus on developing Artificial General Intelligence (AGI). These advancements aim to broaden the applicability of OpenAI’s models while maintaining their reliability and scalability.

Public Perception and Trust Concerns

Regarding AI adoption, trust and public perception matter as much as performance. DeepSeek has drawn some concerns about bias, especially on sensitive or controversial topics. Users have noticed that its responses sometimes avoid strong opinions or critical perspectives, raising questions about how its training data and development environment might influence its outputs. This can be a sticking point for industries or applications where neutrality is critical.

On the other hand, OpenAI has built a solid reputation for being reliable and consistent, but it’s not without its challenges either. As a proprietary platform, OpenAI’s models sometimes feel like a black box, making it harder to understand how decisions are made. This can frustrate users in industries where transparency is non-negotiable, like healthcare or compliance.

Both companies have opportunities to build more trust. DeepSeek’s open-source model has the potential to enhance transparency and collaboration, which could help it address these concerns. Meanwhile, OpenAI’s strong developer ecosystem and track record make it a dependable choice for many. How each company handles these trust issues will be key to how widely they are adopted in the long run.

The Bottom Line

The competition between DeepSeek and OpenAI represents a pivotal moment in AI development, where reasoning models redefine problem-solving and decision-making. DeepSeek emerges with its modular, cost-effective solutions tailored for industries that demand precision, while OpenAI excels in versatility and adaptability with its robust general-purpose models.

Both companies advance their technologies to shape the future of AI, bringing transformative changes across healthcare, finance, education, and more. Their innovations represent the potential of reasoning models and highlight the importance of transparency, trust, and accessibility in adopting AI.

[shanmark54]

Revolutionizing Logistics: The Unprecedented Rise of Automated Guided Vehicles in the Next Decade

In an era marked by technological advancements and the relentless pursuit of efficiency, the Automated Guided Vehicle (AGV) Market is poised for an extraordinary journey from 2024 to 2032. As industries worldwide continue to embrace automation, AGVs stand out as key players in revolutionizing logistics, manufacturing, and warehousing. This article delves into the forecasted trends, innovations, and market dynamics that will shape the AGV landscape over the next eight years.

The Rise of Automation in Industry:

The global industrial landscape is undergoing a paradigm shift, with automation emerging as a driving force for increased productivity and cost-effectiveness. AGVs, equipped with advanced sensing and navigation technologies, are at the forefront of this transformation, offering a versatile solution for material handling and transportation within industrial settings.

Get a sample: https://www.econmarketresearch.com/request-sample/EMR00508

Market Growth Drivers:

Several factors contribute to the projected exponential growth of the AGV market. Increased demand for streamlined and efficient material handling processes, the need for labor cost reduction, and a growing emphasis on workplace safety are some of the primary drivers. AGVs offer a compelling solution to these challenges, providing a seamless blend of automation, precision, and adaptability.

Technological Advancements in AGVs:

The forecasted period is expected to witness significant advancements in AGV technology. Enhanced sensor capabilities, artificial intelligence, and machine learning algorithms will empower AGVs to navigate complex environments, optimize routes, and interact safely with humans and other equipment. These technological strides will not only improve operational efficiency but also contribute to a safer and more collaborative working environment.

Diverse Applications Across Industries:

AGVs are versatile tools with applications spanning various industries. In manufacturing, AGVs play a pivotal role in material transport, reducing downtime and increasing overall production efficiency. Warehousing and logistics companies are adopting AGVs for order picking, goods transportation, and inventory management, leading to faster fulfillment processes. The healthcare sector is also leveraging AGVs for tasks such as hospital logistics and medication delivery, showcasing the broad spectrum of applications for these automated systems.

Smart Warehousing and Intralogistics:

The next decade will witness a transformation in warehouse operations, driven by the integration of smart AGV solutions. AGVs will not only optimize storage and retrieval processes but will also contribute to real-time data analytics for efficient inventory management. Smart intralogistics powered by AGVs will create interconnected and adaptive warehouse ecosystems, ensuring rapid response to changing demands and minimizing errors.

Collaborative and Autonomous AGVs:

The evolution of AGVs includes a shift towards collaboration and autonomy. Collaborative AGVs will work alongside human workers, enhancing productivity and safety. Autonomous AGVs, on the other hand, will navigate without human intervention, making them ideal for repetitive and standardized tasks. The combination of collaborative and autonomous capabilities will result in more flexible and adaptable AGV systems.

Environmental and Cost Considerations:

The AGV market is also responding to the global emphasis on sustainability. Manufacturers are developing eco-friendly AGV models with energy-efficient features, reducing both operational costs and environmental impact. As businesses strive for greater sustainability, AGVs are positioned to play a crucial role in minimizing waste and energy consumption in material handling processes.

Challenges and Solutions:

While the AGV market is poised for substantial growth, challenges such as initial investment costs, system integration complexities, and workforce adaptation to automation may arise. However, industry players are actively addressing these challenges by developing user-friendly interfaces, providing comprehensive training programs, and offering scalable solutions to accommodate businesses of all sizes.

Global Market Dynamics:

The AGV market's growth is not limited to specific regions; it is a global phenomenon. North America, Europe, Asia-Pacific, and other regions are witnessing increased adoption of AGVs across various industries. The market dynamics are shaped by regional trends, regulatory environments, and the unique demands of different industries in each geographic location.

Future Outlook:

As we navigate the forecasted period from 2024 to 2032, the AGV market's trajectory is one of promise and potential. The integration of advanced technologies, collaborative approaches, and a commitment to sustainability positions AGVs as indispensable tools for industries seeking efficient, safe, and cost-effective material handling solutions. The next eight years are poised to witness a transformative journey, marking the ascendancy of AGVs as the cornerstone of automated logistics and manufacturing processes.

Conclusion:

The Automated Guided Vehicle market is on the cusp of a revolutionary decade, with innovation, collaboration, and efficiency at its core. As industries embrace automation to stay competitive in the global market, AGVs emerge as a beacon of progress, offering a seamless blend of technology and practicality. The forecasted period from 2024 to 2032 promises to be a testament to the transformative power of AGVs, reshaping the way we think about logistics, manufacturing, and the future of industry.

Read more details: https://www.econmarketresearch.com/industry-report/automated-guided-vehicle-market/

[techdriveplay]

What Should You Know About Edge Computing?

As technology continues to evolve, so do the ways in which data is processed, stored, and managed. One of the most transformative innovations in this space is edge computing. But what should you know about edge computing? This technology shifts data processing closer to the source, reducing latency and improving efficiency, particularly in environments where immediate action or analysis is…

[namecoder]

hiBotic.com

#robot #ai #artificialintelligence #tech #humanoid #robotics #automation #autonomous #tech #technology #techstartup #branding #brands #avatars #startupIdeas #techrevolution #bot #DomainForSale.

Unlock the realm of futuristic possibilities with HiBotic.com! This 7-character domain is your gateway to a world where innovation and technology collide. 🌐✨

Get this at www.hibotic.com.

See more ideas like this at www.brandtello.com

Act fast, before this opportunity slips away!

[krazytechnews]

What is New in Self-Driving Vehicles in 2024

Self-driving vehicles are autonomous vehicles in their functionalities. Autonomous Vehicles (AVs) are one of the most exciting technology and companies are investing millions. Driverless cars are evolving in their functionalities and it is not far off that everyone would have a driverless vehicle. These cars are driverless and controlled by computer-automated programs. You can say that AVs are…