Spain-based INBRAIN Neuroelectronics has completed patient recruitment for its first-in-human study evaluating a graphene-based cortical interface.

The first-in-human study recruited ten participants, eight of whom underwent surgery using the graphene interface.

It gives surgeons the clear, detailed information they need to protect vital functions, such as speech and movement.

The device performed well throughout these procedures with no failures or safety issues, allowing researchers to collect complete, high-quality datasets from all eight surgical patients.

“The completion of patient enrollment in this first-in-human study marks an important step for INBRAIN and the field of neurotechnology,” said Carolina Aguilar, CEO and Co-founder, INBRAIN Neuroelectronics.

“Graphene has the potential to fundamentally change how we interface with the brain, enabling higher resolution of neural function-specific biomarkers, safer, and more intelligent BCI systems,” Aguilar added.

Neural mapping

INBRAIN’s graphene electrodes are a major advancement in neural technology.

Currently, the standard electrodes used are often too rigid and bulky to capture clear data for surgeries. On the other hand, graphene electrodes offer a major upgrade for brain surgery.

Graphene is often hailed as a “wonder material” made of a single layer of carbon atoms. With its flexibility, it wraps around the brain’s microscopic folds like high-tech cling film.

The newly designed graphene neural interface is ultra-thin and flexible.

Precision is vital in neurosurgery, where even a single millimeter can determine whether a patient retains the ability to speak or move.

The device uses sensitive carbon rather than metal to deliver signals that are much clearer and more precise, enabling surgeons to map and understand brain activity in real time with far greater detail.

In this study, INBRAIN’s graphene interface was tested during brain tumor surgeries to evaluate its safety and performance alongside standard surgical tools.

While the main goal was to ensure the device was safe, the study also tested how well it captured signals, its stability, and how easily it fit into a standard operating room setup.

Interestingly, the device proved highly successful, showing no safety issues or failures across all eight patients.

90-day follow-up phase

Beyond safety, the technology demonstrated its power during awake surgeries, where it successfully captured high-resolution brain signals as patients performed tasks such as naming objects.

“This study demonstrates that graphene can safely interface with the human brain, and capture neural signals with exceptional fidelity and resolution to enable precise decoding of brain and speech-related patterns metals can barely see,” said Dr. Kostas Kostarelos, Co-Founder of INBRAIN Neuroelectronics and Chief Scientific Investigator of the study.

The study, a collaboration between the University of Manchester and the Northern Care Alliance NHS Foundation Trust, is now entering its 90-day follow-up phase.

With full results expected later this year, the era of intelligent BCI therapeutics is slowly becoming a clinical reality.

INBRAIN aims to commercialize next-generation BCI systems that improve surgical accuracy and offer new treatment pathways for neurological disorders.

While this trial focused on brain tumors, the implications go much further.

Reportedly, INBRAIN is combining graphene’s abilities with Microsoft’s AI to create a system that goes beyond recording brain signals—it actually understands them.

With a new partnership at the Mayo Clinic already underway, this technology is quickly moving from a specialized surgical tool to a potential permanent solution for Parkinson’s and epilepsy.

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From left to right: Andy Lonsberry, Path Robotics, Anders Beck, Universal Robots, Dave Coleman, PickNik Robotics.

Artificial intelligence is now a key component of every robotic solution. AI changes the way robots process sensor data, make decisions, and react to the world around them. With AI, robotic systems no longer require manual programming to define the robot’s motions and task sequencing in the workcell.

But is AI ready for real deployments into real-world production applications? What does it take to deliver an AI-driven robotic system for the end customer? At the 2026 Robotics Summit and Expo, a panel from leading AI-driven robotics companies will share their views and best practices learned from the field and real-world deployments. The panel will seek to answer the following questions:

• Is AI making robots easier to program, use, and deploy?
• How does AI change the world of production automation?
• How quickly can robots learn new tasks?
• How much effort and handholding does it take to deploy an AI-driven robot into a production environment for the first time?
• How hard is it to change over the program or help the robot “re-learn” a new skill?

The panelists will also discuss the considerations needed for setting up infrastructure to support AI systems. These will become increasingly crucial as AI robotics evolves from the laboratory and trade show floor to the real world.

Join Anders Beck, VP, AI Robotics Products at Universal Robots, Dave Coleman, Founder & Chief Product Officer at PickNik Robotics, and Andy Lonsberry, Chief Executive Officer at Path Robotics. The session will be moderated by Mike Oitzman, Senior Editor of The Robot Report, an industry veteran who’s watched the robotics industry evolve over the past four decades.

About the 2026 Robotics Summit & Expo

Registration is now open for the Robotics Summit & Expo, the world’s leading technical event for commercial robotics developers. The event is produced by The Robot Report and WTWH Media.

The show will have more than 50 sessions in tracks on artificial intelligence, design and development, enabling technologies, healthcare, and logistics. The Engineering Theater on the show floor will also feature presentations by industry experts.

More than 70 speakers are confirmed from companies such as AWS, Brain Corp, Fictiv, Harmonic Drive, maxon, PickNik Robotics, RealSense, the Robotics and AI Institute, Robust AI, Tesla, Toyota Research Institute, and more.

The 2026 Robotics Summit will also feature a number of networking opportunities. They include a Mix & Mingle Networking Reception after the first day of the show, the Women in Robotics Breakfast, and the ticketed RBR50 Awards Dinner.

The Robotics Summit & Expo is co-located with DeviceTalks Boston, which focuses on medical devices.

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The landscape of artificial intelligence is evolving at an unprecedented pace, and Anthropic’s latest release, Claude Opus 4.5, marks a significant leap forward. This isn’t just another incremental update; it’s a powerful, more efficient, and surprisingly affordable AI model that promises to redefine what’s possible in software engineering, and by extension, has profound implications for the robotics and smart manufacturing sectors.

Anthropic has unleashed its most capable AI model to date, slashing prices by two-thirds while demonstrating state-of-the-art performance, particularly in complex software engineering tasks. This strategic move intensifies the AI race, but for industries like robotics and automation, it signals a new era of accessibility and capability.

Unmatched Performance: AI That Out-Codes Humans?

Perhaps the most startling revelation accompanying Claude Opus 4.5 is its performance on Anthropic’s most challenging internal engineering assessment. The model scored higher than any human job candidate in the company’s history on this rigorous take-home exam. While the test doesn’t measure human soft skills, it undeniably highlights the rapid advancements in AI’s problem-solving and code generation abilities.

On the SWE-bench Verified benchmark, which assesses real-world software engineering tasks, Opus 4.5 achieved an impressive 80.9% accuracy, surpassing even its immediate competitors like OpenAI’s GPT-5.1-Codex-Max and Google’s Gemini 3 Pro. For our ctorobotics.com audience, this means a future where:

• **Robot Programming Accelerates:** Developers can leverage AI to generate, debug, and optimize complex robot control code faster than ever before.
• **Automation Logic Refinement:** AI can assist in designing and refining intricate automation sequences for PLCs and industrial control systems, improving efficiency and reducing errors.
• **Digital Twin & Simulation Enhancement:** AI can generate more realistic and complex simulation scenarios, or even help in auto-generating code for digital twin models.

Efficiency Redefined: Power at a Fraction of the Cost

Beyond raw performance, Anthropic has made a significant move on efficiency and pricing. Claude Opus 4.5 is now priced at just $5 per million input tokens and $25 per million output tokens – a dramatic reduction from its predecessor. This isn’t just good news for Anthropic’s balance sheet; it’s a game-changer for businesses of all sizes looking to integrate frontier AI capabilities.

The model also boasts dramatic efficiency improvements, using up to 76% fewer tokens to achieve similar or better outcomes on key benchmarks. For industrial applications, where every millisecond and every dollar counts, this cost-effectiveness makes advanced AI a far more viable option for widespread deployment:

• **Economical AI Integration:** Lower token costs mean that continuous AI-driven monitoring, optimization, and real-time decision-making in smart factories become financially feasible.
• **Scalable Solutions:** Enterprises can scale their AI applications across multiple production lines or robotic fleets without incurring prohibitive costs.
• **Democratization of Advanced AI:** Startups and smaller innovators in robotics and automation can now access cutting-edge AI capabilities that were previously out of reach.

Self-Improving Agents: The Future of Autonomous Systems

One of the most compelling features highlighted by early customers is the capability of ‘self-improving agents.’ Companies like Rakuten reported that their AI agents, powered by Opus 4.5, could autonomously refine their own capabilities, achieving peak performance in just four iterations. This isn’t the AI rewriting its fundamental code, but rather intelligently optimizing its tools and approaches to solve problems more effectively.

This ‘self-refinement’ capability is particularly exciting for robotics and automation:

• **Adaptive Robotics:** Imagine robots that can autonomously learn and adapt their movements or processes based on real-time feedback and environmental changes, continuously improving their task execution.
• **Intelligent Process Optimization:** AI agents could autonomously identify bottlenecks in a smart factory, propose solutions, and even refine their implementation strategies to maximize throughput or energy efficiency.
• **Proactive Maintenance & Diagnostics:** Self-improving AI could become even better at predicting equipment failures, optimizing maintenance schedules, and diagnosing complex issues in industrial machinery.

Infinite Context, Enhanced Enterprise Features

Anthropic has also rolled out crucial enterprise-focused updates. ‘Infinite chats’ eliminate context window limitations by intelligently summarizing longer conversations, allowing AI to maintain context over extended, complex projects. Integration with Excel for pivot tables and charts, and programmatic tool calling, further enhance its utility for enterprise users.

For the automation and manufacturing world, these features translate to:

• **Smarter HMI & SCADA Systems:** AI capable of understanding vast amounts of historical data and current operational context can provide more intelligent insights and control suggestions to operators.
• **Complex Project Management:** AI can assist in managing intricate system integration projects, pulling together data from various sources and offering coherent summaries and recommendations.
• **Improved Collaboration:** Engineers and operators can use AI as a super-assistant to sift through documentation, analyze logs, and contribute to problem-solving in a more integrated fashion.

The AI Race Heats Up, Driving Innovation for Industry

The rapid release of Opus 4.5, following closely on the heels of other major AI model updates from OpenAI and Google, underscores an intense competitive environment. This race for AI supremacy, however, is a boon for industries like robotics and smart manufacturing. It means a continuous flow of more powerful, efficient, and accessible AI tools that can be directly applied to real-world challenges.

As AI’s performance on technical tasks approaches, and even exceeds, human expert levels, its integration into industrial automation, robot development, and smart factory operations will transition from theoretical discussions to practical implementation. Anthropic’s Claude Opus 4.5 is not just a milestone for AI; it’s a powerful new tool in the hands of engineers and innovators shaping the future of robotics and smart manufacturing.

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In an era defined by rapid digital transformation and escalating cyber threats, the secure deployment of Artificial Intelligence is paramount, especially for organizations handling highly sensitive data. NATO, a global security alliance, has taken a monumental leap forward, partnering with Google Cloud to integrate advanced, sovereign AI capabilities into its most critical operational environments. This multi-million-dollar deal, leveraging Google Distributed Cloud (GDC) with its air-gapped platform, sets a new benchmark for secure, high-performance AI, offering crucial insights for the broader industrial and critical infrastructure sectors.

Unlocking AI Potential in a Sealed Environment

The core of NATO’s digital modernization lies in GDC Air-Gapped. This innovative platform allows defense organizations to execute analytical and AI-intensive workloads within completely isolated, disconnected environments. The primary benefit is absolute data sovereignty and security: sensitive information never traverses the public internet or commercial cloud architectures, remaining firmly within NATO’s sovereign perimeter. For the NATO Communications and Information Agency (NCIA), this means its Joint Analysis, Training and Education Centre (JATEC) can now process critical data and run sophisticated AI models with unprecedented security, bolstering digital readiness across the alliance.

This approach isn’t just about isolation; it’s about enabling advanced capabilities *within* that isolation. GDC Air-Gapped provides hardened infrastructure, parallel compute power, and modern machine learning tools, all integrated into a sealed system. As Tara Brady, President of Google Cloud EMEA, highlighted, this partnership underscores the growing need for trusted, high-performance AI systems that can operate without compromising data exposure, a requirement that resonates deeply with industries like smart manufacturing, energy, and defense production.

AI at Classified Scale: Implications for Industry

One of the most significant aspects of this deployment is the ability to conduct deep analytics, rapid data processing, and modern ML pipelines at cloud scale, even when networks must remain disconnected for security. Traditional AI deployments often rely on vast, connected cloud resources. GDC Air-Gapped bridges this gap by offering self-contained GPU clusters and custom accelerators specifically designed for high-performance, isolated workloads.

For NATO, this translates into faster training simulations, superior threat analysis, and enhanced mission readiness. For industrial robotics, automation, and smart manufacturing, this model offers a powerful template. Imagine factory floors or critical infrastructure facilities where AI-driven predictive maintenance, quality control, or autonomous operations require extreme data privacy and security. The ability to deploy powerful AI capabilities at the edge, fully air-gapped from external networks, could revolutionize operations in critical sectors, ensuring both operational efficiency and data integrity.

Paving the Way for Resilient Digital Transformation

This collaboration aligns perfectly with NCIA’s digital transformation plan and NATO’s commitment to secure AI adoption. As Antonio Calderon, Chief Technology Officer at NCIA, emphasized, strong industry partnerships are crucial for leveraging next-generation technology to enhance operational capabilities and safeguard digital environments. The integration, to be rolled out in the coming months, is built to evolve with future AI and cybersecurity demands, reflecting a long-term vision for resilience.

The NATO-Google Cloud partnership is a clear indicator that specialized, sovereign platforms combining commercial innovation with classified-grade isolation are becoming vital for government and defense agencies worldwide. More broadly, it signals a strategic shift towards AI-ready, secure cloud infrastructures designed for long-term resilience – a model that critical industries grappling with their own digital modernization challenges should keenly observe and potentially emulate for their advanced robotics, automation, and smart manufacturing initiatives.

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In the rapidly evolving world of robotics, automation, and smart manufacturing, software is the crucial backbone. From orchestrating complex robot movements to managing vast Industrial IoT networks and powering sophisticated AI algorithms, the quality and speed of software development directly dictate the pace of industrial advancement. The ability to innovate quickly and efficiently is paramount for companies looking to stay competitive.

JetBrains Harnesses GPT-5 to Revolutionize Coding

This is where industry leader JetBrains steps in, announcing a groundbreaking integration of GPT-5, OpenAI’s latest large language model, across its popular suite of developer tools. This strategic move is poised to empower millions of developers, fundamentally reshaping how they design, reason about, and build software. The integration promises to dramatically reduce development cycles, enhance code quality, and free up developers to focus on higher-level problem-solving rather than repetitive coding tasks, thanks to AI-driven code generation, intelligent debugging, and context-aware suggestions.

Empowering the Next Generation of Industrial Tech

For the robotics and automation sectors, the implications are profound. Whether it’s developing more intuitive human-robot interfaces, optimizing complex path planning algorithms for mobile robots, or crafting the intricate AI models that drive intelligent production lines and autonomous systems, faster and smarter coding directly translates into quicker innovation cycles. The ability for developers to rapidly prototype, test, and deploy sophisticated software solutions means that cutting-edge technologies for smart factories and advanced automation can reach the market at an unprecedented pace, enhancing efficiency and productivity across industries.

The Future of AI-Assisted Development

JetBrains’ adoption of GPT-5 marks a significant milestone in the journey towards AI-assisted development, democratizing access to advanced coding capabilities and potentially lowering the barrier for entry into complex technical fields. By augmenting human ingenuity with powerful AI, we are witnessing a paradigm shift that will not only accelerate the creation of current-generation industrial solutions but also unlock entirely new possibilities for what robots, AI-driven systems, and smart manufacturing technologies can achieve. This collaboration is a testament to the transformative power of AI in enhancing human potential in the digital age.

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In a significant move poised to accelerate global enterprise AI adoption, OpenAI has substantially expanded its data residency options for ChatGPT and its API. This strategic enhancement directly addresses one of the most critical compliance hurdles faced by international businesses, allowing them to store and process their valuable data closer to their operational hubs and in line with local regulatory frameworks.

The Global AI Compliance Hurdle Solved

For too long, the intricacies of data residency have acted as a bottleneck, preventing global enterprises from deploying advanced AI solutions like ChatGPT at scale. Data residency dictates that data must be processed and governed according to the specific laws and customs of the countries where it is stored. Failing to comply can lead to severe penalties, reputational damage, and a loss of trust.

OpenAI’s latest expansion effectively removes this major compliance blocker. Enterprises can now confidently integrate powerful AI tools into their workflows, knowing their data aligns with region-specific data protection acts, such as Europe’s GDPR.

New Regions, Greater Control

ChatGPT Enterprise and Edu subscribers, along with API customers approved for advanced data controls, now have an unprecedented choice of data processing regions. These include:

• Europe (European Economic Area and Switzerland)
• United Kingdom
• United States
• Canada
• Japan
• South Korea
• Singapore
• India
• Australia
• United Arab Emirates

OpenAI has also indicated plans for further expansion, signaling a clear commitment to supporting its global business user base. This control extends to crucial ‘data at rest’ elements, including conversations, uploaded files, custom GPTs, and image-generation artifacts. It’s important to note that, for now, inference residency (where the AI model processes data) remains primarily in the U.S.

Strategic Impact for Robotics, Manufacturing, and Beyond

For industries like robotics, automation, and smart manufacturing – sectors that increasingly rely on AI for efficiency, predictive maintenance, quality control, and human-robot collaboration – this update is transformative. Companies collecting vast amounts of operational data, often across international borders, can now leverage OpenAI’s powerful language models without compromising on data sovereignty or regulatory adherence. This fosters greater trust in AI solutions, enabling innovative applications that require sensitive data handling, from supply chain optimization to advanced quality inspection algorithms.

Navigating the Future of Enterprise AI with Confidence

By offering expanded data residency, OpenAI is not just providing a technical feature; it’s fostering an environment of trust and compliance critical for the mainstream adoption of AI in business. Enterprises can set up new workspaces or projects with their preferred data residency settings, ensuring their AI endeavors are built on a secure and legally sound foundation.

However, enterprises must remain vigilant regarding third-party connectors and integrations within ChatGPT. These external applications may have their own data residency rules, which could default to U.S. processing. Careful evaluation of all components of an AI solution stack is essential for comprehensive compliance.

This move marks a pivotal moment for global businesses looking to harness the full power of generative AI, ensuring that innovation can proceed hand-in-hand with robust data governance.

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This article is a valued contribution from our community and has been edited for clarity and accuracy by DataCamp.

Artificial Intelligence has become essential for software development in today’s rapidly evolving tech landscape. It enables applications to perform tasks that were once considered the exclusive domain of humans, such as understanding and generating human language, recognizing patterns in data, and making intelligent decisions. From chatbots and recommendation systems to image recognition and natural language processing, AI is transforming how we build software.

As a junior data practitioner with a solid technical foundation, you might wonder how to harness AI’s power effectively. That’s why this guide will explore why and how to choose AI frameworks, what frameworks and libraries you should explore, and many other essentials.

Why Choose an AI Framework?

These days, developers rely on corresponding frameworks and libraries more and more to save money, time, and resources while creating AI-driven software. Let’s consider what AI frameworks and libraries are — a collection of pre-built tools and resources that simplify the process of building applications on the basis of Artificial Intelligence. These frameworks provide a foundation for implementing machine learning and deep learning algorithms, making it easier to develop intelligent software.

Now, let’s discover more about why AI frameworks are used for boosting software development flow.

They are cost-effective for IT companies

AI frameworks provide businesses with a cost-effective way to develop custom software applications. By eliminating the need for manual coding and allowing developers to use pre-built components, frameworks can significantly help businesses reduce their development costs. Using frameworks also allows businesses to quickly build sophisticated applications tailored to their specific needs. By leveraging AI and ML power, companies can develop more efficient software solutions than traditional software development methods.

They facilitate app development flow

When selecting frameworks, it’s important to consider their ability to streamline the development process. These frameworks come with pre-implemented algorithms, data handling utilities, and optimization techniques, which enable developers to focus on solving the specific problem at hand rather than getting caught up in the technicalities of AI implementation.

They are a time-saving opportunity

AI frameworks are a tremendous timesaver for modern software development because they simplify creating, testing, and deploying applications. They also provide a complete development environment with debugging tools, testing harnesses, and data visualization capabilities. This speeds up the development process as developers can act quickly without having to manually compile and debug each section of their code. Additionally, AI frameworks offer a wide variety of pre-built models.

These are the most common reasons for implementing AI in business from the very steps of software development for enterprises. Now, let’s consider the key factors when choosing an Artificial Intelligence framework for an IT project.

How to Choose an AI Framework That Fits Your Business Needs

When choosing an AI framework, it’s essential to consider several key factors to ensure it aligns with your project’s requirements and your level of expertise.

Performance

Performance should be a top priority when looking at frameworks. Opting for a framework that can handle data efficiently and provides rapid training and inference times is recommended. The performance of different frameworks can be assessed by evaluating benchmarks and real-world use cases.

Community support

An active and engaged community is an absolute necessity for the development of AI frameworks. It provides unlimited access to an extensive array of resources, tutorials, and community-driven plugins, along with unwavering support for implementing ongoing enhancements and updates to the framework.

Flexibility

When working on AI projects, flexibility is of utmost importance. The best AI framework that provides the ability to experiment with various algorithms is essential to ensure success. Additionally, the framework should be able to adapt to different data types, such as text, images, and audio, and integrate with other technologies without any issues. Thus, you can ensure that your AI projects are efficient, accurate, and effective.

Ease of learning

It’s important to choose a framework that matches your current skill level when starting out as a junior data practitioner. Pay attention to the frameworks that are more beginner-friendly, offering detailed documentation and tutorials. Moreover, consider your learning style and the available resources to aid you in getting started.

When selecting an AI framework, it is crucial to consider various factors, including performance, community support, flexibility, and ease of learning. By doing so, you can ensure that the framework meets the requirements of your project and is compatible with your skill level. Considering these factors will help you find the ideal framework for your data projects.

Open-Source vs. Commercial AI Frameworks: Benefits and Drawbacks

When it comes to choosing the best AI framework for your development project, you have two main options: open-source and commercial frameworks. Each option has its own set of advantages and disadvantages. Understanding the differences between them before making a decision is vital.

Open-source AI frameworks

Open-source frameworks are those released under an open-source license, which grants users the opportunity to operate with the software for any purpose.

Why are open-source frameworks beneficial?

• They’re typically free to use, making them budget-friendly for small projects and startups.
• They often have a strong and active community, which can be used as a valuable resource for learning and troubleshooting.
• You can inspect the source code of open-source frameworks, giving you greater control over your AI implementations.

What are the drawbacks of the open-source AI frameworks?

• Limited support. While community support is helpful, it may not be as responsive or comprehensive as commercial support.
• Complexity. Some open-source frameworks can be complex and challenging for beginners to grasp fully.

Commercial AI frameworks

Commercial frameworks are developed by companies that release their software under proprietary licenses. This means that users of these frameworks are limited in what they can do with the software and also may be subject to additional fees. However, users of commercial frameworks may benefit from extra features and support from the vendor.

The benefits of commercial AI frameworks

• Commercial frameworks typically come with dedicated support teams, ensuring prompt assistance when issues arise.
• They often focus on user-friendliness, making them more accessible to developers of all skill levels.
• Moreover, you may find advanced features and optimizations in commercial frameworks that cater to specific use cases.

When figuring out How to learn AI, you need to consider both the positives and negatives of different frameworks.

The drawbacks of commercial AI frameworks

• They can be expensive, which may be prohibitive for small or bootstrapped projects.
• Using a commercial framework may tie you to a specific vendor and limit your flexibility.

Which to choose? The response relies on your particular project needs and requirements. Moreover, when selecting a framework, you should take into account financial resources, personal expertise, and other factors.

The Top AI Frameworks and Libraries

Software is an important component of streamlining business operations through AI frameworks and libraries. By using software, businesses can automate tasks, reduce manual labor, improve accuracy, save time and money, create insights from data, and more.

Popular AI frameworks such as TensorFlow and PyTorch are used for developing machine learning models. These frameworks provide a comprehensive set of tools that enable developers to easily create and deploy ML models. Other useful AI libraries include Scikit-Learn, Keras, and Caffe. These libraries provide a set of APIs that enable developers to quickly develop applications without needing to write an entire code base from scratch.

PyTorch

Torch is an open-source machine learning library known for its dynamic computational graph and is favored by researchers. The framework is excellent for prototyping and experimentation. Moreover, it’s empowered by growing community support, with tools like PyTorch being built on the library. PyTorch has swiftly become one of the most widely used frameworks out there, useful in all kinds of applications.

Scikit-Learn

Scikit-Learn is a Python library for machine learning. It is an open-source and beginner-friendly tool that offers data mining and machine learning capabilities, as well as comprehensive documentation and tutorials. Scikit-Learn is well-suited for smaller projects and quick model prototyping but may not be the best choice for deep learning tasks.

TensorFlow

TensorFlow is an open-source deep learning framework developed by Google. It’s renowned for its flexibility and scalability, making it suitable for many AI applications. This framework has a large and active community and is equipped with extensive documentation and tutorials.

It also supports deployment on various platforms. However, the learning curve of TensorFlow can be steep for beginners.

Keras

Keras is an open-source high-level neural networks API that runs on top of TensorFlow or other frameworks. It is user-friendly and easy to learn, simplifying the process of operating with deep learning models. Moreover, it’s ideal for quick prototyping. You should only keep in mind that Keras may lack some advanced features for complex tasks.

LangChain

LangChain has recently gained popularity as a framework for large language model (LLM) applications. It allows developers to build applications using LLMs with features like model I/O, data connections, chains, memory, agents, and callbacks. LangChain integrates with various tools, including OpenAI and Hugging Face Transformers, and is used for diverse applications like chatbots, document summarization, and interacting with APIs.

Hugging Face

Hugging Face specializes in easy-to-use AI tools, mainly known for their “Transformers” library, which helps in advanced machine learning tasks like language processing and creating chatbots. They also provide tools for generating images and sounds, efficient ways to handle data in AI models, and simple methods to update large AI models. Additionally, they offer web-friendly versions of these tools, making it easier for beginners and experts alike to experiment with AI in various fields, including natural language processing and computer vision.

OpenNN

OpenNN is a tool used for creating neural networks, a type of AI that mimics how the human brain works. It’s written in C++ and is known for being fast and efficient. OpenNN is used mainly for research and creating AI that can learn and make decisions based on data.

OpenAI

OpenAI provides a range of tools for different AI tasks, including making images or converting text to speech. It’s known for its powerful GPT language models that can understand and generate human-like text. OpenAI’s platform is user-friendly, making it easier for people to use advanced AI in their own projects, especially for creating AI assistants or tools that interact with users in natural language. It’s worth noting that several of the features required a paid premium subscription.

PyBrain

PyBrain is an open-source ML library for Python. It provides a simple and flexible environment for experimenting with various machine learning algorithms and is perfect for researchers, educators, and developers looking for a lightweight Python-based framework for exploring machine learning concepts.

It is lightweight and easy to use for experimentation, supporting a wide range of machine learning algorithms. Moreover, PyBrain’s AI library is good for educational purposes and rapid prototyping.

However, you should take into account that PyBrain has limited documentation and a smaller community compared to mainstream libraries. It may also lack some advanced features found in other frameworks.

IBM Watson

IBM Watson is a suite of AI and machine learning services provided by IBM. It offers tools and solutions for building and deploying AI-powered applications, including natural language processing, computer vision, and predictive analytics.

It can be easily integrated with IBM Cloud for seamless deployment. What is more, robust AI capabilities in the IBM Watson suite are backed by IBM’s expertise. However, the pricing may be a concern for smaller businesses seeking comprehensive AI solutions and consulting services.

Microsoft Cognitive Toolkit (CNTK)

The Microsoft Cognitive Toolkit, or CNTK, is a free and open-source deep learning AI framework developed by Microsoft. It’s known for its efficiency, especially on multi-GPU systems, and is suitable for both research and production deployments.

It is preferred by many researchers, data scientists, and developers working on deep learning projects with access to powerful hardware because it’s highly efficient, particularly for training large models. It also supports multiple neural network types, including feedforward and recurrent networks; additionally, it provides a Python API for ease of use.

But you should be aware that Microsoft CNTK may possess a steeper learning curve compared to more beginner-friendly frameworks.

DL4J (Deeplearning4j)

Deeplearning4j, often abbreviated as DL4J, implies an open-source deep learning framework specifically designed for Java and Scala developers. It provides a comprehensive set of tools for building and deploying deep neural networks in Java-based applications.

DL4J is designed for Java and Scala, making it suitable for enterprise-level applications. The framework also offers support for distributed computing, enabling scalability. The platform includes a wide range of neural network types and pre-processing tools. However, it has a smaller community compared to Python-based frameworks.

More AI frameworks and libraries to consider

Theano

Theano is an open-source numerical computation AI library for Python. While it’s no longer actively developed, it played a significant role in the early days of deep learning.

Why so? For starters, it had an efficient symbolic mathematics library. Theano was also suitable for educational purposes. Although some existing projects may still use it, it’s no longer actively maintained or updated.

MXNet

MXNet is an open-source deep learning framework known for its efficiency and scalability. Additionally, MXNet is efficient for both research and production. It has growing community and industry support, but its community is smaller compared to TensorFlow and PyTorch.

Caffe

Caffe is an open-source deep learning framework. It’s known for its speed and efficiency in computer vision tasks, supporting a variety of deep learning architectures. Caffe is optimized for computer vision applications and excellent for deploying on edge devices. But when choosing it, you should consider its limited flexibility for non-vision tasks.

XGBoost

This is an open-source gradient boosting framework known for its efficiency and performance. Data practitioners working with structured data and classification/regression problems often choose it.

This AI framework excels in structured data tasks and is widely used in data science competitions. XGBoost is known for its exceptional performance for tabular data. The framework supports various programming languages being well-maintained and actively developed. However, you should understand that XGBoost is not designed for deep learning tasks.

Conclusion

We’ve covered some of the most popular AI frameworks, libraries, and other tools for you to consider. You can also check out our list of the Top 5 AI tools for data science to know more about how to boost the productivity of your data processing workflows.

As a junior data practitioner, choosing one of the right AI frameworks or libraries is crucial for your professional growth and project success. While there’s no one-size-fits-all solution, consider your project’s requirements, your familiarity with the framework, and the resources available in the community.

In summary, PyTorch and TensorFlow are excellent choices for deep learning projects, with TensorFlow offering scalability and PyTorch emphasizing flexibility. Scikit-Learn is a go-to for traditional machine learning tasks, while Keras provides a user-friendly entry point to deep learning. XGBoost excels in structured data problems, Caffe is a top choice for computer vision, and MXNet offers efficiency and scalability. Remember that Theano, while no longer actively developed, may still find its use in certain educational contexts. More recent tools such as LangChain and OpenAI give excellent options for LLMs, while Hugging Face is useful for natural language processing in general.

Ultimately, the best AI framework or library is the one that aligns with your specific needs and helps you achieve your goals as a data practitioner. So, roll up your sleeves, dive into the world of AI, and start building smarter software with the right tools.

The post The Top 16 AI Frameworks and Libraries: A Beginner’s Guide appeared first on CTO ROBOTICS Media.

With the incredible pace of advancement in artificial intelligence, we’re seeing the rise of more and more AI frameworks that teams can employ to get their projects off the ground faster. If they want to train a machine learning model, they don’t have to read a paper about it and then write hundreds of lines of code. They can train such a model using a single function call and existing frameworks.

Since many are open-source projects, all contributions and changes are thoroughly vetted before being approved and integrated into the codebase. This ensures good quality and reduces the likelihood of errors in constructing complex machine learning architectures.

Let’s take a look at the most prominent AI frameworks available to teams developing GenAI applications.

What Are AI Frameworks?

AI frameworks are collections of libraries that work together to make creating and deploying AI algorithms easier. AI frameworks are the core ingredients for building advanced, intelligent systems that can learn, adapt, and evolve. They are part of any modern machine learning architecture designed with efficiency in mind.

These frameworks improve the efficiency of algorithm development and deployment by providing pre-configured functions and libraries that allow developers to tailor AI models to specific tasks without having to build the underlying architecture from the ground up.

A significant advantage of these frameworks is the standardization of the development workflow. It ensures that, regardless of the AI endeavor, developers have access to a standardized toolkit and methodology. This uniformity enables the smooth integration of AI elements into various platforms and applications.

Take TensorFlow as an example; its comprehensive libraries significantly reduce development time, contributing to its widespread use in various industries.

How Do AI Frameworks Work?

AI frameworks simplify the creation and execution of complex algorithms by providing pre-built functions and libraries. This allows developers to tailor AI models for specific purposes rather than building the underlying systems from scratch.

These frameworks also standardize the development process. Teams get consistent tools and methods to work with regardless of the AI project. This homogeneity lets them incorporate AI features into various platforms and applications.

Open-Source vs Commercial AI Frameworks: Pros and Cons

When selecting the ideal AI framework for your development project, you’ll be looking at two primary options: open-source and commercial frameworks. Each choice comes with unique pros and cons. Understanding the distinctions between them before making a selection is critical.

Open-source AI frameworks

Open-source frameworks are distributed under an open-source license, allowing users to use the software for any purpose.

Commercial AI frameworks

Commercial frameworks are created by firms that distribute their software under proprietary licenses. This means that users of these frameworks have limited capabilities with the software and may be subject to additional payments. However, customers of commercial frameworks may receive additional features and support from the developer.

So, which AI framework type should you choose?

The answer lies in your specific project demands and requirements. Don’t forget to consider financial resources, available expertise, and other considerations.

Top AI Frameworks and Libraries

TensorFlow

TensorFlow is an open-source deep learning framework created by Google. It offers a rich ecosystem for developing and deploying ML/DL models. Its low-level APIs provide flexibility and control, while high-level APIs such as Keras facilitate model creation.

TensorFlow models can be used for various tasks, but they excel at dealing with unstructured data such as photos, audio, and text. As a result, TensorFlow models are extremely good at image and speech recognition, object identification, natural language processing (NLP), and reinforcement learning. Due to its versatility and customizability, TensorFlow is widely and effectively used in R&D projects.

TensorFlow is best suited for complicated machine/deep learning jobs needing excellent performance and scalability. It works well in both research and production scenarios, particularly when dealing with big, potentially unstructured datasets.

PyTorch

PyTorch is an open-source library created by Facebook’s AI research lab. Along with TensorFlow, it is one of the most prominent deep learning frameworks among practitioners and researchers. PyTorch is well-known for its flexibility and ease of use when developing deep learning models, enabling intuitive model creation and rapid debugging.

PyTorch is a popular tool for applications that require deep learning, such as natural language processing, computer vision, and reinforcement learning. It works really well for cases where you want to experiment quickly with new concepts or complex models.

PyTorch is best suited when flexibility and development speed are essential. It’s a good choice for projects with complicated models requiring regular alterations and fine-grained control over their design.

Keras

Keras is the preferred framework for many deep learning aficionados. Designed to make creating neural networks simple and feasible, Keras sits on top of heavyweight platforms such as TensorFlow and provides a simpler, more intuitive interface ideal for newbies. Think of Keras as a helpful guide through the difficult realm of neural networks.

Keras stands out for its user-friendly approach to developing and training models. It’s extremely flexible, whether you’re prototyping or running production code. It also allows you to experiment with novel deep learning ideas without becoming mired in the complexities of tensor algebra, optimization techniques, etc.

In addition, there is a lot of community support and resources available to help you bring your deep learning projects from concept to fruition.

Hugging Face

Hugging Face is a company that specializes in NLP-focused libraries and transformers. It’s a forum for exchanging models and datasets and presenting interesting ML/DL-based applications. Hugging Face also provides libraries and tools for optimizing and deploying models in production.

Hugging Face’s tools and libraries are widely used in AI for various text and image jobs. They excel at text production, sentiment analysis, named entity recognition, question answering, and chatbot development. Hugging Face models are especially valuable for their transfer learning capabilities, which enable users to get spectacular results with little training data and time.

Hugging Face tools are handy when working on projects that benefit from leveraging cutting-edge pre-trained models. They’re great for academics, developers, and data scientists who need to prototype quickly, achieve high performance, or deliver models into production with little effort. Another reason to choose Hugging Face is its extensive collection of open-source models via the Transformers library.

OpenAI

OpenAI’s API gives developers easy access to the company’s cutting-edge, pre-trained AI models such as ChatGPT, Sora, Dall-E, and others. This API allows developers to integrate AI capabilities into their apps quickly. To make things even easier, teams may use a specific Python module that simplifies communicating with the API.

The OpenAI API can be used for a variety of activities, including text, image, and audio (text-to-speech) production, multi-turn conversation, answering questions based on provided images (multimodality), transcribing audio from supported languages to text files, translation, and more. Furthermore, the API can be used to fine-tune existing models to match the unique requirements of our projects.

The OpenAI framework is ideal when you want access to cutting-edge generative AI models from our application without having to train or host the models on your own. This framework brings advantages to the entire product development process and allows us to experiment with existing models before training ourselves.

Scikit-Learn

Scikit-learn is easily one of the most popular machine learning libraries thanks to its broad functionalities that span all stages of a machine learning project, including data processing and manipulation, feature engineering, model training, and evaluation.

Its comprehensive and intuitive API enables users to get started immediately. Furthermore, the user-friendly interface makes experimenting with alternative models as simple as altering a single line of code.

Scikit-learn is typically used for conventional machine learning tasks like classification, regression, clustering, and dimensionality reduction. Its vast end-to-end functionality allows whole projects to be effectively built using a single tool.

Scikit-learn is best suited for dealing with small- to medium-sized datasets that require strong and dependable implementations of machine learning algorithms. Due to its simple API and comprehensive documentation, it’s a good choice for new and intermediate users. It’s also the best library for individuals who need to prototype and test models quickly.

XGBoost

XGBoost (Extreme Gradient Boosting) is a very efficient and scalable machine learning package for gradient boosting. XGBoost models are well-known for their superior prediction performance. The tool can rapidly and accurately perform a variety of data science issues, such as regression, classification, and ranking.

XGBoost is a popular AI tool for jobs that require structured (tabular) data, such as fraud detection, risk modeling, and churn prediction. Because of its great predictive potential, XGBoost (often together with LightGBM) is a popular technique for getting top scores in ML competitions on platforms like Kaggle.

XGBoost is best suited to situations involving tabular data that require high performance and precision. Its scalability enables it to handle enormous datasets efficiently while training in a reasonable amount of time.

LangChain

LangChain is an open-source AI framework that streamlines the process of developing LLM-based apps. It can serve as a general interface for communicating with LLMs and help with prompts, long-term memory, external datasets, and other agents for activities that an LLM may struggle with, such as mathematics or searches.

Users can dynamically compare different prompts and models using LangChain’s modular architecture, which requires little code changes. It also supports the usage of numerous LLMs chained together. For example, one model may reason through a query, but another creates a response.

LangChain and its community of companion libraries make it easier to manage every stage of the LLM application lifecycle. After building the foundation of your application with LangChain and its third-party integrations, you can deploy it with LangSmith. You can also use the tool to inspect, monitor, and assess what happens at each chain stage. Finally, every chain built with LangChain may be converted into an API using LangServe.

Advanced Features of AI Frameworks

Some AI frameworks come with advanced features such as:

• Distributed Training – Distributed training divides training workloads among numerous mini-processors. These mini-processors, known as worker nodes, run parallel to speed up the training process. Their parallelism can be achieved through either data or model parallelism.
• Visualization Tools – Machine learning visualization shows machine learning models, data, and relationships graphically or interactively. The goal is to make a model’s complex algorithms and data patterns easy for technical and non-technical stakeholders to understand.
• Model Serving and Deployment – The transition from a trained model to a production is difficult. MLOps engineers must overcome several challenges, including scaling up models, ensuring a smooth interface with the current infrastructure, and maintaining high performance and reliability. MLOps tools and frameworks help simplify and streamline the model deployment process.

Benefits of AI Frameworks

AI frameworks establish the groundwork for using machine learning and deep learning algorithms, simplifying the process of developing intelligent systems. They come with several other benefits:

Cost-Effective

AI frameworks provide a cost-effective way for businesses to create custom applications. Frameworks are crucial in reducing development costs since they eliminate the need for considerable manual coding and give the developers ready-to-use components.

Time Savings

AI frameworks tremendously benefit modern software development, accelerating applications’ invention, assessment, and deployment. They offer a comprehensive development ecosystem that includes debuggers, testing frameworks, and data visualization tools.

This shortens the development timeline, allowing developers to go forward faster without having to compile and analyze each code line. Furthermore, AI frameworks provide a large selection of pre-developed models.

App Development Flow

When selecting frameworks, the ability to enhance and expedite development is critical. AI frameworks include pre-coded algorithms, data management utilities, and optimization methodologies, allowing developers to focus on fundamental problem-solving rather than the complexities of AI dynamics.

How to Choose the Right AI Framework

Given the numerous AI frameworks available, selecting the right one for your project might be difficult. As a result, before delving deeply into your alternatives, it makes sense to consider some criteria that will assist you in making an informed decision.

1. Performance

A framework’s speed and scalability vary depending on its implementation, determining its eligibility for distributed computing and hardware utilization, such as many cores or GPUs.

2. Community Support

The framework’s community grows along with its popularity. An active community can help you anytime you experience a problem or require clarification on how something works.

3. Flexibility

A flexible AI framework enables rapid prototyping and modification, which is critical for meeting the field’s changing research and application needs.

4. Ease of Learning

Some frameworks provide a high-level API that makes training machine learning models easier, while others have a steeper curve. You can assess the difficulty by reviewing specific lessons and coding samples.

AI Framework Integration with Popular Tools

Integration with Cloud Services

Artificial intelligence and cloud computing are closely intertwined. Public cloud services today have pre-configured setups and models to facilitate AI application testing and deployment.

AI Frameworks in DevOps Pipelines

More and more teams are using AI solutions to automate and optimize the software development and delivery processes. This includes automating testing and deployment and boosting resource management and security.

Organizations employing AI in DevOps gain speed, accuracy, and reliability from increased software development lifecycle. This, in turn, results in faster deployments, fewer errors, and more overall productivity.

Frameworks and Visualization Tools

By using AI-powered analytics tools, you can do more than visualize data. The future of data discovery revolves around exploring, interacting with, and engaging with your data. As a result, you can uncover underlying trends that may not be visible in prepopulated, static representations. With advanced AI and machine learning capabilities, all users may have access to data and benefit from interactive dashboards.

Automating AI Deployment

AI is integrated into several DevOps lifecycle stages, each with unique benefits. One area where AI is making a significant impact is intelligent monitoring and anomaly detection. AI systems can scan massive volumes of data in real time to find patterns and anomalies that may suggest concerns before they become significant. Proactive monitoring lets teams prevent problems, minimize downtime, and enhance system reliability.

The post Top 8 AI Frameworks: Benefits & How to Choose the Right One appeared first on CTO ROBOTICS Media.

First Law
A robot may not injure a human being or, through inaction, allow a human being to come to harm.

Second Law
A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.

Third Law
A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

–Isaac Asimov’s Three Laws of Robotics

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Robot ethics is a relatively new subdomain of robotics focused on ethical aspects of automation design and deployment.

While its origin can arguably be traced back to Isaac Asimov’s speculative Three Laws of Robotics (above), first published in the 1942 short story ‘Runaround,’ it is only over the past twenty years –spurred by the work of academic researchers, and later by trade organizations and standardization bodies—that the field has evolved to become an important part of the robotics conversation.

For example, The European Union funded RoboLaw project drew on contributions from experts in robot ethics in its development of extensive guidelines for European policymakers and lawmakers governing automation and AI deployments. Meanwhile, the IEEE is working on standards designed to ensure that automation and AI conforms with the principles of ethically-guided design and technology functions for the betterment of society.

Robot ethics combines insights from experts in robotics, AI, computer science, and engineering with insights from experts in philosophy, law, psychology and sociology, in an attempt to ensure that automation designs and deployments do not create ethical hazards for individuals and society. For example, considering the growing impact of industrial automation on society, robot ethics experts ask questions such as ‘Should industrial robots be taxed?’ ‘Does the rise of automation change the debate around proposals for a Universal Basic Income?’ and ‘How can we ensure that automation is designed and deployed with ethical considerations in mind?’

The domain of industrial robots is also included in the growing conversation around robot ethics. The rise of collaborative robots, which enable closer interaction between humans and robots than ever before on factory and warehouse floors, has given extra impetus to the topic.

NOTE: Safety has always been a core principle of industrial robot design, and industrial robots must comply with global and regional standards and laws governing safe design and deployment. Robot ethics builds on this established knowledge to explore the broader ethical considerations involved, such as worker well-being.

Let’s take a look at some resources you can use to begin exploring this vast and interesting robotics subdomain at the intersection of human values and engineering.

1. The British Standards Institute has developed a set of standards for the ethical design of robots and robotic devices. The BS 8611 ‘Ethics design and application robots’ document provides guidelines for the identification of potential ethical harm, alongside guidelines for safe design, ethical protective measures. It also builds on existing safety requirements for industrial, personal care, and medical robots.
2. The IEEE’s Standards Association runs the ‘Global Initiative on the Ethics of Autonomous and Intelligent Systems’ which is a great starting point for learning about ethical issues surrounding both robotics and artificial intelligence.

   The IEEE is currently developing its P7000 series of standards projects, which it describes as a “unique addition” to its collection of over 1,900 global IEEE standards and projects.” That’s certainly true: Traditionally, standards have focused on technology interoperability, functionality, safety, and trade facilitation. In contrast, the IEEE P7000 series is designed to address “specific issues at the intersection of technological and ethical considerations.”

3. Founded in 2012, the Open Roboethics Institute is a non-profit thinktank dedicated to exploring ethical and social implications of robotics and AI.
4. Ethical Issues in Human-Robot Interaction  – A presentation by Kate Darling, MIT Media Lab
   https://www.youtube.com/watch?v=m3gp4LFgPX0
5. Can we apply human ethics to robots? (Via Interesting Engineering)
   https://www.youtube.com/watch?v=dWshtBOgCvs

6. Robot Ethics: The Social and Ethical Implications of Robotics, MIT Press

   The Cobot Experience: AJung Moon & Resolving Human-Cobot Resource Conflicts, Robotiq blog

   How to Judge A ‘Bot, The Economist

   British Standards Institute

   Runa+round, Isaac Asimov

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